AIMC Topic: Transcription Factors

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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...
Transcription Factors Support Vector Machine DNA Models, Molecular Algorithms Protein Binding
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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...
Computational Biology Transcription Factors Data Mining Machine Learning Algorithms Humans Mutation, Missense Polymorphism, Single Nucleotide
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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...
Algorithms Protein Binding Computational Biology Binding Sites Transcription Factors Databases, Genetic 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 Databases, Genetic Chromatin Immunoprecipitation Sequencing Protein Binding Computational Biology Transcription Factors Humans Algorithms Deep Learning
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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...
Cluster Analysis Binding Sites Nucleotide Motifs Transcription Factors Sequence Analysis, DNA Fuzzy Logic Chromatin Immunoprecipitation Protein Binding Computational Biology Humans Algorithms
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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...
Animals Protein Binding Mice Computational Biology Neural Networks, Computer Algorithms Supervised Machine Learning Binding Sites Transcription Factors DNA DNA-Binding Proteins
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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...
Phosphoproteins Phosphotransferases Models, Statistical Proteins Algorithms Machine Learning Computational Biology Transcription Factors
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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...
Amino Acid Motifs Software Chromatin Immunoprecipitation Algorithms Protein Binding DNA Transcription Factors DNA-Binding Proteins Area Under Curve Binding Sites Genomics Computational Biology Neural Networks, Computer
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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...
Etoposide Topoisomerase II Inhibitors Humans Cell Line Machine Learning Datasets as Topic Algorithms Drug Resistance, Neoplasm Antineoplastic Agents DNA Helicases Nuclear Proteins Transcription Factors Precision Medicine Biomarkers, Tumor Leukemia, Myeloid, Acute
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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 ...
Fish Proteins Gene Ontology Oligonucleotide Array Sequence Analysis Meta-Analysis as Topic Supervised Machine Learning Zebrafish Down-Regulation Stress, Physiological Up-Regulation Support Vector Machine Animals MicroRNAs Gene Expression Profiling Principal Component Analysis Transcription Factors
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