AIMC Topic: Protein Interaction Mapping

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Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions.

IEEE/ACM transactions on computational biology and bioinformatics Oct 5, 2018
Emerging evidence has shown that RNA plays a crucial role in many cellular processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological experiments provide a lot of valuable inform...
Neural Networks, Computer Machine Learning RNA Protein Interaction Mapping Caenorhabditis elegans Proteins Algorithms Protein Interaction Maps RNA-Binding Proteins
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Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls.

Scientific reports Sep 3, 2018
We propose an effective machine learning approach to identify group of interacting single nucleotide polymorphisms (SNPs), which contribute most to the breast cancer (BC) risk by assuming dependencies among BCAC iCOGS SNPs. We adopt a gradient tree b...
Prognosis Female Breast Neoplasms Humans Gene Expression Regulation, Neoplastic Estrogen Receptor alpha Finland Support Vector Machine Gene Regulatory Networks Genome, Human Case-Control Studies Protein Interaction Mapping Base Sequence Risk Genetic Predisposition to Disease Genome-Wide Association Study Ubiquitin-Protein Ligases Polymorphism, Single Nucleotide
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Gene Ontology Enrichment Improves Performances of Functional Similarity of Genes.

Scientific reports Aug 14, 2018
There exists a plethora of measures to evaluate functional similarity (FS) between genes, which is a widely used in many bioinformatics applications including detecting molecular pathways, identifying co-expressed genes, predicting protein-protein in...
Protein Interaction Mapping Datasets as Topic Semantics Base Sequence Molecular Sequence Annotation Gene Ontology Gene Expression Profiling Computational Biology
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Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences.

Molecules (Basel, Switzerland) Aug 1, 2018
Machine learning based predictions of proteinā»protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The intensive feature engineering in most of these methods make...
Neural Networks, Computer Animals Protein Binding Humans Amino Acid Sequence Protein Interaction Mapping
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Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions.

PloS one Jun 27, 2018
Protein-protein interactions integrated with disease-gene associations represent important information for revealing protein functions under disease conditions to improve the prevention, diagnosis, and treatment of complex diseases. Although several ...
Protein Interaction Maps Disease Susceptibility Reproducibility of Results Genetic Association Studies Genetic Predisposition to Disease Protein Interaction Mapping Gene Expression Profiling Neural Networks, Computer Computational Biology ROC Curve Algorithms Models, Biological
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Protein-Protein Interactions Prediction via Multimodal Deep Polynomial Network and Regularized Extreme Learning Machine.

IEEE journal of biomedical and health informatics Jun 12, 2018
Predicting the protein-protein interactions (PPIs) has played an important role in many applications. Hence, a novel computational method for PPIs prediction is highly desirable. PPIs endow with protein amino acid mutation rate and two physicochemica...
Protein Interaction Mapping Supervised Machine Learning Yeasts Deep Learning Protein Interaction Maps Computational Biology Proteins Humans Databases, Protein Reproducibility of Results Helicobacter pylori
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Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches.

BMC bioinformatics May 21, 2018
BACKGROUND: Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classif...
Neural Networks, Computer Algorithms Knowledge Discovery Drug Discovery Protein Interaction Mapping
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Network-Based Disease Module Discovery by a Novel Seed Connector Algorithm with Pathobiological Implications.

Journal of molecular biology May 20, 2018
Understanding the genetic basis of complex diseases is challenging. Prior work shows that disease-related proteins do not typically function in isolation. Rather, they often interact with each other to form a network module that underlies dysfunction...
Databases, Protein Models, Biological Disease Susceptibility Protein Interaction Mapping Algorithms Drug Discovery Protein Interaction Maps Humans Neural Networks, Computer
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Annotating activation/inhibition relationships to protein-protein interactions using gene ontology relations.

BMC systems biology Apr 11, 2018
BACKGROUND: Signaling pathways can be reconstructed by identifying 'effect types' (i.e. activation/inhibition) of protein-protein interactions (PPIs). Effect types are composed of 'directions' (i.e. upstream/downstream) and 'signs' (i.e. positive/neg...
Humans Computational Biology Gene Ontology Protein Interaction Mapping
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MTGO: PPI Network Analysis Via Topological and Functional Module Identification.

Scientific reports Apr 3, 2018
Protein-protein interaction (PPI) networks are viable tools to understand cell functions, disease machinery, and drug design/repositioning. Interpreting a PPI, however, it is a particularly challenging task because of network complexity. Several algo...
Myocardial Infarction Humans Gene Ontology Algorithms Protein Interaction Mapping
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