AIMC Topic: Computational Biology

Clear Filters Showing 2931 to 2940 of 4583 articles

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.

PLoS computational biology
UNLABELLED: Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered...

Prediction of lysine propionylation sites using biased SVM and incorporating four different sequence features into Chou's PseAAC.

Journal of molecular graphics & modelling
Lysine propionylation is an important and common protein acylation modification in both prokaryotes and eukaryotes. To better understand the molecular mechanism of propionylation, it is important to identify propionylated substrates and their corresp...

A Regularized Deep Learning Approach for Clinical Risk Prediction of Acute Coronary Syndrome Using Electronic Health Records.

IEEE transactions on bio-medical engineering
OBJECTIVE: Acute coronary syndrome (ACS), as a common and severe cardiovascular disease, is a leading cause of death and the principal cause of serious long-term disability globally. Clinical risk prediction of ACS is important for early intervention...

NoGOA: predicting noisy GO annotations using evidences and sparse representation.

BMC bioinformatics
BACKGROUND: Gene Ontology (GO) is a community effort to represent functional features of gene products. GO annotations (GOA) provide functional associations between GO terms and gene products. Due to resources limitation, only a small portion of anno...

Employing decomposable partially observable Markov decision processes to control gene regulatory networks.

Artificial intelligence in medicine
OBJECTIVE: Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs).

pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC.

Gene
Knowledge of subcellular locations of proteins is crucially important for in-depth understanding their functions in a cell. With the explosive growth of protein sequences generated in the postgenomic age, it is highly demanded to develop computationa...

Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites.

Scientific reports
Matrix Metalloproteases (MMPs) are an important family of proteases that play crucial roles in key cellular and disease processes. Therefore, MMPs constitute important targets for drug design, development and delivery. Advanced proteomic technologies...

Combining Supervised and Unsupervised Learning for Improved miRNA Target Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
MicroRNAs (miRNAs) are short non-coding RNAs which bind to mRNAs and regulate their expression. MiRNAs have been found to be associated with initiation and progression of many complex diseases. Investigating miRNAs and their targets can thus help dev...

MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins.

Computational intelligence and neuroscience
Apoptosis proteins play an important role in the mechanism of programmed cell death. Predicting subcellular localization of apoptosis proteins is an essential step to understand their functions and identify drugs target. Many computational prediction...