AIMC Topic: Transcription Factors

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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...
Neural Networks, Computer Algorithms Deep Learning Transcription Factors Gene Expression Regulation Organ Specificity Binding Sites ROC Curve Protein Binding Computational Biology
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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 ...
Humans Algorithms Protein Binding Support Vector Machine Transcription Factors Binding Sites Nucleotide Motifs
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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...
Binding Sites Chromatin Protein Binding Transcription Factors Nucleotides Neural Networks, Computer Software Deep Learning
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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...
DNA Binding Sites Databases, Genetic Models, Statistical Neural Networks, Computer Proteins Animals Protein Binding Mice Computational Biology Transcription Factors Humans
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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...
Nuclear Proteins Transcription Factors Contrast Media Aged Male Middle Aged DNA-Binding Proteins Kidney Neoplasms Carcinoma, Renal Cell Predictive Value of Tests Mutation Radiographic Image Interpretation, Computer-Assisted Machine Learning Algorithms Retrospective Studies Tomography, X-Ray Computed Female Reproducibility of Results Humans
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An SVM-based method for assessment of transcription factor-DNA complex models.

BMC bioinformatics Dec 21, 2018
BACKGROUND: Atomic details of protein-DNA complexes can provide insightful information for better understanding of the function and binding specificity of DNA binding proteins. In addition to experimental methods for solving protein-DNA complex struc...
Algorithms DNA Protein Binding Transcription Factors Models, Molecular Support Vector Machine
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Deleterious Non-Synonymous Single Nucleotide Polymorphism Predictions on Human Transcription Factors.

IEEE/ACM transactions on computational biology and bioinformatics Nov 21, 2018
Transcription factors (TFs) are the major components of human gene regulation. In particular, they bind onto specific DNA sequences and regulate neighborhood genes in different tissues at different developmental stages. Non-synonymous single nucleoti...
Polymorphism, Single Nucleotide Computational Biology Transcription Factors Data Mining Mutation, Missense Humans Machine Learning Algorithms
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Recurrent Neural Network for Predicting Transcription Factor Binding Sites.

Scientific reports Oct 15, 2018
It is well known that DNA sequence contains a certain amount of transcription factors (TF) binding sites, and only part of them are identified through biological experiments. However, these experiments are expensive and time-consuming. To overcome th...
Transcription Factors Databases, Genetic Binding Sites Algorithms Protein Binding Computational Biology Neural Networks, Computer
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Probe Efficient Feature Representation of Gapped K-mer Frequency Vectors from Sequences Using Deep Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics Aug 31, 2018
Gapped k-mers frequency vectors (gkm-fv) has been presented for extracting sequence features. Coupled with support vector machine (gkm-SVM), gkm-fvs have been used to achieve effective sequence-based predictions. However, the huge computation of a la...
Support Vector Machine Protein Binding Humans Databases, Genetic Computational Biology Algorithms Transcription Factors Deep Learning Chromatin Immunoprecipitation Sequencing
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Sequential Integration of Fuzzy Clustering and Expectation Maximization for Transcription Factor Binding Site Identification.

Journal of computational biology : a journal of computational molecular cell biology Aug 22, 2018
The identification of transcription factor binding sites (TFBSs) is a problem for which computational methods offer great hope. Thus far, the expectation maximization (EM) technique has been successfully utilized in finding TFBSs in DNA sequences, bu...
Fuzzy Logic Cluster Analysis Binding Sites Humans Computational Biology Nucleotide Motifs Algorithms Transcription Factors Chromatin Immunoprecipitation Protein Binding Sequence Analysis, DNA
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