AIMC Topic: Computational Biology

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SENIES: DNA Shape Enhanced Two-Layer Deep Learning Predictor for the Identification of Enhancers and Their Strength.

IEEE/ACM transactions on computational biology and bioinformatics
Identifying enhancers is a critical task in bioinformatics due to their primary role in regulating gene expression. For this reason, various computational algorithms devoted to enhancer identification have been put forward over the years. More featur...

Convolution Neural Networks Using Deep Matrix Factorization for Predicting Circrna-Disease Association.

IEEE/ACM transactions on computational biology and bioinformatics
CircRNAs have a stable structure, which gives them a higher tolerance to nucleases. Therefore, the properties of circular RNAs are beneficial in disease diagnosis. However, there are few known associations between circRNAs and disease. Biological exp...

iDRBP-EL: Identifying DNA- and RNA- Binding Proteins Based on Hierarchical Ensemble Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Identification of DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) from the primary sequences is essential for further exploring protein-nucleic acid interactions. Previous studies have shown that machine-learning-based methods can efficie...

Multi-omics integration method based on attention deep learning network for biomedical data classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Integrating multi-omics data for the comprehensive analysis of the biological processes in human diseases has become one of the most challenging tasks of bioinformatics. Deep learning (DL) algorithms have recently become one...

IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach.

Computational intelligence and neuroscience
. Immunoglobulin proteins (IGP) (also called antibodies) are glycoproteins that act as B-cell receptors against external or internal antigens like viruses and bacteria. IGPs play a significant role in diverse cellular processes ranging from adhesion ...

DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning.

International journal of molecular sciences
Aberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated...

Applications of deep learning in understanding gene regulation.

Cell reports methods
Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven pre...

DeepPRObind: Modular Deep Learner that Accurately Predicts Structure and Disorder-Annotated Protein Binding Residues.

Journal of molecular biology
Current sequence-based predictors of protein-binding residues (PBRs) belong to two distinct categories: structure-trained vs. intrinsic disorder-trained. Since disordered PBRs differ from structured PBRs in several ways, including ability to bind mul...

Computational biology: Role and scope in taming antimicrobial resistance.

Indian journal of medical microbiology
BACKGROUND: Infectious diseases pose many challenges due to increasing threat of antimicrobial resistance, which necessitates continuous research to develop novel strategies for development of new molecules with antibacterial activity. In the era of ...

Prediction of lysine HMGylation sites using multiple feature extraction and fuzzy support vector machine.

Analytical biochemistry
Protein 3-hydroxyl-3-methylglutarylation (HMGylation) is newly discovered lysine acylation modification in mitochondrion. The accurate identification of HMGylation sites is the premise and key to further explore the molecular mechanisms of HMGylation...