MR2CPPIS: Accurate prediction of protein-protein interaction sites based on multi-scale Res2Net with coordinate attention mechanism.

Proteins play a vital role in various biological processes and achieve their functions through protein-protein interactions (PPIs). Thus, accurate identification of PPI sites is essential. Traditional biological methods for identifying PPIs are costly, labor-intensive, and time-consuming.

Predicting miRNA-disease association through combining miRNA function and network topological similarities based on MINE.

Predicting associations between microRNAs (miRNAs) and diseases from the viewpoint of function modules has become increasingly popular. However, existing methods obtained the relations between diseases and miRNAs only through the construction of similarity networks and neglected the complex network characteristic. In this paper, a new method named combining miRNA function similarities and network topology similarities based on module identification in networks (ComSim-MINE) was developed.

Inferring MicroRNA-Disease Associations Based on the Identification of a Functional Module.

Inferring potential associations between microRNAs (miRNAs) and human diseases can help people understand the pathogenesis of complex human diseases. Several computational approaches have been presented to discover novel miRNA-disease associations based on a top-ranked association model. However, some top-ranked miRNAs are not easily used to reveal the association between miRNAs and diseases.

CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer.

Background: Non-coding RNAs (ncRNAs) are emerging as key regulators and play critical roles in a wide range of tumorigenesis. Recent studies have suggested that long non-coding RNAs (lncRNAs) could interact with microRNAs (miRNAs) and indirectly regulate miRNA targets through competing interactions. Therefore, uncovering the competing endogenous RNA (ceRNA) regulatory mechanism of lncRNAs, miRNAs and mRNAs in post-transcriptional level will aid in deciphering the underlying pathogenesis of human polygenic diseases and may unveil new diagnostic and therapeutic opportunities.

Detection of Protein Complexes Based on Penalized Matrix Decomposition in a Sparse Protein⁻Protein Interaction Network.

High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease rapidly when applied to sparse protein⁻protein interaction (PPI) networks.

Discovering Synergistic Drug Combination from a Computational Perspective.

Synergistic drug combinations play an important role in the treatment of complex diseases. The identification of effective drug combination is vital to further reduce the side effects and improve therapeutic efficiency. In previous years, in vitro method has been the main route to discover synergistic drug combinations.

Human disease MiRNA inference by combining target information based on heterogeneous manifolds.

The emergence of network medicine has provided great insight into the identification of disease-related molecules, which could help with the development of personalized medicine. However, the state-of-the-art methods could neither simultaneously consider target information and the known miRNA-disease associations nor effectively explore novel gene-disease associations as a by-product during the process of inferring disease-related miRNAs. Computational methods incorporating multiple sources of information offer more opportunities to infer disease-related molecules, including miRNAs and genes in heterogeneous networks at a system level.

A novel approach to identify the miRNA-mRNA causal regulatory modules in Cancer.

MicroRNAs (miRNAs) play an essential role in many biological processes by regulating the target genes, especially in the initiation and development of cancers. Therefore, the identification of the miRNA-mRNA regulatory modules is important for understanding the regulatory mechanisms. Most computational methods only used statistical correlations in predicting miRNA-mRNA modules, and neglected the fact there are causal relationships between miRNAs and their target genes.

PCE-FR: A Novel Method for Identifying Overlapping Protein Complexes in Weighted Protein-Protein Interaction Networks Using Pseudo-Clique Extension Based on Fuzzy Relation.

Identifying overlapping protein complexes in protein-protein interaction (PPI) networks can provide insight into cellular functional organization and thus elucidate underlying cellular mechanisms. Recently, various algorithms for protein complexes detection have been developed for PPI networks. However, majority of algorithms primarily depend on network topological feature and/or gene expression profile, failing to consider the inherent biological meanings between protein pairs.

Collective Prediction of Disease-Associated miRNAs Based on Transduction Learning.

The discovery of human disease-related miRNA is a challenging problem for complex disease biology research. For existing computational methods, it is difficult to achieve excellent performance with sparse known miRNA-disease association verified by biological experiment. Here, we develop CPTL, a Collective Prediction based on Transduction Learning, to systematically prioritize miRNAs related to disease.

MOEPGA: A novel method to detect protein complexes in yeast protein-protein interaction networks based on MultiObjective Evolutionary Programming Genetic Algorithm.

The identification of protein complexes in protein-protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property.