AI Medical Compendium Topic:
Models, Molecular

Clear Filters Showing 601 to 610 of 629 articles

CoABind: a novel algorithm for Coenzyme A (CoA)- and CoA derivatives-binding residues prediction.

Bioinformatics (Oxford, England) Aug 1, 2018
MOTIVATION: Coenzyme A (CoA)-protein binding plays an important role in various cellular functions and metabolic pathways. However, no computational methods can be employed for CoA-binding residues prediction.
Support Vector Machine Proteins Models, Molecular Protein Binding Computational Biology Vibrio cholerae Acetyltransferases Coenzyme A Bacterial Proteins Protein Conformation Sequence Analysis, Protein Software
View on PubMed DOI

Protein threading using residue co-variation and deep learning.

Bioinformatics (Oxford, England) Jul 1, 2018
MOTIVATION: Template-based modeling, including homology modeling and protein threading, is a popular method for protein 3D structure prediction. However, alignment generation and template selection for protein sequences without close templates remain...
Proteins Models, Molecular Protein Structure, Secondary Sequence Alignment Sequence Analysis, Protein Software Deep Learning
View on PubMed DOI

Protein classification using modified n-grams and skip-grams.

Bioinformatics (Oxford, England) May 1, 2018
MOTIVATION: Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used t...
Proteins Natural Language Processing Models, Molecular Sequence Analysis, Protein Supervised Machine Learning Protein Conformation Molecular Sequence Annotation
View on PubMed DOI

Computational identification of binding energy hot spots in protein-RNA complexes using an ensemble approach.

Bioinformatics (Oxford, England) May 1, 2018
MOTIVATION: Identifying RNA-binding residues, especially energetically favored hot spots, can provide valuable clues for understanding the mechanisms and functional importance of protein-RNA interactions. Yet, limited availability of experimentally r...
Binding Sites Models, Molecular RNA Computational Biology Proteins Databases, Protein Machine Learning Protein Binding
View on PubMed DOI

DeepSF: deep convolutional neural network for mapping protein sequences to folds.

Bioinformatics (Oxford, England) Apr 15, 2018
MOTIVATION: Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a target protein based on the fold of a te...
Models, Molecular Protein Folding Sequence Alignment Sequence Analysis, Protein Software Animals Computational Biology Humans Neural Networks, Computer
View on PubMed DOI

An Approach of Anomaly Detection and Neural Network Classifiers to Measure Cellulolytic Activity.

Combinatorial chemistry & high throughput screening Jan 1, 2018
AIM AND OBJECTIVE: A common method used for massive detection of cellulolytic microorganisms is based on the formation of halos on solid medium. However, this is a subjective method and real-time monitoring is not possible. The objective of this work...
Coloring Agents Hydrolysis Pattern Recognition, Automated Models, Molecular Color Gene Library Congo Red Neural Networks, Computer Machine Learning Cellulose
View on PubMed DOI

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning.

Combinatorial chemistry & high throughput screening Jan 1, 2018
BACKGROUND: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors.
Quantitative Structure-Activity Relationship Thermodynamics Thrombin Models, Molecular Molecular Structure Linear Models Support Vector Machine Anticoagulants Machine Learning Computer Simulation Drug Discovery Humans
View on PubMed DOI

A Network Integration Method for Deciphering the Types of Metabolic Pathway of Chemicals with Heterogeneous Information.

Combinatorial chemistry & high throughput screening Jan 1, 2018
AIM AND OBJECTIVE: A metabolic pathway is an important type of biological pathway, which is composed of a series of chemical reactions. It provides essential molecules and energies for living organisms. To date, several metabolic pathways have been u...
Organic Chemicals Computational Biology Support Vector Machine Models, Molecular Structure-Activity Relationship Metabolic Networks and Pathways Molecular Structure
View on PubMed DOI

Computational Identification of Chemical Compounds with Potential Activity against Leishmania amazonensis using Nonlinear Machine Learning Techniques.

Current topics in medicinal chemistry Jan 1, 2018
Leishmaniasis is a poverty-related disease endemic in 98 countries worldwide, with morbidity and mortality increasing daily. All currently used first-line and second-line drugs for the treatment of leishmaniasis exhibit several drawbacks including to...
Software Models, Molecular Drug Evaluation, Preclinical Antiprotozoal Agents Parasitic Sensitivity Tests Leishmania Machine Learning
View on PubMed DOI

Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility.

Bioinformatics (Oxford, England) Sep 15, 2017
MOTIVATION: The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions b...
Solvents Protein Structure, Secondary Computational Biology Proteins Models, Molecular Neural Networks, Computer Machine Learning
View on PubMed DOI
Popular Topics
Recent Journals