AIMC Topic: Transcription Factors

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Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach.

Scientific reports Apr 25, 2017
Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differ...
Immunohistochemistry Hyperplasia Cellular Microenvironment Female Aged Breast Neoplasms Support Vector Machine Transcription Factors Humans Epithelial Cells Machine Learning Carcinoma, Intraductal, Noninfiltrating Tumor Suppressor Proteins
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Identification of transcription factors that may reprogram lung adenocarcinoma.

Artificial intelligence in medicine Apr 1, 2017
BACKGROUND: Lung adenocarcinoma is one of most threatening disease to human health. Although many efforts have been devoted to its genetic study, few researches have been focused on the transcription factors which regulate tumor initiation and progre...
Artificial Intelligence Gene Regulatory Networks Lung Neoplasms Computational Biology Humans Adenocarcinoma Epigenesis, Genetic Databases, Genetic Cell Transformation, Neoplastic Gene Expression Regulation, Neoplastic Transcription, Genetic Signal Transduction Adenocarcinoma of Lung Transcription Factors
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Imputation for transcription factor binding predictions based on deep learning.

PLoS computational biology Feb 24, 2017
Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard....
Reproducibility of Results Sensitivity and Specificity Protein Binding Transcription Factors Humans Neural Networks, Computer Machine Learning Protein Interaction Mapping Chromatin Immunoprecipitation Hep G2 Cells High-Throughput Nucleotide Sequencing K562 Cells Binding Sites
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A high-order representation and classification method for transcription factor binding sites recognition in Escherichia coli.

Artificial intelligence in medicine Dec 1, 2016
BACKGROUND: Identifying transcription factors binding sites (TFBSs) plays an important role in understanding gene regulatory processes. The underlying mechanism of the specific binding for transcription factors (TFs) is still poorly understood. Previ...
Transcription Factors Humans Escherichia coli Algorithms Binding Sites Protein Binding Computational Biology
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Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

Nucleic acids research Nov 29, 2016
The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF b...
Binding Sites Primary Cell Culture Chromatin Chromatin Assembly and Disassembly Gene Expression Regulation Organ Specificity K562 Cells Histones Human Embryonic Stem Cells Protein Binding Transcription Factors Hep G2 Cells Cell Line Principal Component Analysis DNA CD4-Positive T-Lymphocytes Hepatocytes Humans Machine Learning Algorithms Cell Line, Tumor
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Correcting the impact of docking pose generation error on binding affinity prediction.

BMC bioinformatics Sep 22, 2016
BACKGROUND: Pose generation error is usually quantified as the difference between the geometry of the pose generated by the docking software and that of the same molecule co-crystallised with the considered protein. Surprisingly, the impact of this e...
Software Humans Molecular Docking Simulation Transcription Factors Protein Binding Pyrazines Nuclear Proteins Ligands Protein Conformation
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DiscMLA: An Efficient Discriminative Motif Learning Algorithm over High-Throughput Datasets.

IEEE/ACM transactions on computational biology and bioinformatics May 3, 2016
The transcription factors (TFs) can activate or suppress gene expression by binding to specific sites, hence are crucial regulatory elements for transcription. Recently, series of discriminative motif finders have been tailored to offering promising ...
Area Under Curve High-Throughput Nucleotide Sequencing Nucleotide Motifs Chromatin Immunoprecipitation Humans Machine Learning Algorithms Cell Line, Tumor Computational Biology Transcription Factors Databases, Genetic Sequence Analysis, DNA
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g:Profiler-a web server for functional interpretation of gene lists (2016 update).

Nucleic acids research Apr 20, 2016
Functional enrichment analysis is a key step in interpreting gene lists discovered in diverse high-throughput experiments. g:Profiler studies flat and ranked gene lists and finds statistically significant Gene Ontology terms, pathways and other gene ...
Humans Animals Gene Expression Profiling User-Computer Interface Protein Binding Transcription Factors Internet Plants Gene Ontology Binding Sites Fungi Computer Graphics Vertebrates Insecta Gene Expression Regulation Molecular Sequence Annotation
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Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model.

BMC bioinformatics Jan 11, 2016
BACKGROUND: A living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly con...
Signal Transduction Models, Theoretical Transcription Factors Saccharomyces cerevisiae Neural Networks, Computer Computer Simulation Gene Expression Profiling Systems Biology Gene Expression Regulation, Fungal Saccharomyces cerevisiae Proteins
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Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features.

BMC bioinformatics Jan 11, 2016
BACKGROUND: Understanding the mechanisms by which transcription factors (TF) are recruited to their physiological target sites is crucial for understanding gene regulation. DNA sequence intrinsic features such as predicted binding affinity are often ...
Cell Lineage Chromatin Chromatin Immunoprecipitation Nucleosomes Gene Expression Regulation Genome, Human Histones Binding Sites Algorithms Humans Transcription Factors Machine Learning Protein Processing, Post-Translational DNA
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