AIMC Topic: Protein Interaction Maps

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NewGOA: Predicting New GO Annotations of Proteins by Bi-Random Walks on a Hybrid Graph.

IEEE/ACM transactions on computational biology and bioinformatics
A remaining key challenge of modern biology is annotating the functional roles of proteins. Various computational models have been proposed for this challenge. Most of them assume the annotations of annotated proteins are complete. But in fact, many ...

Omics Data Complementarity Underlines Functional Cross-Communication in Yeast.

Journal of integrative bioinformatics
Mapping the complete functional layout of a cell and understanding the cross-talk between different processes are fundamental challenges. They elude us because of the incompleteness and noisiness of molecular data and because of the computational int...

Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations.

IEEE/ACM transactions on computational biology and bioinformatics
Biomolecular controlled annotations have become pivotal in computational biology, because they allow scientists to analyze large amounts of biological data to better understand test results, and to infer new knowledge. Yet, biomolecular annotation da...

Assessment of Semantic Similarity between Proteins Using Information Content and Topological Properties of the Gene Ontology Graph.

IEEE/ACM transactions on computational biology and bioinformatics
The semantic similarity between two interacting proteins can be estimated by combining the similarity scores of the GO terms associated with the proteins. Greater number of similar GO annotations between two proteins indicates greater interaction aff...

Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier.

Artificial intelligence in medicine
Computational methods are employed in bioinformatics to predict protein-protein interactions (PPIs). PPIs and protein-protein non-interactions (PPNIs) display different levels of development, and the number of PPIs is considerably greater than that o...

A novel hierarchical selective ensemble classifier with bioinformatics application.

Artificial intelligence in medicine
Selective ensemble learning is a technique that selects a subset of diverse and accurate basic models in order to generate stronger generalization ability. In this paper, we proposed a novel learning algorithm that is based on parallel optimization a...

Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids.

International journal of molecular sciences
Information about the interface sites of Protein-Protein Interactions (PPIs) is useful for many biological research works. However, despite the advancement of experimental techniques, the identification of PPI sites still remains as a challenging tas...

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
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 function in precision medicine: deep understanding with machine learning.

FEBS letters
Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and speciali...