AIMC Journal:
Computational biology and chemistry

Showing 171 to 180 of 191 articles

DrugClust: A machine learning approach for drugs side effects prediction.

Computational biology and chemistry Mar 30, 2017
BACKGROUND: Identification of underlying mechanisms behind drugs side effects is of extreme interest and importance in drugs discovery today. Therefore machine learning methodology, linking such different multi features aspects and able to make predi...
Machine Learning Drug Discovery Drug-Related Side Effects and Adverse Reactions
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A L1-regularized feature selection method for local dimension reduction on microarray data.

Computational biology and chemistry Dec 31, 2016
Dimension reduction is a crucial technique in machine learning and data mining, which is widely used in areas of medicine, bioinformatics and genetics. In this paper, we propose a two-stage local dimension reduction approach for classification on mic...
Data Mining Datasets as Topic Linear Models Microarray Analysis
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Development of a sugar-binding residue prediction system from protein sequences using support vector machine.

Computational biology and chemistry Nov 9, 2016
Several methods have been proposed for protein-sugar binding site prediction using machine learning algorithms. However, they are not effective to learn various properties of binding site residues caused by various interactions between proteins and s...
Support Vector Machine Cluster Analysis Proteins Carbohydrates Binding Sites
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SVM and SVR-based MHC-binding prediction using a mathematical presentation of peptide sequences.

Computational biology and chemistry Oct 27, 2016
At present, there are a number of methods for the prediction of T-cell epitopes and major histocompatibility complex (MHC)-binding peptides. Despite numerous methods for predicting T-cell epitopes, there still exist limitations that affect the reliab...
Support Vector Machine Major Histocompatibility Complex
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Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis.

Computational biology and chemistry Sep 30, 2016
In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the avail...
Protein Interaction Maps Diabetes Mellitus Support Vector Machine Data Mining
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A novel fuzzy set based multifactor dimensionality reduction method for detecting gene-gene interaction.

Computational biology and chemistry Sep 29, 2016
BACKGROUND: Gene-gene interaction (GGI) is one of the most popular approaches for finding the missing heritability of common complex traits in genetic association studies. The multifactor dimensionality reduction (MDR) method has been widely studied ...
Humans Fuzzy Logic Epistasis, Genetic
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Predicting protein subcellular localization based on information content of gene ontology terms.

Computational biology and chemistry Sep 14, 2016
Predicting the location where a protein resides within a cell is important in cell biology. Computational approaches to this issue have attracted more and more attentions from the community of biomedicine. Among the protein features used to predict t...
Support Vector Machine Proteins Databases, Protein Gene Ontology Subcellular Fractions
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NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity.

Computational biology and chemistry Sep 13, 2016
Gene Ontology (GO) provides GO annotations (GOA) that associate gene products with GO terms that summarize their cellular, molecular and functional aspects in the context of biological pathways. GO Consortium (GOC) resorts to various quality assuranc...
Computational Biology Gene Ontology
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Recurrent neural network based hybrid model for reconstructing gene regulatory network.

Computational biology and chemistry Aug 16, 2016
One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offe...
Humans Neural Networks, Computer Algorithms Gene Regulatory Networks Models, Biological
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Machine learning optimization of cross docking accuracy.

Computational biology and chemistry May 4, 2016
Performance of small molecule automated docking programs has conceptually been divided into docking -, scoring -, ranking - and screening power, which focuses on the crystal pose prediction, affinity prediction, ligand ranking and database screening ...
Machine Learning Molecular Docking Simulation Drug Design Ligands
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