AIMC Topic: Computational Biology

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Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis.

Scientific reports
Machine learning (ML) is ubiquitous in bioinformatics, due to its versatility. One of the most crucial aspects to consider while training a ML model is to carefully select the optimal feature encoding for the problem at hand. Biophysical propensity s...

Machine learning techniques for protein function prediction.

Proteins
Proteins play important roles in living organisms, and their function is directly linked with their structure. Due to the growing gap between the number of proteins being discovered and their functional characterization (in particular as a result of ...

DeePathology: Deep Multi-Task Learning for Inferring Molecular Pathology from Cancer Transcriptome.

Scientific reports
Despite great advances, molecular cancer pathology is often limited to the use of a small number of biomarkers rather than the whole transcriptome, partly due to computational challenges. Here, we introduce a novel architecture of Deep Neural Network...

Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery.

BioMed research international
Artificial intelligence (AI) proves to have enormous potential in many areas of healthcare including research and chemical discoveries. Using large amounts of aggregated data, the AI can discover and learn further transforming these data into "usable...

MiPepid: MicroPeptide identification tool using machine learning.

BMC bioinformatics
BACKGROUND: Micropeptides are small proteins with length < = 100 amino acids. Short open reading frames that could produces micropeptides were traditionally ignored due to technical difficulties, as few small peptides had been experimentally confirme...

Constructive Prediction of Potential RNA Aptamers for a Protein Target.

IEEE/ACM transactions on computational biology and bioinformatics
Aptamers are short single-stranded nucleic acids that bind to target molecules with high affinity and selectivity. Aptamers are generally identified in vitro by performing SELEX (systematic evolution of ligands by exponential enrichment). Complementi...

A Wrapper Feature Subset Selection Method Based on Randomized Search and Multilayer Structure.

BioMed research international
The identification of discriminative features from information-rich data with the goal of clinical diagnosis is crucial in the field of biomedical science. In this context, many machine-learning techniques have been widely applied and achieved remark...

SDBP-Pred: Prediction of single-stranded and double-stranded DNA-binding proteins by extending consensus sequence and K-segmentation strategies into PSSM.

Analytical biochemistry
Identification of DNA-binding proteins (DNA-BPs) is a hot issue in protein science due to its key role in various biological processes. These processes are highly concerned with DNA-binding protein types. DNA-BPs are classified into single-stranded D...

Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics.

Omics : a journal of integrative biology
Pharmaceutical industry and the art and science of drug development are sorely in need of novel transformative technologies in the current age of digital health and artificial intelligence (AI). Often described as game-changing technologies, AI and m...