AIMC Topic: Chromatin Immunoprecipitation Sequencing
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An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning.
IEEE/ACM transactions on computational biology and bioinformatics
Feb 3, 2021
Knowing the transcription factor binding sites (TFBSs) is essential for modeling the underlying binding mechanisms and follow-up cellular functions. Convolutional neural networks (CNNs) have outperformed methods in predicting TFBSs from the primary D...
Cancer classification based on chromatin accessibility profiles with deep adversarial learning model.
PLoS computational biology
Nov 9, 2020
Given the complexity and diversity of the cancer genomics profiles, it is challenging to identify distinct clusters from different cancer types. Numerous analyses have been conducted for this propose. Still, the methods they used always do not direct...
Machine learning uncovers cell identity regulator by histone code.
Nature communications
Jun 1, 2020
Conversion between cell types, e.g., by induced expression of master transcription factors, holds great promise for cellular therapy. Our ability to manipulate cell identity is constrained by incomplete information on cell identity genes (CIGs) and t...
Humans
Machine Learning
Algorithms
Phenotype
RNA-Seq
Gene Expression Regulation
Cells
Pluripotent Stem Cells
Histone Code
Chromatin Immunoprecipitation Sequencing
Databases, Genetic
Human Umbilical Vein Endothelial Cells
Regenerative Medicine
Gene Regulatory Networks
Epigenesis, Genetic
Transcription Factors
CNN-Peaks: ChIP-Seq peak detection pipeline using convolutional neural networks that imitate human visual inspection.
Scientific reports
May 13, 2020
ChIP-seq is one of the core experimental resources available to understand genome-wide epigenetic interactions and identify the functional elements associated with diseases. The analysis of ChIP-seq data is important but poses a difficult computation...
Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures.
Molecular metabolism
Dec 20, 2019
OBJECTIVE: Type 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome-wide association studies (GWAS) have identified > 400 independent signals that enc...
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...
DECA: harnessing interpretable transformer model for cellular deconvolution of chromatin accessibility profile.
Briefings in bioinformatics
Nov 22, 2024
The assay for transposase-accessible chromatin with sequencing (ATAC-seq) identifies chromatin accessibility across the genome, crucial for gene expression regulating. However, bulk ATAC-seq obscures cellular heterogeneity, while single-cell ATAC-seq...
Integrating scRNA-seq and scATAC-seq with inter-type attention heterogeneous graph neural networks.
Briefings in bioinformatics
Nov 22, 2024
Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression and Assay for Transposase-Accessible Chromatin (ATAC) within individual cells, have become a powerful tool for decipherin...
MLSNet: a deep learning model for predicting transcription factor binding sites.
Briefings in bioinformatics
Sep 23, 2024
Accurate prediction of transcription factor binding sites (TFBSs) is essential for understanding gene regulation mechanisms and the etiology of diseases. Despite numerous advances in deep learning for predicting TFBSs, their performance can still be ...
Supervised learning of enhancer-promoter specificity based on genome-wide perturbation studies highlights areas for improvement in learning.
Bioinformatics (Oxford, England)
Jun 3, 2024
MOTIVATION: Understanding the rules that govern enhancer-driven transcription remains a central unsolved problem in genomics. Now with multiple massively parallel enhancer perturbation assays published, there are enough data that we can utilize to le...
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