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Статті в журналах з теми "GBM approach"
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Skarkova, Veronika, Marketa Krupova, Barbora Vitovcova, Adam Skarka, Petra Kasparova, Petr Krupa, Vera Kralova, and Emil Rudolf. "The Evaluation of Glioblastoma Cell Dissociation and Its Influence on Its Behavior." International Journal of Molecular Sciences 20, no. 18 (September 18, 2019): 4630. http://dx.doi.org/10.3390/ijms20184630.
Повний текст джерелаАнотація:
Purpose: Primary cell lines are a valuable tool for evaluation of tumor behavior or sensitivity to anticancer treatment and appropriate dissociation of cells could preserve genomic profile of the original tissue. The main aim of our study was to compare the influence of two methods of glioblastoma multiforme (GBM) cell derivation (mechanic—MD; enzymatic—ED) on basic biological properties of thus derived cells and correlate them to the ones obtained from stabilized GBM cell line A-172. Methods: Cell proliferation and migration (xCELLigence Real-Time Cell Analysis), expression of microRNAs and protein markers (RT-PCR and Western blotting), morphology (phase contrast and fluorescent microscopy), and accumulation of temozolomide (TMZ) and its metabolite 5-aminoimidazole-4-carboxamide (AIC) inside the cells (LC-MS analysis) were carried out in five different samples of GBM (GBM1, GBM2, GBM32, GBM33, GBM34), with each of them processed by MD and ED types of isolations. The same analyses were done in the A-172 cell line too. Results: Primary GBM cells obtained by ED or MD approaches significantly differ in biological behavior and properties of these cells. Unlike in primary MD GBM cells, higher proliferation, as well as migration, was observed in primary ED GBM cells, which were also associated with the acquired mesenchymal phenotype and higher sensitivity to TMZ. Finally, the same analyses of stabilized GBM cell line A-172 revealed several important differences in measured parameters. Conclusions: GBM cells obtained by MD and ED dissociation show considerable heterogeneity, but based on our results, MD approach should be the preferred method of primary GBM cell isolation
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Verma, Amit, Swetha Gunasekar, Vineeta Goel, Randeep Singh, Ramandeep Singh Arora, Nitesh Rohatgi, A. K. Anand, and Meenu Walia. "A molecular approach to Glioblastoma Multiforme." International Journal of Molecular and Immuno Oncology 1, no. 1 (November 25, 2016): 35. http://dx.doi.org/10.18203/issn.2456-3994.intjmolimmunooncol20164387.
Повний текст джерелаАнотація:
<p>Glioma is a tumor of the central nervous system that occurs in the glial cells, Which it surrounds and protects the nerve cells. Glioblastoma Multiforme (GBM) is the most common and malignant sub-type of gliomas that arises from star-shaped cells called “astrocytes”, which constitute the supportive tissue of the brain. GBM are known to be heterogeneous in outcome with majority having a poor prognosis, thus there is an urgent need for novel therapeutic approaches. The detailed understanding of GBM is established by the combination of histopathology and genomic information of the tumor that aids in the best choice of Personalized Medicine. In this article, seven GBM patients are discussed who underwent tissue diagnosis as well as tumor molecular profiling; the significance of the genes and associated mutations/variations picked up in each individual.</p>
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Friedmann-Morvinski, Dinorah, Rajesh Narasimamurthy, Yifeng Xia, Chad Myskiw, Yasushi Soda та Inder M. Verma. "Targeting NF-κB in glioblastoma: A therapeutic approach". Science Advances 2, № 1 (січень 2016): e1501292. http://dx.doi.org/10.1126/sciadv.1501292.
Повний текст джерелаАнотація:
Glioblastoma multiforme (GBM) is the most common and lethal form of intracranial tumor. We have established a lentivirus-induced mouse model of malignant gliomas, which faithfully captures the pathophysiology and molecular signature of mesenchymal human GBM. RNA-Seq analysis of these tumors revealed high nuclear factor κB (NF-κB) activation showing enrichment of known NF-κB target genes. Inhibition of NF-κB by either depletion of IκB kinase 2 (IKK2), expression of a IκBαM super repressor, or using a NEMO (NF-κB essential modifier)–binding domain (NBD) peptide in tumor-derived cell lines attenuated tumor proliferation and prolonged mouse survival. Timp1, one of the NF-κB target genes significantly up-regulated in GBM, was identified to play a role in tumor proliferation and growth. Inhibition of NF-κB activity or silencing of Timp1 resulted in slower tumor growth in both mouse and human GBM models. Our results suggest that inhibition of NF-κB activity or targeting of inducible NF-κB genes is an attractive therapeutic approach for GBM.
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Pirmoradi, Leila, Nayer Seyfizadeh, Saeid Ghavami, Amir A. Zeki, and Shahla Shojaei. "Targeting cholesterol metabolism in glioblastoma: a new therapeutic approach in cancer therapy." Journal of Investigative Medicine 67, no. 4 (February 14, 2019): 715–19. http://dx.doi.org/10.1136/jim-2018-000962.
Повний текст джерелаАнотація:
Glioblastoma multiforme (GBM) is the most aggressive malignant brain tumor known with a poor survival rate despite current advances in the field of cancer. Additional research into the pathophysiology of GBM is urgently needed given the devastating nature of this disease. Recent studies have revealed the unique cellular physiology of GBM cells as compared with healthy astrocytes. Intriguingly, GBM cells are incapable of de novo cholesterol synthesis via the mevalonate pathway. Thus, the survival of GBM cells depends on cholesterol uptake via low-density lipoprotein receptors (LDLRs) in the form of apolipoprotein-E-containing lipoproteins and ATP-binding cassette transporter A1 (ABCA1) that efflux surplus cholesterol out of cells. Liver X receptors regulate intracellular cholesterol levels in neurons and healthy astrocytes through changes in the expression of LDLR and ABCA1 in response to cholesterol and its derivatives. In GBM cells, due to the dysregulation of this surveillance pathway, there is an accumulation of intracellular cholesterol. Furthermore, intracellular cholesterol regulates temozolomide-induced cell death in glioblastoma cells via accumulation and activation of death receptor 5 in plasma membrane lipid rafts. The mevalonate pathway and autophagy flux are also fundamentally related with implications for cell health and death. Thus, via cholesterol metabolism, the mevalonate pathway may be a crucial player in the pathogenesis and treatment of GBM where our current understanding is still lacking. Targeting cholesterol metabolism in GBM may hold promise as a novel adjunctive clinical therapy for this devastating cancer.
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Tran, David, Son Le, Bo Ma, Darin Falk, and Serge Zolotukhin. "EXTH-51. DEVELOPMENT OF A NOVEL GENE THERAPY APPROACH TARGETING GLIOBLASTOMA FOLLOWING ARTIFICIAL INTELLIGENCE (AI)-DIRECTED IDENTIFICATION OF THE GBM STATE." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi174—vi175. http://dx.doi.org/10.1093/neuonc/noab196.690.
Повний текст джерелаАнотація:
Abstract BACKGROUND Profound heterogeneity has severely hampered therapeutic advancements in GBM. Remarkably, however, GBM exhibits broad clinical and histopathologic overlaps suggesting the presence of a common state. The GBM state embodies network restructuring forced by founding mutations and perpetuated in subclones of GBM stem-like cells (GSCs). Successful targeting of the GBM state promises to circumvent the heterogeneity. METHODS To decipher the GBM state, we applied NETZEN, an AI suite integrating a deep neural network with gene network-based ranking, to first generate the reference GBM gene network from TCGA’s entire GBM RNAseq collection, and then identify the altered master regulatory gene subnetwork in GBM using a dataset containing >30 diverse patient-derived GSC lines and their paired differentiated cells, 6 astrocyte and 3 neuronal precursor cell lines. To develop a gene therapy against the GBM state, we screened a rAAV capsid library through GBM patient-derived xenografts (PDX) to identify variants with specific tropism for GBM cells that can deliver targeting constructs intratumorally. RESULTS We discovered a putative GBM state anchored by developmentally restricted master regulators. To validate its critical role, we deconstructed it using shRNA in a panel of PDX and uniformly observed improved tumor control and survival compared to controls (p< 0.05 in all lines). More notably, when the core GBM state was forcibly reconstructed in astrocytes, transformation into GSC-like cells occurred, as measured by single-cell analysis, neurosphere formation, and most importantly, development of lethal infiltrating brain tumors in 15/15 mice. Finally, selected novel rAAV capsids with 10-40-fold higher specificity for GBM cells were utilized in a shRNA-based rAAV platform to target key master regulators of the validated GBM state. CONCLUSIONS The GBM state is established by a developmental master regulator subnetwork permitting the creation of a first-of-its-kind, heterogeneity-agnostic GBM therapy. This AI-directed R&D program can be expanded to target other tumors.
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Lee, Ho-Sung, In-Hee Lee, Sang-In Park, Minho Jung, Seung Gu Yang, Tae-Wook Kwon, and Dae-Yeon Lee. "Unveiling the Mechanism of the Traditional Korean Medicinal Formula FDY003 on Glioblastoma Through a Computational Network Pharmacology Approach." Natural Product Communications 17, no. 9 (September 2022): 1934578X2211263. http://dx.doi.org/10.1177/1934578x221126311.
Повний текст джерелаАнотація:
Glioblastoma (GBM) is the most common type of primary malignant tumor that develops in the brain, with 0.21 million new cases per year globally and a median survival period of less than 2 years after diagnosis. Traditional Korean medicines have been increasingly suggested as effective and safe therapeutic strategies for GBM. However, their pharmacological effects and mechanistic characteristics remain to be studied. In this study, we employed a computational network pharmacological approach to determine the effects and mechanisms of the traditional Korean medicinal formula FDY003 on GBM. We found that FDY003 treatment decreased the viability of human GBM cells and increased their response to chemotherapeutics. We identified 10 potential active pharmacological compounds of FDY003 and 67 potential GBM-related target genes and proteins. The GBM-related targets of FDY003 were signaling components of various crucial GBM-associated pathways, such as PI3K-Akt, focal adhesion, MAPK, HIF-1, FoxO, Ras, and TNF. These pathways are functional regulators for the determination of cell growth and proliferation, survival and death, and cell division cycle of GBM cells. Together, the overall analyses contribute to the pharmacological basis for the anti-GBM roles of FDY003 and its systematic mechanisms.
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Zupancic, Klemen, Andrej Blejec, Ana Herman, Matija Veber, Urska Verbovsek, Marjan Korsic, Miomir Knezevic, et al. "Identification of plasma biomarker candidates in glioblastoma using an antibody-array-based proteomic approach." Radiology and Oncology 48, no. 3 (September 1, 2014): 257–66. http://dx.doi.org/10.2478/raon-2014-0014.
Повний текст джерелаАнотація:
Abstract Background. Glioblastoma multiforme (GBM) is a brain tumour with a very high patient mortality rate, with a median survival of 47 weeks. This might be improved by the identification of novel diagnostic, prognostic and predictive therapy-response biomarkers, preferentially through the monitoring of the patient blood. The aim of this study was to define the impact of GBM in terms of alterations of the plasma protein levels in these patients. Materials and methods. We used a commercially available antibody array that includes 656 antibodies to analyse blood plasma samples from 17 healthy volunteers in comparison with 17 blood plasma samples from patients with GBM. Results. We identified 11 plasma proteins that are statistically most strongly associated with the presence of GBM. These proteins belong to three functional signalling pathways: T-cell signalling and immune responses; cell adhesion and migration; and cell-cycle control and apoptosis. Thus, we can consider this identified set of proteins as potential diagnostic biomarker candidates for GBM. In addition, a set of 16 plasma proteins were significantly associated with the overall survival of these patients with GBM. Guanine nucleotide binding protein alpha (GNAO1) was associated with both GBM presence and survival of patients with GBM. Conclusions. Antibody array analysis represents a useful tool for the screening of plasma samples for potential cancer biomarker candidates in small-scale exploratory experiments; however, clinical validation of these candidates requires their further evaluation in a larger study on an independent cohort of patients.
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Benouaich Amiel, A., V. Khasminsky, O. Gal, S. Fichman, T. Weiss, T. Siegal, and S. Yust-Katz. "P14.72 Multicentric glioblastoma - A retrospective study of imaging characteristics, treatment approach, pattern of relapse and survival." Neuro-Oncology 21, Supplement_3 (August 2019): iii84. http://dx.doi.org/10.1093/neuonc/noz126.307.
Повний текст джерелаАнотація:
Abstract BACKGROUND Multicentric glioblastoma (m-GBM), defined as well separated tumoral foci, is a rare GBM variant comprising 6–13% of all GBM cases. Published data regarding m-GBM is scarce and is largely reporting on multicentric enhancing foci. We performed a retrospective study to determine the incidence, imaging characteristics, treatment approach, pattern of relapse and prognosis of m-GBM. MATERIAL AND METHODS The neuropathological database of our institution was surveyed for histological diagnosis of adult GBM diagnosed between 01/01/2015 and 31/05/2018. All pre-operative MRI were reviewed to identify patients with m-GBM. We included in the definition of m-GBM well separated enhancing as well as non-enhancing tumor foci. The medical records and follow-up MRI studies were reviewed in order to retrieve the data. RESULTS Of the 170 patients with newly diagnosed GBM 14 (8%) presented with m-GBM. All of them had at least one enhancing lesion and 11 (78.5%) patients had additional well separated non-enhancing tumor foci. The total number of lesions was 37 (19 enhancing and 18 non-enhancing) with a median number of lesions per patient of 2 (range 2 to 4). Median age at diagnosis was 66 (range: 49–79) years. Nine of the patients (64%) underwent surgical resection of the enhancing component whereas 5 patients had only a biopsy. Median follow up was 14.3 (range: 2–30) months. All but one patients were treated by standard concurrent radiotherapy with temozolomide. Median progression free survival is 6.2 (range: 0–13.3) months. Five of the 18 non-enhancing tumor foci eventually displayed contrast enhancement during the course of the disease. At last follow up, 12 patients died, with an overall survival of 12.3 months. Information regarding radiation fields, pattern of disease progression and molecular profile will be presented at the meeting. CONCLUSION m-GBM presents therapeutic dilemas regarding the optimal surgical approach and radiation field planning. Better understanding of the disease course and pattern of progression may help to optimize the therapeutic approach implying particularly to non-enhancing tumor foci.
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D’Amico, Agata Grazia, Grazia Maugeri, Luca Vanella, Valeria Pittalà, Dora Reglodi, and Velia D’Agata. "Multimodal Role of PACAP in Glioblastoma." Brain Sciences 11, no. 8 (July 28, 2021): 994. http://dx.doi.org/10.3390/brainsci11080994.
Повний текст джерелаАнотація:
Glioblastoma multiforme (GBM) is the deadliest form of brain tumors. To date, the GBM therapeutical approach consists of surgery, radiation-therapy and chemotherapy combined with molecules improving cancer responsiveness to treatments. In this review, we will present a brief overview of the GBM classification and pathogenesis, as well as the therapeutic approach currently used. Then, we will focus on the modulatory role exerted by pituitary adenylate cyclase-activating peptide, known as PACAP, on GBM malignancy. Specifically, we will describe PACAP ability to interfere with GBM cell proliferation, as well as the tumoral microenvironment. Considering its anti-oncogenic role in GBM, synthesis of PACAP agonist molecules may open new perspectives for combined therapy to existing gold standard treatment.
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Oh, Michael, Mohammad Hasanain, Simona Migliozzi, Luciano Garofano, Fulvio D'Angelo, Anna Luisa Di Stefano, Julie Lerond, et al. "EXTH-21. DEVELOPMENT OF THERAPEUTIC STRATEGIES BY PATHWAY-BASED MULTI-OMICS APPROACH AND MASTER KINASE ANALYSIS IN GLIOBLASTOMA MULTIFORME." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii214. http://dx.doi.org/10.1093/neuonc/noac209.820.
Повний текст джерелаАнотація:
Abstract Current transcriptomic classification of Glioblastoma Multiforme (GBM) has been ineffective to predict survival and therapeutic vulnerabilities. Recently, we proposed a four-group functional classification of GBM that included proliferative/progenitor, neuronal, mitochondrial and glycolytic/plurimetabolic subtypes with prognostic and therapeutic implications as the mitochondrial subtype carries the best survival and exhibits distinct sensitivity to mitochondrial OXPHOS inhibitors. To uncover novel therapeutic targets for each functional GBM subtype, we focused on protein kinases for their attractive features as both drivers and drug targets, with current availability of 62 FDA-approved inhibitors available for cancer precision therapeutics. We designed an unbiased integrative, machine learning-based proteomics/phosphoproteomics network for the identification of Master Kinases (MKs) responsible for effecting key phenotypic hallmarks of each of the four GBM subtypes. Here we report the identification and validation of protein kinase C delta (PRKCd) and DNA-PKcs as MKs that sustain the glycolytic/plurimetabolic and proliferative/progenitor GBM subtypes, respectively. Genetic and pharmacologic inactivation of PKCd in GBM patient-derived organoids of the glycolytic/plurimetabolic subtype blocked glucose uptake and lipid accumulation, resulting in marked anti-tumor effects. We also validated the role of PKCd in oncometabolic processes at the intersection between insulin, IGF, and lipid metabolism. DNA-PKcs was qualified as MK for the proliferative/progenitor GBM subtype, which is characterized by high basal level of replication stress. Biochemical experiments showed activation of DNA-PK in GBM patient-derived organoids of the proliferative/progenitor subgroup. Targeting DNA-PK in proliferative/progenitor GBM organoids with the clinically tested DNA-PKcs inhibitor nedisertib in association with gamma irradiation, the essential component of the standard of care for patients with GBM, led to an unsustainable level of DNA damage and massive GBM cell death selectively in this GBM subtype. As DNA-PKcs inhibitors have been introduced into clinical trials, our findings suggest that pre-selection of patient with PPR tumors is likely to enhance therapeutic success.
Дисертації з теми "GBM approach"
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Jilesen, Zachary Keavin. "Discovery and Application of Neoepitopes in an Oncolytic Rhabdovirus Vaccine Approach to Treat Glioblastoma Multiforme." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39688.
Повний текст джерелаАнотація:
Glioblastoma multiforme is the most common and lethal primary brain tumour in adults. Its aggressive and invasive phenotype makes it resistant to current standards of care, with a patient median survival following treatment of only 14 months. Potent and safe therapeutics are necessary to improve patient prognosis. Globally, efforts are being made in immunotherapies to combat such deleterious tumours. Preliminary work in the Stojdl lab has developed a novel oncolytic virus platform for brain cancer therapy that is non-toxic and exhibits potent anti-tumour efficacy. This platform is based on the rhabdovirus Farmington, identified for its potent oncolytic properties and engineering malleability. Herein, we begin to show our capability to discover and vaccinate against immunogenic neoepitopes derived from a mouse cancer mutanome. Engineering Farmington virus to express neoepitopes, allows for robust tumour specific immune proliferation following a prime vaccination. Overcoming problems of targeting self-antigen and antigen loss variants, a multi-neoepitope vaccine, presented here, is one of many alternative approaches to help combat cancer resistance. Despite achieving robust anti-tumour immunity by vaccination, selectivity of the tumour microenvironment remains an enormous challenge. Cumulative efforts in immunotherapy research will help drive novel therapeutics, like Farmington, into clinic and, ultimately, improve patient’s prognosis and quality of life.
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Dillenburg, Fabiane Cristine. "An approach for analyzing and classifying microarray data using gene co-expression networks cycles." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/171353.
Повний текст джерелаАнотація:
Uma das principais áreas de pesquisa em Biologia de Sistemas refere-se à descoberta de redes biológicas a partir de conjuntos de dados de microarrays. Estas redes consistem de um grande número de genes cujos níveis de expressão afetam os outros genes de vários modos. Nesta tese, apresenta-se uma nova maneira de analisar os conjuntos de dados de microarrays, com base nos diferentes tipos de ciclos encontrados entre os genes das redes de co-expressão construídas com dados quantificados obtidos a partir dos microarrays. A entrada do método de análise é formada pelos dados brutos, um conjunto de genes de interesse (por exemplo, genes de uma via conhecida) e uma função (ativador ou inibidor) destes genes. A saída do método é um conjunto de ciclos. Um ciclo é um caminho fechado com todos os vértices (exceto o primeiro e o último) distintos. Graças à nova forma de encontrar relações entre os genes, é possível uma interpretação mais robusta das correlações dos genes, porque os ciclos estão associados a mecanismos de feedback, que são muito comuns em redes biológicas. A hipótese é que feedbacks negativos permitem encontrar relações entre os genes que podem ajudar a explicar a estabilidade do processo regulatório dentro da célula. Ciclos de feedback positivo, por outro lado, podem mostrar a quantidade de desequilíbrio de uma determinada célula em um determinado momento. A análise baseada em ciclos permite identificar a relação estequiométrica entre os genes da rede. Esta metodologia proporciona uma melhor compreensão da biologia do tumor. Portanto, as principais contribuições desta tese são: (i) um novo método de análise baseada em ciclos; (ii) um novo método de classificação; (iii) e, finalmente, aplicação dos métodos e a obtenção de resultados práticos. A metodologia proposta foi utilizada para analisar os genes de quatro redes fortemente relacionadas com o câncer - apoptose, glicólise, ciclo celular e NF B - em tecidos do tipo mais agressivo de tumor cerebral (Gliobastoma multiforme - GBM) e em tecidos cerebrais saudáveis. A maioria dos pacientes com GBM morrem em menos de um ano, essencialmente nenhum paciente tem sobrevivência a longo prazo, por isso estes tumores têm atraído atenção significativa. Os principais resultados nesta tese mostram que a relação estequiométrica entre genes envolvidos na apoptose, glicólise, ciclo celular e NF B está desequilibrada em amostras de GBM em comparação as amostras de controle. Este desequilíbrio pode ser medido e explicado pela identificação de um percentual maior de ciclos positivos nas redes das primeiras amostras. Esta conclusão ajuda a entender mais sobre a biologia deste tipo de tumor. O método de classificação baseado no ciclo proposto obteve as mesmas métricas de desempenho como uma rede neural, um método clássico de classificação. No entanto, o método proposto tem uma vantagem significativa em relação às redes neurais. O método de classificação proposto não só classifica as amostras, fornecendo diagnóstico, mas também explica porque as amostras foram classificadas de uma certa maneira em termos dos mecanismos de feedback que estão presentes/ausentes. Desta forma, o método fornece dicas para bioquímicos sobre possíveis experiências laboratoriais, bem como sobre potenciais genes alvo de terapias.One of the main research areas in Systems Biology concerns the discovery of biological networks from microarray datasets. These networks consist of a great number of genes whose expression levels affect each other in various ways. We present a new way of analyzing microarray datasets, based on the different kind of cycles found among genes of the co-expression networks constructed using quantized data obtained from the microarrays. The input of the analysis method is formed by raw data, a set of interest genes (for example, genes from a known pathway) and a function (activator or inhibitor) of these genes. The output of the method is a set of cycles. A cycle is a closed walk, in which all vertices (except the first and last) are distinct. Thanks to the new way of finding relations among genes, a more robust interpretation of gene correlations is possible, because cycles are associated with feedback mechanisms that are very common in biological networks. Our hypothesis is that negative feedbacks allow finding relations among genes that may help explaining the stability of the regulatory process within the cell. Positive feedback cycles, on the other hand, may show the amount of imbalance of a certain cell in a given time. The cycle-based analysis allows identifying the stoichiometric relationship between the genes of the network. This methodology provides a better understanding of the biology of tumors. As a consequence, it may enable the development of more effective treatment therapies. Furthermore, cycles help differentiate, measure and explain the phenomena identified in healthy and diseased tissues. Cycles may also be used as a new method for classification of samples of a microarray (cancer diagnosis). Compared to other classification methods, cycle-based classification provides a richer explanation of the proposed classification, that can give hints on the possible therapies. Therefore, the main contributions of this thesis are: (i) a new cycle-based analysis method; (ii) a new microarray samples classification method; (iii) and, finally, application and achievement of practical results. We use the proposed methodology to analyze the genes of four networks closely related with cancer - apoptosis, glucolysis, cell cycle and NF B - in tissues of the most aggressive type of brain tumor (Gliobastoma multiforme – GBM) and in healthy tissues. Because most patients with GBMs die in less than a year, and essentially no patient has long-term survival, these tumors have drawn significant attention. Our main results show that the stoichiometric relationship between genes involved in apoptosis, glucolysis, cell cycle and NF B pathways is unbalanced in GBM samples versus control samples. This dysregulation can be measured and explained by the identification of a higher percentage of positive cycles in these networks. This conclusion helps to understand more about the biology of this tumor type. The proposed cycle-based classification method achieved the same performance metrics as a neural network, a classical classification method. However, our method has a significant advantage with respect to neural networks. The proposed classification method not only classifies samples, providing diagnosis, but also explains why samples were classified in a certain way in terms of the feedback mechanisms that are present/absent. This way, the method provides hints to biochemists about possible laboratory experiments, as well as on potential drug target genes.
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Medina, Jairzinho Ramos Gilmore Robert. "Gravitoelectromagnetism (GEM) : a group theoretical approach /." Philadelphia, Pa. : Drexel University, 2006. http://hdl.handle.net/1860/1123.
Повний текст джерела
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Augustine-Ohwo, Odaro. "Estimating break points in linear models : a GMM approach." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/estimating-break-points-in-linear-models-a-gmm-approach(804d83e3-dad8-4cda-b1e1-fbfce7ef41b8).html.
Повний текст джерелаАнотація:
In estimating econometric time series models, it is assumed that the parameters remain constant over the period examined. This assumption may not always be valid when using data which span an extended period, as the underlying relationships between the variables in these models are exposed to various exogenous shifts. It is therefore imperative to examine the stability of models as failure to identify any changes could result in wrong predictions or inappropriate policy recommendations. This research proposes a method of estimating the location of break points in linear econometric models with endogenous regressors, estimated using Generalised Method of Moments (GMM). The proposed estimation method is based on Wald, Lagrange Multiplier and Difference type test statistics of parameter variation. In this study, the equation which sets out the relationship between the endogenous regressor and the instruments is referred to as the Jacobian Equation (JE). The thesis is presented along two main categories: Stable JE and Unstable JE. Under the Stable JE, models with a single and multiple breaks in the Structural Equation (SE) are examined. The break fraction estimators obtained are shown to be consistent for the true break fraction in the model. Additionally, using the fixed break approach, their $T$-convergence rates are established. Monte Carlo simulations which support the asymptotic properties are presented. Two main types of Unstable JE models are considered: a model with a single break only in the JE and another with a break in both the JE and SE. The asymptotic properties of the estimators obtained from these models are intractable under the fixed break approach, hence the thesis provides essential steps towards establishing the properties using the shrinking breaks approach. Nonetheless, a series of Monte Carlo simulations conducted provide strong support for the consistency of the break fraction estimators under the Unstable JE. A combined procedure for testing and estimating significant break points is detailed in the thesis. This method yields a consistent estimator of the true number of breaks in the model, as well as their locations. Lastly, an empirical application of the proposed methodology is presented using the New Keynesian Phillips Curve (NKPC) model for U.S. data. A previous study has found this NKPC model is unstable, having two endogenous regressors with Unstable JE. Using the combined testing and estimation approach, similar break points were estimated at 1975:2 and 1981:1. Therefore, using the GMM estimation approach proposed in this study, the presence of a Stable or Unstable JE does not affect estimations of breaks in the SE. A researcher can focus directly on estimating potential break points in the SE without having to pre-estimate the breaks in the JE, as is currently performed using Two Stage Least Squares.
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Saulnier, Steve <1981>. "Bioconjugation and synthetic approach towards enantioenriched gem-difluoromethylene compounds through carbenium ions." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6577/.
Повний текст джерелаАнотація:
Bioconjugation of peptides and asymmetric synthesis of gem-difluoromethylene compounds are areas of the modern organic chemistry for which mild and selective methods continue to be developed. This thesis reports new methodologies for these two areas based on the use of stabilized carbenium ions.
The reaction that makes the bioconjugation of peptides possible takes place via the direct nucleophilic substitution of alcohols and is driven by the spontaneous formation of stabilized carbenium ions in water. By reacting with the thiol group of cysteine in very mild conditions and with a high selectivity, these carbenium ions allow the site-specific ligation of polypeptides containing cysteine and their covalent derivatization with functionalized probes. The ligation of the indole ring of tryptophan, an emerging target in bioconjugation, is also shown and takes place in the same conditions.
The second area investigated is the challenging access to optically active gem-difluoromethylene compounds. We describe a methodology relying on the synthesis of enantioenriched 1,3-benzodithioles intermediates that are shown to be precursors of the corresponding gem-difluoromethylene analogues by oxidative desulfurization-fluorination. This synthesis takes advantage of the highly enantioselective organocatalytic α-alkylation of aldehydes with the benzodithiolylium ion and of the wide possibilities of synthetic transformations offered by the 1,3-benzodithiole group. This approach allows the asymmetric access to complex gem-difluoromethylene compounds through a late-stage fluorination step, thus avoiding the use of fluorinated building blocks.
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Pintat, Stéphane. "Approaches towards the synthesis of gem-difluorinated monosaccharide analogues." Thesis, University of Leicester, 2003. http://hdl.handle.net/2381/30082.
Повний текст джерелаАнотація:
This thesis describes the synthesis of gem-difluorinated cyclic molecules using building block approaches based mainly on ring-closing metathesis (RCM) using commercially available ruthenium catalysts such as Grubbs' catalyst. In the first instance, 1-bromo-1,1-difluoroprop-2-ene was used to synthesise difluorinated dihydropyrans in order to demonstrate that the unprecedented RCM of a substrate containing two fluorine atoms in the allylic position could be achieved. A similar approach allowed the highly diastereoselective synthesis of new 4,4-difluoro-4-deoxyhexoses using a RCM-dihydroxylation sequence. In order to widen the range of available difluorinated monosaccharide analogues, a potentially highly enantioselective, non-RCM based route was developed. This approach relied on the use of (3-bromo-3,3-difluoro-prop-1-ynyl)-benzene as the fluorinated building block and Sharpless asymmetric dihydroxylations to introduce hydroxyl groups enantioselectively. Unfortunately, a poor choice of protecting group prevented access to the desired difluorinated monosaccharide analogues, even if the asymmetric dihydroxylation proved successful and enantioselective. RCM was also used to synthesise different types of difluorocyclooctenones from trifluoroethanol. These difluorinated 8-membered carbocycles showed interesting and unusual conformational behaviour and were investigated by NMR experiments and a simple computational study. These difluorocyclooctenones were also used to synthesise new bicyclic structures, which are effectively conformationally restrained difluorinated monosaccharide analogues.
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Li, Dongfu. "Deep Neural Network Approach for Single Channel Speech Enhancement Processing." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34472.
Повний текст джерелаАнотація:
Speech intelligibility represents how comprehensible a speech is. It is more important than speech quality in some applications. Single channel speech intelligibility enhancement is much more difficult than multi-channel intelligibility enhancement. It has recently been reported that training-based single channel speech intelligibility enhancement algorithms perform better than Signal to Noise Ratio (SNR) based algorithm. In this thesis, a training-based Deep Neural Network (DNN) is used to improve single channel speech intelligibility. To increase the performance of the DNN, the Multi-Resolution Cochlea Gram (MRCG) feature set is used as the input of the DNN. MATLAB objective test results show that the MRCG-DNN approach is more robust than a Gaussian Mixture Model (GMM) approach. The MRCG-DNN also works better than other DNN training algorithms. Various conditions such as different speakers, different noise conditions and reverberation were tested in the thesis.
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Coates, Kendra. "An Evaluation of Growing Early Mindsets (GEM™)." Thesis, University of Oregon, 2016. http://hdl.handle.net/1794/20439.
Повний текст джерелаАнотація:
A growing body of literature and research emphasizes the importance of developing student mindsets and social and emotional learning (SEL) competencies (metacognitive learning skills) across the prenatal (P) to graduate school (20) (P-20) continuum to increase student motivation, engagement, achievement, and overall well-being. There is, however, an absence of research investigating the impact of braiding growth mindset, SEL, and mindfulness principles and practices together on early elementary student and teacher outcomes.
The purpose of my dissertation is to measure the impact of a new PreK–3rd curriculum called Growing Early Mindsets (GEM™) (Coates, in publication) on student and teacher outcomes across the K–3rd continuum in two districts in Oregon. Data collected during the 2014–15 Mindset Works’ study of Growing Early Mindsets (GEM™) was used. Classrooms were assigned to experimental (implemented GEM™) and control groups and given pretest and posttest measures to measure the impact of GEM™ on students’ approaches to learning, social and emotional learning (SEL) competencies, and literacy skills as well as on teacher mindsets, perceptions, confidence, and motivation. Results were statistically significant for students’ approaches to learning and SEL competencies as measured by Teacher Reports and teacher’s beliefs as measured by the Teacher Mindset Survey. Results were not statistically significant for students’ approaches to learning and SEL competencies as measured by Student Surveys, nor students’ oral reading fluency as measured by district-administered oral reading fluency measures. All experimental teachers reported that GEM™ changed their perceptions of their own and others’ learning and growth, increased their confidence to integrate growth mindset and SEL practices, and increased their motivation to improve their overall teaching practices. While the results are somewhat promising, the findings raise many questions that need further exploration.10000-01-01
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Xu, Zhifeng. "Best practice of risk modelling in motor insurance : using GLM and Machine Learning approach." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/20405.
Повний текст джерелаАнотація:
Mestrado em Actuarial ScienceO pricing na atividade seguradora está a tornar-se cada vez mais interessante e desafi- ador pelo facto de a dimensão dos dados a analisar estar a crescer de forma explosiva. Torna-se assim urgente para as seguradoras reconsiderar a forma de lidar com este vol- ume de dados. Para implementar modelos sofisticados de pricing para produtos de seguro automóvel, aplicámos técnicas de machine learning, incluindo modelos GLM penalizados e métodos de boosting, que ajudam a identificar as características mais importantes de entre uma grande quantidade de variáveis candidatas. Estes métodos também permitem detetar potenciais interações sem testar as inúmeras combinações bidimensionais. Para um uso eficiente desses métodos, é necessário compreender o objetivo do modelo, as hipóteses que o suportam e dominar as metodologias estatísticas. Embora haja alguma evidência de um maior poder preditivo dos modelos baseados em machine learning quando comparados com os tradicionais GLM, estes últimos beneficiam de uma estrutura, mais conveniente e mais interpretável. O modelo GLM é mais fácil de ex- plicar às partes interessadas o que nos levou a utilizar os GLM na modelação do risco, mas absorvendo os ensinamentos dados pelos modelos de machine learning. A avaliação dos modelos é realizada pela análise dos resíduos quer na fase de treino quer de validação quer ainda de teste. Após a revisão pela equipa, aplicam-se alguns ajustes em cada modelo para reforçar a sua significância e a sua robustez. Espera-se que eles tenham alto poder preditivo nos dados fora da amostra e possam, portanto, ser usados no futuro.
Insurance pricing nowadays is getting more and more interesting and challenging due to the fact that the dimension of analysable data is evolutionarily exploding. It is an urgent call for insurers to reconsider how to deal with the data more accurately and precisely. To implement pricing sophistication in motor insurance products, we apply cutting edge machine learning techniques including penalized GLM and boosting methods, which help us identify the important features among massive amount of candidate variables, and detect potential interactions without trying the endless two-way combinations manually. In order to sufficiently make use of these methods, we need to deeply understand the research objective, preliminary assumptions and statistical backgrounds. Although there is some evidence indicating the existence of higher predictive power of machine learning models compared with traditional GLM (Generalized Linear Models), GLM is more convenient and interpretable, especially for multiplicative models. GLM model is easier to be demonstrated to stakeholder, therefore we still achieve our risk models in GLM, but absorbing the insights from our machine learning results. The evaluation of models is done by progression, it is generally performed by residual analysis of the training or validation dataset, and testing errors for the holdout dataset. After peer review, we apply some adjustment in each model, to get models that are significant and robust. They are expected to have high predictive power in the out-of- sample data, thus can be used in the future.
info:eu-repo/semantics/publishedVersion
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Mote, Shekhar Raj. "EVALUATION OF STATE-OF-THE-ART PRECIPITATION ESTIMATES: AN APPROACH TO VALIDATE MULTI-SATELLITE PRECIPITATION ESTIMATES." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/theses/2364.
Повний текст джерелаАнотація:
Availability of precipitation data is very important in every aspect related to hydrology. Readings from the ground stations are reliable and are used in hydrological models to do various analysis. However, the predictions are always associated with uncertainties due to the limited number of ground stations, which requires interpolation of the data. Meanwhile, groundbreaking approach in capturing precipitation events from vantage point through satellites in space has created a platform to not only merge ground data with satellite estimates to produce more accurate result, but also to find the data where ground stations are not available or scarcely available. Nevertheless, the data obtained through these satellite missions needs to be verified on its temporal and spatial resolution as well as the uncertainties associated before we make any decisions on its basis. This study focuses on finding and evaluating data obtained from two multi-satellite precipitation measurements missions: i) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) ii) Global Precipitation Measurement (GPM) mission. GPM is the latest mission launched on Feb 28, 2014 after the successful completion of TRMM mission which collected valuable data for 17 years since its launch in November 1997. Both near real time and final version precipitation products for TMPA and GPM are considered for this study. Two study areas representing eastern and western parts of the United States of America (USA) are considered: i) Charlotte (CLT) in North Carolina ii) San Francisco (SF) in California. Evaluation is carried out for daily accumulated rainfall estimates and single rainfall events. Statistical analysis and error categorization of daily accumulated rainfall estimates were analyzed in two parts: i) Ten yeas data available for TMPA products were considered for historical analysis ii) Both TMPA and GPM data available for a ten-month common period was considered for GPM Era analysis. To study how well the satellite estimates with their finest temporal and spatial resolution capture single rainfall event and to explore their engineering application potential, an existing model of SF watershed prepared in Infoworks Integrated Catchment Model (ICM) was considered for hydrological simulation. Infoworks ICM is developed and maintained by Wallingford Software in the UK and SF watershed model is owned by San Francisco Public Works (SFPW). The historical analysis of TMPA products suggested overestimation of rainfall in CLT region while underestimation in SF region. This underestimation was largely associated with missed-rainfall events and negative hit events in SF. This inconsistency in estimation was evident in GPM products as well. However, in the study of single rainfall events with higher magnitude of rainfall depth in SF, the total rainfall volume and runoff volume generated in the watershed were over-estimated. Hence, satellite estimates in general tends to miss rainfall events of lower magnitude and over-estimate rainfall events of higher magnitude. From statistical analysis of GPM Era data, it was evident that GPM has been able to correct this inconsistency to some extent where it minimized overestimation in CLT region and minimized negative error due to underestimation in SF. GPM products fairly captured the hydrograph shape of outflow in SF watershed in comparison to TMPA. From this study, it can be concluded that even though GPM precipitation estimates could not quiet completely replace ground rain gage measurements as of now, with the perpetual updating of algorithms to correct its associated error, it holds realistic engineering application potential in the near future.
Книги з теми "GBM approach"
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Rutherford, Andrew, and Andrew Rutherford. ANOVA and ANCOVA: A GLM approach. 2nd ed. Hoboken, N.J: Wiley, 2011.
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Rutherford, Andrew. ANOVA and ANCOVA: A GLM approach. 2nd ed. Hoboken, N.J: Wiley, 2011.
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Introducing ANOVA and ANCOVA: A GLM approach. London ; Thousand Oaks, Calif: SAGE, 2001.
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Syntactic theory: A unified approach. 2nd ed. London: Arnold, 1999.
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Borsley, Robert D. Syntactic theory: A unified approach. London: E. Arnold, 1991.
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Gbéto, Flavien. Le maxi du Centre-Bénin et du Centre-Togo: Une approche autosegmentale et dialectologique d'un parler Gbe de la section Fon. Köln: R. Köppe, 1997.
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Rutherford, Andrew. ANOVA and ANCOVA: A GLM Approach. Wiley & Sons, Incorporated, John, 2012.
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Rutherford, Andrew. ANOVA and ANCOVA: A GLM Approach. Wiley & Sons, Limited, John, 2013.
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Rutherford, Andrew. ANOVA and ANCOVA: A GLM Approach. Wiley & Sons, Incorporated, John, 2012.
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Rutherford, Andrew. ANOVA and ANCOVA: A GLM Approach. Wiley & Sons, Incorporated, John, 2012.
Знайти повний текст джерелаЧастини книг з теми "GBM approach"
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Mendonça, Ana, Joana Pereira, Rita Reis, Victor Alves, António Abelha, Filipa Ferraz, João Neves, Jorge Ribeiro, Henrique Vicente, and José Neves. "A Case-Based Reasoning Approach to GBM Evolution." In Computational Collective Intelligence, 489–98. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98446-9_46.
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Dietz, Ekkehart. "Estimation of Heterogeneity — A GLM-Approach." In Advances in GLIM and Statistical Modelling, 66–71. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2952-0_11.
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Kintiraki, Evangelia, and Dimitrios G. Goulis. "Medical Monitoring of Preexisting DM and GDM." In Comprehensive Clinical Approach to Diabetes During Pregnancy, 119–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89243-2_7.
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Inkmann, Joachim. "The Conditional Moment Approach to GMM Estimation." In Lecture Notes in Economics and Mathematical Systems, 6–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56571-7_2.
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Stamou, Maria I., and Marie-France Hivert. "Fetal Origin of Adult Disease: The Case of GDM." In Comprehensive Clinical Approach to Diabetes During Pregnancy, 93–116. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89243-2_6.
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Gerstein, Larry. "The local-global approach to lattices." In Graduate Studies in Mathematics, 175–206. Providence, Rhode Island: American Mathematical Society, 2008. http://dx.doi.org/10.1090/gsm/090/09.
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Körner, T. "Another approach to the inverse function theorem." In Graduate Studies in Mathematics, 383–86. Providence, Rhode Island: American Mathematical Society, 2003. http://dx.doi.org/10.1090/gsm/062/20.
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Liu, Yong-Bo, and Xin-Yu Wang. "Gene flow mitigation by ecological approaches." In Gene flow: monitoring, modeling and mitigation, 125–36. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789247480.0009.
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Abstract With an increased area of cultivating genetically modified (GM) plants worldwide, the ecological risks of transgenic plants released into the environment have caused concern. One of the risks is the occurrence of gene flow between GM plants and non-GM plants, including their wild relatives. Gene flow data from oilseed rape (Brassica napus), cotton (Gossypium hirsutum), maize (Zea mays), soybean (Glycine max), rice (Oryza sativa), and wheat (Triticum aestivum) indicate that the frequency of pollen-mediated gene flow is negatively related with distance between donor and recipient plants, and the frequency is relatively high in closely related species. We discuss five main ecological approaches to mitigate gene flow from GM plants to non-GM plants, including distance isolation, border or trap crops, barrier crops, agricultural practices, and through biological means. The required isolation distance has been adopted in managing GM crops in some countries, and cultivating tall crops, or border or trap crops, can decrease the requisite isolation distance to mitigate gene flow. Combining several approaches is more effective than a single approach in mitigating gene flow, because the frequency of pollen-mediated gene flow depends on plant genotype, flowering time, wind speed and direction, and other factors. Thus, in the framework of biosafety assessment of GM plants, mitigating the occurrence of gene flow between GM and non-GM plants is a key step to decrease the ecological risk of post- commercial cultivation of GM plants.
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Smith, Hal, and Horst Thieme. "Topological approaches to persistence." In Graduate Studies in Mathematics, 177–230. Providence, Rhode Island: American Mathematical Society, 2010. http://dx.doi.org/10.1090/gsm/118/09.
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Korn, Ralf, and Elke Korn. "The mean-variance approach in a one-period model." In Graduate Studies in Mathematics, 1–9. Providence, Rhode Island: American Mathematical Society, 2000. http://dx.doi.org/10.1090/gsm/031/01.
Повний текст джерелаТези доповідей конференцій з теми "GBM approach"
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Şeker, F., A. Erkent, N. Ergüder, E. Barçin, F. Uyulur, N. Lack, M. Gönen, H. Wakimoto, and T. Bagci-Onder. "PO-196 Identification of novel molecular players of GBM cell dispersal through anin vitroprofiling approach." In Abstracts of the 25th Biennial Congress of the European Association for Cancer Research, Amsterdam, The Netherlands, 30 June – 3 July 2018. BMJ Publishing Group Ltd, 2018. http://dx.doi.org/10.1136/esmoopen-2018-eacr25.232.
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Harputlu Aksu, Şeniz, and Erman Çakıt. "Classifying mental workload using EEG data: A machine learning approach." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001820.
Повний текст джерелаАнотація:
Mental workload is related to the difference between the available mental resource capacity of the operator and the mental resource required by the job. To decide the number of tasks assigned to operator and the difficulty levels of those tasks, it is important to know the operator's mental workload. An overload occurs if the amount of resources required by the task exceeds the available capacity of the person. Mental workload analysis helps to recognize the mental fatigue, evaluate the human performance of different level tasks and adjust cognitive sources for safe and efficient human-machine interactions. Excessive levels of mental workload can lead to errors or delays in information processing. Monitoring brain activity has been verified to be sensitive and consistent reflector of mental workload changes. Classification, regression, clustering, anomaly detection, dimensionality reduction, and reward maximization are common machine learning models. Classification of mental workload has critical importance in the domain of human factors and ergonomics. In recent years, with the need to analyze continuous and large-scale data obtained by physiological methods, the use of machine learning algorithms has become widespread in estimating and classifying mental workload. The objectives of the current study were two-fold: (1) to investigate the relationship among EEG features, task difficulty levels and subjective self-assessment (NASA-TLX) scores and (2) to develop machine learning algorithms for classifying mental workload using EEG features. N-back tasks have been commonly used in the literature. In this study, N-back memory tests were performed at four different difficulty levels. As the number of n increases, so does the difficulty of the task. Four participants performed the tests. Seventy EEG features (5 frequency band power for 14 channels) were selected as independent variables. One output variable reflecting the difficulty level of N-Back memory was classified. The machine learning algorithms used in our study were K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) algorithms. As the task difficulty increased, theta activity in prefrontal and frontal regions increased. Especially frontal theta power, parietal and occipital gamma power were significantly correlated to perceived workload scores obtained via NASA-TLX. Prefrontal beta-high activity had a significant negative relationship with self-assessment workload ratings. Prefrontal and frontal theta, prefrontal beta-high, occipital, parietal and temporal gamma and occipital alpha activities were found to be the most effective parameters. The results obtained for the four classes of classification problem reached the accuracy of 68% with EEG features as input and the Random Forest algorithm. In addition, the results obtained for the two classes of classification problem reached the accuracy of 87% with EEG features as input and the GBM algorithm. The results from the analysis indicate that EEG signals play an important role in the classification of mental workload. Another remarkable result was high classification performance of GBM, LightGBM and XGBoost algorithms that have been developed in the recent past and therefore not frequently used in studies on this subject in the literature.
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Ogwu, Jessica, Emmanuel Ikpesu, and Kingsley Ogbonna. "Natural Gas Spot Price Prediction Using a Machine Learning Datacentric Approach." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/211979-ms.
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Abstract The ability to accurately predict natural gas prices asides being beneficial to stakeholders of the natural gas market also have positive economic impacts on energy management and environmental sustainability. This paper explores the application of machine learning algorithms for the purpose of accurately predicting monthly natural gas spot prices. Henry Hub natural gas spot price data from January 2001 to November 2021 were utilized alongside four machine learning algorithms namely; Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest Regressor and Gradient Boosting Machine (GBM). The models were trained with 11 variables with 80% of the dataset utilized for training and 20% for testing purposes. A 10-fold cross validation technique was implemented for model validation purposes. The accuracy of each model was evaluated using the Root Mean Square error metric. After model evaluation, all four models generated distinct results, with the Artificial Neural Network model having the most accurate prediction of all four models.
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Stripay, Jennifer L., Brett M. Stevens, Addie L. Bardin, and Mark D. Noble. "Abstract B14: Targeting a network of cancer control nodes through rescue of c-Cbl: A novel therapeutic approach in GBM." In Abstracts: AACR Special Conference: Advances in Brain Cancer Research; May 27-30, 2015; Washington, DC. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.brain15-b14.
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Calabrese, Matteo, Martin Cimmino, Martina Manfrin, Francesca Fiume, Dimos Kapetis, Maura Mengoni, Silvia Ceccacci, et al. "An Event Based Machine Learning Framework for Predictive Maintenance in Industry 4.0." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97917.
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Abstract Predictive Maintenance concerns the smart monitoring of machine to avoid possible future failures, since because it is better to intervene before the damage occurs, saving time and money. In this paper, a Predictive Maintenance methodology based on Machine learning approach is presented and it is applied to a real cutting machine, a woodworking machinery in a real industrial group, producing accurate estimations. This kind of strategy is important to deal with maintenance problems given the ever increasing need to reduce downtime and associated costs. The Predictive Maintenance methodology implemented allows dynamical decision rules that have to be considered for maintenance prediction using a combined approach on Azure Machine Learning Studio. The Three models (RF, GBM and XGBM) allowed the accurately predict machine down ever gripped bearing thanks to the pre-processing phases.
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Tavares, Gabriel, Saulo Mastelini, and Sylvio Jr. "User Classification on Online Social Networks by Post Frequency." In XIII Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação, 2017. http://dx.doi.org/10.5753/sbsi.2017.6076.
Повний текст джерелаАнотація:
This paper proposes a technique for classifying user accounts on social networks to detect fraud in Online Social Networks (OSN). The main purpose of our classification is to recognize the patterns of users from Human, Bots or Cyborgs. Classic and consolidated approaches of Text Mining employ textual features from Natural Language Processing (NLP) for classification, but some drawbacks as computational cost, the huge amount of data could rise in real-life scenarios. This work uses an approach based on statistical frequency parameters of the user posting to distinguish the types of users without textual content. We perform the experiment over a Twitter dataset and as learn-based algorithms in classification task we compared Random Forest (RF), Support Vector Machine (SVM), k-nearest Neighbors (k-NN), Gradient Boosting Machine (GBM) and Extreme Gradient Boosting (XGBoost). Using the standard parameters of each algorithm, we achieved accuracy results of 88% and 84% by RF and XGBoost, respectively
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Ma, Liang, Lei Gao, Yichen Luo, Huayong Yang, Bin Zhang, Changchun Zhou, JinGyu Ock, and Wei Li. "Flow Analysis of a Porous Polymer-Based Three-Dimensional Cell Culture Device for Drug Screening." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6313.
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A porous polymer-based three-dimensional (3D) cell culture device has been developed as an in vitro tissue model system for the cytotoxicity of anticancer drug test. The device had two chambers connected in tandem, each loaded with a 3D scaffold made of highly biocompatible poly (lactic acid) (PLA). Hepatoma cells (HepG2) and glioblastoma multiforme (GBM) cancer cells were cultured in the two separate porous scaffolds. A peristaltic pump was adopted to realize a perfusion cell culture. In this study, we focus on cell viability inside the 3D porous scaffolds under flow-induced shear stress effects. A flow simulation was conducted to predict the shear stress based on a realistic representation of the porous structure. The simulation results were correlated to the cell variability measurements at different flow rates. It is shown that the modeling approach presented in this paper can be useful for shear stress predication inside porous scaffolds and the computational fluid dynamics model can be an effective way to optimize the operation parameters of perfused 3D cell culture devices.
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Mess, Griffin, Rasika Thombre, Max Kerensky, Eli Curry, Fariba Abhabaglou, Safwan Alomari, Henry Brem, Nicholas Theodore, Betty Tyler, and Amir Manbachi. "Designing a Murine Model of Human Glioblastoma Brain Tumor: Development of a Platform for Validation Using Ultrasound Elastography." In 2022 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/dmd2022-1025.
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Abstract Glioblastoma Multiforme (GBM) is a malignant brain cancer with low overall survival. Therefore, researchers are looking to augment its current therapeutic regimen, which includes surgical tumor resection, chemotherapy and radiation. A promising treatment modality, focused ultrasound, has been used as a non-invasive treatment for GBM through multiple approaches such as thermal ablation, immunomodulation, and blood brain barrier disruption. In order to develop these treatments for clinical trials, testing in animal models needs to be performed to investigate the efficacy of the treatment in complex biological environments, as well as to evaluate any side-effects. The more biologically relevant the animal model is to human anatomy, the more applicable the results will be for translation to clinical trials. Here, we report a human GBM rat model, which utilizes an IDH-wildtype, EGFRvIII mutant patient-derived xenograft in athymic rats. The in vivo tumor growth rate was assessed over a period of 20 days to evaluate reproducibility and to develop the model for future testing of FUS in the treatment of GBM.
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Alusta, Gamal, Hossein Algdamsi, Ahmed Amtereg, Ammar Agnia, Ahmed Alkouh, and Bacem Kcharem. "Integration of Self Organizing Map and Date Driven Methods to Predict Oil Formation Volume Factor: North Africa Crude Oil Examples." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205782-ms.
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Abstract In this paper we introduce for the first time an innovative approach for deriving Oil Formation Volume Factor (Bo) by mean of artificial intelligence method. In a new proposed application Self-Organizing Map (SOM) technology has been merged with statistical prediction methods integrating in a single step dimensionality reduction, extraction of input data structure pattern and prediction of formation volume factor Bo. The SOM neural network method applies an unsupervised training algorithm combined with back propagation neural network BPNN to subdivide the entire set of PVT input into different patterns identifying a set of data that have something in common and run individual MLFF ANN models for each specific PVT cluster and computing Bo. PVT data for more than two hundred oil samples (total of 804 data points) were collected from the north African region representing different basin and covering a greater geographical area were used in this study. To establish clear Bound on the accuracy of Bo determination several statistical parameters and terminology included in the presentation of the result from SOM-Neural Network solution. the main outcome is the reduction of error obtained by the new proposed competitive Learning Structure integration of SOM and MLFF ANN to less than 1 % compared to other method. however also investigated in this work five independents means of model driven and data driven approach for estimating Bo theses are 1) Optimal Transformations for Multiple Regression as introduced by (McCain, 1998) using alternating conditional expectations (ACE) for selecting multiple regression transformations 2), Genetic programing and heuristic modeling using Symbolic Regression (SR) and cross validation for model automatic tuning 3) Machine learning predictive model (Nearest Neighbor Regression, Kernel Ridge regression, Gaussian Process Regression (GPR), Random Forest Regression (RF), Support Vector Regression (SVM), Decision Tree Regression (DT), Gradient Boosting Machine Regression (GBM), Group modeling data handling (GMDH). Regression Model Accuracy Metrics (Average absolute relative error, R-square), diagnostic plot was used to address the more adequate techniques and model for predicting Bo.
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Manasipov, Roman, Denis Nikolaev, Dmitrii Didenko, Ramez Abdalla, and Michael Stundner. "Physics Informed Machine Learning for Production Forecast." In SPE Reservoir Characterisation and Simulation Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212666-ms.
Повний текст джерелаАнотація:
Abstract Understanding the reservoir behavior is vital knowledge required for various aspects of the reservoir management cycle such as production optimization and establishment of the field development strategy. Reservoir simulation is the most accurate tool for production forecast, but often it is very expensive from aspects of computational time and investment in the model building process. In this work, the machine learning methods for accurate production forecast that honor the material balance constraints are presented. The presented hybrid model approach consists of several main components. The material balance constraints are necessary during the training process to avoid unphysical solutions and to honor conservation laws. For this reason, the Capacitance Resistance Model (CRM) was chosen due to its intuitive form and flexibility in describing reservoirs of various complexities. Another part of the solution is represented by powerful machine learning methods such as Generalized Additive Models (GAM), Gradient Boosting, and Convolutional and Recurrent Neural Networks. Neural Networks and Gradient Boosting methods are very popular machine learning techniques. However, in this work, it is demonstrated that GAM can also produce results comparable to the former methods while holding additional attractive properties. The basis functions of GAM are the splines, which are smooth functions with continuous derivatives. Such properties are very useful for optimization tasks. GAM is an extension of standard Generalized Linear Models (GLM), which provides rich tools for model explainability. It is hence also advantageous for the understanding how the reservoir behaves through such models. The implemented approach was applied to the publicly available data with an existing history matched reservoir model for the offshore field with several injectors and producers. This allowed us to compare results and build machine learning models that describe communication between wells and can be further analyzed though the simulation model. Machine learning methods are constantly improving at solving difficult problems, while it often suffers from nonphysical solutions and unexplainable models. The presented method holds the properties of explainable regression models while providing powerful predictability capabilities within material balance constraints. By no means does it try to replace the reservoir simulation but offers a complementary solution, which is reliable and necessary in cases where there is no full reservoir model available.
Звіти організацій з теми "GBM approach"
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Hasell, Douglas K. New Approach for 2D Readout of GEM Detectors. Office of Scientific and Technical Information (OSTI), October 2011. http://dx.doi.org/10.2172/1030606.
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Idris, Iffat. Documentation of Survivors of Gender-based Violence (GBV). Institute of Development Studies (IDS), July 2021. http://dx.doi.org/10.19088/k4d.2021.103.
Повний текст джерелаАнотація:
This review is largely based on grey literature, in particular policy documents and reports by international development organizations. While there was substantial literature on approaches and principles to GBV documentation, there was less on remote service delivery such as helplines – much of this only in the wake of the COVID-19 pandemic. In addition, very little was found on actual examples of GBV documentation in developing contexts. By definition, gender featured strongly in the available literature; the particular needs of persons with disabilities were also addressed in discussions of overall GBV responses, but far less in GBV documentation. GBV documentation refers to the recording of data on individual GBV incidents in order to provide/refer survivors with/to appropriate support, and the collection of data of GBV incidents for analysis and to improve GBV responses. The literature notes that there are significant risks associated with GBV documentation, in relation to data protection. Failure to ensure information security can expose survivors, in particular, to harm, e.g. reprisal attacks by perpetrators, stigma, and ostracism by their families/ communities. This means that GBV documentation must be carried out with great care. A number of principles should always be applied when documenting GBV cases in order to protect survivors and prevent potential negative effects: do no harm, survivor-centered approach, survivor autonomy, informed consent, non-discrimination, confidentiality, and data protection (information security).
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Kurozumi, Takushi, Ryohei Oishi, and Willem Van Zandweghe. Sticky Information Versus Sticky Prices Revisited: A Bayesian VAR-GMM Approach. Federal Reserve Bank of Cleveland, November 2022. http://dx.doi.org/10.26509/frbc-wp-202234.
Повний текст джерелаАнотація:
Several Phillips curves based on sticky information and sticky prices are estimated and compared using Bayesian VAR-GMM. This method derives expectations in each Phillips curve from a VAR and estimates the Phillips curve parameters and the VAR coefficients simultaneously. Quasi-marginal likelihood-based model comparison selects a dual stickiness Phillips curve in which, each period, some prices remain unchanged, consistent with micro evidence. Moreover, sticky information is a more plausible source of inflation inertia in the Phillips curve than other sources proposed in previous studies. Sticky information, sticky prices, and unchanged prices in each period are all needed to better describe inflation dynamics.
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Savani, Manu, and Alastair Stewart. Making Market Systems Work for Women Dairy Farmers in Bangladesh: A final evaluation of Oxfam's Gendered Enterprise and Markets programme in Bangladesh. Oxfam GB, December 2019. http://dx.doi.org/10.21201/2019.5365.
Повний текст джерелаАнотація:
Gendered Enterprise and Markets (GEM) is Oxfam GB’s approach to market systems development. The GEM approach facilitates change in market systems and social norms, with the aim of ensuring more sustainable livelihood opportunities for marginalized women and men. The GEM DFID AidMatch Programme (June 2014–February 2018) worked within the soya, milk and vegetable value chains targeting women smallholder farmers in areas of poverty. The programme aimed to benefit 63,600 people (10,600 smallholder households) living in Zambia, Tajikistan and Bangladesh through increases in household income, women having greater influence over key livelihood decisions within their households and communities, and engaging in livelihoods more resilient to shocks, such as natural disasters and market volatility. The GEM programme in Bangladesh was implemented under Oxfam Bangladesh’s flagship REE-CALL programme (Resilience, through Economic Empowerment, Climate Adaptation, Leadership and Learning). GEM operated in seven districts across Bangladesh, with the project activities implemented by seven local partners. The project aimed to establish 84 producer groups for smallholder dairy farmers, and this was achieved during the first year. Building on these local networks, GEM aimed to deliver a suite of training and support covering assertiveness, rights and leadership skills, agricultural practice and disaster risk management. The evaluation was designed to investigate if and how the GEM programme might have contributed to its intended outcomes – not only in the lives of individual women smallholder farmers targeted by the programme but also in changes in their communities and the larger market system. It also sought to capture any potential unintended outcomes of the programme.
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Stewart, Alastair, and Miranda Morgan. A Final Evaluation of Oxfam's Gendered Enterprise and Markets Programme (2014-18): Summary of findings. Oxfam GB, December 2019. http://dx.doi.org/10.21201/2019.5358.
Повний текст джерелаАнотація:
Gendered Enterprise and Markets (GEM) is Oxfam GB’s approach to market systems development. The GEM approach facilitates change in market systems and social norms, with the aim of ensuring more sustainable livelihood opportunities for marginalized women and men. The GEM DFID AidMatch Programme (June 2014–February 2018) worked within the soya, milk and vegetable value chains targeting women smallholder farmers in areas of poverty. The programme aimed to benefit 63,600 people (10,600 smallholder households) living in Zambia, Tajikistan and Bangladesh through increases in household income, women having greater influence over key livelihood decisions within their households and communities, and engaging in livelihoods more resilient to shocks, such as natural disasters and market volatility. This evaluation was designed to investigate if and how the GEM programme contributed to its intended outcomes – not only in the lives of individual women smallholder farmers targeted by the programme but also in terms of changes in their communities and the larger market system. It also sought to capture any potential unintended outcomes of the programme. This summary report outlines the key findings from the three individual country evaluations in Bangladesh, Tajikistan and Zambia - for which the full reports are also available.
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Morgan, Miranda, and Alastair Stewart. Making Market Systems Work for Women Farmers in Tajikistan: A final evaluation of Oxfam's Gendered Enterprise and Markets programme in Tajikistan. Oxfam GB, December 2019. http://dx.doi.org/10.21201/2019.5372.
Повний текст джерелаАнотація:
Gendered Enterprise and Markets (GEM) is Oxfam GB’s approach to market systems development. The GEM approach facilitates change in market systems and social norms, with the aim of ensuring more sustainable livelihood opportunities for marginalized women and men. The GEM DFID AidMatch Programme (June 2014–February 2018) worked within the soya, milk and vegetable value chains targeting women smallholder farmers in areas of poverty. The programme aimed to benefit 63,600 people (10,600 smallholder households) living in Zambia, Tajikistan and Bangladesh through increases in household income, women having greater influence over key livelihood decisions within their households and communities, and engaging in livelihoods more resilient to shocks, such as natural disasters and market volatility. In Tajikistan, the Gendered Enterprise and Markets (GEM) programme has been implemented in five districts of Khatlon Province by Oxfam in partnership with local public organizations, League of Women Lawyers of Tajikistan (LWL) and Neksigol Mushovir. The GEM programme in Tajikistan sought to directly improve the livelihoods of an estimated 3,000 smallholder farmers (60 percent women) in fruit and vegetable value chains through improved production skills, resilience to climate risks, access to market opportunities and greater engagement with market players, and strengthened ability to influence private sector and government actors. The evaluation was designed to investigate if and how the GEM programme might have contributed to its intended outcomes – not only in the lives of individual women smallholder farmers targeted by the programme but also to changes in their communities and the larger market system. It also sought to capture any potential unintended outcomes of the programme.
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Morgan, Miranda, Alastair Stewart, and Simone Lombardini. Making Market Systems Work for Women Farmers in Zambia: A final evaluation of Oxfam's Gendered Enterprise and Markets programme in the Copperbelt region of Zambia. Oxfam GB, December 2019. http://dx.doi.org/10.21201/2019.5389.
Повний текст джерелаАнотація:
Gendered Enterprise and Markets (GEM) is Oxfam GB’s approach to market systems development. The GEM approach facilitates change in market systems and social norms, with the aim of ensuring more sustainable livelihood opportunities for marginalized women and men. The GEM DFID AidMatch Programme (June 2014–February 2018) worked within the soya, milk and vegetable value chains targeting women smallholder farmers in areas of poverty. The programme aimed to benefit 63,600 people (10,600 smallholder households) living in Zambia, Tajikistan and Bangladesh through increases in household income, women having greater influence over key livelihood decisions within their households and communities, and engaging in livelihoods more resilient to shocks, such as natural disasters and market volatility. In Zambia, the GEM programme has been implemented in four districts of the Copperbelt Province in coordination with implementing partners Heifer Programmes International and the Sustainable Agricultural Programme (SAP). The GEM programme in the Copperbelt seeks to directly improve the livelihoods of an estimated 4,000 smallholder farmers (75 percent women) in the dairy and soya value chains through improved production skills, resilience to climate risks, access to market opportunities, greater engagement with market players and strengthened ability to influence private sector and government actors. The evaluation was designed to investigate if and how the GEM programme might have contributed to its intended outcomes – not only in the lives of individual women smallholder farmers targeted by the programme but also to changes in their communities and the larger market system. It also sought to capture any potential unintended outcomes of the programme.
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Sengupta, S. K., and J. S. Boyle. Statistical intercomparison of global climate models: A common principal component approach with application to GCM data. Office of Scientific and Technical Information (OSTI), May 1993. http://dx.doi.org/10.2172/10173301.
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Ranzi, Gianluca, Alberto Ferrarotti, and Giuseppe Piccardo. OVERVIEW OF THE DYNAMIC APPROACH FOR THE FULL AND PARTIAL INTERACTION ANALYSIS WITHIN THE GENERALISED BEAM TEHORY (GBT). The Hong Kong Institute of Steel Construction, December 2018. http://dx.doi.org/10.18057/icass2018.p.171.
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Wroblewski, Angela, Bente Knoll, Barbara Pichler, Elisabeth Reitinger, Birgit Hofleitner, Barbara Egger, Victoria Englmaier, Peter Koller, and Arn Sauer. Chancen feministischer Evaluation. Methodische Herausforderungen bei der Evaluation von Gender Mainstreaming und Gleichstellungspolitiken. Working Paper 119. Edited by Angela Wroblewski. IHS - Institute for Advanced Studies, May 2018. http://dx.doi.org/10.22163/fteval.2018.502.
Повний текст джерелаАнотація:
Studies in the context of gender mainstreaming, gender equality policy or feminist issues often face specific challenges in connection with the empirical approach. The Gender Mainstreaming Working Group (AK GM) of the German Evaluation Society (DeGEval) focused on the choice of adequate methods and research designs for the evaluation of gender mainstreaming measures, gender equality policies and feminist evaluation at its spring conference 2017, which took place at the IHS on 11 May 2017 and is documented in this volume.