Advanced biological technologies are producing large-scale protein-protein interaction (PPI) data at an ever increasing pace, which enable us to identify protein complexes from PPI networks. Pair-wise interactions can be modeled as a graph, where vertices represent proteins and edges PPIs. However most of current algorithms detect based on deterministic graphs, whose either present or absent. Neighboring information is neglected in these methods. Based the uncertain graph model, we propose...

Many computational methods have been proposed to identify essential proteins by using the topological features of interactome networks. However, precision protein discovery still needs be improved. Researches show that majority hubs (essential proteins) in yeast network are due their involvement complex biological modules and can classified into two categories: date party hubs. In this study, combining with gene expression profiles, we propose a new method predict based on overlapping...

Protein complexes play a significant role in understanding the underlying mechanism of most cellular functions. Recently, many researchers have explored computational methods to identify protein from protein-protein interaction (PPI) networks. One group focus on detecting local dense subgraphs which correspond by considering neighbors. The drawback this kind approach is that global information networks ignored. Some such as Markov Clustering algorithm (MCL), PageRank-Nibble are proposed find...

Recently, an increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) can participate in various crucial biological processes and also be used as the most promising biomarkers for treatment certain diseases such coronary artery disease cancers. Due to costs time complexity, possible disease-related lncRNAs verified by traditional experiments is very limited. Therefore, recent years, it has been popular use computational models predict potential disease-lncRNA...

<title>Abstract</title> Background: Protein function prediction serves as a fundamental cornerstone in bioinformatics, offering critical insights into the intricate biological processes and molecular mechanisms that form basis of life. Precise annotation protein functions is indispensable for unraveling disease mechanisms, identifying drug targets, propelling forward synthetic biology applications. Nevertheless, this task remains complex due to diverse characteristics multi-omics data...

Essential proteins are distinctly important for an organism's survival and development crucial to disease analysis drug design as well. Large-scale protein-protein interaction (PPI) data sets exist in Saccharomyces cerevisiae, which provides us with a valuable opportunity predict identify essential from PPI networks. Many network topology-based computational methods have been designed detect proteins. However, these limited by the completeness of available data. To break out restraints, some...

Automated annotation of protein function is challenging. As the number sequenced genomes rapidly grows, overwhelming majority proteins can only be annotated computationally. Under new conditions or stimuli, not and location would changed, but also their interactions. This dynamic feature interactions, however, was considered in existing prediction algorithms. Taking nature interactions into consideration, we construct a weighted interactome network (DWIN) by integrating protein-protein...

Accurate annotation of protein functions is still a big challenge for understanding life in the post-genomic era. Many computational methods based on protein-protein interaction (PPI) networks have been proposed to predict function proteins. However, precision these predictions needs be improved, due incompletion and noise PPI networks. Integrating network topology biological information could improve accuracy prediction may also lead discovery multiple types between Current algorithms...

Human activity recognition, tracking and classification is an essential trend in assisted living systems that can help support elderly people with their daily activities. Traditional recognition approaches depend on vision-based or sensor-based techniques. Nowadays, a novel promising technique has obtained more attention, namely device-free human neither requires the target object to wear carry device nor install cameras perceived area. The for uses only signals of common wireless local area...

The survival of human beings is inseparable from microbes. More and more studies have proved that microbes can affect physiological processes in various aspects are closely related to some diseases. In this paper, based on known microbe-disease associations, a bidirectional weighted network was constructed by integrating the schemes normalized Gaussian interactions recommendations firstly. And then, newly network, computational model called BWNMHMDA developed predict potential relationships...

The accurate annotation of protein functions is great significance in elucidating the phenomena life, treating disease and developing new medicines. Various methods have been developed to facilitate prediction these by combining interaction networks (PINs) with multi-omics data. However, it still challenging make full use multiple biological improve performance annotation.We presented NPF (Network Propagation for Functions prediction), an integrative function predicting framework assisted...

There is no criterion to distinguish synchronous and non-synchronous multiple primary cutaneous melanomas (MPMs). This study aimed MPMs compare the survivals of them using Surveillance, Epidemiology, End Results database.Synchronous were distinguished by fitting double log transformed distribution time interval between first second (TIFtS) through a piecewise linear regression. The overall melanoma-specific compared Kaplan-Meier method Cox proportional hazard model modeling occurrence as...

Protein complexes play an important role in biological processes. Recent developments experiments have resulted the publication of many high-quality, large-scale protein-protein interaction (PPI) datasets, which provide abundant data for computational approaches to prediction protein complexes. However, precision complex still needs be improved due incompletion and noise PPI networks. There exist diverse relationships among proteins after integrating multiple sources information. Considering...

In recent years, lncRNAs (long-non-coding RNAs) have been proved to be closely related the occurrence and development of many serious diseases that are seriously harmful human health. However, most lncRNA-disease associations not found yet due high costs time complexity traditional bio-experiments. Hence, it is quite urgent necessary establish efficient reasonable computational models predict potential between diseases.In this manuscript, a novel prediction model called TCSRWRLD proposed...

Growing evidence shows that microbes in human body and surface play critical roles the development of many diseases. Predicting underlying associations between diseases is essential for deeply understanding pathogenesis However, biological experiments to find relationship usually laborious time-consuming, which presents need effective computational tools. In this study, we propose a model node-information-based Link Propagation Human Microbe-Disease Association prediction (LPHMDA) prioritize...

Essential proteins are important for the survival and reproduction of organisms. Many computational methods have been proposed to identify essential proteins, due production vast amounts protein-protein interaction (PPI) data. It has demonstrated that PPI networks graph-theoretic characteristics as so-called small-world scale-free. The traditional metrics cannot really reflect relationship between when identifying from networks. In this paper, we construct a diffusion distance network (DSN)...

Abstract Background Essential proteins are an important part of the cell and closely related to life activities cell. Hitherto, Protein-Protein Interaction (PPI) networks have been adopted by many computational methods predict essential proteins. Most current approaches focus mainly on topological structure PPI networks. However, those relying solely network low detection accuracy for Therefore, it is necessary integrate with other biological information identify Results In this paper, we...

The identification of essential proteins can help in understanding the minimum requirements for cell survival and development. Ever-increasing amounts high-throughput data provide us with opportunities to detect from protein interaction networks (PINs). Existing network-based approaches are limited by poor quality underlying PIN data, which exhibits high rates false positive negative results. To overcome this problem, researchers have focused on prediction combining PINs other biological has...

Abstract Background The accurate characterization of protein functions is critical to understanding life at the molecular level and has a huge impact on biomedicine pharmaceuticals. Computationally predicting function been studied in past decades. Plagued by noise errors protein–protein interaction (PPI) networks, researchers have undertaken focus fusion multi-omics data recent years. A model that appropriately integrates network topologies with biological preserves their intrinsic...

Computational methods accurately prioritizing latent disorder genes require for all kinds of biological information. But the defection a single type data has negative impact on identification causing diseases. To address limitation, computing approaches often integrate different data. In study, novel algorithm PDGPC (Predicting Disease Genes with Protein Complexes) is proposed. It utilizes protein subcellular localizations to improve reliability protein-protein interactions and constructs...

Detecting functional modules in Protein-Protein Interaction (PPI) networks is essential to understand gene function, biological pathways and cellular organisation. Majority of methods predict via the static PPI networks. However, systems are highly dynamic regulated by Considering inherent within these networks, we build time course terms expression profiles. And then a novel framework for identifying with core-attachment structure has been proposed accordance Our algorithm generates cores...

Accurate annotation of protein function is the key to understanding life at molecular level and has great implications for biomedicine pharmaceuticals. The rapid developments high-throughput technologies have generated huge amounts protein-protein interaction (PPI) data, which prompts emergence computational methods determine function. Plagued by errors noises hidden in PPI these undertaken focus on prediction functions integrating topology networks multi-source biological data. Despite...