Ju Xiang

Changsha University of Science and Technology

Leukemia is a malignant disease of progressive accumulation characterized by high morbidity and mortality rates, and investigating its disease genes is crucial for understanding its etiology and pathogenesis. Network propagation methods have emerged and been widely employed in disease gene prediction. However, most of the network propagation method...

Identification of protein complex is an important issue in the field of system biology, which is crucial to understanding the cellular organization and inferring protein functions. Recently, many computational methods have been proposed to detect protein complexes from protein-protein interaction (PPI) networks. However, most of these methods only...

Major depressive disorder (MDD) is a pressing global health issue. Its pathogenesis remains elusive, but numerous studies have revealed its intricate associations with various biological factors. Consequently, there is an urgent need for a comprehensive multi-omics resource to help researchers in conducting multi-omics data analysis for MDD. To add...

Tripterygium glycosides (TG) have been reported to ameliorate Alzheimer's disease (AD), although the mechanism involved remains to be determined. In the present study, the lncRNA and circRNA expression profiles of an AD mouse model treated with TG were assessed using microarrays. lncRNAs, mRNAs, and circRNAs in the hippocampi of 3 AD+normal saline...

Protein complexes play an essential role in living cells. Detecting protein complexes is crucial to understand protein functions and treat complex diseases. Due to high time and resource consumption of experiment approaches, many computational approaches have been proposed to detect protein complexes. However, most of them are only based on protein...

Introduction: Insulin has an effect on neurodegenerative diseases. However, the role and mechanism of insulin in vascular dementia (VD) and its underlying mechanism are unknown. In this study, we aimed to investigate the effects and mechanism of insulin on VD. Methods: Experimental rats were randomly assigned to control (CK), Sham, VD, and insulin...

Vascular dementia (VaD) is the second most prevalent dementia, which is attributable to neurovascular dysfunction. Currently, no approved pharmaceuticals are available. Taohong Siwu decoction (TSD) is a traditional Chinese medicine prescription with powerful antiapoptosis and anti-inflammatory properties. In this study, a network pharmacology appro...

The study of disease-gene associations is an important topic in the field of computational biology. The accumulation of massive amounts of biomedical data provides new possibilities for exploring potential relations between diseases and genes through computational strategy, but how to extract valuable information from the data to predict pathogenic...

Coronavirus disease 2019 (COVID-19), a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently spreading rapidly around the world. Since SARS-CoV-2 seriously threatens human life and health as well as the development of the world economy, it is very urgent to identify effective drugs against this virus. However,...

Network embedding has attracted a lot of attention in different fields recently. It represents nodes in a network into a low-dimensional and dense space while preserving the structural properties of the network. Some methods (e.g. motif2Vec, RUM, and MODEL) have been proposed to preserve the higher-order structures, i.e., motifs in embedding space,...

The identification of disease-causing genes is critical for mechanistic understanding of disease etiology and clinical manipulation in disease prevention and treatment. Yet the existing approaches in tackling this question are inadequate in accuracy and efficiency, demanding computational methods with higher identification power. Here, we proposed...

Biomedical data mining is very important for the research of complex diseases, and disease-gene discovery is one of the most representative topics in this field. Multiscale module structure (MMS) that widely exists in biological networks can provide useful insight for disease research. However, how to effectively mine information in MMS to enhance...

Hubs are generally defined as nodes with a high degree centrality, and they are 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 with a new way to analyze the essentiality of proteins. Unfortunately, most of the...

The detection of protein complexes is of great significance for understanding the cellular organizations and protein functions. Most of the existing methods just search the local topological information to mine dense subgraphs as protein complexes, ignoring the global topological information. To tackle this issue, we propose the DPCMNE method to de...

The nonstructured abstract were supplied as following: Estrogen receptor is involved in the pathogenesis of recurrent spontaneous abortion (RSA). The ESR1 and ESR2 genes can mediate nongenomic estrogen responses. This study aimed to assess the genetic association between the ESR1 and ESR2 genes polymorphisms and RSA susceptibility in a Chinese Han...

Motivation: Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the m...

Motivation Biomarkers with prognostic ability and biological interpretability can be used to support decision-making in the survival analysis. Genes usually form functional modules to play synergistic roles, such as pathways. Predicting significant features from the functional level can effectively reduce the adverse effects of heterogeneity and ob...

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex d...

MicroRNA (miRNA) is a class of non-coding single-stranded RNA molecules encoded by endogenous genes with a length of about 22 nucleotides. MiRNAs have been successfully identified as differentially expressed in various cancers. There is evidence that disorders of miRNAs are associated with a variety of complex diseases. Therefore, inferring potenti...

Protein complex detection is an important issue in the field of system biology, which is crucial to understanding the cellular organization and inferring protein functions. In recent years, various computational methods have been proposed to detect protein complexes from protein-protein interaction (PPI) networks. Unfortunately, most of these metho...

Studies have found that long non-coding RNAs (lncRNAs) play important roles in many human biological processes, and it is critical to explore potential lncRNA–disease associations, especially cancer-associated lncRNAs. However, traditional biological experiments are costly and time-consuming, so it is of great significance to develop effective comp...

Purpose: To identify the molecular etiology of a Chinese family with nonsyndromic macular dystrophy. Methods: Ophthalmic examinations were performed, and genomic DNA was extracted from available family members. Whole exome sequencing of two members (the proband and her unaffected mother) and Sanger sequencing in available family members were per...

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cance...

Complex diseases are caused by a variety of factors, and their diagnosis, treatment and prognosis are usually difficult. Proteins play an indispensable role in living organisms and perform specific biological functions by interacting with other proteins or biomolecules, their dysfunction may lead to diseases, it is a natural way to mine disease-rel...

Objective: Schizophrenia is a complex mental disorder with high heritability. The hypothalamic-pituitary-adrenal (HPA) axis, which is the stress system of the neuroendocrine system, is considered to impact psychotic disorders. We hypothesized that polymorphisms of HPA axis genes might be involved in the development of schizophrenia. Methods: A c...

Background Tripterygium glycoside (TG) has been suggested to have protective effects on the diseases of the central nervous system including Alzheimer’s disease (AD). The mechanisms involving lncRNA-associated competing endogenous RNAs (ceRNAs) were shown to play important roles in the development of AD. However, the ceRNA mechanism of TG in treati...

Motivation Identifying disease-related genes is important for the study of human complex diseases. Module structures or community structures are ubiquitous in biological networks. Although the modular nature of human diseases can provide useful insights, the mining of information hidden in multiscale module structures has received less attention in...

The prediction of genes related to diseases is important to the study of the diseases due to high cost and time consumption of biological experiments. Network propagation is a popular strategy for disease-gene prediction. However, existing methods focus on the stable solution of dynamics while ignoring the useful information hidden in the dynamical...

Identifying biomarkers of heterogeneous complex diseases has always been one of the focuses in medical research. In previous studies, the powerful network propagation methods have been applied to finding marker genes related to specific diseases, but existing methods are mostly based on a single network, which may be greatly affected by the incompl...

In order to improve the accuracy of the classification of the big data of disease gene detection, an algorithm for the classification of the big data of disease gene detection based on the complex network technology was proposed. On the basis of complex network technology, a distance-based membership function is first established. Considering the d...

Alzheimer’s disease (AD) is acommon neurodegenerative disease in the aged population. Tripterygium glycoside (TG) has been reported to protect the nervous system. However, the effect of TG on AD is still unknown. We aimed to explore the effect of TG on AD. Thirty-two C57BL/6J mice were randomly selected and assigned to the normal control, AD model,...

Diabetes-related diseases (DRDs), especially cancers pose a big threat to public health. Although people have explored pathological pathways of a few common DRDs, there is a lack of systematic studies on important biological processes (BPs) connecting diabetes and its related diseases/cancers. We have proposed and compared 10 protein–protein intera...

Network embedding has attracted considerable attention in recent years. It represents nodes in a network into a low-dimensional vector space while keeping the properties of the network. Some methods (e.g. ComE, MNMF, and CARE) have been proposed to preserve the community property in network embedding, and they have obtained good results in some dow...

Objective: Postpartum depression (PPD) is an episode of major depressive disorder that affecting women of childbearing age. 5-HTTLPR is 1 of the most extensively investigated polymorphisms in PPD. However, the previous results were inconsistent and inclusive. Hence, we performed a meta-analysis to precisely evaluate the association between 5-HTTLP...

Background: Drug discovery is known for the large amount of money and time it consumes and the high risk it takes. Drug repositioning has, therefore, become a popular approach to save time and cost by finding novel indications for approved drugs. In order to distinguish these novel indications accurately in a great many of latent associations betw...

Objectives Allergic rhinitis (AR) is a chronic inflammatory disease of nasal mucosa. Loss of function of Th17 cells and regulatory T (Treg) cells plays a role in the pathogenesis of AR. IL18, FOXP3, and IL13 are key genes in the development of AR. However, the genetic associations between IL18, FOXP3 and IL13 genes polymorphisms and AR risk were in...

In this study, we proposed an ensemble learning method simultaneously integrating a low-rank matrix completion model and a ridge regression model to predict anticancer drug response on cancer cell lines. The model was applied to two benchmark datasets including the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer...

With the development of high throughput technologies, there are more and more protein–protein interaction (PPI) networks available, which provide a need for efficient computational tools for network alignment. Network alignment is widely used to predict functions of certain proteins, identify conserved network modules, and study the evolutionary re...

Aims: Hypothalamic-pituitary-adrenocortical axis gene polymorphisms have been reported to affect aggressive behavior. Corticotropin releasing hormone binding protein (CRHBP) polymorphisms have been shown to contribute to the susceptibility to stress-related disorders, including aggressive behavior. However, no study has been conducted on the relati...

Identifying disease-related genes is of importance for understanding of molecule mechanisms of diseases, as well as diagnosis and treatment of diseases. Many computational methods have been proposed to predict disease-related genes, but how to make full use of multi-source biological data to enhance the ability of disease-gene prediction is still c...

Identifying disease-related microRNAs (miRNAs) is crucial to understanding the etiology and pathogenesis of many diseases. However, existing computational methods are facing a few dilemmas such as lacking “negative samples” (i.e. confirmed unrelated miRNA-disease pairs). In this study, we proposed LRMCMDA, a low-rank matrix completion-based method...

Community detection in complex networks is an important issue in network science. Several statistical measures have been proposed and widely applied to detecting the communities in various complex networks. However, due to the lack of flexibility resolution, some of them have to encounter the resolution limit and thus are not compatible with multi-...

Breast cancer is a heterogeneous disease with many clinically distinguishable molecular subtypes each corresponding to a cluster of patients. Identification of prognostic and heterogeneous biomarkers for breast cancer is to detect cluster-specific gene biomarkers which can be used for accurate survival prediction of breast cancer outcomes. In this...

Since similar complex diseases are much alike in clinical symptoms, patients are easily misdiagnosed and mistreated. It is crucial to accurately predict the disease status and identify markers with high sensitivity and specificity for classifying similar complex diseases. Many approaches incorporating network information have been put forward to pr...

Many methods have been proposed to detect communities/modules in various networks such as biological molecular networks and disease networks, while optimizing statistical measures for community structures is one of the most popular ways for community detection. Surprise, which is a statistical measure of interest for community detection, has good p...

With the increasing availability of social networks and biological networks, detecting network community structure has become more and more important. However, most traditional methods for detecting community structure have limitations in dimension reduction or parameter optimization. In this paper, we propose a Density-Canopy-Kmeans clustering alg...

Breast cancer is a malignant disease that is caused by multiple factors, and the prognosis of breast cancer patients is the focus of medical research. In the present study, we have proposed a novel computational framework which identifies the prognostic biomarkers of breast cancer based on multiple network fusion and multiple scoring strategies. In...

Background: Identifying possible drug-target interactions (DTIs) has become an important task in drug research and development. Although high-throughput screening is becoming available, experimental methods narrow down the validation space because of extremely high cost, low success rate, and time consumption. Therefore, various computational model...

Optimizing statistical measures for community structure is one of the most popular strategies for community detection, but many of them lack the flexibility of resolution and thus are incompatible with multi-scale communities of networks. Here, we further studied a statistical measure of interest for community detection, asymptotic surprise which i...

Two-dimensional (2D) materials exhibit massive potential in research and development in the scientific world due to their unique electrical, optical, thermal and mechanical properties. Graphene is an earliest found two-dimensional material, which has many excellent properties, such as high carrier mobility and large surface area. However, single la...

Community structures are ubiquitous in various complex networks, implying that the networks commonly be composed of groups of nodes with more internal links and less external links. As an important topic in network theory, community detection is of importance for understanding the structure and function of the networks. Optimizing statistical measu...

Network-based computational approaches in the prediction of genes that are associated with diseases are of considerable importance in uncovering the molecular basis of human diseases. Here, we proposed a novel disease-gene-prediction method by combining path-based structure with community structure characteristics in human protein-protein networks....

Optimizing statistical measures for community structure is one of the most popular strategies for community detection, but many of them lack the flexibility of resolution and thus are incompatible with multi-scale communities of networks. Here, we further studied a statistical measure of interest for community detection, asymptotic surprise, an asy...

Module or community structures widely exist in complex networks, and optimizing statistical measures is one of the most popular approaches for revealing and identifying such structures in real-world applications. In this paper, we focus on critical behaviors of (Quasi-)Surprise, a type of statistical measure of interest for community structure, acc...

Objective The estrogen receptor (ER) and the human epidermal growth factor receptor 2 (HER2) each play an important role in female cancers. This study aimed to investigate the genetic association between three common single nucleotide polymorphisms (SNPs) and the risk of ovarian cancer. The SNPs investigated in this study were ESR2 rs1271572 and rs...

The serotonin receptor gene (5-HT2A) has been reported to be a susceptible factor in behavioral and psychological symptoms of dementia (BPSD) in Alzheimer’s disease (AD). However, previous results were conflicting. We aim to investigate the association of 5-HT2A T102C with BPSD in AD using a meta-analysis. Studies were collected using PubMed, Web o...

Community structure is a common topological property of complex networks, which attracted much attention from various fields. Optimizing quality functions for community structures is a kind of popular strategy for community detection, such as Modularity optimization. Here, we introduce a general definition of Modularity, by which several classical...

Community detection is one of important issues in the research of complex networks. In literatures, many methods have been proposed to detect community structures in the networks, while they also have the scope of application themselves. In this paper, we investigate an important measure for community detection, Surprise (Aldecoa and Marín, Sci. Re...