AIMC Topic: Gene Expression Profiling

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Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data.

Bioinformatics (Oxford, England) Jun 1, 2018
MOTIVATION: Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different...
Humans Software Female Colorectal Neoplasms Gene Expression Regulation, Neoplastic Breast Neoplasms Support Vector Machine Neoplasms Colonic Neoplasms Gene Expression Profiling Oligonucleotide Array Sequence Analysis Sequence Analysis, RNA
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DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.

Bioinformatics (Oxford, England) May 1, 2018
MOTIVATION: While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a tim...
Computational Biology Gene Expression Regulation, Neoplastic Antineoplastic Combined Chemotherapy Protocols Support Vector Machine Neoplasms Humans Gene Expression Profiling Software Deep Learning Cell Line, Tumor
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Identification of recurrent risk-related genes and establishment of support vector machine prediction model for gastric cancer.

Neoplasma Mar 14, 2018
This study sought to investigate genes related to recurrent risk and establish a support vector machine (SVM) classifier for prediction of recurrent risk in gastric cancer (GC).Based on the gene expression profiling dataset GSE26253, feature genes th...
Support Vector Machine Neoplasm Recurrence, Local Humans Stomach Neoplasms Gene Expression Profiling Gene Expression Regulation, Neoplastic
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Deep learning sharpens views of cells and genes.

Nature Jan 4, 2018
Machine Learning Neoplasms Gene Expression Profiling Image Processing, Computer-Assisted Myocardial Infarction Humans Retina Neural Networks, Computer
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ICG: a wiki-driven knowledgebase of internal control genes for RT-qPCR normalization.

Nucleic acids research Jan 4, 2018
Real-time quantitative PCR (RT-qPCR) has become a widely used method for accurate expression profiling of targeted mRNA and ncRNA. Selection of appropriate internal control genes for RT-qPCR normalization is an elementary prerequisite for reliable ex...
Animals Gene Expression Profiling Real-Time Polymerase Chain Reaction Mice Humans Genes, Essential Knowledge Bases RNA, Untranslated Databases, Nucleic Acid
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Machine-Learning Prediction of Drug-Induced Cardiac Arrhythmia: Analysis of Gene Expression and Clustering.

Critical reviews in biomedical engineering Jan 1, 2018
A marked delay in the electrical repolarization of heart ventricles is characterized by prolongation of the Q-T wave (QT) interval on a surface electrocardiogram. Such a delay can lead to potentially life-threatening cardiac arrhythmia (torsades de p...
Rats Electrocardiography Support Vector Machine Cluster Analysis Signal Processing, Computer-Assisted Heart Drug Evaluation, Preclinical Heart Ventricles Humans Male Machine Learning Drug Design Gene Expression Profiling Mice Female Arrhythmias, Cardiac Reproducibility of Results MAP Kinase Signaling System False Positive Reactions Sensitivity and Specificity Gene Expression Regulation Animals Torsades de Pointes Clinical Trials as Topic
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Machine learning and deep analytics for biocomputing: call for better explainability.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2018
The goals of this workshop are to discuss challenges in explainability of current Machine Leaning and Deep Analytics (MLDA) used in biocomputing and to start the discussion on ways to improve it. We define explainability in MLDA as easy to use inform...
Cluster Analysis Decision Support Techniques Gene Expression Profiling Single-Cell Analysis Computational Biology Humans Artificial Intelligence Neural Networks, Computer Machine Learning Algorithms
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Single subject transcriptome analysis to identify functionally signed gene set or pathway activity.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2018
Analysis of single-subject transcriptome response data is an unmet need of precision medicine, made challenging by the high dimension, dynamic nature and difficulty in extracting meaningful signals from biological or stochastic noise. We have propose...
Diet, High-Fat Gene Ontology Oligonucleotide Array Sequence Analysis Mice, Inbred Strains Mice Computational Biology Databases, Genetic Mice, Inbred C57BL Mice, 129 Strain Mice, Inbred DBA Mice, Inbred NZB Humans Precision Medicine Female Animals Male Gene Expression Profiling Gene Regulatory Networks
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Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests.

Nucleic acids research Dec 1, 2017
The discovery of microRNAs (miRNAs) remains an important problem, particularly given the growth of high-throughput sequencing, cell sorting and single cell biology. While a large number of miRNAs have already been annotated, there may well be large n...
Gene Expression Profiling Ribonuclease III Humans Machine Learning RNA, Plant Single-Cell Analysis Sequence Analysis, RNA Software Mice MicroRNAs Genomics High-Throughput Nucleotide Sequencing Algorithms RNA, Small Untranslated Animals
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Using neural networks for reducing the dimensions of single-cell RNA-Seq data.

Nucleic acids research Sep 29, 2017
While only recently developed, the ability to profile expression data in single cells (scRNA-Seq) has already led to several important studies and findings. However, this technology has also raised several new computational challenges. These include ...
Databases, Genetic Cluster Analysis RNA Gene Expression Profiling Datasets as Topic Humans Gene Expression Regulation Single-Cell Analysis Protein Interaction Mapping Neural Networks, Computer Computational Biology Multifactor Dimensionality Reduction Software Sequence Analysis, RNA
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