AIMC Topic: Gene Expression Profiling

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GOATOOLS: A Python library for Gene Ontology analyses.

Scientific reports Jul 18, 2018
The biological interpretation of gene lists with interesting shared properties, such as up- or down-regulation in a particular experiment, is typically accomplished using gene ontology enrichment analysis tools. Given a list of genes, a gene ontology...
Alzheimer Disease Gene Ontology Gene Expression Regulation, Developmental Software Algorithms Biomarkers Animals Gene Expression Profiling Mice Computational Biology Disease Models, Animal
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Identification of 170 New Long Noncoding RNAs in .

BioMed research international Jul 11, 2018
Long noncoding RNAs (lncRNAs) are transcripts generally longer than 200 nucleotides with no or poor protein coding potential, and most of their functions are also poorly characterized. Recently, an increasing number of studies have shown that lncRNAs...
Gene Ontology Genome Exons Schistosoma mansoni Gene Expression Profiling Mice RNA, Long Noncoding Humans Female Animals Male
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Drug response prediction by ensemble learning and drug-induced gene expression signatures.

Genomics Jul 6, 2018
Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Recently, considerable amount of drug-induced gene expression data has become publicly available, in ad...
Animals Humans Cell Survival Machine Learning Antineoplastic Agents Software Inhibitory Concentration 50 Gene Expression Profiling Drug Resistance, Neoplasm Cell Line, Tumor
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Assessment of Functional Phosphatidylinositol 3-Kinase Pathway Activity in Cancer Tissue Using Forkhead Box-O Target Gene Expression in a Knowledge-Based Computational Model.

The American journal of pathology Jul 6, 2018
The phosphatidylinositol 3-kinase (PI3K) pathway is commonly activated in cancer. Tumors are potentially sensitive to PI3K pathway inhibitors, but reliable diagnostic tests that assess functional PI3K activity are lacking. Because PI3K pathway activi...
Female Breast Neoplasms Gene Expression Profiling Computational Biology Humans Phosphatidylinositol 3-Kinases Superoxide Dismutase Forkhead Transcription Factors Cell Proliferation Knowledge Bases Phosphorylation Diagnostic Tests, Routine
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Machine Learning Helps Identify New Drug Mechanisms in Triple-Negative Breast Cancer.

IEEE transactions on nanobioscience Jul 2, 2018
This paper demonstrates the ability of mach- ine learning approaches to identify a few genes among the 23,398 genes of the human genome to experiment on in the laboratory to establish new drug mechanisms. As a case study, this paper uses MDA-MB-231 b...
Humans Female Antineoplastic Agents Gene Expression Profiling Triple Negative Breast Neoplasms Cell Line, Tumor Cluster Analysis Single-Cell Analysis Unsupervised Machine Learning Genomics Metformin Gene Expression Regulation, Neoplastic
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Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions.

PloS one Jun 27, 2018
Protein-protein interactions integrated with disease-gene associations represent important information for revealing protein functions under disease conditions to improve the prevention, diagnosis, and treatment of complex diseases. Although several ...
ROC Curve Protein Interaction Maps Neural Networks, Computer Genetic Predisposition to Disease Protein Interaction Mapping Algorithms Genetic Association Studies Computational Biology Reproducibility of Results Models, Biological Gene Expression Profiling Disease Susceptibility
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Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?

RNA (New York, N.Y.) Jun 25, 2018
RNA sequencing (RNA-seq) is becoming a prevalent approach to quantify gene expression and is expected to gain better insights into a number of biological and biomedical questions compared to DNA microarrays. Most importantly, RNA-seq allows us to qua...
RNA Organ Specificity Alternative Splicing Gene Expression Profiling Sequence Analysis, RNA Supervised Machine Learning Data Mining Humans Animals
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A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.

Scientific reports Jun 11, 2018
Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal sam...
Sensitivity and Specificity Male Gene Expression Profiling Molecular Diagnostic Techniques Young Adult Adult Asthma Sequence Analysis, RNA Predictive Value of Tests Nasal Mucosa Humans Machine Learning Prospective Studies Female
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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,...
Algorithms Melanoma Skin Neoplasms Gene Expression Profiling Gene Regulatory Networks Computational Biology Gene Expression Regulation, Neoplastic Models, Genetic Humans Machine Learning Software Proto-Oncogene Proteins c-mdm2 Bayes Theorem Wnt-5a Protein Models, Biological Tumor Suppressor Protein p53
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An Efficient Mixed-Model for Screening Differentially Expressed Genes of Breast Cancer Based on LR-RF.

IEEE/ACM transactions on computational biology and bioinformatics Apr 23, 2018
To screen differentially expressed genes quickly and efficiently in breast cancer, two gene microarray datasets of breast cancer, GSE15852 and GSE45255, were downloaded from GEO. By combining the Logistic Regression and Random Forest algorithm, this ...
Oligonucleotide Array Sequence Analysis Algorithms Female Breast Neoplasms Gene Expression Profiling Gene Regulatory Networks Logistic Models Databases, Genetic Decision Trees Humans Machine Learning
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