AIMC Topic: Computational Biology

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An Analytical Review of Computational Drug Repurposing.

IEEE/ACM transactions on computational biology and bioinformatics
Drug repurposing is a vital function in pharmaceutical fields and has gained popularity in recent years in both the pharmaceutical industry and research community. It refers to the process of discovering new uses and indications for existing or faile...

A Fast Linear Neighborhood Similarity-Based Network Link Inference Method to Predict MicroRNA-Disease Associations.

IEEE/ACM transactions on computational biology and bioinformatics
Increasing evidences revealed that microRNAs (miRNAs) play critical roles in important biological processes. The identification of disease-related miRNAs is critical to understand the molecular mechanisms of human diseases. Most existing computationa...

A Novel Drug Repositioning Approach Based on Collaborative Metric Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Computational drug repositioning, which is an efficient approach to find potential indications for drugs, has been used to increase the efficiency of drug development. The drug repositioning problem essentially is a top-K recommendation task that rec...

An Improved Muti-Task Learning Algorithm for Analyzing Cancer Survival Data.

IEEE/ACM transactions on computational biology and bioinformatics
Survival analysis is a popular branch of statistics. At present, many algorithms (like traditional multi-tasking learning model) cannot be applied well in practice because of censored data. Although using some model (like parametric regression model)...

iPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou's 5-Step Rule and General Pseudo Amino Acid Composition.

IEEE/ACM transactions on computational biology and bioinformatics
Protein phosphorylation is one of the key mechanism in prokaryotes and eukaryotes and is responsible for various biological functions such as protein degradation, intracellular localization, the multitude of cellular processes, molecular association,...

Prediction of PCR amplification from primer and template sequences using recurrent neural network.

Scientific reports
We have developed a novel method to predict the success of PCR amplification for a specific primer set and DNA template based on the relationship between the primer sequence and the template. To perform the prediction using a recurrent neural network...

IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier.

BMC bioinformatics
BACKGROUND: Identifying lncRNA-disease associations not only helps to better comprehend the underlying mechanisms of various human diseases at the lncRNA level but also speeds up the identification of potential biomarkers for disease diagnoses, treat...

Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning.

Computational and mathematical methods in medicine
A key enzyme in human immunodeficiency virus type 1 (HIV-1) life cycle, integrase (IN) aids the integration of viral DNA into the host DNA, which has become an ideal target for the development of anti-HIV drugs. A total of 1785 potential HIV-1 IN inh...

SteroidXtract: Deep Learning-Based Pattern Recognition Enables Comprehensive and Rapid Extraction of Steroid-Like Metabolic Features for Automated Biology-Driven Metabolomics.

Analytical chemistry
Despite the vast amount of metabolic information that can be captured in untargeted metabolomics, many biological applications are looking for a biology-driven metabolomics platform that targets a set of metabolites that are relevant to the given bio...

Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox.

Genome biology
The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing i...