AI Medical Compendium Journal:
Computational biology and chemistry

Showing 141 to 150 of 191 articles

System-level responses to cisplatin in pro-apoptotic stages of breast cancer MCF-7 cell line.

Computational biology and chemistry
Cisplatin ceases cell division and induces apoptosis in cancer cell lines. It is well established that cisplatin alters the expression of many genes involved in several cellular processes and pathways including transcription, p53 signaling pathway, a...

Improving neural protein-protein interaction extraction with knowledge selection.

Computational biology and chemistry
Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. Meanwhile, knowledge bases (KBs) contain huge amounts of structured information of protein entities and thei...

A deep learning ensemble for function prediction of hypothetical proteins from pathogenic bacterial species.

Computational biology and chemistry
Protein function prediction is a crucial task in the post-genomics era due to their diverse irreplaceable roles in a biological system. Traditional methods involved cost-intensive and time-consuming molecular biology techniques but they proved to be ...

Protein secondary structure prediction using neural networks and deep learning: A review.

Computational biology and chemistry
Literature contains over fifty years of accumulated methods proposed by researchers for predicting the secondary structures of proteins in silico. A large part of this collection is comprised of artificial neural network-based approaches, a field of ...

Efficient utilization on PSSM combining with recurrent neural network for membrane protein types prediction.

Computational biology and chemistry
Position-Specific Scoring Matrix (PSSM) is an excellent feature extraction method that was proposed early in protein classifying prediction, but within the restriction of feature shape in PSSM, researchers make a lot attempts to process it so that PS...

DM-RPIs: Predicting ncRNA-protein interactions using stacked ensembling strategy.

Computational biology and chemistry
ncRNA-protein interactions (ncRPIs) play an important role in a number of cellular processes, such as post-transcriptional modification, transcriptional regulation, disease progression and development. Since experimental methods are expensive and tim...

THPep: A machine learning-based approach for predicting tumor homing peptides.

Computational biology and chemistry
In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agent...

Predicting drug-target interaction network using deep learning model.

Computational biology and chemistry
BACKGROUND: Traditional methods for drug discovery are time-consuming and expensive, so efforts are being made to repurpose existing drugs. To find new ways for drug repurposing, many computational approaches have been proposed to predict drug-target...

Time-dependent AI-Modeling of the anticancer efficacy of synthesized gallic acid analogues.

Computational biology and chemistry
BACKGROUND/AIM: Main objective of this study is mapping of the anticancer efficacy of synthesized gallic acid analogues using modeling and artificial intelligence (AI) over a large range of concentrations and exposure times to explore the underline m...

Identification of coenzyme-binding proteins with machine learning algorithms.

Computational biology and chemistry
The coenzyme-binding proteins play a vital role in the cellular metabolism processes, such as fatty acid biosynthesis, enzyme and gene regulation, lipid synthesis, particular vesicular traffic, and β-oxidation donation of acyl-CoA esters. Based on th...