The rapid development of proteomics and high-throughput technologies has produced a large amount Protein-Protein Interaction (PPI) data, which makes it possible for considering dynamic properties protein interaction networks (PINs) instead static properties. Identification complexes from PINs becomes vital scientific problem understanding cellular life in the post genome era. Up to now, plenty models or methods have been proposed construction identify complexes. However, most constructed...

Biological functions of a cell are typically carried out through protein complexes. The detection complexes is therefore great significance for understanding the cellular organizations and functions. In past decades, many computational methods have been proposed to detect However, most existing just search local topological information mine dense subgraphs as complexes, ignoring global information. To tackle this issue, we propose DPCMNE method via multi-level network embedding. It can...

Hubs are generally defined as nodes with a high degree centrality, and they important for maintaining the stability of complex networks. Previous studies have shown that hub proteins tend to be essential in protein-protein interaction (PPI) networks, providing us new way analyze essentiality proteins. Unfortunately, most existing leverage static PPI networks both incomplete noisy ignore temporal spatial characteristics Benefiting from development high-throughput technologies, abundant...

Proteins drive virtually all cellular-level processes. The proteins that are critical to cell proliferation and survival defined as essential. These essential implicated in key metabolic regulatory networks, important the context of rational drug design efforts. computational identification benefits from publicly available protein interaction datasets. Scientists have developed several algorithms use these datasets predict proteins. However, a comprehensive web platform facilitates analysis...

Protein complexes play an essential role in living cells. Detecting protein is crucial to understand functions and treat complex diseases. Due high time resource consumption of experiment approaches, many computational approaches have been proposed detect complexes. However, most them are only based on protein-protein interaction (PPI) networks, which heavily suffer from the noise PPI networks. Therefore, we propose a novel core-attachment method, named CACO, human complexes, by integrating...

Identifying disease-related genes is an important issue in computational biology. Module structure widely exists biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods the which can provide useful insights for study genes. However, mining effective utilization module still challenging such issues as a disease gene prediction.We propose hybrid disease-gene prediction method integrating multiscale (HyMM), utilize information from...

Detecting protein complexes from protein-protein interaction (PPI) networks provides biologists an opportunity to efficiently understand the cellular organizations and functions. Existing computational methods just focus on mining high-density regions as by searching local topological information of a PPI network ignore global information. To address this limitation, in study, we present novel complex detection method based hierarchical compressing embedding, named DPC-HCNE. The proposed can...

With the advances in high-throughput technology, a large number of protein interactions data have been burgeoning recent years, which makes it possible for considering dynamic properties interaction networks(PINs) instead static properties. To address limitation existing PIN analysis approaches, this paper, we proposed new model-based scheme construction Spatial and Temporal Active Protein Interaction Network (ST-APIN) by integrating time-course gene expression subcellular location...

Identification of protein complex is an important issue in the field system biology, which crucial to understanding cellular organization and inferring functions. Recently, many computational methods have been proposed detect complexes from protein-protein interaction (PPI) networks. However, most these only focus on local information proteins PPI network, are easily affected by noise network. Meanwhile, it's still challenging complexes, especially for overlapping cases. To address issues,...

Abstract Leukemia is a malignant disease of progressive accumulation characterized by high morbidity and mortality rates, investigating its genes crucial for understanding etiology pathogenesis. Network propagation methods have emerged been widely employed in gene prediction, but most them focus on static biological networks, which hinders their applicability effectiveness the study diseases. Moreover, there currently lack special algorithms identification leukemia genes. Here, we proposed...

Cell heterogeneity analysis is an important and urgent task in single cell data research. Numerous type identification methods have been proposed to address the issue. Due high rate of dropout complex biological background, it still a challenging obtain accurate clusters cells. In this study, we propose robust clustering method based on subspace learning partial imputation, called RCSLI. RCSLI incorporates modified variable genes selection utilizes self-expression scRNA-seq learn sparse...

Abstract Motivation Identifying disease-related genes is important for the study of human complex diseases. Module structures or community are ubiquitous in biological networks. Although modular nature diseases can provide useful insights, mining information hidden multiscale module has received less attention disease-gene prediction. Results We propose a hybrid method, HyMM, to predict more effectively by integrating from structures. HyMM consists three key steps: extraction modules, gene...

Abstract Biomedical data mining is very important for the research of complex diseases, and disease-gene discovery one most representative topics in this field. Multiscale module structure (MMS) that widely exists biological networks can provide useful insight disease research. However, how to effectively mine information MMS enhance ability challenging. Thus, we propose a type novel hybrid methods (HyMSMK) by integrating multiscale kernel (MSMK) derived from profile (MSMP). We extract MSMP...