AIMC Topic: Chromatin Immunoprecipitation

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Prediction of protein-ligand binding affinity from sequencing data with interpretable machine learning.

Nature biotechnology May 23, 2022
Protein-ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions....
DNA Ligands Binding Sites Chromatin Immunoprecipitation Machine Learning Protein Binding Transcription Factors
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DeepG4: A deep learning approach to predict cell-type specific active G-quadruplex regions.

PLoS computational biology Aug 12, 2021
DNA is a complex molecule carrying the instructions an organism needs to develop, live and reproduce. In 1953, Watson and Crick discovered that DNA is composed of two chains forming a double-helix. Later on, other structures of DNA were discovered an...
Deep Learning G-Quadruplexes Genome Chromatin Immunoprecipitation Neural Networks, Computer Algorithms Neoplasms Humans
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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...
Protein Binding Computational Biology Transcription Factors Chromatin Immunoprecipitation DNA Humans Binding Sites Machine Learning Algorithms
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Exploratory Gene Ontology Analysis with Interactive Visualization.

Scientific reports May 24, 2019
The Gene Ontology (GO) is a central resource for functional-genomics research. Scientists rely on the functional annotations in the GO for hypothesis generation and couple it with high-throughput biological data to enhance interpretation of results. ...
Gene Ontology Software Design Chromatin Immunoprecipitation User-Computer Interface Genome-Wide Association Study Genomics Sequence Analysis, RNA Humans Software Animals
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DeepHistone: a deep learning approach to predicting histone modifications.

BMC genomics Apr 4, 2019
MOTIVATION: Quantitative detection of histone modifications has emerged in the recent years as a major means for understanding such biological processes as chromosome packaging, transcriptional activation, and DNA damage. However, high-throughput exp...
Chromosome Mapping Chromatin Chromatin Immunoprecipitation Protein Processing, Post-Translational Gene Expression Regulation Histones Polymorphism, Single Nucleotide Epigenomics Humans 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...
Humans Algorithms Binding Sites Nucleotide Motifs Chromatin Immunoprecipitation Sequence Analysis, DNA Fuzzy Logic Cluster Analysis Protein Binding 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...
Neural Networks, Computer DNA DNA-Binding Proteins Binding Sites Amino Acid Motifs Chromatin Immunoprecipitation Genomics Computational Biology Software Transcription Factors Algorithms Area Under Curve Protein Binding
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ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data.

BMC bioinformatics Nov 14, 2017
BACKGROUND: With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-...
Binding Sites Enhancer Elements, Genetic Chromosomes CCCTC-Binding Factor Chromatin Immunoprecipitation Animals Histone Code Cluster Analysis Unsupervised Machine Learning Promoter Regions, Genetic Mice Genomics Sequence Analysis, DNA
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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....
Sensitivity and Specificity Protein Binding Transcription Factors Neural Networks, Computer Machine Learning Reproducibility of Results Binding Sites Protein Interaction Mapping Chromatin Immunoprecipitation Hep G2 Cells High-Throughput Nucleotide Sequencing K562 Cells Humans
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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 ...
Databases, Genetic Sequence Analysis, DNA Area Under Curve Chromatin Immunoprecipitation Cell Line, Tumor Computational Biology High-Throughput Nucleotide Sequencing Humans Transcription Factors Nucleotide Motifs Machine Learning Algorithms
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