AIMC Topic: Models, Genetic

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Machine learning identifies a core gene set predictive of acquired resistance to EGFR tyrosine kinase inhibitor.

Journal of cancer research and clinical oncology May 25, 2018
PURPOSE: Acquired resistance (AR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) is a major issue worldwide, for both patients and healthcare providers. However, precise prediction is currently infeasible due to the lack o...
Machine Learning Algorithms Reproducibility of Results Humans Models, Genetic Drug Resistance, Neoplasm ErbB Receptors Protein Kinase Inhibitors Neoplasms Transcriptome Cell Line, Tumor
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Control of Gene Regulatory Networks Using Bayesian Inverse Reinforcement Learning.

IEEE/ACM transactions on computational biology and bioinformatics Apr 26, 2018
Control of gene regulatory networks (GRNs) to shift gene expression from undesirable states to desirable ones has received much attention in recent years. Most of the existing methods assume that the cost of intervention at each state and time point,...
Tumor Suppressor Protein p53 Proto-Oncogene Proteins c-mdm2 Wnt-5a Protein Gene Regulatory Networks Computational Biology Gene Expression Regulation, Neoplastic Models, Genetic Bayes Theorem Models, Biological Melanoma Skin Neoplasms Gene Expression Profiling Machine Learning Software Algorithms Humans
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Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia.

PLoS genetics Apr 23, 2018
Hybridization and gene flow between species appears to be common. Even though it is clear that hybridization is widespread across all surveyed taxonomic groups, the magnitude and consequences of introgression are still largely unknown. Thus it is cru...
Software Computer Simulation Animals Genetic Variation Haplotypes Species Specificity Drosophila Models, Genetic Evolution, Molecular Supervised Machine Learning Genetic Speciation Drosophila simulans Hybridization, Genetic Gene Flow Genetics, Population Genome, Insect
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A hierarchical clustering method for dimension reduction in joint analysis of multiple phenotypes.

Genetic epidemiology Apr 22, 2018
Genome-wide association studies (GWAS) have become a very effective research tool to identify genetic variants of underlying various complex diseases. In spite of the success of GWAS in identifying thousands of reproducible associations between genet...
Cluster Analysis Multifactor Dimensionality Reduction Humans Phenotype Computer Simulation Pulmonary Disease, Chronic Obstructive Genome-Wide Association Study Models, Genetic
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Statistics versus machine learning.

Nature methods Apr 3, 2018
Machine Learning Gene Expression Regulation Models, Genetic Data Interpretation, Statistical
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PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation.

International journal of molecular sciences Mar 29, 2018
Protein-protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Therefore, detecting whether a pair of proteins interact is of great importance for the study of molecular...
Software Models, Genetic Databases, Protein Support Vector Machine Saccharomyces cerevisiae Saccharomyces cerevisiae Proteins
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Sequential regulatory activity prediction across chromosomes with convolutional neural networks.

Genome research Mar 27, 2018
Models for predicting phenotypic outcomes from genotypes have important applications to understanding genomic function and improving human health. Here, we develop a machine-learning system to predict cell-type-specific epigenetic and transcriptional...
Regulatory Sequences, Nucleic Acid Animals Gene Expression Profiling Humans Genomics Polymorphism, Single Nucleotide Models, Genetic Neural Networks, Computer Promoter Regions, Genetic Computational Biology Machine Learning Gene Expression Regulation Epigenomics Chromosomes
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A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets.

BMC bioinformatics Mar 27, 2018
BACKGROUND: Testing predefined gene categories has become a common practice for scientists analyzing high throughput transcriptome data. A systematic way of testing gene categories leads to testing hundreds of null hypotheses that correspond to nodes...
Models, Genetic Models, Theoretical Bayes Theorem Markov Chains Gene Ontology Quantitative Trait Loci Computer Simulation Transcriptome ROC Curve Humans Algorithms
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Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations.

Scientific reports Mar 23, 2018
Gene Ontology (GO) enrichment analysis is ubiquitously used for interpreting high throughput molecular data and generating hypotheses about underlying biological phenomena of experiments. However, the two building blocks of this analysis - the ontolo...
Molecular Sequence Annotation Gene Ontology Models, Genetic Databases, Genetic Humans Evolution, Molecular Reproducibility of Results Computational Biology
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Generalising better: Applying deep learning to integrate deleteriousness prediction scores for whole-exome SNV studies.

PloS one Mar 14, 2018
Many automatic classifiers were introduced to aid inference of phenotypical effects of uncategorised nsSNVs (nonsynonymous Single Nucleotide Variations) in theoretical and medical applications. Lately, several meta-estimators have been proposed that ...
Sequence Analysis, DNA Machine Learning Internet Polymorphism, Single Nucleotide Models, Genetic Exome
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