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Gene Expression Regulation

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Deep-learning augmented RNA-seq analysis of transcript splicing.

Nature methods
A major limitation of RNA sequencing (RNA-seq) analysis of alternative splicing is its reliance on high sequencing coverage. We report DARTS (https://github.com/Xinglab/DARTS), a computational framework that integrates deep-learning-based predictions...

TADKB: Family classification and a knowledge base of topologically associating domains.

BMC genomics
BACKGROUND: Topologically associating domains (TADs) are considered the structural and functional units of the genome. However, there is a lack of an integrated resource for TADs in the literature where researchers can obtain family classifications a...

Machine learning polymer models of three-dimensional chromatin organization in human lymphoblastoid cells.

Methods (San Diego, Calif.)
We present machine learning models of human genome three-dimensional structure that combine one dimensional (linear) sequence specificity, epigenomic information, and transcription factor binding profiles, with the polymer-based biophysical simulatio...

Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence.

Proceedings of the National Academy of Sciences of the United States of America
Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological ...

Analysis of differentially expressed genes in rheumatoid arthritis and osteoarthritis by integrated microarray analysis.

Journal of cellular biochemistry
BACKGROUND: Rheumatoid arthritis (RA) and osteoarthritis (OA) were two major types of joint diseases. This study aimed to explore the mechanism underlying OA and RA and analyze their difference by integrated analysis of multiple gene expression data ...

Significant improvement of miRNA target prediction accuracy in large datasets using meta-strategy based on comprehensive voting and artificial neural networks.

BMC genomics
BACKGROUND: Identifying mRNA targets of miRNAs is critical for studying gene expression regulation at the whole-genome level. Multiple computational tools have been developed to predict miRNA:mRNA interactions. Nonetheless, many of these tools are de...

Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome.

BMC bioinformatics
BACKGROUND: To further our understanding of immunopeptidomics, improved tools are needed to identify peptides presented by major histocompatibility complex class I (MHC-I). Many existing tools are limited by their reliance upon chemical affinity data...

Deep learning identifies genome-wide DNA binding sites of long noncoding RNAs.

RNA biology
Long noncoding RNAs (lncRNAs) can exert their function by interacting with the DNA via triplex structure formation. Even though this has been validated with a handful of experiments, a genome-wide analysis of lncRNA-DNA binding is needed. In this pap...

Changes in the expression of four ABC transporter genes in response to imidacloprid in Bemisia tabaci Q (Hemiptera: Aleyrodidae).

Pesticide biochemistry and physiology
Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae), a globally invasive species complex that causes serious damage to field crops, has developed resistance to imidacloprid and many other pesticides. Insect detoxify to pesticides may partially depend...

Artificial Neural Network to Predict Varicocele Impact on Male Fertility through Testicular Endocannabinoid Gene Expression Profiles.

BioMed research international
The relationship between varicocele and fertility has always been a matter of debate because of the absence of predictive clinical indicators or molecular markers able to define the severity of this disease. Even though accumulated evidence demonstra...