Zeitschriftenartikel: „Genomic classification“

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Murthy, H. N., S. C. Hiremath und A. N. Pyati. „Genomic Classification in Guizotia (Asteraceae).“ CYTOLOGIA 60, Nr. 1 (1995): 67–73. http://dx.doi.org/10.1508/cytologia.60.67.

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Akbani, Rehan, Kadir C. Akdemir, B. Arman Aksoy, Monique Albert, Adrian Ally, Samirkumar B. Amin, Harindra Arachchi et al. „Genomic Classification of Cutaneous Melanoma“. Cell 161, Nr. 7 (Juni 2015): 1681–96. http://dx.doi.org/10.1016/j.cell.2015.05.044.

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Doranga, Saroj, Rajeev Nepal und Pratigya Timsina. „Automated Classification of Genetic Mutations in Cancer using Machine Learning“. Scientific Researches in Academia 1, Nr. 1 (23.11.2023): 108–23. http://dx.doi.org/10.3126/sra.v1i1.60140.

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Efforts to decipher the genomic data of cancer and its implications for treatment face challenges. Robust preclinical models reflecting human cancer's genomic diversity, along with comprehensive genetic and pharmacological annotations, can greatly aid in this endeavor. Large collections of cancer cell lines effectively capture the genomic diversity and provide valuable insights into the response to anti-cancer drugs. In this study, we demonstrate significant agreement and biological consistency between drug sensitivity measurements and their corresponding genomic predictors from two publicly available pharmacogenomics databases: The Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer. Despite ongoing efforts to identify cancer-related metabolic changes that may reveal vulnerabilities to targeted drugs, systematic evaluations of metabolism in relation to functional genomics features and associated dependencies are still uncommon. To gain further insights into the metabolic diversity of cancer, we analyzed 225 metabolites in 928 cell lines representing over 20 cancer types using liquid chromatography-mass spectrometry (LC-MS) in the Cancer Cell Line Encyclopedia (CCLE). The analysis revealed missing data for various features, with certain percentages exceeding 40%, leading to the removal of 12 features according to standard procedures. Further analysis revealed 25 unique chromosomes and 4 unique Variant_Types in the dataset. Model performance assessment showed an accuracy score of 96% using a logistic regression model.

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Faillot, Simon, Thomas Foulonneau, Mario Néou, Stéphanie Espiard, Simon Garinet, Anna Vaczlavik, Anne Jouinot et al. „Genomic classification of benign adrenocortical lesions“. Endocrine-Related Cancer 28, Nr. 1 (Januar 2021): 79–95. http://dx.doi.org/10.1530/erc-20-0128.

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Benign adrenal tumors cover a spectrum of lesions with distinct morphology and steroid secretion. Current classification is empirical. Beyond a few driver mutations, pathophysiology is not well understood. Here, a pangenomic characterization of benign adrenocortical tumors is proposed, aiming at unbiased classification and new pathophysiological insights. Benign adrenocortical tumors (n = 146) were analyzed by transcriptome, methylome, miRNome, chromosomal alterations and mutational status, using expression arrays, methylation arrays, miRNA sequencing, SNP arrays, and exome or targeted next-generation sequencing respectively. Pathological and hormonal data were collected for all tumors. Pangenomic analysis identifies four distinct molecular categories: (1) tumors responsible for overt Cushing, gathering distinct tumor types, sharing a common cAMP/PKA pathway activation by distinct mechanisms; (2) adenomas with mild autonomous cortisol excess and non-functioning adenomas, associated with beta-catenin mutations; (3) primary macronodular hyperplasia with ARMC5 mutations, showing an ovarian expression signature; (4) aldosterone-producing adrenocortical adenomas, apart from other benign tumors. Epigenetic alterations and steroidogenesis seem associated, including CpG island hypomethylation in tumors with no or mild cortisol secretion, miRNA patterns defining specific molecular groups, and direct regulation of steroidogenic enzyme expression by methylation. Chromosomal alterations and somatic mutations are subclonal, found in less than 2/3 of cells. New pathophysiological insights, including distinct molecular signatures supporting the difference between mild autonomous cortisol excess and overt Cushing, ARMC5 implication into the adreno-gonadal differentiation faith, and the subclonal nature of driver alterations in benign tumors, will orient future research. This first genomic classification provides a large amount of data as a starting point.

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Ornella, L., P. Pérez, E. Tapia, J. M. González-Camacho, J. Burgueño, X. Zhang, S. Singh et al. „Genomic-enabled prediction with classification algorithms“. Heredity 112, Nr. 6 (15.01.2014): 616–26. http://dx.doi.org/10.1038/hdy.2013.144.

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Spino, Marissa, und Matija Snuderl. „Genomic Molecular Classification of CNS Malignancies“. Advances In Anatomic Pathology 27, Nr. 1 (Januar 2020): 44–50. http://dx.doi.org/10.1097/pap.0000000000000254.

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Graur, Dan, Yichen Zheng und Ricardo B. R. Azevedo. „An Evolutionary Classification of Genomic Function“. Genome Biology and Evolution 7, Nr. 3 (28.01.2015): 642–45. http://dx.doi.org/10.1093/gbe/evv021.

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Kundra, Ritika, Hongxin Zhang, Robert Sheridan, Sahussapont Joseph Sirintrapun, Avery Wang, Angelica Ochoa, Manda Wilson et al. „OncoTree: A Cancer Classification System for Precision Oncology“. JCO Clinical Cancer Informatics, Nr. 5 (März 2021): 221–30. http://dx.doi.org/10.1200/cci.20.00108.

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PURPOSE Cancer classification is foundational for patient care and oncology research. Systems such as International Classification of Diseases for Oncology (ICD-O), Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), and National Cancer Institute Thesaurus (NCIt) provide large sets of cancer classification terminologies but they lack a dynamic modernized cancer classification platform that addresses the fast-evolving needs in clinical reporting of genomic sequencing results and associated oncology research. METHODS To meet these needs, we have developed OncoTree, an open-source cancer classification system. It is maintained by a cross-institutional committee of oncologists, pathologists, scientists, and engineers, accessible via an open-source Web user interface and an application programming interface. RESULTS OncoTree currently includes 868 tumor types across 32 organ sites. OncoTree has been adopted as the tumor classification system for American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE), a large genomic and clinical data-sharing consortium, and for clinical molecular testing efforts at Memorial Sloan Kettering Cancer Center and Dana-Farber Cancer Institute. It is also used by precision oncology tools such as OncoKB and cBioPortal for Cancer Genomics. CONCLUSION OncoTree is a dynamic and flexible community-driven cancer classification platform encompassing rare and common cancers that provides clinically relevant and appropriately granular cancer classification for clinical decision support systems and oncology research.

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Kim, Jong-Won. „Diagnostic Classification and Genomic Analyses of Cancer“. Laboratory Medicine Online 11, Nr. 4 (01.10.2021): 223–29. http://dx.doi.org/10.47429/lmo.2021.11.4.223.

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Joly, Yann, Hilary Burton, Bartha Maria Knoppers, Ida Ngueng Feze, Tom Dent, Nora Pashayan, Susmita Chowdhury et al. „Life insurance: genomic stratification and risk classification“. European Journal of Human Genetics 22, Nr. 5 (16.10.2013): 575–79. http://dx.doi.org/10.1038/ejhg.2013.228.

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Edgar, R. C., und E. W. Myers. „PILER: identification and classification of genomic repeats“. Bioinformatics 21, Suppl 1 (01.06.2005): i152—i158. http://dx.doi.org/10.1093/bioinformatics/bti1003.

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Shames, David S., und Ignacio I. Wistuba. „The evolving genomic classification of lung cancer“. Journal of Pathology 232, Nr. 2 (10.12.2013): 121–33. http://dx.doi.org/10.1002/path.4275.

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Maleina, M. N. „The Concept and Classification of Genomic (Genetic) Information“. Lex Russica, Nr. 7 (23.07.2020): 50–58. http://dx.doi.org/10.17803/1729-5920.2020.164.7.050-058.

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The importance of genomic information has now increased due to the possibility of its practical use. Meanwhile, the understanding of the term “genomic information” is specified based on different criteria. Genomic information is proposed to be classified depending on the following criteria: 1) the origin of a biological sample, 2) the place of fixation and storage of genomic information, 3) the purpose of use, 4) the completeness of examination, 5) the relation of a person to the acquisition of his or her genomic information, 6) the scope of content. Genomic information can be presented as a generic concept referring to all biological objects, as a special concept (species) referring only to humans, and as subspecies reflecting specificity of such information in a particular area of activity. Genomic information of a living being (human, animal, plant, microorganism) is understood as data on certain fragments of deoxyribonucleic acid (sometimes ribonucleic acid) on the basis of which the living being is identified or other permitted activity is carried out.Human genomic information is defined as biometric personal data extracted from certain fragments of deoxyribonucleic acid (sometimes ribonucleic acid) of a living individual or corpse, on the basis of which it is possible to identify, determine genetic predispositions or extract patterns of the development of the human being obtained voluntarily, and, in cases provided for by the law, forced to be fixed in a biological sample and/or stored in an information map or database.It is proved that the existing laws on information or a new law dedicated to regulation of the application of genomic technologies should be amended instead of adopting a special law “On Genetic Information”.

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WANG, JING-DOO. „COMPARING VIRUS CLASSIFICATION USING GENOMIC MATERIALS ACCORDING TO DIFFERENT TAXONOMIC LEVELS“. Journal of Bioinformatics and Computational Biology 11, Nr. 06 (Dezember 2013): 1343003. http://dx.doi.org/10.1142/s0219720013430038.

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In this paper, three genomic materials — DNA sequences, protein sequences, and regions (domains) are used to compare methods of virus classification. Virus classes (categories) are divided by various taxonomic level of virus into three datasets for 6 order, 42 family, and 33 genera. To increase the robustness and comparability of experimental results of virus classification, the classes are selected that contain at least 10 instances, and meanwhile each instance contains at least one region name. Experimental results show that the approach using region names achieved the best accuracies — reaching 99.9%, 97.3%, and 99.0% for 6 orders, 42 families, and 33 genera, respectively. This paper not only involves exhaustive experiments that compare virus classifications using different genomic materials, but also proposes a novel approach to biological classification based on molecular biology instead of traditional morphology.

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Grimont, Patrick A. D. „Use of DNA reassociation in bacterial classification“. Canadian Journal of Microbiology 34, Nr. 4 (01.04.1988): 541–46. http://dx.doi.org/10.1139/m88-092.

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The reassociation properties of DNA provide invaluable taxonomic tools. Different methods may give different reassociation values. However, the thermal stability of reassociated DNA strands (a measurement that seems independent of method) is useful in delineating genomic species. Although many phenotypically defined species have been confirmed by DNA reassociation, some medically important genomic species previously had been split into several nomenspecies on the basis of a few characteristics whereas some environmental genomic species had been lumped into unidentifiable aggregates. It might take some time before the nomenclature can be adapted to new taxonomic findings.

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Kareem, Noora. „GENOMIC BIOMARKERS IN ENDOMETRIAL CARCINOMA“. Iraqi Journal of Medical Sciences 17, Nr. 2 (30.06.2019): 100–102. http://dx.doi.org/10.22578/ijms.17.2.1.

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Endometrial carcinoma is the second most common gynecological cancer in developing countries after cervical carcinoma and its incidence is increasing due to the rise in the rate of obesity. Diagnosis depend on invasive test (biopsy) with no routine screening investigation available for either general population or high-risk group, there are several types of biomarkers that can be used for diagnosis, prognosis and management but none are available for routine clinical practice. Following the discovery of the new gene-based classifications of endometrial cancer, the use of these gene-based biomarkers will be the cornerstone in the early diagnosis and management for endometrial carcinoma patients in the coming years. Keywords:Endometrial carcinoma, screening, PTEN, miRNA, P53, circulating tumor DNA, genomic classification Citation:Kareem NM. Genomic biomarkers in endometrial carcinoma. Iraqi JMS. 2019; 17(2): 100-102. doi: 10.22578/IJMS.17.2.1

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Surrey, Lea F., Minjie Luo, Fengqi Chang und Marilyn M. Li. „The Genomic Era of Clinical Oncology: Integrated Genomic Analysis for Precision Cancer Care“. Cytogenetic and Genome Research 150, Nr. 3-4 (2016): 162–75. http://dx.doi.org/10.1159/000454655.

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Genomic alterations are important biological markers for cancer diagnosis and prognosis, disease classification, risk stratification, and treatment selection. Chromosomal microarray analysis (CMA) and next-generation sequencing (NGS) technologies are superb new tools for evaluating cancer genomes. These state-of-the-art technologies offer high-throughput, highly accurate, targeted and whole-genome evaluation of genomic alterations in tumor tissues. The application of CMA and NGS technologies in cancer research has generated a wealth of useful information about the landscape of genomic alterations in cancer and their implications in cancer care. As the knowledge base in cancer genomics and genome biology grows, the focus of research is now shifting toward the clinical applications of these technologies to improve patient care. Although not yet standard of care in cancer, there is an increasing interest among the cancer genomics communities in applying these new technologies to cancer diagnosis in the Clinical Laboratory Improvement Amendments (CLIA)-certified laboratories. Many clinical laboratories have already started adopting these technologies for cancer genomic analysis. We anticipate that CMA and NGS will soon become the major diagnostic means for cancer genomic analysis to meet the increasing demands of precision cancer care.

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Meysman, Pieter, Kathleen Marchal und Kristof Engelen. „DNA Structural Properties in the Classification of Genomic Transcription Regulation Elements“. Bioinformatics and Biology Insights 6 (Januar 2012): BBI.S9426. http://dx.doi.org/10.4137/bbi.s9426.

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It has been long known that DNA molecules encode information at various levels. The most basic level comprises the base sequence itself and is primarily important for the encoding of proteins and direct base recognition by DNA-binding proteins. A more elusive level consists of the local structural properties of the DNA molecule wherein the DNA sequence only plays an indirect supportive role. These properties are nevertheless an important factor in a large number of biomolecular processes and can be considered as informative signals for the presence of a variety of genomic features. Several recent studies have unequivocally shown the benefit of relying on such DNA properties for modeling and predicting genomic features as diverse as transcription start sites, transcription factor binding sites, or nucleosome occupancy. This review is meant to provide an overview of the key aspects of these DNA conformational and physicochemical properties. To illustrate their potential added value compared to relying solely on the nucleotide sequence in genomics studies, we discuss their application in research on transcription regulation mechanisms as representative cases.

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Murugesan, Karthikeyan, Radwa Sharaf, Meagan Montesion, Jay A. Moore, James Pao, Dean C. Pavlick, Garrett M. Frampton et al. „Genomic Profiling of Combined Hepatocellular Cholangiocarcinoma Reveals Genomics Similar to Either Hepatocellular Carcinoma or Cholangiocarcinoma“. JCO Precision Oncology, Nr. 5 (August 2021): 1285–96. http://dx.doi.org/10.1200/po.20.00397.

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PURPOSE Combined hepatocellular cholangiocarcinoma (cHCC-CCA) is a rare, aggressive primary liver carcinoma, with morphologic features of both hepatocellular carcinomas (HCC) and liver cholangiocarcinomas (CCA). METHODS The genomic profiles of 4,975 CCA, 1,470 HCC, and 73 cHCC-CCA cases arising from comprehensive genomic profiling in the course of clinical care were reviewed for genomic alterations (GA), tumor mutational burden, microsatellite instability status, genomic loss of heterozygosity, chromosomal aneuploidy, genomic ancestry, and hepatitis B virus status. RESULTS In cHCC-CCA, GA were most common in TP53 (65.8%), TERT (49.3%), and PTEN (9.6%), and 24.6% cHCC-CCA harbored potentially targetable GA. Other GA were predominantly associated with either HCC or CCA, including, but not limited to, TERT, FGFR2, IDH1, and presence of hepatitis B virus. On the basis of these features, a machine learning (ML) model was trained to classify a cHCC-CCA case as CCA-like or HCC-like. Of cHCC-CCA cases, 16% (12/73) were ML-classified as CCA-like and 58% (42/73) cHCC-CCA were ML-classified as HCC-like. The ML model classified more than 70% of cHCC-CCA as CCA-like or HCC-like on the basis of genomic profiles, without additional clinico-pathologic input. CONCLUSION These findings demonstrate the use of ML for classification as based on a targeted exome panel used during routine clinical care. Classification of cHCC-CCA by genomic features alone creates insights into the biology of the disease and warrants further investigation for relevance to clinical care.

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Szczepińska, Teresa, Ayatullah Faruk Mollah und Dariusz Plewczynski. „Genomic Marks Associated with Chromatin Compartments in the CTCF, RNAPII Loop and Genomic Windows“. International Journal of Molecular Sciences 22, Nr. 21 (27.10.2021): 11591. http://dx.doi.org/10.3390/ijms222111591.

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The nature of genome organization into two basic structural compartments is as yet undiscovered. However, it has been indicated to be a mechanism of gene expression regulation. Using the classification approach, we ranked genomic marks that hint at compartmentalization. We considered a broad range of marks, including GC content, histone modifications, DNA binding proteins, open chromatin, transcription and genome regulatory segmentation in GM12878 cells. Genomic marks were defined over CTCF or RNAPII loops, which are basic elements of genome 3D structure, and over 100 kb genomic windows. Experiments were carried out to empirically assess the whole set of features, as well as the individual features in classification of loops/windows, into compartment A or B. Using Monte Carlo Feature Selection and Analysis of Variance, we constructed a ranking of feature importance for classification. The best simple indicator of compartmentalization is DNase-seq open chromatin measurement for CTCF loops, H3K4me1 for RNAPII loops and H3K79me2 for genomic windows. Among DNA binding proteins, this is RUNX3 transcription factor for loops and RNAPII for genomic windows. Chromatin state prediction methods that indicate active elements like promoters, enhancers or heterochromatin enhance the prediction of loop segregation into compartments. However, H3K9me3, H4K20me1, H3K27me3 histone modifications and GC content poorly indicate compartments.

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Morales, J. Alejandro, Román Saldaña, Manuel H. Santana-Castolo, Carlos E. Torres-Cerna, Ernesto Borrayo, Adriana P. Mendizabal-Ruiz, Hugo A. Vélez-Pérez und Gerardo Mendizabal-Ruiz. „Deep Learning for the Classification of Genomic Signals“. Mathematical Problems in Engineering 2020 (05.05.2020): 1–9. http://dx.doi.org/10.1155/2020/7698590.

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Genomic signal processing (GSP) is based on the use of digital signal processing methods for the analysis of genomic data. Convolutional neural networks (CNN) are the state-of-the-art machine learning classifiers that have been widely applied to solve complex problems successfully. In this paper, we present a deep learning architecture and a method for the classification of three different functional genome types: coding regions (CDS), long noncoding regions (LNC), and pseudogenes (PSD) in genomic data, based on the use of GSP methods to convert the nucleotide sequence into a graphical representation of the information contained in it. The obtained accuracy scores of 83% and 84% when classifying between CDS vs. LNC and CDS vs. PSD, respectively, indicate the feasibility of employing this methodology for the classification of these types of sequences. The model was not able to differentiate from PSD and LNC. Our results indicate the feasibility of employing CNN with GSP for the classification of these types of DNA data.

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Skutkova, Helena, Martin Vitek, Petr Babula, Rene Kizek und Ivo Provaznik. „Classification of genomic signals using dynamic time warping“. BMC Bioinformatics 14, Suppl 10 (2013): S1. http://dx.doi.org/10.1186/1471-2105-14-s10-s1.

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Xu, Lin, Cong Sun, Chen Fang, Aharon Oren und Xue-Wei Xu. „Genomic-based taxonomic classification of the family Erythrobacteraceae“. International Journal of Systematic and Evolutionary Microbiology 70, Nr. 8 (01.08.2020): 4470–95. http://dx.doi.org/10.1099/ijsem.0.004293.

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The family Erythrobacteraceae , belonging to the order Sphingomonadales , class Alphaproteobacteria , is globally distributed in various environments. Currently, this family consist of seven genera: Altererythrobacter , Croceibacterium , Croceicoccus , Erythrobacter , Erythromicrobium , Porphyrobacter and Qipengyuania . As more species are identified, the taxonomic status of the family Erythrobacteraceae should be revised at the genomic level because of its polyphyletic nature evident from 16S rRNA gene sequence analysis. Phylogenomic reconstruction based on 288 single-copy orthologous clusters led to the identification of three separate clades. Pairwise comparisons of average nucleotide identity, average amino acid identity (AAI), percentage of conserved protein and evolutionary distance indicated that AAI and evolutionary distance had the highest correlation. Thresholds for genera boundaries were proposed as 70 % and 0.4 for AAI and evolutionary distance, respectively. Based on the phylo-genomic and genomic similarity analysis, the three clades were classified into 16 genera, including 11 novel ones, for which the names Alteraurantiacibacter, Altericroceibacterium, Alteriqipengyuania, Alteripontixanthobacter, Aurantiacibacter, Paraurantiacibacter, Parerythrobacter, Parapontixanthobacter, Pelagerythrobacter, Tsuneonella and Pontixanthobacter are proposed. We reclassified all species of Erythromicrobium and Porphyrobacter as species of Erythrobacter . This study is the first genomic-based study of the family Erythrobacteraceae , and will contribute to further insights into the evolution of this family.

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HU, TaoTao, und ZeGuang HAN. „Genomic Mutations and Molecular Classification of Liver Cancer“. SCIENTIA SINICA Vitae 44, Nr. 2 (01.02.2014): 119–24. http://dx.doi.org/10.1360/052013-356.

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Dabney, Alan R., und John D. Storey. „Optimality Driven Nearest Centroid Classification from Genomic Data“. PLoS ONE 2, Nr. 10 (03.10.2007): e1002. http://dx.doi.org/10.1371/journal.pone.0001002.

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Papaemmanuil, Elli, Moritz Gerstung, Lars Bullinger, Verena I. Gaidzik, Peter Paschka, Nicola D. Roberts, Nicola E. Potter et al. „Genomic Classification and Prognosis in Acute Myeloid Leukemia“. New England Journal of Medicine 374, Nr. 23 (09.06.2016): 2209–21. http://dx.doi.org/10.1056/nejmoa1516192.

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Fantini, Damiano, und Joshua J. Meeks. „Genomic classification and risk stratification of bladder cancer“. World Journal of Urology 37, Nr. 9 (12.11.2018): 1751–57. http://dx.doi.org/10.1007/s00345-018-2558-2.

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Errico, Alessia. „ALL classification—integration of genomic and cytogenetic data“. Nature Reviews Clinical Oncology 11, Nr. 8 (08.07.2014): 440. http://dx.doi.org/10.1038/nrclinonc.2014.117.

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Paul, Bobby, K. Kavia Raj, Thokur Sreepathy Murali und K. Satyamoorthy. „Species-specific genomic sequences for classification of bacteria“. Computers in Biology and Medicine 123 (August 2020): 103874. http://dx.doi.org/10.1016/j.compbiomed.2020.103874.

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Michels, Evi, Jo Vandesompele, Katleen De Preter, Jasmien Hoebeeck, Joëlle Vermeulen, Alexander Schramm, Jan J. Molenaar et al. „ArrayCGH-based classification of neuroblastoma into genomic subgroups“. Genes, Chromosomes and Cancer 46, Nr. 12 (Dezember 2007): 1098–108. http://dx.doi.org/10.1002/gcc.20496.

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Stoyanova, Radka, Alan Pollack, Charles Lynne, Merce Jorda, Nicholas Erho, Lucia L. C. Lam, Christine Buerki, Elai Davicioni und Adrian Ishkanian. „Using radiogenomics to characterize MRI-guided prostate cancer biopsy heterogenity.“ Journal of Clinical Oncology 33, Nr. 7_suppl (01.03.2015): 25. http://dx.doi.org/10.1200/jco.2015.33.7_suppl.25.

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25 Background: Current methods for prostate cancer risk stratification are often insufficient to accurately predict outcome after definitive therapy. As tumor multi-focality and genetic heterogeneity can lead to diagnostic prostate biopsy sampling bias, we hypothesize that quantitative imaging with multiparametric (MP)-MRI will more accurately direct targeted biopsies to index lesions associated with highest risk clinical and genomic features, and improve accuracy of current risk classification systems. Methods: Regionally distinct prostate habitats were delineated on MP-MRI (T2w, perfusion and diffusion imaging). Directed biopsies were performed on 17 habitats from 6 patients using MRI-ultrasound fusion. Biopsy location was characterized with 51 radiographic features (including intensity, volume, perfusion, and diffusion paramters). Transcriptome-wide analysis of 1.4 million RNA probes was performed on RNA from each habitat. Genomics features with insignificant expression values (<0.25) and interquartile range <0.5 were filtered, leaving ~2K features. Results: High quality genomic data was derived from 17 (100%) biopsies and clustered by patient origin. Using only prostate cancer related genomic features for hierarchical clustering, samples clustered by Gleason score (GS), indicating these biopsies contain prognostic signal. Similarly, when principal component analysis was performed on 51 imaging features, the primary source of variance segregated the samples into high (≥7) and low (6) GS. Pearson’s correlation analysis identified 152 genomic features that were highly associated with the imaging features (|r| > 0.7). Furthermore, genomic features were found to be significantly enriched for prostate cancer related pathways (p < 0.05), representing a potential biologically meaningful link between imaging and genomic data. Conclusions: MP-MRI-targeted diagnostic biopsies can potentially improve risk classification by directing pathological and genomic analysis to highest risk index lesions. This is the first demonstration of a link between quantitative imaging features (radiomics) with genomic features in MRI-directed prostate biopsies.

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Adanur Dedeturk, Beyhan, Ahmet Soran und Burcu Bakir-Gungor. „Blockchain for genomics and healthcare: a literature review, current status, classification and open issues“. PeerJ 9 (30.09.2021): e12130. http://dx.doi.org/10.7717/peerj.12130.

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The tremendous boost in the next generation sequencing technologies and in the “omics” technologies resulted in the generation of hundreds of gigabytes of data per day. Nowadays, via integrating -omics data with other data types, such as imaging and electronic health record (EHR) data, panomics studies attempt to identify novel and potentially actionable biomarkers for personalized medicine applications. In this respect, for the accurate analysis of -omics data and EHR, there is a need to establish secure and robust pipelines that take the ethical aspects into consideration, regulate privacy and ownership issues, and data sharing. These days, blockchain technology has picked up significant attention in diverse fields, including genomics, since it offers a new solution for these problems from a different perspective. Blockchain is an immutable transaction ledger, which offers secure and distributed system without a central authority. Within the system, each transaction can be expressed with cryptographically signed blocks, and the verification of transactions is performed by the users of the network. In this review, firstly, we aim to highlight the challenges of EHR and genomic data sharing. Secondly, we attempt to answer “Why” or “Why not” the blockchain technology is suitable for genomics and healthcare applications in detail. Thirdly, we elucidate the general blockchain structure based on the Ethereum, which is a more suitable technology for the genomic data sharing platforms. Fourthly, we review current blockchain-based EHR and genomic data sharing platforms, evaluate the advantages and disadvantages of these applications, and classify these applications using different metrics. Finally, we conclude by discussing the open issues and introducing our suggestion on the topic. In summary, to facilitate the diagnosis, monitoring and therapy of diseases with the effective analysis of -omics data with other available data types, through this review, we put forward the possible implications of the blockchain technology to life sciences and healthcare.

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Orozco-Arias, Simon, Luis Humberto Lopez-Murillo, Johan S. Piña, Estiven Valencia-Castrillon, Reinel Tabares-Soto, Luis Castillo-Ossa, Gustavo Isaza und Romain Guyot. „Genomic object detection: An improved approach for transposable elements detection and classification using convolutional neural networks“. PLOS ONE 18, Nr. 9 (21.09.2023): e0291925. http://dx.doi.org/10.1371/journal.pone.0291925.

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Analysis of eukaryotic genomes requires the detection and classification of transposable elements (TEs), a crucial but complex and time-consuming task. To improve the performance of tools that accomplish these tasks, Machine Learning approaches (ML) that leverage computer resources, such as GPUs (Graphical Processing Unit) and multiple CPU (Central Processing Unit) cores, have been adopted. However, until now, the use of ML techniques has mostly been limited to classification of TEs. Herein, a detection-classification strategy (named YORO) based on convolutional neural networks is adapted from computer vision (YOLO) to genomics. This approach enables the detection of genomic objects through the prediction of the position, length, and classification in large DNA sequences such as fully sequenced genomes. As a proof of concept, the internal protein-coding domains of LTR-retrotransposons are used to train the proposed neural network. Precision, recall, accuracy, F1-score, execution times and time ratios, as well as several graphical representations were used as metrics to measure performance. These promising results open the door for a new generation of Deep Learning tools for genomics. YORO architecture is available at https://github.com/simonorozcoarias/YORO.

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Ye, Taoyu, Sen Li und Yang Zhang. „Genomic pan-cancer classification using image-based deep learning“. Computational and Structural Biotechnology Journal 19 (2021): 835–46. http://dx.doi.org/10.1016/j.csbj.2021.01.010.

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Durastanti, Claudio, Emilio N. M. Cirillo, Ilaria De Benedictis, Mario Ledda, Antonio Sciortino, Antonella Lisi, Annalisa Convertino und Valentina Mussi. „Statistical Classification for Raman Spectra of Tumoral Genomic DNA“. Micromachines 13, Nr. 9 (25.08.2022): 1388. http://dx.doi.org/10.3390/mi13091388.

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We exploit Surface-Enhanced Raman Scattering (SERS) to investigate aqueous droplets of genomic DNA deposited onto silver-coated silicon nanowires, and we show that it is possible to efficiently discriminate between spectra of tumoral and healthy cells. To assess the robustness of the proposed technique, we develop two different statistical approaches, one based on the Principal Components Analysis of spectral data and one based on the computation of the ℓ2 distance between spectra. Both methods prove to be highly efficient, and we test their accuracy via the Cohen’s κ statistics. We show that the synergistic combination of the SERS spectroscopy and the statistical analysis methods leads to efficient and fast cancer diagnostic applications allowing rapid and unexpansive discrimination between healthy and tumoral genomic DNA alternative to the more complex and expensive DNA sequencing.

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Mahmood Aamir, Khalid, Muhammad Bilal, Muhammad Ramzan, Muhammad Attique Khan, Yunyoung Nam und Seifedine Kadry. „Classification of Retroviruses Based on Genomic Data Using RVGC“. Computers, Materials & Continua 69, Nr. 3 (2021): 3829–44. http://dx.doi.org/10.32604/cmc.2021.017835.

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NEMAN, JOSH, GEORGE SOMLO und RAHUL JANDIAL. „Classification of Genomic Changes in Breast Cancer Brain Metastasis“. Neurosurgery 67, Nr. 2 (01.08.2010): N18—N19. http://dx.doi.org/10.1227/01.neu.0000386966.69913.79.

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Smith, Donald B., und Peter Simmonds. „Classification and Genomic Diversity of Enterically Transmitted Hepatitis Viruses“. Cold Spring Harbor Perspectives in Medicine 8, Nr. 9 (12.03.2018): a031880. http://dx.doi.org/10.1101/cshperspect.a031880.

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Czekaj, Tomasz, Wen Wu und Beata Walczak. „Classification of genomic data: Some aspects of feature selection“. Talanta 76, Nr. 3 (30.07.2008): 564–74. http://dx.doi.org/10.1016/j.talanta.2008.03.045.

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Jaimes-Díaz, Hueman, Adda Jeanette García-Chéquer, Alfonso Méndez-Tenorio, Juan Carlos Santiago-Hernández, Rogelio Maldonado-Rodríguez und Kenneth Loren Beattie. „Bacterial classification using genomic fingerprints obtained by virtual hybridization“. Journal of Microbiological Methods 87, Nr. 3 (Dezember 2011): 286–94. http://dx.doi.org/10.1016/j.mimet.2011.08.014.

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Szpiech, Zachary A., Alexandra Blant und Trevor J. Pemberton. „GARLIC: Genomic Autozygosity Regions Likelihood-based Inference and Classification“. Bioinformatics 33, Nr. 13 (16.02.2017): 2059–62. http://dx.doi.org/10.1093/bioinformatics/btx102.

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Betlach, Alyssa M., Dominiek Maes, Laura Garza‐Moreno, Pablo Tamiozzo, Marina Sibila, Freddy Haesebrouck, Joaquim Segalés und Maria Pieters. „Mycoplasma hyopneumoniaevariability:Current trends and proposed terminology for genomic classification“. Transboundary and Emerging Diseases 66, Nr. 5 (04.06.2019): 1840–54. http://dx.doi.org/10.1111/tbed.13233.

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Magierowicz, Marion, Cécile Tomowiak, Xavier Leleu und Stéphanie Poulain. „Working Toward a Genomic Prognostic Classification of Waldenström Macroglobulinemia“. Hematology/Oncology Clinics of North America 32, Nr. 5 (Oktober 2018): 753–63. http://dx.doi.org/10.1016/j.hoc.2018.05.007.

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Poobalan, P., und S. Pannirselvam. „Semi-supervised Clustering Based Feature Selection with Multiobjective Genomic Search Class-based Classification Method for NIDPS“. Indian Journal of Science and Technology 15, Nr. 19 (19.05.2022): 948–55. http://dx.doi.org/10.17485/ijst/v15i19.297.

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Tekaia, Fredj, Antonio Lazcano und Bernard Dujon. „The Genomic Tree as Revealed from Whole Proteome Comparisons“. Genome Research 9, Nr. 6 (01.06.1999): 550–57. http://dx.doi.org/10.1101/gr.9.6.550.

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The availability of a number of complete cellular genome sequences allows the development of organisms’ classification, taking into account their genome content, the loss or acquisition of genes, and overall gene similarities as signatures of common ancestry. On the basis of correspondence analysis and hierarchical classification methods, a methodological framework is introduced here for the classification of the available 20 completely sequenced genomes and partial information for Schizosaccharomyces pombe, Homo sapiens, and Mus musculus. The outcome of such an analysis leads to a classification of genomes that we call a genomic tree. Although these trees are phenograms, they carry with them strong phylogenetic signatures and are remarkably similar to 16S-like rRNA-based phylogenies. Our results suggest that duplication and deletion events that took place through evolutionary time were globally similar in related organisms. The genomic trees presented here place the Archaea in the proximity of the Bacteria when the whole gene content of each organism is considered, and when ancestral gene duplications are eliminated. Genomic trees represent an additional approach for the understanding of evolution at the genomic level and may contribute to the proper assessment of the evolutionary relationships between extant species.

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Scholer, Anthony Joseph, Mary Garland-Kledzik, Debopyria Ghosh, Juan Santamaria-Barria, Adam Khader, Javier Orozco, Melanie Goldfarb und Diego M. Marzese. „Exploring the genomic landscape of hepatobiliary cancers to establish a novel molecular subtype classification.“ Journal of Clinical Oncology 38, Nr. 4_suppl (01.02.2020): 562. http://dx.doi.org/10.1200/jco.2020.38.4_suppl.562.

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562 Background: The current understanding of the genomic landscape of hepatobiliary cancer (HBC) is limited. Recent genomic and epigenomic studies have demonstrated that various cancers of different tissue origins can have similar molecular phenotypes. Therefore, the aim of this study is to evaluate the genomic alterations of HBCs as a first step towards creating a novel molecular subtype classification. Methods: A multidimensional analysis of next-generation sequencing for the genomic landscape of HBCs was conducted using mutational data from the AACR-Genomics Evidence Neoplasia Information Exchange database (v. 5.0). From 61 gene mutation platforms, we found 42 genes common to all HBC cases. Associations between histomolecular characteristics of HBCs (hepatocellular (HCC), cholangiocarcinoma (CCA), and gallbladder carcinoma (GBC)) with gene mutations (classified by COSMIC CENSUS) were analyzed using Pearson’s χ2 test. Results: A total of 1,017 alterations were identified in 61 genes (516 missense variant, 157 gene amplifications, 101 inactivating mutations, 106 truncating mutations, 84 upstream gene variants, 37 gene homozygous deletions, 16 gene rearrangements) in 329 patients: 115 (35%) CCA, 87 (26.4%) GBC, and 127 (38.6%) HCC. The majority 77.8% (256) of tumors harbored at least two mutations and 38.9% (128) had at least one alteration, with GBC having a higher average number of alterations (3.28) than HCC (3.23) and CCA (2.49) However, HCCs had the higher maximum number of alterations compared to CCA and GBC (p < 0.05). The ten genes most frequently altered across all the HBCs were TP53, TERT, CTNNB1, KRAS, ARID1A, CDKN2A, IDH1, PIK3CA, MYC, and SMAD4 with disparities in the distribution of genes altered repeatedly observed (p < 0.001). IDH1 mutations were associated with CCA, CTNNB1 and TERT mutations with HCC, and TP53 mutations with both HCC and GBC. Conclusions: HBC subtypes appear to have unique mutational landscapes, but also significant overlap of genetic signatures. Therefore, further exploratory genetic and epigenomic research is needed to develop a histomolecular classification algorithm that can be used for prognostic and therapeutic stratification of these cancers.

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Lominadze, Zurabi, Mohammed Rifat Shaik, Dabin Choi, Duha Zaffar, Lopa Mishra und Kirti Shetty. „Hepatocellular Carcinoma Genetic Classification“. Cancer Journal 29, Nr. 5 (September 2023): 249–58. http://dx.doi.org/10.1097/ppo.0000000000000682.

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Abstract Hepatocellular carcinoma (HCC) represents a significant global burden, with management complicated by its heterogeneity, varying presentation, and relative resistance to therapy. Recent advances in the understanding of the genetic, molecular, and immunological underpinnings of HCC have allowed a detailed classification of these tumors, with resultant implications for diagnosis, prognostication, and selection of appropriate treatments. Through the correlation of genomic features with histopathology and clinical outcomes, we are moving toward a comprehensive and unifying framework to guide our diagnostic and therapeutic approach to HCC.

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Guo, Charles C., und Bogdan Czerniak. „Bladder Cancer in the Genomic Era“. Archives of Pathology & Laboratory Medicine 143, Nr. 6 (23.01.2019): 695–704. http://dx.doi.org/10.5858/arpa.2018-0329-ra.

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Context.— Bladder cancer is a heterogeneous disease that exhibits a wide spectrum of clinical and pathologic features. The classification of bladder cancer has been traditionally based on morphologic assessment with the aid of immunohistochemistry. However, recent genomic studies have revealed that distinct alterations of DNA and RNA in bladder cancer may underlie its diverse clinicopathologic features, leading to a novel molecular classification of this common human cancer. Objective.— To update recent developments in genomic characterization of bladder cancer, which may shed insights on the molecular mechanisms underlying the origin of bladder cancer, dual-track oncogenic pathways, intrinsic molecular subtyping, and development of histologic variants. Data Sources.— Peer-reviewed literature retrieved from PubMed search and authors' own research. Conclusions.— Bladder cancer is likely to arise from different uroprogenitor cells through papillary/luminal and nonpapillary/basal tracks. The intrinsic molecular subtypes of bladder cancer referred to as luminal and basal exhibit distinct expression signatures, clinicopathologic features, and sensitivities to standard chemotherapy. Genomic characterization of bladder cancer provides new insights to understanding the biological nature of this complex disease, which may lead to more effective treatment.

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Demir, Derya. „Insights into the New Molecular Updates in Acute Myeloid Leukemia Pathogenesis“. Genes 14, Nr. 7 (10.07.2023): 1424. http://dx.doi.org/10.3390/genes14071424.

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As our understanding of the biologic basis of acute myeloid leukemia evolves, so do the classification systems used to describe this group of cancers. Early classification systems focused on the morphologic features of blasts and other cell populations; however, the explosion in genomic technologies has led to rapid growth in our understanding of these diseases and thus the refinement of classification systems. Recently, two new systems, the International Consensus Classification system and the 5th edition of the World Health Organization classification of tumors of hematopoietic and lymphoid tissues, were published to incorporate the latest genomic advances in blood cancer. This article reviews the major updates in acute myeloid leukemia in both systems and highlights the biologic insights that have driven these changes.

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Li, Lanlan, Yeying Yang, Qi Zhang, Jiao Wang, Jiehui Jiang und Alzheimer’s Disease Neuroimaging Initiative. „Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer’s Disease or Mild Cognitive Impairment“. Behavioural Neurology 2021 (14.07.2021): 1–15. http://dx.doi.org/10.1155/2021/3359103.

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Objectives. Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis. Methods. In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer’s Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. All data were randomly assigned to one training/validation group and one test group at a ratio of 9 : 1. And fivefold cross-validation was used. Results. We compared classification results from the DLG model to those from traditional GWAS analysis among the three groups. For the AD and HC groups, the accuracy, sensitivity, and specificity of classification were, respectively, 98.78 ± 1.50 % , 98.39 % ± 2.50 % , and 99.44 % ± 1.11 % using the DLG model, while 71.38 % ± 0.63 % , 63.13 % ± 2.87 % , and 85.59 % ± 6.66 % using traditional GWAS. Similar results were obtained from the other two intergroup classifications. Conclusion. The DLG model can achieve higher accuracy and sensitivity when applied to progression of AD. More importantly, we discovered several novel genetic biomarkers of AD progression, including rs6311 and rs6313 in HTR2A, rs1354269 in NAV2, and rs690705 in RFC3. The roles of these novel loci in AD should be explored in future research.

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