AIMC Topic: Computational Biology

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DeepCNPP: Deep Learning Architecture to Distinguish the Promoter of Human Long Non-Coding RNA Genes and Protein-Coding Genes.

Studies in health technology and informatics
Promoter region of protein-coding genes are gradually being well understood, yet no comparable studies exist for the promoter of long non-coding RNA (lncRNA) genes which has emerged as a global potential regulator in multiple cellular process and dif...

A deep learning model based on sparse auto-encoder for prioritizing cancer-related genes and drug target combinations.

Carcinogenesis
Prioritization of cancer-related genes from gene expression profiles and proteomic data is vital to improve the targeted therapies research. Although computational approaches have been complementing high-throughput biological experiments on the under...

NetGO: improving large-scale protein function prediction with massive network information.

Nucleic acids research
Automated function prediction (AFP) of proteins is of great significance in biology. AFP can be regarded as a problem of the large-scale multi-label classification where a protein can be associated with multiple gene ontology terms as its labels. Bas...

HerGePred: Heterogeneous Network Embedding Representation for Disease Gene Prediction.

IEEE journal of biomedical and health informatics
The discovery of disease-causing genes is a critical step towards understanding the nature of a disease and determining a possible cure for it. In recent years, many computational methods to identify disease genes have been proposed. However, making ...

DeepCrystal: a deep learning framework for sequence-based protein crystallization prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Protein structure determination has primarily been performed using X-ray crystallography. To overcome the expensive cost, high attrition rate and series of trial-and-error settings, many in-silico methods have been developed to predict cr...

Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships.

Bioinformatics (Oxford, England)
MOTIVATION: Gene expression prediction is one of the grand challenges in computational biology. The availability of transcriptomics data combined with recent advances in artificial neural networks provide an unprecedented opportunity to create predic...

Automated Metadata Suggestion During Repository Submission.

Neuroinformatics
Knowledge discovery via an informatics resource is constrained by the completeness of the resource, both in terms of the amount of data it contains and in terms of the metadata that exists to describe the data. Increasing completeness in one of these...

AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification.

Nucleic acids research
ChIP-seq is a technique to determine binding locations of transcription factors, which remains a central challenge in molecular biology. Current practice is to use a 'control' dataset to remove background signals from a immunoprecipitation (IP) 'targ...

DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning.

Nucleic acids research
Interactions between regulatory elements are of crucial importance for the understanding of transcriptional regulation and the interpretation of disease mechanisms. Hi-C technique has been developed for genome-wide detection of chromatin contacts. Ho...

Identifying antimicrobial peptides using word embedding with deep recurrent neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Antibiotic resistance constitutes a major public health crisis, and finding new sources of antimicrobial drugs is crucial to solving it. Bacteriocins, which are bacterially produced antimicrobial peptide products, are candidates for broad...