Drug repositioning is an efficient drug discovery method for identifying associations between present drugs and new diseases, offering considerable development time and cost savings. Although existing methods have been widely applied, they fail to fully capture the complex semantics between drugs and diseases, and are deficient in terms of model interpretability. In this paper, we propose a novel method using a hierarchical attention mechanism aggregating meta-path information for drug-disease association prediction (MPHAM), aiming at effectively integrating heterogeneous information from various sources to enhance prediction accuracy and model interpretability. First, considering the wide range of biological interactions between drugs and diseases, we construct a heterogeneous information network (HIN) to utilize data on drugs, proteins, and diseases. Then, we introduce a meta-path-based feature fusion strategy designed to effectively capture the complex semantics between nodes in the network. By defining meta-paths of multiple lengths and types, information about different relationship types is systematically integrated to generate high-quality node feature representations. Furthermore, the feature fusion strategy incorporates a multi-layer attention mechanism that dynamically assigns weights to the contributions of various meta-paths in the feature aggregation process, significantly improving the model's capacity to capture important semantic information. Experimental results demonstrate that MPHAM can effectively predict drug-disease association by integrating complex meta-path information, and the prediction accuracy is better than five state-of-the-art methods. The case studies of three classical drugs further demonstrate the more accurate predictive performance of MPHAM in drug-candidate disease association prediction.

Transcription functions as a pivotal biological process in cell biology, which is required to complete the binding of transcription factors (TFs) to transcription factor binding sites (TFBSs) on the DNA. Accurate prediction of TFBSs can provide great potential to regulate the expression of interested genes, which can facilitate exploration of new drugs and treatment for diseases. Although many deep learning-based models have been proposed for predicting TFBSs, existing models still have problems, including the use of convolutional processing of DNA sequences that loses information about the DNA double helix structure and fails to adequately account for the stereoscopic structure of DNA shape data in three dimensions. In this paper, we propose a novel model called DeepCTMS, in which both sequence features and shape features of DNA slices are effectively fused to derive high-quality representations for the task of TFBS prediction. A sequence feature processing module is first used to extract the DNA double helix structure features of DNA slices. The three-dimensional features of DNA shape data are extracted by employing a convolutional triple attention (CTA) module for the shape data of a DNA slice. Finally, a multi-scale fusion feature processing (MSFFP) module is used to fuse sequence features and shape features to obtain representations with significantly aligned semantics of both features. Ablation experiments, t-SNE visual analysis, and cross-cell line validation results demonstrate that DeepCTMS consistently outperforms benchmark models on prediction performance and generalization ability on 165 ChIP-seq datasets.

Identifying drug–target interactions (DTIs) is critical for discovery and repurposing. Existing deep models often ignore frequency-domain structure and rely on global pooling that dilutes binding-critical signals. We present TriPeakDTI, which couples adaptive frequency-domain analysis with a peak-guided attention mechanism. Using the discrete cosine transform (DCT), TriPeakDTI learns per-modality spectral weights for drugs and targets to capture multi-scale patterns—low frequencies for global conformations and high frequencies for local flexibility—during single-modal extraction. A bidirectional, peak-guided fusion module preserves token-level interaction evidence via peak detection, cross-modal alignment, and strength-aware aggregation, preventing information washout. Across three benchmark datasets, TriPeakDTI outperforms seven state-of-theart methods.

Accurate prediction of phage-host interactions remains a fundamental challenge that impedes the clinical deployment of phage therapy. Current graph neural network-based methods rely on superficial sequence features, failing to adequately integrate deep biological semantic information. In this work, we present DSHMGAT (Dual-Stream Hierarchical Mixed-Routing Graph Attention Network), a novel framework that incorporates agent-generated semantic embeddings to mitigate this semantic deficiency. By employing a ReAct-driven agent with function calling capabilities to access domain-specific databases, our approach reduces hallucinations inherent in biological LLM applications. DSHMGAT adopts a dualstream hierarchical graph neural network that simultaneously captures genomic sequences and biological semantic representations, enabling effective cross-modal information integration. Inspired by mixture-of-experts architectures, we develop a mixed-routing attention mechanism that improves learning flexibility through dynamic weight allocation between routing heads and shared heads. DSHMGAT achieves an AUC of 0.9486 on the benchmark dataset which outperforms established approaches in our comparative analysis. Ablation experiments reveal that the observed improvements can be attributed to the combined effects of multimodal fusion, cross-modal interaction and mixed-routing mechanism.

Microbial-disease association prediction has always been a frontier research direction in bioinformatics, which includes two tasks: predicting association relationships and association categories (Decrease, Increase). At present, The study methods based on statistical analysis rely heavily on biological prior knowledge, the traditional microbial experiments are time-consuming and costly. In this paper, we propose a novel model that predict microbe-disease association based on an enhanced relational graph convolutional network. Firstly, we construct a heterogeneous network containing microbial abundance changes and disease associations, microbial similarity, and disease similarity. Then, this paper proposes a feature difference enhancement module based on graph similarity, which aims to enhance the difference of feature representation between different association categories. It is fused with the relational graph convolutional network to form an enhanced relational graph convolutional network and complete feature coding work. Thus, the multi-category feature representation between nodes can be effectively extracted and the influence of edges on the graph structure can be considered. Finally, a neural network was used to complement nonlinear feature representation. The experimental results show that the proposed model can efficiently predict the association categories between microbial abundance changes and diseases.

The biomedical literature suggests that drug interventions can affect microbial communities of the human body and thereby treat disease. Screening of microbe-drug associations is therefore a key research topic in the field of antimicrobial drug development and drug repositioning. Existing models have not adequately integrated the multimodal representations of biomedical entities and the microbe-drug association networks from different sources. In addition, the positive negative sample imbalance of the heterogeneous networks and the sparsity of the association matrices decrease the accuracy of the prediction results. In this paper, we proposed a multimodal joint representation framework (MJRFMDA) for microbe-drug association prediction. First, we represented microbial and drug features through multimodal attributes. Secondly, we aggregated neighbor information and updated node attributes through GCN and GAT in microbe-drug association networks. Then we aggregated the embedded representations of multimodal networks through a joint representation layer based on a graph-level attention mechanism. Finally, we employed neural inductive matrix completion to capture the nonlinear associations between microbes and drugs, and reconstructed the association score matrix. The experimental results show that MJRFMDA outperforms the six selected state-of-the-art models.

Computational drug repositioning can identify potential associations between drugs and diseases. This technology has been shown to be effective in accelerating drug development and reducing experimental costs. Although there has been plenty of research for this task, existing methods are deficient in utilizing complex relationships among biological entities, which may not be conducive to subsequent simulation of drug treatment processes. In this article, we propose a heterogeneous graph embedding method called HMLKGAT to infer novel potential drugs for diseases. More specifically, we first construct a heterogeneous information network by combining drug-disease, drug-protein and disease-protein biological networks. Then, a multi-layer graph attention model is utilized to capture the complex associations in the network to derive representations for drugs and diseases. Finally, to maintain the relationship of nodes in different feature spaces, we propose a multi-kernel learning method to transform and combine the representations. Experimental results demonstrate that HMLKGAT outperforms six state-of-the-art methods in drug-related disease prediction, and case studies of five classical drugs further demonstrate the effectiveness of HMLKGAT.

Detecting drug side effects is crucial in development of drugs. As publicly available biomedical data expands, researchers have devised numerous computational methods for predicting drug-side effect associations (DSAs). Among these, network-based approaches have gained significant attention in the biomedical field. However, the challenge of data scarcity poses a significant hurdle for existing DSAs prediction models. While various data augmentation methods have been created to solve the proble, most rely on random alterations to the original networks, neglecting the causality of DSAs’ existence, thus impacting the predictive performance negatively. In this paper, we introduce a counterfactual inference-based data augmentation method to enhance performance. First,a heterogeneous information network (HIN) is construct by integrating multiple biomedical data sources. We employ community detection on the HIN to preform a counterfactual inference-based method, deriving augmented links and an augmented HIN. Subsequently, we apply a meta-path-based graph neural network to obtain high-quality representations of drugs and side effects, enabling the prediction of DSAs. Our comprehensive experiments confirm the effectiveness of this counterfactual inference-based data augmentation for DSAs prediction.

Drug repositioning is the process of identifying potential associations between approved drugs and diseases (DDAs) to unveil novel therapeutic applications. Unlike traditional drug discovery approaches, a key advantage of drug repositioning lies in its capacity to leverage the existing knowledge and safety profiles of established medications, leading to significant reductions in both the time and costs associated with drug development. While various methods have been proposed to address this challenge using diverse strategies, the conventional approach for training DDAs prediction models typically relies on random sampling of unknown drug-disease pairs to construct negative samples. However, this method may inadvertently introduce unwanted noise or errors by erroneously categorizing some genuine DDAs as negative samples, thereby leaving room for improvement in current methodologies. In this paper, we introduce a novel negative sample selection algorithm for DDAs prediction that explicitly incorporates causal knowledge inherent in DDAs. To accomplish this, we first construct a heterogeneous information network (HIN) that encompasses various biological entities associated with DDAs and their interconnections. Subsequently, we utilize the outcomes of community detection within the HIN as a form of counterfactual inference, resulting in the development of a negative sample selection algorithm based on a thoughtfully designed counterfactual question. By combining the known DDAs (i.e., positive samples) with the newly generated negative samples, we train a prediction model that incorporates a graph learning module to acquire representations of drugs and diseases. Comprehensive experiments confirm the effectiveness of our proposed model for DDAs prediction.

Microbe-drug interactions, which refer to the effects of drugs on microorganisms, play a crucial role in the realm of studying antibiotic-resistant bacteria and the development of antimicrobial agents. With the rapid progress in biomedical field, numerous experimental results containing validated microbe-drug interactions have been available in scientific articles. However, since failing to employ domain knowledge, traditional natural language processing methods encounter challenges in accurately identifying microbe and drug entities. Moreover, the unstructured characteristics and semantic complexity of biomedical literature pose difficulties for conventional text mining approaches to accurately grasp the syntactic features. In this paper, we present a novel microbial-drug relation extraction model called D-GCN, in which dual graph convolutional networks are used. Specifically, the drug database Drugbank is leveraged as external domain knowledge, while the graph convolutional network-based model SemGCN is utilized to learn meaningful features from biomedical texts. In addition, the attention graph convolutional network A-GCN is introduced to capture crucial syntactic features contained in texts. The experimental results show that the proposed model achieves better performance over the selected baseline models, which means D-GCN can not only accurately recognize microbial and drug entity representations, but also effectively identify the microbe-drug interactions.

Drug-drug interactions (DDIs) are compound effects when patients take two or more drugs at the same time, which may weaken the efficacy of drugs or cause unexpected side effects. Thus, accurately predicting DDIs is of great significance for the drug development and the drug safety surveillance. Although many methods have been proposed for the task, the biological knowledge related to DDIs is not fully utilized and the complex semantics among drug-related biological entities are not effectively captured in existing methods, leading to suboptimal performance. Moreover, the lack of interpretability for the predicted results also limits the wide application of existing methods for DDIs prediction. In this study, we propose a novel framework for predicting DDIs with interpretability. Specifically, we construct a heterogeneous information network (HIN) by explicitly utilizing the biological knowledge related to the procedure of inducing DDIs. To capture the complex semantics in HIN, a meta-path-based information fusion mechanism is proposed to learn high-quality representations of drugs. In addition, an attention mechanism is designed to combine semantic information obtained from meta-paths with different lengths to obtain final representations of drugs for DDIs prediction. Comprehensive experiments are conducted on 2410 approved drugs, and the results of predictive performance comparison show that our proposed framework outperforms selected representative baselines on the task of DDIs prediction. The results of ablation study and cold-start scenario indicate that the meta-path-based information fusion mechanism red is beneficial for capturing the complex semantics among drug-related biological entities. Moreover, the results of case study demonstrate that the designed attention mechanism is able to provide partial interpretability for the predicted DDIs. Therefore, the proposed method will be a feasible solution to the task of predicting DDIs.

In the treatment of bacterial infectious diseases, overuse of antibiotics may lead to not only bacterial resistance to antibiotics but also dysbiosis of beneficial bacteria which are essential for maintaining normal human life activities. Instead, phage therapy, which invades and lyses specific pathogenic bacteria without affecting beneficial bacteria, becomes more and more popular to treat bacterial infectious diseases. For the effective phage therapy, it requires to accurately predict potential phage-host interactions from heterogeneous information network consisting of bacteria and phages. Although many models have been proposed for predicting phage-host interactions, most methods fail to consider fully the sparsity and unconnectedness of phage-host heterogeneous information network, deriving the undesirable performance on phage-host interactions prediction. To address the challenge, we propose an effective model called GERMAN-PHI for predicting Phage-Host Interactions via Graph Embedding Representation learning with Multi-head Attention mechaNism. In GERMAN-PHI, the multi-head attention mechanism is utilized to learn representations of phages and hosts from multiple perspectives of phage-host associations, addressing the sparsity and unconnectedness in phage-host heterogeneous information network. More specifically, a module of GAT with talking-heads is employed to learn representations of phages and bacteria, on which neural induction matrix completion is conducted to reconstruct the phage-host association matrix. Results of comprehensive experiments demonstrate that GERMAN-PHI performs better than the state-of-the-art methods on phage-host interactions prediction. In addition, results of case study for two high-risk human pathogens show that GERMAN-PHI can predict validated phages with high accuracy, and some potential or new associated phages are provided as well.

Inferring microbe-metabolite interactions is conducive to understand how microbes affect human health, and specific microbial metabolites can be used as biomarkers for the diagnosis and treatment. Most of the existing methods only utilize the known mechanisms between microbiome and metabolome, while ignore the intragroup biological interactions(including microbe-microbe correlations and metabolite-metabolite correlations). In this paper, we propose a microbe-metabolite heterogeneous network fusion model based on graph convolution network (MMHNF) for inferring microbe-metabolite interactions. The proposed model not only utilizes the common properties of microbe-metabolite interactions, but also applies the properties of microbe-microbe and metabolite-metabolite correlation networks. In addition, it can learn low-dimensional and effective feature representations from multisource heterogeneous networks by applying the graph convolution network. Comprehensive experiments are performed on both simulated and experimental data, and the results show that MMHNF outperforms selected baselines. Moreover, results of case studies on inflammatory bowel disease (IBD) demonstrate further the effectiveness of the proposed model.

Microbes are closely related to human diseases, and the internal interactions among microbes influence disease production and development as well. Therefore, accurately predicting the associations between microbes and diseases becomes very important. This field is being carried out by a large number of researchers. However, existing methods have two deficiencies: (i) they fail to utilize the pathogenic impact of microbial interactions; (ii) they ignore the noise effect of unknown microbe-disease associations for training the prediction model. In this paper, we propose a novel model for above the shortcomings. Results of extensive experiments show that the proposed model achieves better performance over SOTA methods. In addition, the results of a case study on Type 1 diabetes demonstrate the effectiveness of the predicted associations between microbes and diseases derived from our model.

Drug side effect is an important entity in the biomedical field, and identifying the association of the drug-side effects is a very important issue in pharmacological studies and drug risk-benefit. Traditional side effect discovery methods are mainly based on pharmacological experiments. These methods can detect the side effects of some drugs, but the identification process is time-consuming, expensive, and fails to identify some rare side effects. In recent years, with the expansion of massive biomedical data, computational-based methods are widely developed and applied for the task of drug-side effect association(DSA) prediction. However, existing methods cannot fully utilize public biomedical databases, and the complex semantic associations between drugs and side effects are not effectively captured, which leads to suboptimal model prediction performance. In this study, we develop a novel meta-path-based graph neural network model for drug-side effect association prediction. In the proposed model, we first construct a heterogeneous information network(HIN) by fusing multiple biological datasets. And then, a novel meta-path-based feature learning module is designed to learn high-quality representations of drugs and side effects. Finally, with the learned features, the prediction module utilizes a fully connected neural network to make prediction. In addition, comprehensive experiments is conducted, the results demonstrate the effectiveness of our model, indicating that the method will be a viable approach for DSA prediction tasks.

Compared with traditional methods, drug repositioning is a viable solution to drug discovery. Drug repositioning usually applies the procedure of drug-disease associations (DDAs) prediction, which can reduce the cost and time of drug development and improve the success rate of drug discovery. In this paper, we develop a new multi-task learning framework based on heterogeneous graph convolutional network (MTHGCN) to recognize potential DDAs. In MTHGCN, a heterogeneous information network is constructed by combining multiple biological datasets. And then, a module based on graph convolutional networks is utilized to learn low-dimensional representations of drugs and diseases. Finally, we design two types of auxiliary tasks to help to train the target DDAs prediction task based on the multi-task learning mechanism. We conduct comprehensive experiments on MTHGCN. The results demonstrate the effectiveness of MTHGCN for drug repositioning.

Drug-drug interactions (DDIs) refer to the compound effects that may impair the effectiveness of drugs or cause unexpected side effects when two or more drugs are taken together. Therefore, it is very important to accurately predict DDIs for the drug development and drug safety monitoring. Many methods have been proposed to accomplish this task, but the existing methods fail to make full use of the biological knowledge related to DDIs, and do not effectively capture the complex semantics between biological entities related to drugs, resulting in poor performance. In this paper, we propose a novel DDIs prediction framework based on heterogeneous information network (HIN). More specifically, we construct the HIN which combines biological knowledge related to DDIs. In order to capture the complex semantics in HIN, a meta-path-based fusion mechanism is proposed to obtain high-quality drugs’ representations. Moreover, we design a meta-path level attention to combine semantics obtained from meta-paths with different lengths to obtain the final representations of drugs for DDIs prediction. The experimental results demonstrate that the framework performs better than the selected representative baselines on 2410 approved drugs.

Microbial interactions are of great importance for maintaining ecological balance and regulating human health. Most of the previous studies focus on the paired relationships and pay less attention to the higher-order interaction relationships in the microbial communities. The hypergraph was applied to establish higher-order interaction networks among microbes in microbial communities and the result of hypergraph clustering depends on hyperedge weights. So, we adopt simplex and take advantage of its volume for reconstructing each hyperedge weight to improve hypergraph clustering. We proposed a novel hypergraph clustering algorithm based on simplex (HCBS) here to detect the higher-order interaction modules in the network in a manner of clustering. The HCBS algorithm achieves the hyperedge weight from a unique higher-order relationship by calculating the joint contribution of all nodes in each hyperedge. The maximum modularity was utilized to optimize the clustering number of the hypergraph in the paper. The experimental results illustrate that the HCBS algorithm emphasis the differences of hyperedge weights and it is very effective in detecting microbial higher-order modules.

Microbial ecosystems are complex, by analyzing co-occurrence modules of microbial communities, we can better understand the conditions of microbial interactions in each environment, and help understand the interaction patterns that maintain the stability of microbial communities. Imbalances in human microbiome are closely related to human disease. Previous modular clustering analysis was based only on the relationship between paired microorganisms. In this paper, we propose calculating the logical relationship between microbial triplet in human body by information entropy and construct a hypergraph based on the triplet network. Based on the hypergraph clustering, we proposed a novel hypergraph clustering algorithm based on intra-class scatter matrix (HCIS) to reconstruct hyperedge similarity, and selected the optimal cluster number by maximizing modularity to analyze higher-order module of microorganisms. The clustering results verify the effectiveness and feasibility of HCIS algorithm for higher-order microbial module analysis.

BACKGROUND: The traditional methods of visualizing high-dimensional data objects in low-dimensional metric spaces are subject to the basic limitations of metric space. These limitations result in multidimensional scaling that fails to faithfully represent non-metric similarity data. RESULTS: Multiple maps t-SNE (mm-tSNE) has drawn much attention due to the construction of multiple mappings in low-dimensional space to visualize the non-metric pairwise similarity to eliminate the limitations of a single metric map. mm-tSNE regularization combines the intrinsic geometry between data points in a high-dimensional space. The weight of data points on each map is used as the regularization parameter of the manifold, so the weights of similar data points on the same map are also as close as possible. However, these methods use standard momentum methods to calculate parameters of gradient at each iteration, which may lead to erroneous gradient search directions so that the target loss function fails to achieve a better local minimum. In this article, we use a Nesterov momentum method to learn the target loss function and correct each gradient update by looking back at the previous gradient in the candidate search direction. By using indirect second-order information, the algorithm obtains faster convergence than the original algorithm. To further evaluate our approach from a comparative perspective, we conducted experiments on several datasets including social network data, phenotype similarity data, and microbiomic data. CONCLUSIONS: The experimental results show that the proposed method achieves better results than several versions of mm-tSNE based on three evaluation indicators including the neighborhood preservation ratio (NPR), error rate and time complexity.

From a biological standpoint, due to the special combination of complex symptoms, some type of complex diseases is difficult to be accurately diagnosed. Known as phenotypic overlap, these sets of disease-related symptoms reveal a common pathological and physiological mechanism. Researchers attempt to visualize the phenotypic relationships between different human diseases from the perspective of machine learning, but traditional methods of visualizing high-dimensional data objects into low-dimensional would be subject to fundamental limitations of metric spaces. Our method is primarily based on the multiple maps t-SNE regularization, which is a probabilistic method for visualizing data points in multiple low-dimensional spaces. We use the Nesterov accelerated gradient method to learn the objective loss function. This method thought to counterweigh too high velocities by “peeking ahead” actual objective values in the candidate search direction, thus providing a larger and timelier correction to velocity. Experiments results on several dataset show that the proposed method outperforms the original version of mm-tSNE and mm-tSNE with regularization, as measured by the neighborhood preservation ratio. This suggests the modified mm-tSNE regularization can be applied directly in other domain including social and biological datasets.

Gene (microRNA) identification is a key step in understanding the cellular mechanisms. Compared with biological experiments, computational prediction of disease genes is cheaper and more effortless. In this study, we analyzed the properties of tumor-associated microRNA in mouse and found that tumor-associated genes display 8distinguishingfeatures when compared with genes not yet known to be involved in tumor. The features of tumor-associated genes tend to located at network center and interact with each other were found by analyze the network characteristics. In addition, the features of the tumor-associated genes tend to be involved in certain biological processes and show certain phenotypes also were found through enrichment analysis. Based on these features, a machine-learning algorithm SVM were developed to predict new tumor-associated genes in mouse. Using the machine-learning algorithm, 120 tumor-associated genes were predicted with a posterior probability more than 0.9. We verified the accuracy of the identification framework with the data set of tumor-associated genes, and the result shows that this method is feasible.

Genome-wide association studies (GWAS) of T2D have discovered a number of loci that contribute to susceptibility to the disease. In this paper, we classified and identified the suspected risky Loci of T2D with computational method based on the known T2D GWAS-associated SNPs. The framework includes two parts: we first classified the SNPs based on their features of position and function through a simplified classification decision tree which was constructed by C4.5 decision tree algorithm; we then identified whether the genes associated with the suspected risky SNPs are associated with T2D by using random walk algorithm with Restart Model on the PPI network of T2D GWAS-associated genes among proteins and interactors. Based on the classification of SNP associated with T2D, we analyzed molecular pathogenesis of T2D. We verified the accuracy and reliability of the classification and identification framework with the data set of GWAS-associated SNPs. The result shows that this method is reliable. It provides a significant way to identify and classify the suspected risky Loci associated with T2D and further insights into the molecular pathogenesis of T2D.

Known as phenotypic overlapping, some disease-rel ated symptoms share a common pathologi cal and physiological mechanism. Researchers attempt to visualize the phenotypic relationships between different human diseases from the perspective of machine learning, but traditional visualization methods may be subject to fundamental limitations of metric spaces. Multiple maps t-SNE regularization method, a probabilistic method for visualizing data points in multiple low-dimensional spaces has been proposed to address the limitation. However, the convergence speed is low when apply on the scale dataset. We use the RMSProp with Nesterov momentum method to learn the objective loss function. This method normalize the gradients by applying an exponential moving average of gradient magnitude for each iteration parameter and use Nesterov momentum to counterweigh too high velocities by “peeking ahead” actual objective values in the candidate search direction. This method convergent faster than the original method of convergence speed. Experiments results on several dataset shows that the proposed method outperforms the several version of mm-tSNE with or without regularization, as measured by the neighborhood preservation ratio and error rate. This suggests the modified mm-tSNE regularization can be applied directly in other domain including social, biological and microbiomic datasets.

The microbiota living in the human body plays a very important role in our health and disease, so the identification of microbes associated with diseases will contribute to improving medical care and to better understanding of microbe functions, interactions. However, the known associations between the diseases and microbes are very less. We proposed a new method for prioritization of candidate microbes to predict disease-microbe relationships that based on the random walking on the heterogeneous network. Here, we first constructed a heterogeneous network by connecting the disease network and microbe network using the disease-microbe relationship information, then extended the random walk to the heterogeneous network, finally we used leave-one-out cross-validation to evaluate the method and ranked the candidate disease-causing microbes. We used the algorithm to disclose some potential association between disease and microbe that cannot be found by microbe network or disease network alone. Furthermore, we studied three representative diseases, Type 2 diabetes, Asthma and Psoriasis, and presented the potential microbes associated with these diseases, respectively. We confirmed that the discovery of the associations will be a good clinical solution for disease mechanism understanding, diagnosis and therapy.

Detection of protein complexes and functional modules plays a crucial role for strengthening the comprehension of cellular organization and biological functions on the dynamic protein-protein interaction network. In this article, we put forward a new strategy to identify temporal protein complexes. Integrating time-course gene expression data into static protein interaction data, a series of time-sequenced subnetworks were constructed. Then we combined the network topology and gene ontology information for defining the distance between proteins in PPI network. A novel method to find the cluster centers and then form initial clusters was based on the idea that cluster centers are usually recognized as nodes with higher densities than their neighbors and with a relatively larger distance from other cluster centers. Finally, inspired by the brainstorming discussion process, two ways are introduced to update the initial clusters for achieving the optimal results. After the filtering and merging procedure, experimental results demonstrated that the proposed strategy had a good performance comparing with the other four advanced algorithms - MCODE, FAG-EC, HC-PIN, and CNC.

While much progress has been made on the genetic analysis of osteoporosis in the past 20 years, there are a lot of genes and SNPs that are associated with osteoporosis through GWAS. In this paper, we aim to identify the risky SNPs associated with osteoporosis by algorithms based on the known osteoporosis GWAS-associated SNPs. The whole framework of our prediction method includes two steps: Firstly, we identify whether the associated genes of the suspected risky SNPs is osteoporosis GWAS-associated genes by the method of random walk algorithm on the PPI network of osteoporosis GWAS-associated genes. Then, we classify the positive result SNPs based on their features of position and function through ID3 decision tree algorithm. We verify the accuracy of the prediction framework with the data set of GWAS-associated SNPs, and the result shows that the method is feasible. It provides a more convenient way to identify the risky SNPs of osteoporosis associated.

Essential proteins are indispensable to support a cell function that provides an effective way to analyze the critical activities and processing in protein-protein interaction. By means of deleting essential protein, we can evaluate the importance of essential proteins in PPIs before and after the events, However, the elimination of protein node method (EPNM) would destroy the topology of PPI network and effect to exactly assess other protein node. Therefore, this paper proposes the Network Node Correlation and Invalidating Protein Node (NNCIP) Algorithm which is not only keep the integrity of network topology but also consider to local correlated behaviors of PPI network. Furthermore, we define a novel measure of essential protein that describes proteins function decomposed in biological cells division process. The experimental results showed that NNCIP performs significantly better than classical centrality measures DC, CC, BC, EC and WCC, and has the obvious advantage in predicted accuracy.

The detection of temporal protein complexes would be a great aid in furthering our knowledge of the dynamic features and molecular mechanism in cell life activities. Inspired by the idea of that the tighter a protein's neighbors inside a module connect, the greater the possibility that the protein belongs to the module, we propose a novel clustering algorithm CNC (Clustering based on Neighbor Closeness) and apply it to the time course protein interaction networks (TCPINs) to detect temporal protein complexes. Our novel algorithm has better performance on identifying protein complexes than five state-of-the-art algorithms—Hunter, MCODE, CFinder, SPICI, and ClusterONE—in terms of matching degree and accuracy metric, meanwhile it obtains many protein complexes with strong biological significance.

BACKGROUND: The identification of protein functional modules would be a great aid in furthering our knowledge of the principles of cellular organization. Most existing algorithms for identifying protein functional modules have a common defect -- once a protein node is assigned to a functional module, there is no chance to move the protein to the other functional modules during the follow-up processes, which lead the erroneous partitioning occurred at previous step to accumulate till to the end. RESULTS: In this paper, we design a new algorithm ADM (Adaptive Density Modularity) to detect protein functional modules based on adaptive density modularity. In ADM algorithm, according to the comparison between external closely associated degree and internal closely associated degree, the partitioning of a protein-protein interaction network into functional modules always evolves quickly to increase the density modularity of the network. The integration of density modularity into the new algorithm not only overcomes the drawback mentioned above, but also contributes to identifying protein functional modules more effectively. CONCLUSIONS: The experimental result reveals that the performance of ADM algorithm is superior to many state-of-the-art protein functional modules detection techniques in aspect of the accuracy of prediction. Moreover, the identified protein functional modules are statistically significant in terms of "Biological Process" annotated in Gene Ontology, which provides substantial support for revealing the principles of cellular organization.

Disease-causing genes prioritization is very important for understanding mechanisms of diseases and biomedical applications, such as drug design. Previous studies have shown that promising candidate genes are mostly ranked according to their relatedness to known disease genes or closely related disease processes. Therefore, a dangling gene (isolated gene) with no edges in the network can not be effectively prioritized. These approaches tend to prioritize those genes that are highly connected in the PPI network and perform poorly when they are applied to loosely connected disease genes. Motivated by this observation, we propose a new disease-causing genes prioritization method that based on network diffusion and rank concordance (NDRC). The method is evaluated by leave-one-out cross validation on 1931 diseases in which at least one gene is known to be involved, and it is able to rank the true causal gene first in 849 of all 2542 cases, and as the experimental results suggest that NDRC significantly outperforms other existing methods such as RWR, VAVIEN, DADA and PRINCE on identifying loosely connected disease genes and which successfully put dangling genes as potential candidate disease genes. Furthermore, we apply NDRC method to study two representative diseases, Meckel syndrome 1 and Peroxisome biogenesis disorder 1A (Zellweger). Our study has also found that the complex disease-causing genes are divided into several modules that are closely associated with different disease phenotype.

The identification of modules in complex networks is important for the understanding of systems. Recent studies have shown those functional modules can be identified from the protein interaction a network, what's more, the complex modules have not only relatively high density, but also have high coefficient of affinity. However, these analyses are challenging because of the presence of unreliable interactions in PPT network. In this paper, in order to mine overlapping functional modules with various and effective biological characteristics, we propose a novel algorithm based on Connected Affinity and Multi-level Seed Extension (CAMSE). First, CAMSE integrates protein-protein interactions (PPI) with the protein-protein Connected Coefficient (CC) inferred from protein complexes collected in the MIPS database to enhance the modularization and biological character of the interaction network. Then we complete the seed selection, inner kernel extensions and outer extension to get core candidate function modules step by step. Finally, we integrated the modules with high repeat rate. The experimental results show that CAMSE can detect the functional modules much more effectively and accurately when it compared with other state-of-art algorithms CPM, CACE and IPC-MCE.

Breast cancer is a leading cause of cancer-related deaths in women worldwide. Discovery of breast cancer-related disease genes is becoming very important to researcher and opens a new way to investigate pathogenic mechanism of breast cancer. Many studies have shown that the availability of human genome-wide protein-protein interactions (PPI) provides us with new opportunity for discovering disease-genes by topological features in PPI network. Therefore, it's a novel idea that map disease genes to proteins and predict disease genes by analyzing modular feature of the human PPI network. In this paper, we propose a Closely Associated Degree (CAD) algorithm based on the variation of density modularity. The CAD algorithm is first tested on the yeast PPI network, and then applied to discover breast cancer-related disease genes. The experimental results show that CAD gets 25 breast cancer-related functional modules that include known disease genes of breast cancer. Further analyzing these functional modules, four breast cancer-related disease genes have been discovered which play a significant role in breast cancer.

In recent years, there are growing interests in developing novel approaches for inferring dynamic interactions in biological systems including gene transcription network and microbial interaction networks. Multivariate Vector Autoregression (MVAR) model is one of these efficient methods. Variants of MVAR with different penalties or regularizations can avoid the problem of over-fitting and provide great potential in high-dimensional data analysis. In this paper, we developed a novel regularization methods for MVAR via weighted fusion which consider the correlation among variables. The weighted fusion can potentially incorporate information redundancy among correlated variables for estimation and variable selection. Weighted fusion is also useful when the number of predictors p is larger than the number of observations n. In theory, we discuss the grouping effect of weighted fusion regularization for linear models. We then apply the proposed model on several time series data sets especially a time series dataset of human gut microbiomes. The experimental results indicate that the new approach has better performance that several other VAR-based models and we demonstrate its capability of extracting relevant microbial interactions.

To identify the underlying disease gene of human genetic disorders is a challenging and meaningful task in bioinformatics research. Recently, several methods we re developed based on PPI network, motivated by the observation that the disease genes of the same or similar diseases tend to lie close to each other in the PPI network. However, most of these methods based on the direct neighbors or shortest distance between disease genes, which ignore the global information of the PPI network. We develop a novel method for predicting disease gene based on function flow with PPI network. First, we map the known disease genes and candidate disease genes in linkage interval to PPI network. Then, simulate the process of function flow model which propagates information from the known disease genes to other genes (proteins) over the PPI network, and each protein gets a function score. Finally, candidate disease genes are ranked according to function score, the genes (proteins) with high score are considered as disease genes. The experimental results show our method is effective, and it can identify disease genes more accurately.

BACKGROUND: In recent years, many protein complex mining algorithms, such as classical clique percolation (CPM) method and markov clustering (MCL) algorithm, have developed for protein-protein interaction network. However, most of the available algorithms primarily concentrate on mining dense protein subgraphs as protein complexes, failing to take into account the inherent organizational structure within protein complexes. Thus, there is a critical need to study the possibility of mining protein complexes using the topological information hidden in edges. Moreover, the recent massive experimental analyses reveal that protein complexes have their own intrinsic organization. METHODS: Inspired by the formation process of cliques of the complex social network and the centrality-lethality rule, we propose a new protein complex mining algorithm called Multistage Kernel Extension (MKE) algorithm, integrating the idea of critical proteins recognition in the Protein- Protein Interaction (PPI) network,. MKE first recognizes the nodes with high degree as the first level kernel of protein complex, and then adds the weighted best neighbour node of the first level kernel into the current kernel to form the second level kernel of the protein complex. This process is repeated, extending the current kernel to form protein complex. In the end, overlapped protein complexes are merged to form the final protein complex set. RESULTS: Here MKE has better accuracy compared with the classical clique percolation method and markov clustering algorithm. MKE also performs better than the classical clique percolation method both on Gene Ontology semantic similarity and co-localization enrichment and can effectively identify protein complexes with biological significance in the PPI network.

A novel algorithm based on Connected Affinity Clique Extension (CACE) for mining overlapping functional modules in protein interaction network is proposed in this paper. In this approach, the value of protein connected affinity is interpreted as the reliability and possibility of interaction which is inferred from protein complexes. The protein interaction network is constructed as a weighted graph, and the weigh is dependent on the connected affinity coefficient. The experimental results of our CACE in two test data sets show that the CACE can detect the functional modules much more effective and accurate compared with other state-of-art algorithms CPM and IPC-MCE.

Density modularity can overcome this defect, but it use Simulated Annealing (SA) algorithm to search the maximal density modularity, which can't ensure to rapidly search the global optimal solution of problem. Based on this consideration, we propose a Closely Associated Degree (CAD) algorithm to discover protein functional module which continuously improve density modularity of PPI network. CAD first analyze the associated degree of protein node, then join it into the maximal associated degree module. When all modular structure are stable, CAD merges the pair of module that can bring the maximum increment of density modularity. This process is continually repeated that make density modularity to grow rapidly. Experimental results show that the CAD algorithm can effectively and accurately identify protein functional modules with biological significance in large-scale PPI network.

The Elimination of Protein Node Method (EPNM) destroy network topology and leading to the non-connectivity of the network. In this paper, we define accessibility of protein nodes, and develop a new Network Capital Assessment and Invalidating Protein (NCAIP) algorithm which is based on the network capital assessment and invalidating protein node. NCAIP algorithm evaluates the importance of nodes and identifies essential proteins in protein-protein interaction network by analyzing the declining extent of network capital before and after that protein node was invalidated. The experimental results show that NCAIP algorithm has high accuracy on the identification of essential proteins.

Inspired by the formation process of cliques of the complex social network and the centrality-lethality rule, and integrating the idea of critical proteins recognition in the Protein-Protein Interaction (PPI) network, we propose a new protein complex mining algorithm called MKE (Multistage Kernel Extension). MKE first recognizes the nodes with high degree as the first level kernel of protein complex, then adds the weighted best neighbor node of the first level kernel into the current kernel to form the second level kernel of the protein complex, this process is repeated, extending the current kernel to form protein complex. Overlapped protein complexes are merged to form the final protein complex set. The results show that MKE has better accuracy compared with the classical clique percolation method. MKE also performs better than markov clustering algorithm on Gene Ontology semantic similarity and co-localization enrichment and can effectively identify protein complexes with biological significance.

In order to overcome the limitations of global modularity and the deficiency of local modularity, we introduce a hybrid modularity measure LGQ (Local-Global Quantification) which adopts a suitable modularity adjustable parameter to control the balance of global detecting capability and local search capability in Protein-Protein Interaction (PPI) network. On the other hand, a new protein complex mining algorithm called BN-LGQ has been proposed, which integrates the definitions of best neighbor node and the modularity increment. And by comparison with other known algorithms, the experimental results show BN-LGQ performs a better accuracy on predicting protein complexes and has a higher match with the reference protein complexes. Moreover, it can identify protein complexes with better biological significance in PPI network.

Recent studies have shown that protein complex is composed of core and attachment proteins, and proteins inside the core are highly co-expressed. Based on this new concept, we reconstruct weighted PPI network by using gene expression data, and develop a novel protein complex identification algorithm from the angle of edge(PCIA-GeCo). First, we select the edge with high co-expressed coefficient as seed to form the preliminary cores. Then, the preliminary cores are filtered according to the weighted density of complex core to obtain the unique core. Finally, the protein complexes are generated by identifying attachment proteins for each core. A comprehensive comparison in term of F-measure, Coverage rate between our method and three other existing algorithms HUNTER, COACH and CORE has been made by comparing the predicted complexes against benchmark complexes. The evaluation results show our method PCIA-GeCo is effective; it can identify protein complexes more accurately.