AIMC Topic: Gene Expression Profiling

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Gene Ontology Enrichment Improves Performances of Functional Similarity of Genes.

Scientific reports Aug 14, 2018
There exists a plethora of measures to evaluate functional similarity (FS) between genes, which is a widely used in many bioinformatics applications including detecting molecular pathways, identifying co-expressed genes, predicting protein-protein in...
Gene Expression Profiling Protein Interaction Mapping Base Sequence Molecular Sequence Annotation Gene Ontology Computational Biology Datasets as Topic Semantics
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Integration of Gene Expression Profile Data to Screen and Verify Hub Genes Involved in Osteoarthritis.

BioMed research international Aug 14, 2018
Osteoarthritis (OA) is one of the most common diseases worldwide, but the pathogenic genes and pathways are largely unclear. The aim of this study was to screen and verify hub genes involved in OA and explore potential molecular mechanisms. The expre...
Computational Biology Osteoarthritis Gene Ontology Humans Gene Expression Profiling Transcriptome China Gene Regulatory Networks
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Prediction of Acquired Taxane Resistance Using a Personalized Pathway-Based Machine Learning Method.

Cancer research and treatment Aug 10, 2018
PURPOSE: This study was conducted to develop and validate an individualized prediction model for automated detection of acquired taxane resistance (ATR).
Machine Learning ROC Curve Antineoplastic Agents Algorithms Drug Resistance, Neoplasm Databases, Genetic Gene Expression Profiling Precision Medicine Taxoids Models, Statistical Pharmacogenetics Bridged-Ring Compounds Reproducibility of Results
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Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis.

International journal of molecular sciences Aug 10, 2018
Toxicity prediction is very important to public health. Among its many applications, toxicity prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and c...
Humans Transcriptome Natural Language Processing Machine Learning Animals Gene Expression Profiling
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Establishment of a SVM classifier to predict recurrence of ovarian cancer.

Molecular medicine reports Aug 8, 2018
Gene expression data using retrieved ovarian cancer (OC) samples were used to identify genes of interest and a support vector machine (SVM) classifier was subsequently established to predict the recurrence of OC. Three datasets (GSE17260, GSE44104 an...
Ovarian Neoplasms Neoplasm Recurrence, Local Gene Expression Regulation, Neoplastic Models, Genetic Support Vector Machine Humans Female Gene Expression Profiling Protein Interaction Maps Gene Regulatory Networks
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Deep Neural Networks for In Situ Hybridization Grid Completion and Clustering.

IEEE/ACM transactions on computational biology and bioinformatics Aug 7, 2018
Transcriptome in brain plays a crucial role in understanding the cortical organization and the development of brain structure and function. Two challenges, incomplete data and high dimensionality of transcriptome, remain unsolved. Here, we present a ...
In Situ Hybridization Computational Biology Cluster Analysis Atlases as Topic Image Processing, Computer-Assisted Mice Neural Networks, Computer Animals Brain Gene Expression Profiling Transcriptome
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Predict effective drug combination by deep belief network and ontology fingerprints.

Journal of biomedical informatics Aug 3, 2018
The synergistic effect of drug combination is one of the most desirable properties for treating cancer. However, systematically predicting effective drug combination is a significant challenge. We report here a novel method based on deep belief netwo...
Deep Learning Cell Line, Tumor Drug Synergism Gene Regulatory Networks Humans Gene Ontology Neoplasms Databases, Pharmaceutical Gene Expression Profiling Computational Biology Antineoplastic Combined Chemotherapy Protocols Drug Combinations
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Improving eQTL Analysis Using a Machine Learning Approach for Data Integration: A Logistic Model Tree Solution.

Journal of computational biology : a journal of computational molecular cell biology Jul 27, 2018
Expression quantitative trait loci (eQTL) analysis is an emerging method for establishing the impact of genetic variations (such as single nucleotide polymorphisms) on the expression levels of genes. Although different methods for evaluating the impa...
Humans Machine Learning Gene Expression Regulation Algorithms Quantitative Trait Loci Gene Expression Profiling Genotype Gene Regulatory Networks Computational Biology Polymorphism, Single Nucleotide Logistic Models
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Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles.

PloS one Jul 26, 2018
The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysing genetic networks based on gene expression profiles and interaction datasets. To construct a gene ...
Computational Biology Databases, Genetic Epistasis, Genetic Machine Learning Gene Expression Profiling Gene Regulatory Networks
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RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes.

BMC genomics Jul 20, 2018
BACKGROUND: Although different quality controls have been applied at different stages of the sample preparation and data analysis to ensure both reproducibility and reliability of RNA-seq results, there are still limitations and bias on the detectabi...
Sequence Analysis, RNA Humans Epigenesis, Genetic Arabidopsis Machine Learning Transcription, Genetic Histone Code Gene Expression Profiling
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