AIMC Topic: Protein Interaction Maps

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Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk.

BMC medical genomics
BACKGROUND: With the development of sequencing technology, more and more long non-coding RNAs (lncRNAs) have been identified. Some lncRNAs have been confirmed that they play an important role in the process of development through the dosage compensat...

Word and Sentence Embedding Tools to Measure Semantic Similarity of Gene Ontology Terms by Their Definitions.

Journal of computational biology : a journal of computational molecular cell biology
The gene ontology (GO) database contains GO terms that describe biological functions of genes. Previous methods for comparing GO terms have relied on the fact that GO terms are organized into a tree structure. Under this paradigm, the locations of tw...

Integrating network topology, gene expression data and GO annotation information for protein complex prediction.

Journal of bioinformatics and computational biology
The prediction of protein complexes based on the protein interaction network is a fundamental task for the understanding of cellular life as well as the mechanisms underlying complex disease. A great number of methods have been developed to predict p...

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
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...

Protein function prediction from protein-protein interaction network using gene ontology based neighborhood analysis and physico-chemical features.

Journal of bioinformatics and computational biology
Protein Function Prediction from Protein-Protein Interaction Network (PPIN) and physico-chemical features using the Gene Ontology (GO) classification are indeed very useful for assigning biological or biochemical functions to a protein. They also lea...

Artificial Fish Swarm Optimization Based Method to Identify Essential Proteins.

IEEE/ACM transactions on computational biology and bioinformatics
It is well known that essential proteins play an extremely important role in controlling cellular activities in living organisms. Identifying essential proteins from protein protein interaction (PPI) networks is conducive to the understanding of cell...

Establishment of a SVM classifier to predict recurrence of ovarian cancer.

Molecular medicine reports
Gene expression data using retrieved ovarian cancer (OC) samples were used to identify genes of interest and a support vector machine (SVM) classifier was subsequently established to predict the recurrence of OC. Three datasets (GSE17260, GSE44104 an...

Mining features for biomedical data using clustering tree ensembles.

Journal of biomedical informatics
The volume of biomedical data available to the machine learning community grows very rapidly. A rational question is how informative these data really are or how discriminant the features describing the data instances are. Several biomedical datasets...

PROSES: A Web Server for Sequence-Based Protein Encoding.

Journal of computational biology : a journal of computational molecular cell biology
Recently, the number of the amino acid sequences shared in online databases is growing rapidly in huge amounts. By using sequence-derived features, machine learning algorithms are successfully applied to prediction of protein functional classes, prot...

Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions.

PloS one
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 ...