Готові списки джерел за темами / Personalized predictive medicine

Зміст

  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Добірка наукової літератури з теми "Personalized predictive medicine"

Автор: Grafiati
Опубліковано: 2 листопада 2024

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Personalized predictive medicine".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Статті в журналах з теми "Personalized predictive medicine"

1

Nassimbwa, Kabanda D. "The Role of Biomarkers in Personalized Cancer Treatment." RESEARCH INVENTION JOURNAL OF PUBLIC HEALTH AND PHARMACY 3, no. 2 (2024): 3–33. http://dx.doi.org/10.59298/rijpp/2024/323033.

Повний текст джерела
Анотація:
Biomarkers are vital instruments in modern medicine, providing information about biological processes and disease progression. Biomarkers in oncology have had a substantial impact on the development of personalised cancer treatments by predicting therapy responses and outcomes. The study investigates the different types of biomarkers, such as diagnostic, prognostic, and predictive biomarkers, and their function in precision medicine. Personalised cancer treatment, which is guided by biomarker testing, improves patient outcomes by adapting medicines to individual genetic profiles. However, obstacles such as biomarker validation, resistance to targeted medicines, and regulatory barriers persist. Overcoming these obstacles will propel future advances in biomarker-driven oncology. Keywords: Biomarkers, personalized medicine, cancer, precision oncology, diagnostic biomarkers.
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Giglia, Giuseppe, Giuditta Gambino, and Pierangelo Sardo. "Through Predictive Personalized Medicine." Brain Sciences 10, no. 9 (2020): 594. http://dx.doi.org/10.3390/brainsci10090594.

Повний текст джерела
Анотація:
Neuroblastoma (NBM) is a deadly form of solid tumor mostly observed in the pediatric age. Although survival rates largely differ depending on host factors and tumor-related features, treatment for clinically aggressive forms of NBM remains challenging. Scientific advances are paving the way to improved and safer therapeutic protocols, and immunotherapy is quickly rising as a promising treatment that is potentially safer and complementary to traditionally adopted surgical procedures, chemotherapy and radiotherapy. Improving therapeutic outcomes requires new approaches to be explored and validated. In-silico predictive models based on analysis of a plethora of data have been proposed by Lombardo et al. as an innovative tool for more efficacious immunotherapy against NBM. In particular, knowledge gained on intracellular signaling pathways linked to the development of NBM was used to predict how the different phenotypes could be modulated to respond to anti-programmed cell death-ligand-1 (PD-L1)/programmed cell death-1 (PD-1) immunotherapy. Prediction or forecasting are important targets of artificial intelligence and machine learning. Hopefully, similar systems could provide a reliable opportunity for a more targeted approach in the near future.
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Reena Dhan, Archana, and Binod Kumar. "Machine Learning for Healthcare: Predictive Analytics and Personalized Medicine." International Journal of Science and Research (IJSR) 13, no. 6 (2024): 1307–13. http://dx.doi.org/10.21275/mr24608013906.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Farooq, Faisal, Balaji Krishnapuram, Romer Rosales, Shipeng Yu, Jude Shavlik, and Raju Kucherlapati. "Predictive Models in Personalized Medicine." ACM SIGHIT Record 1, no. 1 (2011): 23–25. http://dx.doi.org/10.1145/1971706.1971714.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

DOSAY-AKBULUT, Mine. "A Review on Determination and Future of the Predictive and Personalized Medicine." International Journal of Biology 8, no. 1 (2015): 32. http://dx.doi.org/10.5539/ijb.v8n1p32.

Повний текст джерела
Анотація:
<p class="1Body">Medicine contents’ have extended to predictive, personalized, preventive and participatory medicine (P4). ‘Personalized medicine' focuses on the prediction of potential benefits or risks for individuals as possible as in detailed. Biomarker discovery, biocomputing and nanotechnology have opened a new horizon on ‘personalized medicine’ (including disease detection, diagnosis and therapy by using individual's molecular profile) and ‘predictive medicine’ (to predict disease development, progression and clinical outcome, by using the genetic and molecular information).</p><p class="1Body">Personalized medicine can be applied to a lot of different areas. P4 medicine, based on use of marker-assisted diagnosis and targeted therapies, comes from an individual's molecular profile, will form a new way on drugs development and medicine administration. Genetic screening aimed to identify carrier and affected individuals in a particular population. Molecular diagnostic test, including genome-derived tests are getting more attention within the medicine with genotyping, RNA expression analyses, metabolic profiling, and other biomarkers. Genomics research has getting more attention on the biomedical research, translational science, and personalized medicine; divided into 3 main parts: 1) genomics to biology, 2) genomics to health, and 3) genomics to society.</p><p class="1Body">We conducted a literature search via PubMed databases with using “personalized medicine”, and “application areas of P4” keywords, and summarized some of new studies.</p><p class="1Body">Personalized medicine is described as an individualized treatment based on the individual's genetic variants. In other words, “for predicting health, preventing and preempting disease, and personalizing treatment depending on the each person’ unique biology", has a speedy improvement.</p>
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Nie, Shuming. "Nanotechnology for personalized and predictive medicine." Nanomedicine: Nanotechnology, Biology and Medicine 2, no. 4 (2006): 305. http://dx.doi.org/10.1016/j.nano.2006.10.115.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Workman, Paul, Paul A. Clarke, and Bissan Al-Lazikani. "Personalized Medicine: Patient-Predictive Panel Power." Cancer Cell 21, no. 4 (2012): 455–58. http://dx.doi.org/10.1016/j.ccr.2012.03.030.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Rizvi, S. Mohd Shiraz, Farzana Mahdi, Abbas Ali Mahdi, Tabrez Jafar, and Saliha Rizvi. "PERSONALIZED MEDICINE: ROLE OF ASYMMETRIC DIMETHYLARGININE AS A PREDICTIVE MARKER OF CAD." Era's Journal of Medical Research 7, no. 1 (2020): 86–91. http://dx.doi.org/10.24041/ejmr2020.15.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Mathieu, Thierry, Laurent Bermont, Jean-Christophe Boyer, et al. "Lexical fields of predictive and personalized medicine." Annales de biologie clinique 70, no. 6 (2012): 651–58. http://dx.doi.org/10.1684/abc.2012.0767.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Martin, Greg, and Dean Jones. "The Road to Personalized and Predictive Medicine." American Journal of Respiratory and Critical Care Medicine 188, no. 2 (2013): 257. http://dx.doi.org/10.1164/rccm.201212-2248le.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.

Дисертації з теми "Personalized predictive medicine"

1

Bragazzi, Nicola Luigi [Verfasser], and Norbert [Akademischer Betreuer] Hampp. "Nanogenomics and Nanoproteomics Enabling Personalized, Predictive and Preventive Medicine / Nicola Luigi Bragazzi. Betreuer: Norbert Hampp." Marburg : Philipps-Universität Marburg, 2014. http://d-nb.info/1051935334/34.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Park, Keon-Young. "Predicting patient-to-patient variability in proteolytic activity and breast cancer progression." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53479.

Повний текст джерела
Анотація:
About one in eight women in the United States will develop breast cancer over the course of her lifetime. Moreover, patient-to-patient variability in disease progression continues to complicate clinical decisions in diagnosis and treatment for breast cancer patients. Early detection of tumors is a key factor influencing patient survival, and advancements in diagnostic and imaging techniques has allowed clinicians to spot smaller sized lesions. There has also been an increase in premature treatments of non-malignant lesions because there is no clear way to predict whether these lesions will become invasive over time. Patient variability due to genetic polymorphisms has been investigated, but studies on variability at the level of cellular activity have been extremely limited. An individual’s biochemical milieu of cytokines, growth factors, and other stimuli contain a myriad of cues that pre-condition cells and induce patient variability in response to tumor progression or treatment. Circulating white blood cells called monocytes respond to these cues and enter tissues to differentiate into monocyte-derived macrophages (MDMs) and osteoclasts that produce cysteine cathepsins, powerful extracellular matrix proteases. Cathepsins have been mechanistically linked to accelerated tumor growth and metastasis. This study aims to elucidate the variability in disease progression among patients by examining the variability of protease production from tissue-remodeling macrophages and osteoclasts. Since most extracellular cues initiate multiple signaling cascades that are interconnected and dynamic, this current study uses a systems biology approach known as cue-signal-response (CSR) paradigm to capture this complexity comprehensively. The novel and significant finding of this study is that we have identified and predicted donor-to-donor variability in disease modifying cysteine cathepsin activities in macrophages and osteoclasts. This study applied this novel finding to the context of tumor invasion and showed that variability in tumor associated macrophage cathepsin activity and their inhibitor cystatin C level mediates variability in cancer cell invasion. These findings help to provide a minimally invasive way to identify individuals with particularly high remodeling capabilities. This could be used to give insight into the risk for tumor invasion and develop a personalized therapeutic regime to maximize efficacy and chance of disease free survival.
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Cheng, Chih-Wen. "Development of integrated informatics analytics for improved evidence-based, personalized, and predictive health." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54872.

Повний текст джерела
Анотація:
Advanced information technologies promise a massive influx of individual-specific medical data. These rich sources offer great potential for an increased understanding of disease mechanisms and for providing evidence-based and personalized clinical decision support. However, the size, complexity, and biases of the data pose new challenges, which make it difficult to transform the data to useful and actionable knowledge using conventional statistical analysis. The so-called “Big Data” era has created an emerging and urgent need for scalable, computer-based data mining methods that can turn data into useful, personalized decision support knowledge in a flexible, cost-effective, and productive way. The goal of my Ph.D. research is to address some key challenges in current clinical deci-sion support, including (1) the lack of a flexible, evidence-based, and personalized data mining tool, (2) the need for interactive interfaces and visualization to deliver the decision support knowledge in an accurate and effective way, (3) the ability to generate temporal rules based on patient-centric chronological events, and (4) the need for quantitative and progressive clinical predictions to investigate the causality of targeted clinical outcomes. The problem statement of this dissertation is that the size, complexity, and biases of the current clinical data make it very difficult for current informatics technologies to extract individual-specific knowledge for clinical decision support. This dissertation addresses these challenges with four overall specific aims: Evidence-Based and Personalized Decision Support: To develop clinical decision support systems that can generate evidence-based rules based on personalized clinical conditions. The systems should also show flexibility by using data from different clinical settings. Interactive Knowledge Delivery: To develop an interactive graphical user interface that expedites the delivery of discovered decision support knowledge and to propose a new visualiza-tion technique to improve the accuracy and efficiency of knowledge search. Temporal Knowledge Discovery: To improve conventional rule mining techniques for the discovery of relationships among temporal clinical events and to use case-based reasoning to evaluate the quality of discovered rules. Clinical Casual Analysis: To expand temporal rules with casual and time-after-cause analyses to provide progressive clinical prognostications without prediction time constraints. The research of this dissertation was conducted with frequent collaboration with Children’s Healthcare of Atlanta, Emory Hospital, and Georgia Institute of Technology. It resulted in the development and adoption of concrete application deliverables in different medical settings, including: the neuroARM system in pediatric neuropsychology, the PHARM system in predictive health, and the icuARM, icuARM-II, and icuARM-KM systems in intensive care. The case studies for the evaluation of these systems and the discovered knowledge demonstrate the scope of this research and its potential for future evidence-based and personalized clinical decision support.
Стилі APA, Harvard, Vancouver, ISO та ін.
4

IANZA, ANNA. "VALIDATION OF PREDICTIVE AND PROGNOSTIC BIOMARKERS AS A GUIDE FOR A PERSONALIZED APPROACH IN SOLID TUMOURS." Doctoral thesis, Università degli Studi di Trieste, 2020. http://hdl.handle.net/11368/2973745.

Повний текст джерела
Анотація:
Breast cancer (BC), Colorectal Cancer (CRC) and Non-Small Cell Lung Cancer (NSCLC) are among the most commonly diagnosed solid tumors, and occupy the first places in the mortality rankings. Compared to an old fashioned one-size-fits-all approach, precision medicine offers the possibility to accurately choose the most appropriate therapeutic strategy, that fits the patients not only from the clinical (age, comorbidities) but also from a molecular point of view. A genetic and biological understanding of the tumor, integrated with a weighted analysis of results can help the clinician in designing a therapeutic pathway that, ideally from the start, gives the patients the best response rates. The aim of my research is to evaluate the markers that have the greatest impact on the prediction of therapy response. Mutational analysis revolutionized the NSCLC treatment paradigm and, consequently, improved the prognosis. EGFR mutated patients benefit from target therapy with tyrosine kinase inhibitors. A fluid and longitudinal monitoring of mutational status is becoming a key factor in disease management. Firstly we extracted circulating free DNA (cfDNA) from the plasma of 30 patients with EGFR-mutated NSCLC and assessed mutational status with real-time PCR. We then monitored such mutation during target therapy in 19 patients. The liquid biopsy had a sensitivity of 60% in confirming the tissue mutation. Patients whose EGFR mutation was not detectable on plasma had a longer Progression free survival (PFS) and Overall survival (OS). Next step will be assessing if cfDNA analysis allows early detection of resistance mutation such as T790M. Next part of my research focused on luminal BC, working partially retrospectively on data from a phase III study of 90 ER-positive, HER2 negative locally advanced breast cancer patients that were randomly assigned 1:1 to receive Let 2,5 mg daily and metronomic oral Cyc 50 mg daily with (arm B; n=45) or without (arm A, n=45) sorafenib 400 mg/bid daily for six months as neoadjuvant treatment. The predictive role of Ki67, SUV variations and metabolic response and its changes with regards to clinical response and survival was analyzed. The serum of 32 patients was analyzed via Luminex Multiplex Panel technology. 38 analytes (cytokines and growth factors) were simultaneously measured according to arm of treatment and time of sample collection (before and after treatment). Patients were divided into groups according to response to therapy (RECIST). Then we investigated a possible link between chemotherapy-induced RNA disruption and survival/progression. Analysis were performed on 40 biopsies taken at baseline and 15 days after the beginning of the neoadjuvant therapy. The RNA for each sample or subdivided sample was then assessed using the RNA Disruption Assay. The maximum RNA disruption Index (RDI) value for each patient at day 15 was used for all analyses. Finally, We investigated the discordance of mutational status between primary and metastatic site in colorectal cancer.Patients with metastatic CRC who underwent surgery of both primary and metastasis were retrospectively evaluated, and mutational status assessment of K-RAS, N-RAS, BRAF and PIK3CA was performed on 21 patients. Median DFS was 20.5 months (95% CI 9.9-29.6) in patients with concordance in mutational status versus 10.4 months (95% CI 6.1-not reached) in patients with discordance (p=0.01) and median OS was 35.9 months (95% CI 26.3-not reached) in patients with concordance in mutational status 25.6 months (95% CI 6.6-not reached) in patients with discordance (p=0.038). In conclusion discordance seems related to clinical outcome. Overall my results show that new strategies and technologies allow the researchers and the clinicians to strive for a better and more complete understanding of solid tumors complex evolution, an integrated and focused approach to the early disease could become the future of disease management.
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Wang, Hao. "Screening multi-omics biomarkers for suboptimal health status." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2431.

Повний текст джерела
Анотація:
Background Suboptimal health status (SHS) is an intermediate health status between ideal health and diseases. It is characterized by chronic fatigue, perception of health complaints and a cluster of physical symptoms lasting for more than three months. SHS is recognised as a subclinical, reversible stage of chronic diseases. Objectives Study I. To investigate the prevalence of SHS in a cross-sectional study. Study II. To screen transcriptomic biomarkers for SHS in a case-control study. Study III. To screen metabolomics biomarkers for SHS in a case-control study. Materials and Methods Study I. A cross-sectional study was conducted from September 2017 to November 2017. SHS questionnaire-25 was used to assess the SHS levels of the participants. Study II. The RNA sequencing (RNA-Seq)-based transcriptome analysis was firstly conducted on buffy coat samples collected from 30 participants with SHS and 30 age- and sex-matched healthy controls. Study III. The liquid chromatography-mass spectrometry (LC-MS)- based untargeted metabolomics analysis was conducted on plasma samples collected from 50 SHS participants and 50 age- and sex-matched healthy controls. Result In Study I, a total of 4839 Chinese university students enrolled in this study. The prevalence of SHS was 8.39%, with the prevalence of 6.57% among males and 9.60% among females. The multivariate logistic regression results showed that SHS was significantly associated with age (Odd ratio (OR) = 1.193, P = 0.019), female (OR = 1.437, P = 0.002), sleep duration (OR = 0.609, P < 0.001), insomnia symptoms (OR = 1.238, P < 0.001), anxiety symptoms (OR = 1.025, P = 0.019), and depression symptoms (OR = 1.082, P < 0.001). In study II, transcriptome analysis identified a total of 46 differentially expressed genes, in which 22 transcripts were significantly increased and 24 transcripts were decreased in the SHS group. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that several biological processes were related to SHS, such as ATP-binding cassette transporter and neurodegeneration. A combination of transcripts can distinguish SHS individuals from the healthy controls with a sensitivity of 83.3%, a specificity of 90.0%, and an area under the receiver operating characteristic curve (AUC) of 0.938. In study III, metabolomics analysis identified a total of 24 significantly altered metabolites as the candidate biomarkers for SHS. Pathway analysis revealed that sphingolipid metabolism, taurine metabolism, and steroid hormone biosynthesis are the disturbed metabolic pathways related to SHS. A combination of four metabolic biomarkers (sphingosine, pregnanolone, taurolithocholate sulfate, cervonyl carnitine) can distinguish SHS individuals from the controls with a sensitivity of 94.0%, a specificity of 90.0%, and an AUC of 0.977. Conclusion SHS is prevalent in Chinese university students. Older age, female, insomnia, depression, and anxiety symptoms are risk factors for SHS, while longer sleep duration is a protective factor for SHS. Blood transcripts and metabolites are valuable biomarkers for SHS identification. These findings suggest the potential utility of SHS-related transcriptomic and metabolomic biomarkers for the Predictive, Preventive, and Personalized Medicine (PPPM) of chronic diseases.
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Shen, Yuanyuan. "Ordinal Outcome Prediction and Treatment Selection in Personalized Medicine." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17463982.

Повний текст джерела
Анотація:
In personalized medicine, two important tasks are predicting disease risk and selecting appropriate treatments for individuals based on their baseline information. The dissertation focuses on providing improved risk prediction for ordinal outcome data and proposing score-based test to identify informative markers for treatment selection. In Chapter 1, we take up the first problem and propose a disease risk prediction model for ordinal outcomes. Traditional ordinal outcome models leave out intermediate models which may lead to suboptimal prediction performance; they also don't allow for non-linear covariate effects. To overcome these, a continuation ratio kernel machine (CRKM) model is proposed both to let the data reveal the underlying model and to capture potential non-linearity effect among predictors, so that the prediction accuracy is maximized. In Chapter 2, we seek to develop a kernel machine (KM) score test that can efficiently identify markers that are predictive of treatment difference. This new approach overcomes the shortcomings of the standard Wald test, which is scale-dependent and only take into account linear effect among predictors. To do this, we propose a model-free score test statistics and implement the KM framework. Simulations and real data applications demonstrated the advantage of our methods over the Wald test. In Chapter 3, based on the procedure proposed in Chapter 2, we further add sparsity assumption on the predictors to take into account the real world problem of sparse signal. We incorporate the generalized higher criticism (GHC) to threshold the signals in a group and maintain a high detecting power. A comprehensive comparison of the procedures in Chapter 2 and Chapter 3 demonstrated the advantages and disadvantages of difference procedures under different scenarios.<br>Biostatistics
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Reggiani, Francesco. "Development and assessment of bioinformatics methods for personalized medicine." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3424693.

Повний текст джерела
Анотація:
The human genome is a source of information for researchers that study complex diseases with the perspective of a better understanding of the pathologies and the development of new therapeutic strategies. Starting from the beginning of the current century, a growing number of technologies devoted to DNA sequencing have emerged, generally referred to as Next Generation Sequencing (NGS) technologies. NGS gradually decreased the cost of sequencing a human genome to around US$1000, enabling the use of these technologies for clinical and research purposes, such as Genome-wide association studies (GWAS). GWAS studies have enlightened the presence of disease- associated loci, in particular variants that could be used to evaluate the risk of an individual to develop a disease. Unfortunately, different sources of errors are able to impair the interpretation and use of NGS data: on the one hand, we have noise related to the process of DNA sequencing and read alignment errors, which could lead to false positive calls or artifacts. On the other hand, variants could be poor predictors for the manifestation of their associated disease. Nowadays the challenge of genomic data interpretation has driven the research towards the development of methods for the analysis and interpretation of genomic variations, eventually predicting the probability of a patient to develop a definite disease. A fair evaluation of these tools is essential to understand the applicability of the presented methods in clinical practice. The Critical Assessment of Genome Interpretation (CAGI) has been developed with the aim of defining the current state of art in terms of methods for predicting the impact of genomic changes at molecular and phenotype levels. CAGI is a community-driven experiment in which different prediction methods, developed by a set of invited groups, are evaluated on a common dataset. Unfortunately, no common guidelines were given to evaluate the tools presented in CAGI experiments, this has made the comparison between different CAGI experiments cumbersome, since different mathematical indexes and scripts have been used to evaluate the involved methods. My PhD project has been focused on the development of software for the assessment of machine learning methods in regression and multiple phenotype challenges. This tool is based on state of the art assessment principles, derived from literature or previous CAGI experiments. This software is available as an R package and has been used to repeat or perform new assessments on a wide range of CAGI experiments. The knowledge acquired during the development of this project was used to evaluate two CAGI 5 challenges: Pericentriolar Material 1 (PCM1) and Intellectual Disability (ID) panel. The experience I have acquired, through the development of all previously mentioned works, has led the improvement and assessment of a machine learning method. In particular, I have developed a software for the prediction of cholesterol levels, based on genotype data. Eventually I have tested the reliability of this method. This tool was the milestone in a project founded by the Italian Ministry of Health.<br>Il genoma umano è una risorsa ricca di informazioni per i ricercatori che si dedicano allo studio delle patologie complesse. L’obiettivo di questo genere di ricerche è giungere ad una migliore comprensione di queste malattie e quindi sviluppare nuove strategie terapeutiche per la cura dei pazienti affetti. Dall’inizio di questo secolo, un numero crescente di tecnologie per il sequenziamento del DNA sono state sviluppate, sono conosciute come tecnologie “Next Generation Sequencing” (NGS). Le tecnologie NGS hanno gradualmente diminuito il costo del sequenziamento di un genoma umano fino a circa 1000 dollari, ciò ha consentito l’utilizzo di questi strumenti nella pratica clinica e nella ricerca, in particolare negli studi di associazione genome-wide o “Genome-wide association studies” (GWAS). Questi lavori hanno portato alla luce l’associazione di alcune varianti con alcune patologie o caratteri complessi. Queste varianti potrebbero essere utilizzate per valutare il rischio che un individuo sviluppi una particolare patologia. Sfortunatamente diverse sorgenti di errore sono in grado di ostacolare l’uso e l’interpretazione dei dati genomici: da una parte abbiamo il rumore legato al processo di sequenziamento e gli errori di allineamento delle reads. Dall’altra parte gli SNP non sempre possono essere utilizzati in modo affidabile per predire l’insorgenza della malattia a cui sono stati associati. Il Critical Assessment of Genome Interpretation è stato organizzato con l’obiettivo di definire lo stato dell’arte nei metodi che stimano l’effetto di variazioni genetiche a livello molecolare o fenotipico. Negli anni il CAGI ha dato vita a più competizioni in cui diversi gruppi di ricerca hanno testato i loro metodi di predizione su diversi dataset condivisi. L’assenza di linee generali su come condurre la valutazione delle performance dei predittori, ha reso difficile un confronto fra metodi sviluppati in edizioni diverse del CAGI. In questo contesto, il progetto di dottorato si è focalizzato nello sviluppo di un software per la valutazione di metodi di apprendimento automatici basati sulla regressione o la predizione di fenotipi multipli. Questo strumento si fonda su criteri di analisi della performance, derivanti dalla letteratura e da precedenti esperimenti del CAGI. Questo software è stato sviluppato in R ed utilizzato per ripetere o valutare ex novo la qualità dei predittori in un gran numero di esperimenti del CAGI. Le conoscenze acquisite durante lo sviluppo di questo progetto, sono state utilizzate per valutare due competizioni del CAGI 5: la Pericentriolar Material 1 (PCM1) e il Pannello per le Disabilità Intellettive (ID). L’esperienza derivante dal completamento dei lavori precedentemente elencati, ha guidato lo sviluppo e il miglioramento delle prestazioni di un metodo predittivo. In particolare è stato sviluppato un software per la predizione dei livelli di colesterolo, basato su dati genotipici, di cui è stata testata la validità con criteri matematici allo stato dell’arte. Questo strumento è stato la pietra portante di un progetto fondato dal Ministero della Salute Italiano.
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Alderdice, Matthew. "Personalised medicine in rectal cancer : understanding and predicting response to neoadjuvant chemoradiotherapy." Thesis, Queen's University Belfast, 2017. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.725327.

Повний текст джерела
Анотація:
Around 12-15% of patients with locally advanced rectal cancer (LARC) undergo a pathologically complete response (Tumour Regression Grade 4 - TRG4) to neoadjuvant chemoradiotherapy; the remainder exhibit a spectrum of tumour regression (TRG1-3). Understanding therapy-related genomic alterations may help us better predict response, progression-free and overall survival, and also identify both novel and repurposed treatment strategies based on the underlying biology of the disease. The Northern Ireland Biobank provided 48 formalin fixed paraffin embedded (FFPE) rectal cancer biopsies and matched resections following neoadjuvant therapy (discovery cohort). These were analysed using high-throughput gene expression microarray, DNA mutational profiling and microsatellite instability profiling. Differential gene expression analysis (analysis of variance) was performed contrasting tumour regression grades in both biopsies and resections to identify predictive and therapy related features. Real time PCR was utilised for microarray validation while immunohistochemistry (IHC) was employed to measure CD56+ cell populations in an independent (validation) cohort (n=150). A NK cell-like gene expression signature was observed following long course chemoradiotherapy in a tumour regression-dependent manner. CD56+ NK cel, populations were measured by IHC and found to be significantly higher in TRG3 patients. Furthermore, it was observed that patients positive for CD56 ceils after therapy had a better overall survival (HR=0.282, 95%C,=0.109-0.729, x2=7.854, p=.OO5). In silico drug selection using QUADrATiC analysis identified clinically relevant therapeutic FDA-approved compounds based upon the NK cell-like signature. We demonstrated that identifying an independently validated predictive signature from biopsies for LARC patients treated with LCPCRT was not possible. However, we identified a novel post-therapeutic NK-like transcription signature in patients responding to neoadjuvant chemoradiotherapy. Furthermore, CD56 positive patients had better overall survival. Therefore, harnessing an NK-like response after therapy may improve outcomes for locally advanced rectal cancer patients.
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Alcenat, Stéphane. "Assurance maladie et tests génétiques." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCB002.

Повний текст джерела
Анотація:
Cette thèse apporte trois contributions majeures. Le premier chapitre, un article paru dans la Revue Française d’Économie n°2/vol XXXIV, propose une revue de la littérature sur les implications de la régulation des tests génétiques de prédisposition sur le marché de l'assurance santé. Nous montrons que les formes actuelles de régulation réalisent un arbitrage entre la maximisation du bien-être social ex ante et l’incitation à la prévention. Cet arbitrage est conditionné par la façon dont l’acquisition de l’information affecte les comportements de prévention et de révélation des agents, la discrimination des risques par les assureurs ainsi que la nature des contrats. Le deuxième chapitre étudie théoriquement l’impact de la reclassification sur la prévention, la décision de faire le test ainsi que sur le bien-être social dans la réglementation d’obligation de divulgation. En particulier, nous montrons qu’en fonction du coût de l’effort de prévention la valeur individuelle de l’information génétique avec reclassification peut être plus élevée que sans reclassification. De plus, nous montrons comment les préférences temporelles affectent la valeur individuelle de l’information génétique. D’après nos résultats, le bien-être social est strictement plus élevé sans reclassification qu'avec reclassification. Le dernier chapitre étudie et caractérise des contrats incitatifs pouvant être mis en œuvre pour développer la médecine personnalisée avec des traitements très efficaces, dans un contexte d'aléa moral sur l’effort fourni par la firme pour améliorer l'efficacité du médicament. Nous considérons un modèle dans lequel l'autorité de santé a trois possibilités. Il peut appliquer le même traitement (standard ou nouveau traitement) à l'ensemble de la population ou mettre en œuvre la médecine personnalisée, c'est-à-dire utiliser les informations génétiques pour proposer le traitement le plus adapté à chaque patient. Nous caractérisons d'abord le contrat de remboursement de médicaments d'une entreprise produisant un nouveau traitement avec un test génétique compagnon lorsque l'entreprise peut entreprendre un effort pour améliorer la qualité des médicaments. Ensuite, nous déterminons les conditions sous lesquelles la médecine personnalisée doit être mise en œuvre lorsque cet effort est observable et quand il ne l'est pas. Enfin, nous montrons comment la non observabilité de l'effort affecte la décision de l'autorité sanitaire de mettre en œuvre la médecine personnalisée avec des traitements hautement efficaces<br>This thesis includes three main contributions. The first chapter, an article published in 2019 in the “Revue Française d’Économie n°2/vol XXXIV”, provides a literature review on the implications of genetic testing regulations on the health insurance market. We show that the choice of a regulation results from a trade-off between the maximization of ex-ante social welfare and incentive to undertake prevention actions. Indeed, this trade-off depends on the way information acquisition impacts prevention and revelation behaviors of agents, as well as of its impact on insurance contract. The second chapter studies theoretically how reclassification impacts testing and prevention decision as well as social welfare in the Disclosure Duty regulation. In particular, we show that the incentives of agents to take genetic with reclassification can be higher than without reclassification according to the effort cost. In addition, we show how time preferences affect the incentive to take genetic testing. Finally, we show that the social welfare is strictly higher without reclassification than with reclassification. The last chapter studies and characterizes contracts that can be implemented to develop personalized medicine with highly effective treatment in context of moral hazard about firm effort to improve drug efficacy. It also studies how the non-observability of effort impacts the decision of a health authority to implement personalized medicine with highly effective treatments. We consider a model in which the health authority has three possibilities. It can apply either the same treatment (a standard or a new treatment) to the whole population or implement personalized medicine, i.e., use genetic information to offer the most suitable treatment to each patient. We first characterize the drug reimbursement contract of a firm producing a new treatment with a companion genetic test when the firm can undertake an effort to improve drug quality. Then, we determine the conditions under which personalized medicine should be implemented when this effort is observable and when it is not. Finally, we show how the unobservability of effort affects the conditions under which the health authority implements personalized medicine
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Cornec-Le, Gall Emilie. "Polykystose rénale autosomique dominante : de la génétique moléculaire au développement d'outils pronostiques." Thesis, Brest, 2015. http://www.theses.fr/2015BRES0030.

Повний текст джерела
Анотація:
La Polykystose Rénale Autosomique Dominante (PKRAD) est une des pathologies héréditaires les plus fréquentes et affecte environ un individu sur 1000. Elle se caractérise par une importante variabilité clinique, notamment dans l’âge de survenue de l’insuffisance rénale terminale. Deux gènes sont en cause : le gène PKD1 situé sur le chromosome 16 (85% des cas) et le gène PKD2 situé sur le chromosome 4 (15% des cas). Les progrès majeurs dans la compréhension des mécanismes moléculaires impliqués ont permis le développement de stratégies thérapeutiques spécifiques, et de nouvelles questions surgissent : quels patients traiter ? Quand débuter les traitements ? La cohorte Genkyst, qui vise à inclure tous les patients suivis pour PKRAD dans la région Grand Ouest, nous a d’abord permis de décrire la variabilité génétique rencontrée dans la PKRAD. Nous avons ensuite démontré l’existence de fortes corrélations génotype-phénotype, en rapportant l’influence sur l’âge de survenue de l’insuffisance rénale terminale non seulement du gène en cause, mais aussi du type de mutation pour le gène PKD1. Enfin, l’analyse des données cliniques et génétiques de 1341 patients nous a permis de développer un algorithme pronostique, baptisé le PROPKD score, permettant de stratifier le risque de progression vers l’insuffisance rénale terminale. Nous espérons que ces travaux participeront à l’individualisation de la prise en charge des patients atteints de PKRAD, ce qui est un enjeu crucial à l’arrivée des nouveaux traitements<br>Autosomal Dominant Polycystic Kidney Disease (ADPKD) is one of the most frequent Mendelian inherited disorders, and affects approximately one individual out of 1000. ADPKD is marked by a high clinical variability, especially regarding age at end-stage renal disease (ESRD). Two genes are identified: PKD1 located on the chromosome 16 (85% of the pedigrees) and PKD2 located on the chromosome 4 (15% of the pedigrees). Substantial progress in understanding the cellular mechanisms underlying ADPKD has triggered the development of targeted therapies, and new questions are arising: which patients should be treated? When should we begin these treatments? Thanks to Genkyst cohort, which aims to include all consenting ADPKD patients from the western part of France, we first described the important allelic variability encountered in ADPKD. Secondly, we demonstrated the important influence of not only the gene involved, but also of PKD1 mutation type. Last, the analysis of clinical and genetic characteristics of 1341 patients from the Genkyst cohort allowed us to develop a prognostic algorithm, named the PROPKD score for predicting renal outcome in ADPKD. Our hope is that these works will participate in the development of individualized medicine in ADPKD, which is crucial in the context of the emerging targeted therapies
Стилі APA, Harvard, Vancouver, ISO та ін.

Книги з теми "Personalized predictive medicine"

1

Olga, Golubnitschaja, ed. Predictive diagnostics and personalized treatment: Dream or reality. Nova Science Publishers, 2009.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Grech, Godfrey, and Iris Grossman, eds. Preventive and Predictive Genetics: Towards Personalised Medicine. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15344-5.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Podbielska, Halina, and Marko Kapalla, eds. Predictive, Preventive, and Personalised Medicine: From Bench to Bedside. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34884-6.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Chaari, Lotfi, ed. Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11800-6.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Chaari, Lotfi, ed. Digital Health in Focus of Predictive, Preventive and Personalised Medicine. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49815-3.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Berliner, Leonard, and Heinz U. Lemke, eds. An Information Technology Framework for Predictive, Preventive and Personalised Medicine. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12166-6.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Hood, Leroy, and Nathan Price. Age of Scientific Wellness: Why the Future of Medicine Is Personalized, Predictive, Data-Rich, and in Your Hands. Harvard University Press, 2023.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Age of Scientific Wellness: Why the Future of Medicine Is Personalized, Predictive, Data-Rich, and in Your Hands. Harvard University Press, 2023.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Age of Scientific Wellness - Why the Future of Medicine Is Personalized, Predictive, Data-Rich, and in Your Hands. Harvard University Press, 2023.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Mansnérus, Juli, Raimo Lahti, and Amanda Blick, eds. Personalized medicine: Legal and ethical challenges. University of Helsinki, Faculty of Law, 2020. http://dx.doi.org/10.31885/9789515169419.

Повний текст джерела
Анотація:
This anthology deals with the legal and ethical challenges regarding personalized (precision) medicine and healthcare. It can also be regarded as the final report of a research project on the legal and ethical aspects of personalized medicine. It complements the reported results of the consortium ‘Personalised medicine to predict and prevent Type 1 Diabetes (P4 Diabetes)’ which were briefly presented in the booklet entitled ‘Better, Smarter, Now: Personalised Health – From Genes to Society (pHealth)’, Academy of Finland, Helsinki 2019. The articles of this anthology are not limited to the aspects of predicting and preventing Type 1 Diabetes only, as the name of the consortium would suggest. The list of participating researchers indicates that many-sided medical expertise was represented in the consortium and, in addition, computational data analysis as well as legal and ethical issues were covered by the participating sites of research. A comprehensive examination of the issues of personalized medicine requires multidisciplinary approaches. In this anthology, the legally and ethically oriented mainstream of writings has been complemented with an article of a computer scientist in order to recognize the possibilities and challenges of machine learning when interpreting the patient’s need for help. It is our hope that this anthology would be useful both for the academic community and for the decision-makers in the fields of healthcare and (personalized) medicine. It is also advisable that the anthology would give an impetus for further research activity in these new spheres of medical law and biolaw.
Стилі APA, Harvard, Vancouver, ISO та ін.

Частини книг з теми "Personalized predictive medicine"

1

Richter, Kneginja, Nikola Gjorgov, and Stojan Bajraktarov. "Predictive, Preventive, and Personalized Approach in Sleep Medicine." In Predictive, Preventive, and Personalised Medicine: From Bench to Bedside. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34884-6_14.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Regierer, Babette, Valeria Zazzu, Ralf Sudbrak, Alexander Kühn, and Hans Lehrach. "Future of Medicine: Models in Predictive Diagnostics and Personalized Medicine." In Molecular Diagnostics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/10_2012_176.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Hazin, Hesham, and David Dosik. "Personalized Chemotherapy for Hepatocellular Carcinoma." In An Information Technology Framework for Predictive, Preventive and Personalised Medicine. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12166-6_6.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Andrews, Russell J. "Wearable Revolution: Predictive, Preventive, Personalized Medicine (PPPM) Par Excellence." In Predictive, Preventive, and Personalised Medicine: From Bench to Bedside. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34884-6_19.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Kapalla, Marko, Dagmar Kapallová, and Ladislav Turecký. "Healthcare Overview in the Slovak Republic and Implementation of Predictive, Preventive and Personalized Medicine." In Advances in Predictive, Preventive and Personalised Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4602-2_5.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Brown, Paul M. "Effectiveness, Cost Effectiveness, and Financial Viability of Personalized Medicine: A Role for Comparative Effectiveness Research?" In Advances in Predictive, Preventive and Personalised Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4602-2_21.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Marcus-Kalish, Mira, and Hamutal Meiri. "Simultaneous Systematic Approach to Enable Predictive, Preventive and Personalized Medicine – Women Healthcare as a Case Study." In Advances in Predictive, Preventive and Personalised Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4602-2_17.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Trovato, Guglielmo M., and Francesco Basile. "Italian Healthcare System in the Global Context: The Cultural Challenge of Predictive, Preventive and Personalized Medicine." In Advances in Predictive, Preventive and Personalised Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4602-2_2.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Kugler, Andrea, Chiara Kertu, and Kurt Krapfenbauer. "The Economic Challenge of Predictive, Preventive and Personalized Medicine: The Case Study of Lung, Head and Neck Cancer." In Advances in Predictive, Preventive and Personalised Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4602-2_22.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Kinkorová, Judita, and Ondřej Topolčan. "An Overview of the Healthcare System in the Czech Republic with Respect to Predictive, Preventive and Personalized Medicine." In Advances in Predictive, Preventive and Personalised Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4602-2_6.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.

Тези доповідей конференцій з теми "Personalized predictive medicine"

1

Lella, Luigi, Ignazio Licata, Gianfranco Minati, Christian Pristipino, Antonio De Belvis, and Roberta Pastorino. "Predictive AI Models for the Personalized Medicine." In 12th International Conference on Health Informatics. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007472203960401.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Hood, Lee. "Systems medicine, transformational technologies and the emergence of predictive, personalized, preventive and participatory (P4) medicine." In the Conference. ACM Press, 2009. http://dx.doi.org/10.1145/1654059.1657026.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Hood, Leroy. "“Systems biology and systems medicine: From reactive to predictive, personalized, preventive and participatory (P4) medicine”." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4649061.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Affan, Affan, Jacek M. Zurada, and Tamer Inane. "Patient-Specific Modeling and Model Predictive Control Approach to Personalized Optimal Anemia Management." In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2023. http://dx.doi.org/10.1109/embc40787.2023.10340171.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Zdereva, E. A., M. Tsyganov, N. V. Litvyakov, and M. K. Ibragimova. "PREDICTIVE AND PROGNOSTIC SIGNIFICANCE OF EXPRESSION AND ABERRATIONS OF THE DNA COPY NUMBER OF CHEMOSENSITIVITY GENES IN PATIENTS WITH BREAST CANCER." In I International Congress “The Latest Achievements of Medicine, Healthcare, and Health-Saving Technologies”. Kemerovo State University, 2023. http://dx.doi.org/10.21603/-i-ic-42.

Повний текст джерела
Анотація:
Increasingly, researchers are focusing on the susceptibility of breast tumors (BC) to certain chemotherapy drugs and personalizing studies based on an assessment of this susceptibility. One such personalized approach is the assessment of chemotherapy gene expression and aberrations in the number of DNA copies — deletions and amplifications that affect gene activity. Comprehensive assessment of gene expression of chemotherapy drugs is important not only for understanding the heterogeneity and molecular biology of breast cancer, but also for obtaining a more accurate prognosis of the disease. Thus, the aim of the work is to analyze the relationship between the expression of chemosensitivity genes ERCC1, RRM1, TOP1, TOP2α, TUBB3, TYMS, GSTP1 and the effect of neoadjuvant chemotherapy (NCT).
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Cyganov, M. M., M. K. Ibragimova, and A. A. Hozyainova. "PREDICTIVE AND PROGNOSTIC SIGNIFICANCE OF PALB2 GENE MUTATIONS IN BREAST TUMORS." In I International Congress “The Latest Achievements of Medicine, Healthcare, and Health-Saving Technologies”. Kemerovo State University, 2023. http://dx.doi.org/10.21603/-i-ic-146.

Повний текст джерела
Анотація:
It has been shown that the loss of PALB2 function, due to mutations or chromosomal&#x0D; aberrations, may have an impact on the effectiveness of chemotherapy treatment and disease&#x0D; prognosis in patients with various oncological diseases. Thus, the aim of this work was to&#x0D; evaluate the predictive and prognostic potential of DNA copy number aberrations and PALB2&#x0D; gene mutations in breast tumors.&#x0D; Materials and methods. The study included 66 patients with breast cancer (BC). To&#x0D; evaluate the presence of DNA copy number aberrations (CNA), microarray analysis was&#x0D; used with the CytoScanTMHD Array high density microarrays. Gene mutations were&#x0D; assessed by sequencing on the MiSeq™ Sequencing System using the Accel-Amplicon&#x0D; BRCA1, BRCA2, and PALB2 Panel. Results. The presence of the PALB2 CNA gene is not&#x0D; associated with the effectiveness of neoadjuvant chemotherapy (NAC), p=0.07. The&#x0D; presence of a deletion is determined by 100% metastatic-free survival, versus 68% in the&#x0D; group with normal copy number (log-rank test p=0.04). It has been established that&#x0D; elimination of the frameshift deletion c.2552delA mutation during NAC leads to an&#x0D; objective response to treatment. Identified missense mutations (c.2993G&gt;A; c.2014G&gt;C;&#x0D; c.1010T&gt;C) were observed in patients with tumor progression. But only c.1010T&gt;C has&#x0D; pathogenic significance. The presence of mutations in the PALB2 gene is not associated&#x0D; with metastatic survival (log-rank test p=0.15). Conclusion. Currently, there is little data on&#x0D; the impact of disorders in the PALB2 gene on the effectiveness of treatment and prognosis&#x0D; of the disease, but the study of this gene has great potential for testing focused on diagnosis,&#x0D; prevention, and a personalized approach to the treatment of cancer patients
Стилі APA, Harvard, Vancouver, ISO та ін.
7

Occhipinti, Annalisa, and Claudio Angione. "A Computational Model of Cancer Metabolism for Personalised Medicine." In Building Bridges in Medical Science 2021. Cambridge Medicine Journal, 2021. http://dx.doi.org/10.7244/cmj.2021.03.001.3.

Повний текст джерела
Анотація:
Cancer cells must rewrite their ‘‘internal code’’ to satisfy the demand for growth and proliferation. Such changes are driven by a combination of genetic (e.g., genes’ mutations) and non-genetic factors (e.g., tumour microenvironment) that result in an alteration of cellular metabolism. For this reason, understanding the metabolic and genomic changes of a cancer cell can provide useful insight on cancer progression and survival outcomes. In our work, we present a computational framework that uses patient-specific data to investigate cancer metabolism and provide personalised survival predictions and cancer development outcomes. The proposed model integrates patient-specific multi-omics data (i.e., genomic, metabolomic and clinical data) into a metabolic model of cancer to produce a list of metabolic reactions affecting cancer progression. Quantitative and predictive analysis, through survival analysis and machine learning techniques, is then performed on the list of selected reactions. Since our model performs an analysis of patient-specific data, the outcome of our pipeline provides a personalised prediction of survival outcome and cancer development based on a subset of identified multi-omics features (genomic, metabolomic and clinical data). In particular, our work aims to develop a computational pipeline for clinicians that relates the omic profile of each patient to their survival probability, based on a combination of machine learning and metabolic modelling techniques. The model provides patient-specific predictions on cancer development and survival outcomes towards the development of personalised medicine.
Стилі APA, Harvard, Vancouver, ISO та ін.
8

Nguyen, Giang T. T., and Duc-Hau Le. "A matrix completion method for drug response prediction in personalized medicine." In the Ninth International Symposium. ACM Press, 2018. http://dx.doi.org/10.1145/3287921.3287974.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Cojbasic, Zarko. "Machine Learning for Personalized Medicine: Clinical Outcome Prediction and Diagnosis : Plenary Talk." In 2019 IEEE 13th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2019. http://dx.doi.org/10.1109/saci46893.2019.9111519.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

Biswas, Sougatamoy, Vinod Kumar, and Smritilekha Das. "Multiclass classification models for Personalized Medicine prediction based on patients Genetic Variants." In 2021 IEEE International Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES). IEEE, 2021. http://dx.doi.org/10.1109/tribes52498.2021.9751631.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.

Звіти організацій з теми "Personalized predictive medicine"

1

Manski, Charles. Probabilistic Prediction for Binary Treatment Choice: with Focus on Personalized Medicine. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w29358.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Zhang, Yu, Chaoliang Sun, Hengxi Xu, et al. Connectivity-Based Subtyping of De Novo Parkinson Disease: Biomarkers, Medication Effects and Longitudinal Progression. Progress in Neurobiology, 2024. http://dx.doi.org/10.60124/j.pneuro.2024.10.04.

Повний текст джерела
Анотація:
Parkinson's disease (PD) is characterized by divergent clinical symptoms and prognosis, suggesting the presence of distinct subtypes. Identifying these subtypes is crucial for understanding the underlying pathophysiology, predicting disease progression, and developing personalized treatments. In this study, we propose a connectivity-based subtyping approach, which measures each patient's deviation from the reference structural covariance networks established in healthy controls. Using data from the Parkinson's Progression Markers Initiative, we identified two distinct subtypes of de novo PD patients: 248 patients with typical cortical-striato-thalamic dysfunctions and 41 patients showing weakened dorsal raphe nucleus (DRN)-to-cortical/striatal projections. The proposed subtyping approach demonstrated high stability in terms of random sampling of healthy or diseased population and longitudinal prediction at follow-up visits, outperforming the traditional motor phenotypes. Compared to the typical PD, patients with the DRN-predominant subtype were characterized by less server motor symptoms at baseline and distinct imaging biomarkers, including larger striatal volumes, higher concentration of cerebrospinal fluid amyloid-β and amyloid-β/t(p)-tau ratio. Subtype-specific associations and drug effects were identified that the DRN subtype exhibited more pronounced medication effects on motor symptoms, potentially regulated by DRN serotonergic modulation through striatal dopaminergic neurons. The DRN serotonergic inputs also regulated non-motor symptoms, the aggregation of CSF biomarkers and the conversion to more severe disease states. Our findings suggest that the DRN-predominant subtype represents a unique clinical and biological phenotype of PD characterized by an enhanced response to anti-parkinsonian treatment, more favorable prognosis and slower progression of dopamine depletion. This study may contribute to clinical practice of precision medicine, early invention and individualized treatments in PD and other neurodegenerative diseases.
Стилі APA, Harvard, Vancouver, ISO та ін.
Ми пропонуємо знижки на всі преміум-плани для авторів, чиї праці увійшли до тематичних добірок літератури. Зв'яжіться з нами, щоб отримати унікальний промокод!

До бібліографії