AIMC Journal:
Computational biology and chemistry

Showing 181 to 190 of 191 articles

Multi-instance multi-label distance metric learning for genome-wide protein function prediction.

Computational biology and chemistry Feb 13, 2016
Multi-instance multi-label (MIML) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with not only multiple instances but also multiple class labels. To find an ...
Proteins Algorithms Machine Learning Genome
View on PubMed DOI

Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.

Computational biology and chemistry Sep 28, 2015
Lipocalins are short in sequence length and perform several important biological functions. These proteins are having less than 20% sequence similarity among paralogs. Experimentally identifying them is an expensive and time consuming process. The co...
Databases, Protein Machine Learning Lipocalins
View on PubMed DOI

APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction.

Computational biology and chemistry Sep 5, 2015
Tertiary protein structure prediction is one of the most challenging problems in structural bioinformatics. Despite the advances in algorithm development and computational strategies, predicting the folded structure of a protein only from its amino a...
Knowledge Bases Proteins Protein Structure, Tertiary Protein Conformation Algorithms Computational Biology
View on PubMed DOI

Machine Learnable Fold Space Representation based on Residue Cluster Classes.

Computational biology and chemistry Jul 30, 2015
MOTIVATION: Protein fold space is a conceptual framework where all possible protein folds exist and ideas about protein structure, function and evolution may be analyzed. Classification of protein folds in this space is commonly achieved by using sim...
Cluster Analysis Machine Learning Protein Folding Proteins
View on PubMed DOI

MATEPRED-A-SVM-Based Prediction Method for Multidrug And Toxin Extrusion (MATE) Proteins.

Computational biology and chemistry Jul 29, 2015
The growth and spread of drug resistance in bacteria have been well established in both mankind and beasts and thus is a serious public health concern. Due to the increasing problem of drug resistance, control of infectious diseases like diarrhea, pn...
Support Vector Machine Amino Acids Reproducibility of Results Drug Resistance Organic Cation Transport Proteins Hydrophobic and Hydrophilic Interactions Dipeptides Position-Specific Scoring Matrices
View on PubMed DOI

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder.

Computational biology and chemistry Mar 23, 2015
Protein-protein interactions (PPIs) play essential roles in many biological processes. In protein-protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (I...
Gene Ontology Molecular Targeted Therapy Support Vector Machine Protein Interaction Mapping Computational Biology Drug Discovery Databases, Protein Proteins Protein Interaction Maps Humans
View on PubMed DOI

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

Computational biology and chemistry Mar 18, 2015
Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene...
Neoplasms Algorithms Oligonucleotide Array Sequence Analysis Transcriptome Humans Artificial Intelligence
View on PubMed DOI

A semi-supervised learning approach for RNA secondary structure prediction.

Computational biology and chemistry Feb 20, 2015
RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because o...
RNA Supervised Machine Learning Stochastic Processes Nucleic Acid Conformation
View on PubMed DOI

BagReg: Protein inference through machine learning.

Computational biology and chemistry Feb 7, 2015
Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have ...
Computational Biology Proteins Databases, Protein Machine Learning
View on PubMed DOI

Identifying microRNAs involved in cancer pathway using support vector machines.

Computational biology and chemistry Jan 29, 2015
Since Ambros' discovery of small non-protein coding RNAs in the early 1990s, the past two decades have seen an upsurge in the number of reports of predicted microRNAs (miR), which have been implicated in various functions. The correlation of miRs wit...
Support Vector Machine Gene Expression Regulation, Neoplastic Neoplastic Processes Neoplasms Algorithms MicroRNAs
View on PubMed DOI
Popular Topics
Popular Journals