AIMC Topic: Transcription Factors

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Representation learning of genomic sequence motifs with convolutional neural networks.

PLoS computational biology Dec 19, 2019
Although convolutional neural networks (CNNs) have been applied to a variety of computational genomics problems, there remains a large gap in our understanding of how they build representations of regulatory genomic sequences. Here we perform systema...
Computer Simulation Deep Learning Genomics Humans Neural Networks, Computer DNA Genome, Human Binding Sites Amino Acid Motifs Computational Biology Transcription Factors Databases, Genetic
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BindSpace decodes transcription factor binding signals by large-scale sequence embedding.

Nature methods Aug 12, 2019
The decoding of transcription factor (TF) binding signals in genomic DNA is a fundamental problem. Here we present a prediction model called BindSpace that learns to embed DNA sequences and TF labels into the same space. By training on binding data f...
Chromatin Immunoprecipitation Machine Learning Algorithms Protein Binding DNA Binding Sites Computational Biology Humans Transcription Factors
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Inferring Gene Regulatory Networks of Metabolic Enzymes Using Gradient Boosted Trees.

IEEE journal of biomedical and health informatics Jul 30, 2019
Metabolic reprogramming is a hallmark of cancer. In cancer cells, transcription factors (TFs) govern metabolic reprogramming through abnormally increasing or decreasing the transcription rate of metabolic enzymes, which provides cancer cells growth a...
Lung Neoplasms Transcription Factors Enzymes Humans Machine Learning Transcriptome Gene Regulatory Networks Algorithms Gene Expression Profiling
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Mechanistic interpretation of non-coding variants for discovering transcriptional regulators of drug response.

BMC biology Jul 30, 2019
BACKGROUND: Identification of functional non-coding variants and their mechanistic interpretation is a major challenge of modern genomics, especially for precision medicine. Transcription factor (TF) binding profiles and epigenomic landscapes in refe...
Male Protein Binding Polymorphism, Single Nucleotide Gene Expression Regulation Models, Genetic Transcription Factors Humans Machine Learning Female
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Visualizing complex feature interactions and feature sharing in genomic deep neural networks.

BMC bioinformatics Jul 19, 2019
BACKGROUND: Visualization tools for deep learning models typically focus on discovering key input features without considering how such low level features are combined in intermediate layers to make decisions. Moreover, many of these methods examine ...
Neural Networks, Computer Algorithms Deep Learning Genomics Histones Histone Code Transcription Factors Databases, Genetic Base Sequence
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Cross-Cell-Type Prediction of TF-Binding Site by Integrating Convolutional Neural Network and Adversarial Network.

International journal of molecular sciences Jul 12, 2019
Transcription factor binding sites (TFBSs) play an important role in gene expression regulation. Many computational methods for TFBS prediction need sufficient labeled data. However, many transcription factors (TFs) lack labeled data in cell types. W...
Protein Binding Computational Biology Transcription Factors Algorithms Deep Learning ROC Curve Gene Expression Regulation Organ Specificity Binding Sites Neural Networks, Computer
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MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites.

BMC bioinformatics May 1, 2019
BACKGROUND: Transcription factors (TFs) play important roles in the regulation of gene expression. They can activate or block transcription of downstream genes in a manner of binding to specific genomic sequences. Therefore, motif discovery of these ...
Nucleotide Motifs Transcription Factors Support Vector Machine Binding Sites Humans Algorithms Protein Binding
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FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data.

Methods (San Diego, Calif.) Mar 26, 2019
Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all valid TF/cell type pairs is not experimentally feasible. To address this issue, we developed a convolutional-recurrent neural network model, call...
Software Deep Learning Protein Binding Binding Sites Chromatin Transcription Factors Nucleotides Neural Networks, Computer
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Prediction of TF-Binding Site by Inclusion of Higher Order Position Dependencies.

IEEE/ACM transactions on computational biology and bioinformatics Jan 10, 2019
Most proposed methods for TF-binding site (TFBS) predictions only use low order dependencies for predictions due to the lack of efficient methods to extract higher order dependencies. In this work, we first propose a novel method to extract higher or...
Transcription Factors Protein Binding Binding Sites Databases, Genetic Mice Humans Proteins Models, Statistical Computational Biology Neural Networks, Computer DNA Animals
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Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status.

AJR. American journal of roentgenology Jan 2, 2019
OBJECTIVE: The purpose of this study is to evaluate the potential value of machine learning (ML)-based high-dimensional quantitative CT texture analysis in predicting the mutation status of the gene encoding the protein polybromo-1 (PBRM1) in patient...
Kidney Neoplasms Carcinoma, Renal Cell Mutation Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Female DNA-Binding Proteins Humans Reproducibility of Results Machine Learning Nuclear Proteins Aged Contrast Media Algorithms Transcription Factors Male Predictive Value of Tests Retrospective Studies Middle Aged
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