AIMC Topic: Transcription Factors

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Weakly-Supervised Convolutional Neural Network Architecture for Predicting Protein-DNA Binding.

IEEE/ACM transactions on computational biology and bioinformatics Aug 7, 2018
Although convolutional neural networks (CNN) have outperformed conventional methods in predicting the sequence specificities of protein-DNA binding in recent years, they do not take full advantage of the intrinsic weakly-supervised information of DNA...
Neural Networks, Computer Algorithms Animals Protein Binding DNA DNA-Binding Proteins Supervised Machine Learning Mice Binding Sites Computational Biology Transcription Factors
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AdaSampling for Positive-Unlabeled and Label Noise Learning With Bioinformatics Applications.

IEEE transactions on cybernetics Apr 2, 2018
Class labels are required for supervised learning but may be corrupted or missing in various applications. In binary classification, for example, when only a subset of positive instances is labeled whereas the remaining are unlabeled, positive-unlabe...
Proteins Phosphoproteins Phosphotransferases Machine Learning Algorithms Computational Biology Transcription Factors Models, Statistical
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High-Order Convolutional Neural Network Architecture for Predicting DNA-Protein Binding Sites.

IEEE/ACM transactions on computational biology and bioinformatics Mar 26, 2018
Although Deep learning algorithms have outperformed conventional methods in predicting the sequence specificities of DNA-protein binding, they lack to consider the dependencies among nucleotides and the diverse binding lengths for different transcrip...
Neural Networks, Computer Software Algorithms Protein Binding Amino Acid Motifs Area Under Curve Chromatin Immunoprecipitation Genomics DNA Computational Biology DNA-Binding Proteins Transcription Factors Binding Sites
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A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.

Nature communications Jan 3, 2018
Cancers that appear pathologically similar often respond differently to the same drug regimens. Methods to better match patients to drugs are in high demand. We demonstrate a promising approach to identify robust molecular markers for targeted treatm...
Nuclear Proteins Transcription Factors Cell Line Datasets as Topic Leukemia, Myeloid, Acute Antineoplastic Agents DNA Helicases Drug Resistance, Neoplasm Etoposide Topoisomerase II Inhibitors Humans Machine Learning Algorithms Precision Medicine Biomarkers, Tumor
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Effect of abiotic and biotic stress factors analysis using machine learning methods in zebrafish.

Comparative biochemistry and physiology. Part D, Genomics & proteomics Nov 11, 2017
In order to understand the mechanisms underlying stress responses, meta-analysis of transcriptome is made to identify differentially expressed genes (DEGs) and their biological, molecular and cellular mechanisms in response to stressors. The present ...
Gene Ontology Oligonucleotide Array Sequence Analysis Animals Gene Expression Profiling Transcription Factors Support Vector Machine MicroRNAs Principal Component Analysis Down-Regulation Meta-Analysis as Topic Stress, Physiological Supervised Machine Learning Up-Regulation Zebrafish Fish Proteins
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McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes.

Genome biology Oct 26, 2017
Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target g...
Machine Learning Animals Transcription Factors Sequence Analysis, DNA Embryonic Development Deoxyribonuclease I Enhancer Elements, Genetic Promoter Regions, Genetic Genes, Reporter Drosophila melanogaster Gene Expression Regulation, Developmental Nucleotide Motifs Histone Code
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The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies.

PloS one Oct 5, 2017
Understanding the molecular machinery involved in transcriptional regulation is central to improving our knowledge of an organism's development, disease, and evolution. The building blocks of this complex molecular machinery are an organism's genomic...
Nucleotides Machine Learning Binding Sites Transcription Factors Support Vector Machine
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EMQIT: a machine learning approach for energy based PWM matrix quality improvement.

Biology direct Aug 1, 2017
BACKGROUND: Transcription factor binding affinities to DNA play a key role for the gene regulation. Learning the specificity of the mechanisms of binding TFs to DNA is important both to experimentalists and theoreticians. With the development of high...
Binding Sites Position-Specific Scoring Matrices Transcription Factors Models, Molecular Datasets as Topic Gene Expression Regulation ROC Curve Computational Biology Models, Genetic Machine Learning Software
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Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4.

Journal of chemical information and modeling Jul 10, 2017
Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with differ...
Drug Discovery Nuclear Proteins Transcription Factors Cell Line Humans Machine Learning Molecular Docking Simulation Protein Conformation Structure-Activity Relationship Cell Cycle Proteins Drug Evaluation, Preclinical Molecular Targeted Therapy
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TIGERi: modeling and visualizing the responses to perturbation of a transcription factor network.

BMC bioinformatics May 31, 2017
BACKGROUND: Transcription factor (TF) networks play a key role in controlling the transfer of genetic information from gene to mRNA. Much progress has been made on understanding and reverse-engineering TF network topologies using a range of experimen...
Transcriptome Gene Regulatory Networks Machine Learning Mice Transcription Factors Animals Mice, Knockout Binding Sites Mitogen-Activated Protein Kinase 14
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