AIMC Topic: Proteins

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Predicting drug target interactions using meta-path-based semantic network analysis.

BMC bioinformatics Apr 12, 2016
BACKGROUND: In the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and li...
Databases, Factual Pharmaceutical Preparations Drug Design Data Mining Pharmacological Phenomena Machine Learning Proteins Models, Chemical
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The Virtual Screening of the Drug Protein with a Few Crystal Structures Based on the Adaboost-SVM.

Computational and mathematical methods in medicine Apr 3, 2016
Using the theory of machine learning to assist the virtual screening (VS) has been an effective plan. However, the quality of the training set may reduce because of mixing with the wrong docking poses and it will affect the screening efficiencies. To...
Models, Statistical Combinatorial Chemistry Techniques Proteins Computational Biology Support Vector Machine Cathepsin K Chemistry, Pharmaceutical Ligands Binding Sites Algorithms Hydrogen Imaging, Three-Dimensional Drug Design Crystallization Databases, Protein Humans Reproducibility of Results Protein Binding Protein Conformation ROC Curve Area Under Curve Software
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Super-Thresholding: Supervised Thresholding of Protein Crystal Images.

IEEE/ACM transactions on computational biology and bioinformatics Mar 16, 2016
In general, a single thresholding technique is developed or enhanced to separate foreground objects from background for a domain of images. This idea may not generate satisfactory results for all images in a dataset, since different images may requir...
Supervised Machine Learning Crystallization Proteins Algorithms Databases, Protein Image Processing, Computer-Assisted
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conSSert: Consensus SVM Model for Accurate Prediction of Ordered Secondary Structure.

Journal of chemical information and modeling Mar 15, 2016
Accurate prediction of protein secondary structure remains a crucial step in most approaches to the protein-folding problem, yet the prediction of ordered secondary structure, specifically beta-strands, remains a challenge. We developed a consensus s...
Protein Structure, Secondary Support Vector Machine Proteins Consensus
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UniProt-DAAC: domain architecture alignment and classification, a new method for automatic functional annotation in UniProtKB.

Bioinformatics (Oxford, England) Mar 7, 2016
MOTIVATION: Similarity-based methods have been widely used in order to infer the properties of genes and gene products containing little or no experimental annotation. New approaches that overcome the limitations of methods that rely solely upon sequ...
Amino Acid Sequence Knowledge Bases Molecular Sequence Annotation Proteins Databases, Protein
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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 ...
Genome Algorithms Proteins Machine Learning
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A Comparative Analysis Between k-Mers and Community Detection-Based Features for the Task of Protein Classification.

IEEE transactions on nanobioscience Feb 3, 2016
Machine learning algorithms are widely used to annotate biological sequences. Low-dimensional informative feature vectors can be crucial for the performance of the algorithms. In prior work, we have proposed the use of a community detection approach ...
Algorithms Proteins Supervised Machine Learning Computational Biology
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Active machine learning-driven experimentation to determine compound effects on protein patterns.

eLife Feb 3, 2016
High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or...
Microscopy High-Throughput Screening Assays Cell Physiological Phenomena Cytosol Optical Imaging Drug Evaluation, Preclinical Supervised Machine Learning Automation, Laboratory Proteins
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Sequence-based prediction of protein-peptide binding sites using support vector machine.

Journal of computational chemistry Feb 2, 2016
Protein-peptide interactions are essential for all cellular processes including DNA repair, replication, gene-expression, and metabolism. As most protein-peptide interactions are uncharacterized, it is cost effective to investigate them computational...
Proteins Binding Sites Peptides Support Vector Machine
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DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins.

Drug discovery today Jan 25, 2016
Application of computational methods in drug discovery has received increased attention in recent years as a way to accelerate drug target prediction. Based on 443 sequence-derived protein features, we applied the most commonly used machine learning ...
Humans Algorithms Drug Discovery Machine Learning Proteins
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