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Transcription Factors

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Predicting TF-DNA Binding Motifs from ChIP-seq Datasets Using the Bag-Based Classifier Combined With a Multi-Fold Learning Scheme.

IEEE/ACM transactions on computational biology and bioinformatics
The rapid development of high-throughput sequencing technology provides unique opportunities for studying of transcription factor binding sites, but also brings new computational challenges. Recently, a series of discriminative motif discovery (DMD) ...

Biologically relevant transfer learning improves transcription factor binding prediction.

Genome biology
BACKGROUND: Deep learning has proven to be a powerful technique for transcription factor (TF) binding prediction but requires large training datasets. Transfer learning can reduce the amount of data required for deep learning, while improving overall...

Evaluation of deep learning approaches for modeling transcription factor sequence specificity.

Genomics
As a key component of gene regulation, transcription factors (TFs) play an important role in a number of biological processes. To fully understand the underlying mechanism of TF-mediated gene regulation, it is therefore critical to accurately identif...

HOX cluster and their cofactors showed an altered expression pattern in eutopic and ectopic endometriosis tissues.

Reproductive biology and endocrinology : RB&E
Endometriosis is major gynecological disease that affects over 10% of women worldwide and 30%-50% of these women have pelvic pain, abnormal uterine bleeding and infertility. The cause of endometriosis is unknown and there is no definite cure mainly b...

Gene Ontology representation for transcription factor functions.

Biochimica et biophysica acta. Gene regulatory mechanisms
Transcription plays a central role in defining the identity and functionalities of cells, as well as in their responses to changes in the cellular environment. The Gene Ontology (GO) provides a rigorously defined set of concepts that describe the fun...

Discovering differential genome sequence activity with interpretable and efficient deep learning.

PLoS computational biology
Discovering sequence features that differentially direct cells to alternate fates is key to understanding both cellular development and the consequences of disease related mutations. We introduce Expected Pattern Effect and Differential Expected Patt...

Prioritizing and characterizing functionally relevant genes across human tissues.

PLoS computational biology
Knowledge of genes that are critical to a tissue's function remains difficult to ascertain and presents a major bottleneck toward a mechanistic understanding of genotype-phenotype links. Here, we present the first machine learning model-FUGUE-combini...

DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence.

International journal of molecular sciences
Predicting in vivo protein-DNA binding sites is a challenging but pressing task in a variety of fields like drug design and development. Most promoters contain a number of transcription factor (TF) binding sites, but only a small minority has been id...

Machine learning predicts nucleosome binding modes of transcription factors.

BMC bioinformatics
BACKGROUND: Most transcription factors (TFs) compete with nucleosomes to gain access to their cognate binding sites. Recent studies have identified several TF-nucleosome interaction modes including end binding (EB), oriented binding, periodic binding...