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The primary sequences of DNA, RNA and protein have been used as the dominant information source of existing machine learning tools, especially for contexts not fully explored by wet-experimental approaches. Since molecular markers are profoundly orchestrated in the living organisms, those markers that cannot be unambiguously recovered from the primary sequence often help to predict other biological events. To the best of our knowledge, there is no current tool to build and deploy machine learning models that consider genomic evidence. We therefore developed the WHISTLE server, the first machine learning platform based on genomic coordinates. It features convenient covariate extraction and model web deployment with 46 distinct genomic features integrated along with the conventional sequence features. We showed that, when predicting m6A sites from SRAMP project, the model integrating genomic features substantially outperformed those based on only sequence features. The WHISTLE server should be a useful tool for studying biological attributes specifically associated with genomic coordinates, and is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/whi2.

Drug repositioning invovles exploring novel usages for existing drugs. It plays an important role in drug discovery, especially in the pre-clinical stages. Compared with the traditional drug discovery approaches, computational approaches can save time and reduce cost significantly. Since drug repositioning relies on existing drug-, disease-, and target-centric data, many machine learning (ML) approaches have been proposed to extract useful information from multiple data resources. Deep learning (DL) is a subset of ML and appears in drug repositioning much later than basic ML. Nevertheless, DL methods have shown great performance in predicting potential drugs in many studies. In this article, we review the commonly used basic ML and DL approaches in drug repositioning. Firstly, the related databases are introduced, while all of them are publicly available for researchers. Two types of preprocessing steps, calculating similarities and constructing networks based on those data, are discussed. Secondly, the basic ML and DL strategies are illustrated separately. Thirdly, we review the latest studies focused on the applications of basic ML and DL in identifying potential drugs through three paths: drug-disease associations, drug-drug interactions, and drug-target interactions. Finally, we discuss the limitations in current studies and suggest several directions of future work to address those limitations.

Accumulated studies have discovered that circular RNAs (CircRNAs) are closely related to many complex human diseases. Due to this close relationship, CircRNAs can be used as good biomarkers for disease diagnosis and therapeutic targets for treatments. However, the number of experimentally verified circRNA-disease associations are still fewer and also conducting wet-lab experiments are constrained by the small scale and cost of time and labour. Therefore, effective computational methods are required to predict associations between circRNAs and diseases which will be promising candidates for small scale biological and clinical experiments. In this paper, we propose novel computational models based on Graph Convolution Networks (GCN) for the potential circRNA-disease association prediction. Currently most of the existing prediction methods use shallow learning algorithms. Instead, the proposed models combine the strengths of deep learning and graphs for the computation. First, they integrate multi-source similarity information into the association network. Next, models predict potential associations using graph convolution which explore this important relational knowledge of that network structure. Two circRNA-disease association prediction models, GCN based Node Classification (GCN-NC) and GCN based Link Prediction (GCN-LP) are introduced in this work and they demonstrate promising results in various experiments and outperforms other existing methods. Further, a case study proves that some of the predicted results of the novel computational models were confirmed by published literature and all top results could be verified using gene-gene interaction networks.

MicroRNAs (miRNAs) have been proved to play critical roles in diverse biological processes, including the human disease development process. Exploring the potential associations between miRNAs and diseases can help us better understand complex disease mechanisms. Given that traditional biological experiments are expensive and time-consuming, computational models can serve as efficient means to uncover potential miRNA-disease associations. This study presents a new computational model based on variational graph auto-encoder with matrix factorization (VGAMF) for miRNA-disease association prediction. More specifically, VGAMF first integrates four different types of information about miRNAs into an miRNA comprehensive similarity network and two types of information about diseases into a disease comprehensive similarity network, respectively. Then, VGAMF gets the non-linear representations of miRNAs and diseases, respectively, from those two comprehensive similarity networks with variational graph auto-encoders. Simultaneously, a non-negative matrix factorization is conducted on the miRNA-disease association matrix to get the linear representations of miRNAs and diseases. Finally, a fully connected neural network combines linear and non-linear representations of miRNAs and diseases to get the final predicted association score for all miRNA-disease pairs. In the 10-fold cross-validation experiments, VGAMF achieves an average AUC of 0.9280 on HMDD v2.0 and 0.9470 on HMDD v3.2, which outperforms other competing methods. Besides, the case studies on colon cancer and esophageal cancer further demonstrate the effectiveness of VGAMF in predicting novel miRNA-disease associations.

Circular RNAs (circRNAs) are a special kind of non-coding RNA. They play important regulatory role in diseases through interactions of miRNAs associated with the diseases. Due to their insensitivity to nucleases, they are more stable than linear RNAs. It is thus imperative to integrate available information for predicting circRNA-disease associations in humans. Here, we propose a computational model to predict circRNA-disease associations based on accelerated attributed network embedding (AANE) algorithm and autoencoder(AE). First, we use AANE algorithm to extract low-dimensional features of circRNAs and diseases and then stacked autoencoder (SAE) to automatically extract in-depth features. The features obtained by AANE and the SAE are integrated and XGBoost is used as a binary classifier to get the predicted results. The proposed model has an average area under the receiver operating characteristic curve value of 0.8800 in 5-fold cross validation and 0.8988 in 10-fold cross validation. The factors that can affect the performance of the model are discussed and some common diseases are used as case studies. Results indicated that the model has great performance in predicting circRNA-disease associations.

Biomolecules, such as microRNAs, circRNAs, lncRNAs and genes, are functionally interdependent in human cells, and all play critical roles in diverse fundamental and vital biological processes. The dysregulations of such biomolecules can cause diseases. Identifying the associations between biomolecules and diseases can uncover the mechanisms of complex diseases, which is conducive to their diagnosis, treatment, prognosis and prevention. Due to the time consumption and cost of biologically experimental methods, many computational association prediction methods have been proposed in the past few years. In this study, we provide a comprehensive review of machine learning-based approaches for predicting disease–biomolecule associations with multi-view data sources. Firstly, we introduce some databases and general strategies for integrating multi-view data sources in the prediction models. Then we discuss several feature representation methods for machine learning-based prediction models. Thirdly, we comprehensively review machine learning-based prediction approaches in three categories: basic machine learning methods, matrix completion-based methods and deep learning-based methods, while discussing their advantages and disadvantages. Finally, we provide some perspectives for further improving biomolecule–disease prediction methods.

Circular RNA (circRNA) is a novel non-coding endogenous RNAs. Evidence has shown that circRNAs are related to many biological processes and play essential roles in different biological functions. Although increasing numbers of circRNAs are discovered using high-throughput sequencing technologies, these techniques are still time-consuming and costly. In this study, we propose a computational method to predict circRNA-disesae associations which is based on metapath2vec++ and matrix factorization with integrated multiple data (called PCD MVMF). To construct more reliable networks, various aspects are considered. Firstly, circRNA annotation, sequence, and functional similarity networks are established, and disease-related genes and semantics are adopted to construct disease functional and semantic similarity networks. Secondly, metapath2vec++ is applied on an integrated heterogeneous network to learn the embedded features and initial prediction score. Finally, we use matrix factorization, take similarity as a constraint, and optimize it to obtain the final prediction results. Leave-one-out cross-validation, five-fold cross-validation, and f-measure are adopted to evaluate the performance of PCD MVMF. These evaluation metrics verify that PCD MVMF has better prediction performance than other methods. To further illustrate the performance of PCD MVMF, case studies of common diseases are conducted. Therefore, PCD MVMF can be regarded as a reliable and useful circRNA-disease association prediction tool.

Automatic extracting of knowledge from massive data samples, i.e., big data analytics (BDA), has emerged as a vital task in almost all scientific research fields. The BDA problems are rather difficult to solve due to their large-scale, high-dimensional, and dynamic properties, while the problems with small data are usually hard to handle due to insufficient data samples and incomplete information. Such difficulties lead to the search-based data analytics problem, where a data analysis task is modeled as a complex, dynamic, and computationally expensive optimization problem and then solved by using an iterative algorithm. In this paper, we intend to present an extensive and in-depth discussion on the utilizing of evolutionary computation (EC) based optimization methods [including evolutionary algorithms (EAs) and swarm intelligence (SI)] for solving search-based data analysis problems. Then, as an example for illustration, we provide a comprehensive review of the applications of state-of-the-art EC methods for different types of data mining problems in bioinformatics. Here, the detailed analysis and discussion are conducted on three types of data samples, which include sequences data, network data, and image data. Finally, we survey the challenges faced by EC methods and the trend for future directions. Based on the applications of EC methods for search-based data analysis problems involving inexact and uncertain information, the insights of data analytics are able to understand better, and more efficient algorithms could be designed to solve real-world complex BDA problems.

Pathway-specific protein domain (PSPD) are associated with specific pathways. Many protein domains are pervasive in various biological processes, whereas other domains are linked to specific pathways. Many human disease pathways, such as cancer pathways and signaling pathway-related diseases, have caused the loss of functional PSPD. Therefore, the creation of an accurate method to predict its roles is a critical step toward human disease and pathways. In this study, we proposed a deep learning model based on a two-dimensional neural network (2D-CNN-PSPD) with a pathway-specific protein domain association prediction. In terms of the purposes of a sub-pathway, its parent pathway and its super pathway are linked to the Uni-Pathway. We also proposed a dipeptide composition (DPC) model and a dipeptide deviation (DDE) model of feature extraction profiles as PSSM. Then, we predicted the proteins associated with the same sub-pathway or with the same organism. The DDE model and DPC model of the PSSM feature profile input was associated with our proposed 2D-CNN method. We deployed several parameters to optimize the model’s output performance and used the hyperparameter optimization approach to find the best model for our dataset based on the 10-fold cross-validation results. Ultimately, we assessed the predictive performance of the current model by using independent datasets and cross-validation datasets. Therefore, we enhanced the efficiency of deep learning methods. PSPD is involved in any known pathway and then follow the association in different stages of the pathway hierarchy with other proteins. Our proposed method could identify 2D-CNN-PSPD with 0.83% sensitivity, 0.92% specificity, 87.27% accuracy, and 0.75% accuracy. We provided an important method for the analysis of PSPD proteins in the proposed research, and our achievements might promote computational biological research. We concluded our proposed model architecture in the future, the use of the latest features, and the multi-one structure to predict different types of molecules, such as DNA, RNA, and disease-pathway specific proteins associations.

Circular RNAs (circRNAs) play important roles in various biological processes, as essential non -coding RNAs that have effects on transcriptional and posttranscriptional gene expression regulation. Recently, many studies have shown that circRNAs can be regarded as micro RNA (miRNA) sponges, which are known to be associated with certain diseases. Therefore efficient computation methods are needed to explore miRNA-circRNA interactions, but only very few computational methods for predicting the associations between miRNAs and circRNAs exist. In this study, we adopt an improved random walk computational method, named KRWRMC, to express complicated associations between miRNAs and circRNAs. Our major contributions can be summed up in two points. First, in the conventional Random Walk Restart Heterogeneous (RWRH) algorithm, the computational method simply converts the circRNA/miRNA similarity network into the transition probability matrix; in contrast, we take the influence of the neighbor of the node in the network into account, which can suggest or stress some potential associations. Second, our proposed KRWRMC is the first computational model to calculate large numbers of miRNA-circRNA associations, which can be regarded as biomarkers to diagnose certain diseases and can thus help us to better understand complicated diseases. The reliability of KRWRMC has been verified by Leave One Out Cross Validation (LOOCV) and 10-fold cross validation, the results of which indicate that this method achieves excellent performance in predicting potential miRNA-circRNA associations.

Background Increasing numbers of evidences have illuminated that metabolites can respond to pathological changes. However, identifying the diseases-related metabolites is a magnificent challenge in the field of biology and medicine. Traditional medical equipment not only has the limitation of its accuracy but also is expensive and time-consuming. Therefore, it’s necessary to take advantage of computational methods for predicting potential associations between metabolites and diseases. Results In this study, we develop a computational method based on KATZ algorithm to predict metabolite-disease associations (KATZMDA). Firstly, we extract data about metabolite-disease pairs from the latest version of HMDB database for the materials of prediction. Then we take advantage of disease semantic similarity and the improved disease Gaussian Interaction Profile (GIP) kernel similarity to obtain more reliable disease similarity and enhance the predictive performance of our proposed computational method. Simultaneously, KATZ algorithm is applied in the domains of metabolomics for the first time. Conclusions According to three kinds of cross validations and case studies of three common diseases, KATZMDA is worth serving as an impactful measuring tool for predicting the potential associations between metabolites and diseases.

Research on disease-related genes has always been a visible focus in the biological field. This study can help us to reveal the hidden mechanism of human diseases. Although many methods have been developed, the accuracy of identifying disease-genes remains challenging. We propose a computation-based method for predicting disease-genes. The investigations are inspired by the information loss model for evaluating similarities between network nodes and human protein complexes that reflect interactions between genes. Furthermore, we also combine other data such as lncRNA to construct a triple heterogeneous network and design a network propagation algorithm applied to the heterogeneous network (InLPCH). This algorithm effectively reduces the number of false positives in the biological networks when predicting disease-genes and combines the multiple propagation paths of the heterogeneous network to improve prediction accuracy. We conduct extensive experiments over disease-genes dataset. The InLPCH demonstrates high performance in comparison with six other state-of-the-art algorithms.

Essential proteins are regarded as the fundamental components of living organisms. The identification of essential proteins greatly contributes to the understanding of cellular functions and biological mechanisms. There are a variety of experimental as well as computational methods which have been used for essential protein detection. However, it is still a big challenge to further improve the precision of essential proteins prediction. In this paper, we introduce a novel essential proteins exploration method named RWEP, which adopts random walk algorithm and integrates the topological and biological properties to determine protein essentiality in protein–protein interaction (PPI) networks. In this method, first, PPIs are weighted based on topology of networks, gene expression and GO annotation data. Then each protein in a PPI network is assigned an initial score by exploiting subcellular localization and protein complexes information. Finally, we apply a random walk with restart (RWR) algorithm on the weighted PPI networks to iteratively score proteins. To demonstrate the performance of RWEP, we have carried out a series of experiments on four different yeast datasets (DIP, MIPS, Krogan and Gavin). The computational experiments confirm the efficiency of RWEP in predicting essential proteins. Compared with other state-of-the-art essential proteins identification methods, RWEP achieves a superior performance in terms of various evaluation criteria.

Circular RNAs (circRNAs) are widely expressed in highly diverged eukaryotes. Although circRNAs have been known for many years, their function remains unclear. Interaction with RNA-binding protein (RBP) to influence post-transcriptional regulation is considered to be an important pathway for circRNA function, such as acting as an oncogenic RBP sponge to inhibit cancer. In this study, we design a deep learning framework, CRPBsites, to predict the binding sites of RBPs on circRNAs. In this model, the sequences of variable-length binding sites are transformed into embedding vectors by word2vec model. Bidirectional LSTM is used to encode the embedding vectors of binding sites, and then they are fed into another LSTM decoder for decoding and classification tasks. To train and test the model, we construct four datasets that contain sequences of variable-length binding sites on circRNAs, and each set corresponds to an RBP, which is overexpressed in bladder cancer tissues. Experimental results on four datasets and comparison with other existing models show that CRPBsites has superior performance. Afterwards, we found that there were highly similar binding motifs in the four binding site datasets. Finally, we applied well-trained CRPBsites to identify the binding sites of IGF2BP1 on circCDYL, and the results proved the effectiveness of this method. In conclusion, CRPBsites is an effective prediction model for circRNA-RBP interaction site identification. We hope that CRPBsites can provide valuable guidance for experimental studies on the influence of circRNA on post-transcriptional regulation.

Metabolites have been shown to be closely related to the occurrence and development of many complex human diseases by a large number of biological experiments; investigating their correlation mechanisms is thus an important topic, which attracts many researchers. In this work, we propose a computational method named LGBMMDA, which is based on the Light Gradient Boosting Machine (LightGBM) to predict potential metabolite–disease associations. This method extracts the features from statistical measures, graph theoretical measures, and matrix factorization results, utilizing the principal component analysis (PCA) process to remove noise or redundancy. We evaluated our method compared with other used methods and demonstrated the better areas under the curve (AUCs) of LGBMMDA. Additionally, three case studies deeply confirmed that LGBMMDA has obvious superiority in predicting metabolite–disease pairs and represents a powerful bioinformatics tool.

Graphs can be used to effectively represent complex data structures. Learning these irregular data in graphs is challenging and still suffers from shallow learning. Applying deep learning on graphs has demonstrated good performance in many applications including social analysis, bioinformatics etc. Message passing graph convolution network is a powerful method which has expressive power to learn graph structures. Meanwhile, circular ribonucleic acid (circRNA) is a type of non-coding RNA which plays a critical role in human diseases. Identifying the associations between circRNAs and diseases is important for diagnosis and treatment of complex diseases. However, there are limited number of known associations between them and conducting biological experiments to identify new associations is time consuming and expensive. As a result, there is a need of building efficient and feasible computation methods to predict potential circRNA-disease associations. In this paper, we propose a novel graph convolution network framework to learn features from a graph built with multi-source similarity information to predict circRNA-disease associations. First we use multi-source information of circRNA similarity, disease and circRNA Gaussian Interaction Profile (GIP) kernel similarity to extract the features using first graph convolution. Then we predict disease associations for each circRNA with a second graph convolution. Proposed framework with five-fold cross validation on various experiments shows promising results in predicting circRNA-disease association and outperforms other existing methods.

Accumulating evidence shows that circular RNAs (circRNAs) have significant roles in human health and in the occurrence and development of diseases. Biological researchers have identified disease-related circRNAs that could be considered as potential biomarkers for clinical diagnosis, prognosis, and treatment. However, identification of circRNA-disease associations using traditional biological experiments is still expensive and time-consuming. In this study, we propose a novel method named MSFCNN for the task of circRNA-disease association prediction, involving two-layer convolutional neural networks on a feature matrix that fuses multiple similarity kernels and interaction features among circRNAs, miRNAs, and diseases. First, four circRNA similarity kernels and seven disease similarity kernels are constructed based on the biological or topological properties of circRNAs and diseases. Subsequently, the similarity kernel fusion method is used to integrate the similarity kernels into one circRNA similarity kernel and one disease similarity kernel, respectively. Then, a feature matrix for each circRNA-disease pair is constructed by integrating the fused circRNA similarity kernel and fused disease similarity kernel with interactions and features among circRNAs, miRNAs, and diseases. The features of circRNA-miRNA and disease-miRNA interactions are selected using principal component analysis. Finally, taking the constructed feature matrix as an input, we used two-layer convolutional neural networks to predict circRNA-disease association labels and mine potential novel associations. Five-fold cross validation shows that our proposed model outperforms conventional machine learning methods, including support vector machine, random forest, and multilayer perception approaches. Furthermore, case studies of predicted circRNAs for specific diseases and the top predicted circRNA-disease associations are analyzed. The results show that the MSFCNN model could be an effective tool for mining potential circRNA-disease associations.

The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.

Cumulative experimental studies have demonstrated the critical roles of microRNAs (miRNAs) in the diverse fundamental and important biological processes, and in the development of numerous complex human diseases. Thus, exploring the relationships between miRNAs and diseases is helpful with understanding the mechanisms, the detection, diagnosis, and treatment of complex diseases. As the identification of miRNA-disease associations via traditional biological experiments is time-consuming and expensive, an effective computational prediction method is appealing. In this study, we present a deep learning framework with variational graph auto-encoder for miRNA-disease association prediction (VGAE-MDA). VGAE-MDA first gets the representations of miRNAs and diseases from the heterogeneous networks constructed by miRNA-miRNA similarity, disease-disease similarity, and known miRNA-disease associations. Then, VGAE-MDA constructs two sub-networks: miRNA-based network and disease-based network. Combining the representations based on the heterogeneous network, two variational graph auto-encoders (VGAE) are deployed for calculating the miRNA-disease association scores from two sub-networks, respectively. Lastly, VGAE-MDA obtains the final predicted association score for a miRNA-disease pair by integrating the scores from these two trained networks. Unlike the previous model, the VGAE-MDA can mitigate the effect of noises from random selection of negative samples. Besides, the use of graph convolutional neural (GCN) network can naturally incorporate the node features from the graph structure while the variational autoencoder (VAE) makes use of latent variables to predict associations from the perspective of data distribution. The experimental results show that VGAE-MDA outperforms the state-of-the-art approaches in miRNA-disease association prediction. Besides, the effectiveness of our model has been further demonstrated by case studies.

Background Circular RNA (circRNA) has been extensively identified in cells and tissues, and plays crucial roles in human diseases and biological processes. circRNA could act as dynamic scaffolding molecules that modulate protein-protein interactions. The interactions between circRNA and RNA Binding Proteins (RBPs) are also deemed to an essential element underlying the functions of circRNA. Considering cost-heavy and labor-intensive aspects of these biological experimental technologies, instead, the high-throughput experimental data has enabled the large-scale prediction and analysis of circRNA-RBP interactions. Results A computational framework is constructed by employing Positive Unlabeled learning (P-U learning) to predict unknown circRNA-RBP interaction pairs with kernel model MFNN (Matrix Factorization with Neural Networks). The neural network is employed to extract the latent factors of circRNA and RBP in the interaction matrix, the P-U learning strategy is applied to alleviate the imbalanced characteristics of data samples and predict unknown interaction pairs. For this purpose, the known circRNA-RBP interaction data samples are collected from the circRNAs in cancer cell lines database (CircRic), and the circRNA-RBP interaction matrix is constructed as the input of the model. The experimental results show that kernel MFNN outperforms the other deep kernel models. Interestingly, it is found that the deeper of hidden layers in neural network framework does not mean the better in our model. Finally, the unlabeled interactions are scored using P-U learning with MFNN kernel, and the predicted interaction pairs are matched to the known interactions database. The results indicate that our method is an effective model to analyze the circRNA-RBP interactions. Conclusion For a poorly studied circRNA-RBP interactions, we design a prediction framework only based on interaction matrix by employing matrix factorization and neural network. We demonstrate that MFNN achieves higher prediction accuracy, and it is an effective method.

A variety of microbial communities are renowned as “a forgotten organ” throughout human body, which have significant impacts on the human health and disease. Identifying the associations between microbes and diseases can provide us with valuable insights for understand the complex disease pathogenesis as well as the diagnosis and therapy, prevention, prognosis drug discovery. Because the experiment-based methods need a long and sampled time series to identify the microbe-disease associations, computational methods provide a valuable insight into understanding complex diseases. However, discovering novel and effective microbial candidates for complex diseases with computational prediction models is still limited. Here, we developed a new method to predict the potential microbe-disease associations by integrating Multiple Data sources and Path-based HeteSim scores for Human Microbe-Disease Associations (MDPH_HMDA). First of all, a heterogeneous network was constructed, in which microbe similarity was measured by the microbe-microbe functional similarity and Gaussian interaction profile kernel similarity for microbes, disease similarity was measured by the symptom-based human disease similarity and Gaussian interaction profile kernel similarity for diseases. Then, normalized HeteSim measure was employed to weight the known microbe-disease pairs, and the HeteSim scores of microbe-disease-disease path and microbe-microbe-disease path were integrated to calculate the relatedness scores for potential microbe-disease associations. Additionally, MDPH_HMDA achieved a reliable prediction performance with AUCs (the area under the ROC curve) of 0.9015 in the leave-one-out cross validation, and the results showed that our method could be effective to find the potential associations between microbes and microbes. Furthermore, representative diseases including type 2 diabetes, colorectal carcinoma, asthma and inflammatory bowel disease (IBD), in which the potential microbes associated with these diseases were ranked as candidate disease-causing microbes, respectively. The reliable performance showed that our proposed method could serve as a powerful computational tool to identify more microbe-disease associations and benefit to medical scientific progress in terms of the human medical improvement.

More and more clinical observations have implied that microbes have great effects on human diseases. Understanding the relations between microbes and diseases are of profound significance for disease prevention and therapy. In this paper, we propose a predictive model based on the known microbe-disease associations to discover potential microbe-disease associations through integrating Learning Graph Representations and a modified Scoring mechanism on the Heterogeneous network (called LGRSH). Firstly, the similarity networks for microbe and disease are obtained based on the similarity of Gaussian interaction profile kernel. Then, we construct a heterogeneous network including these two similarity networks and microbe-disease associations' network. After that, the embedding algorithm Node2vec is implemented to learn representations of nodes in the heterogeneous network. Finally, according to these low-dimensional vector representations, we calculate the relevance between each microbe and disease by utilizing a modified rule-based inference method. By comparison with three other methods including LRLSHMDA, KATZHMDA and BiRWHMDA, LGRSH performs better than others. Moreover, in case studies of asthma, Chronic Obstructive Pulmonary Disease and Inflammatory Bowel Disease, there are 8, 8, and 10 out of the top-10 discovered disease-related microbes were validated respectively, demonstrating that LGRSH performs well in predicting potential microbe-disease associations.

Essential proteins are indispensable to cellular life. Identification of essential proteins plays a critical role in the survival and development of life process and understanding the function of cell machinery. The experimental methods are usually costly and time-consuming, in order to overcome these limitations, many computational methods have been proposed to discover essential proteins based on the topological features of PPI networks and other biological information. In this paper, a novel method named RSG is proposed to predict essential proteins based on RNA-Seq, subcellular localization and GO annotation datasets. First, the experiments show that the RNA-Seq data is more advantageous than traditional gene expression data in predicting essential proteins, meanwhile, the protein essentiality is closely related to the subcellular localization information and protein GO terms through data analysis. A new weighted PPI network is constructed, which integrates the GO terms information with Pearson correlation coefficient of RNA-Seq data. Then, the weighted edge clustering coefficient is developed to measure the connectivity of protein nodes. RSG determines the essentiality based on not only the subcellular localization information but also the co-expressed level and functional similarity characterized by RNA-Seq and GO annotation data. The experimental results on two species (Saccharomyces cerevisiae and Drosophila melanogaster), the performance of RSG was compared with other centrality methods, the results show that RSG has a better performance in predicting essential proteins.

Protein complexes are very important for investigating the characteristics of biological processes. Identifying protein complexes from proteinprotein interaction (PPI) networks is one of the recent research endeavors. The critical step of the seed-growth algorithms used for identifying protein complexes from PPI networks is to detect seed nodes (proteins) from which protein complexes are growing up in PPI networks. Topology potential was proposed to understand the evolution behavior and organizational principles of complex networks such as PPI networks. Furthermore, PPI networks are inherently dynamic in nature. In this study, we proposed a new seed-growing algorithm (called TP-WDPIN) for identifying protein complexes, which employs the concept of topology potential to detect significant proteins and mine protein complexes from Weighted Dynamic PPI Networks. To investigate the performance of the method, the TP-WDPIN algorithm was applied to four PPI databases and compared the obtained results to those produced by six other competing algorithms. Experimental results have demonstrated that the proposed TP-WDPIN algorithm exhibits better performance than other methods such as MCODE, MCL, CORE, CSO, ClusterONE, COACH when experimenting with four PPI databases (DIP, Krogan, MIPS, Gavin).

While deep learning models have demonstrated outstanding performance in medical image segmentation tasks, histological annotations for training deep learning models are usually challenging to obtain, due to the effort and experience required to carefully delineate tissue structures. In this study, we propose an unsupervised method, termed as tissue cluster level graph cut (TisCut), for segmenting histological images into meaningful compartments (e.g., tumor or non-tumor regions), which aims at assisting histological annotations for downstream supervised models. The TisCut consists of three modules. First, histological tissue objects are clustered based on their spatial proximity and morphological features. The Voronoi diagram is then constructed based on tissue object clustering. In the last module, morphological features computed from the Voronoi diagram are integrated into a region adjacency graph. Image partition is then performed to divide the image into meaningful compartments by using the graph cut algorithm. The TisCut has been evaluated on three histological image sets for necrosis and melanoma detections. Experiments show that the TisCut could provide a comparative performance with U-Net models, which achieves about 70% and 85% Jaccard index coefficients in partitioning brain and skin histological images, respectively. In addition, it shows the potential to be used for generating histological annotations when training masks are difficult to collect for supervised segmentation models.

How to quickly find a set of solutions with good diversity and convergence is the main goal of multi-objective optimization evolutionary algorithms (MOEAs). In this paper, a crossover operator based on uniform design and selection strategy based on decomposition is designed to help MOEAs to improve the search efficiency, and an improvement decomposition-based multi-objective evolutionary algorithm with uniform design is proposed. Firstly, a multi-objective problem is transformed into a set of single problems based on a set of direction vectors, and all single problems are optimized simultaneously. Secondly, a crossover operator based on uniform design which can search decision space along the descent (ascent) directions is designed to improve the search efficiency of the algorithm. Thirdly, in order to improve the convergence performance of the algorithm, a sub-population strategy is used to optimize each sub-problem. Moreover, a selection strategy is designed to help the crossover operators to balance between the global searching and the local searching. Comparing with some efficient state-of-the-art algorithms, e.g., NSGAII and MOEA/D, on some benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence.

Background Protein complexes are the cornerstones of many biological processes and gather them to form various types of molecular machinery that perform a vast array of biological functions. In fact, a protein may belong to multiple protein complexes. Most existing protein complex detection algorithms cannot reflect overlapping protein complexes. To solve this problem, a novel overlapping protein complexes identification algorithm is proposed. Results In this paper, a new clustering algorithm based on overlay network chain in quotient space, marked as ONCQS, was proposed to detect overlapping protein complexes in weighted PPI networks. In the quotient space, a multilevel overlay network is constructed by using the maximal complete subgraph to mine overlapping protein complexes. The GO annotation data is used to weight the PPI network. According to the compatibility relation, the overlay network chain in quotient space was calculated. The protein complexes are contained in the last level of the overlay network. The experiments were carried out on four PPI databases, and compared ONCQS with five other state-of-the-art methods in the identification of protein complexes. Conclusions We have applied ONCQS to four PPI databases DIP, Gavin, Krogan and MIPS, the results show that it is superior to other five existing algorithms MCODE, MCL, CORE, ClusterONE and COACH in detecting overlapping protein complexes.

Circular RNAs (circRNAs) are extensively expressed in cells and tissues, and play crucial roles in human diseases and biological processes. Recent studies have reported that circRNAs could function as RNA binding protein (RBP) sponges, meanwhile RBPs can also be involved in back-splicing. The interaction with RBPs is also considered an important factor for investigating the function of circRNAs. Hence, it is necessary to understand the interaction mechanisms of circRNAs and RBPs, especially in human cancers. Here, we present a novel method based on deep learning to identify cancer-specific circRNA&ndash, RBP binding sites (CSCRSites), only using the nucleotide sequences as the input. In CSCRSites, an architecture with multiple convolution layers is utilized to detect the features of the raw circRNA sequence fragments, and further identify the binding sites through a fully connected layer with the softmax output. The experimental results show that CSCRSites outperform the conventional machine learning classifiers and some representative deep learning methods on the benchmark data. In addition, the features learnt by CSCRSites are converted to sequence motifs, some of which can match to human known RNA motifs involved in human diseases, especially cancer. Therefore, as a deep learning-based tool, CSCRSites could significantly contribute to the function analysis of cancer-associated circRNAs.

Combining large-scale biological data, using computational methods to mine potential disease-gene associations is a popular strategy. At the same time, bio-inspired intelligent optimization has always been a hot research field of intelligent computing. In this study, we apply the pigeon-inspired optimization (PIO) algorithm to the identification of human disease-genes. The problem of predicting disease-genes is translated into a single-objective optimization problem. A reasonable objective function is designed to measure the association between genes and inquiring diseases in a heterogeneous network, and the corresponding probability matrix is generated. The experimental results show that the proposed method (PDG-PIO) can accurately identify disease-genes.

Circular RNA (circRNA) is a closed-loop structural non-coding RNA molecule which plays a significant role during the gene regulation processes. There are many previous studies shown that circRNAs can be regarded as the sponges of miRNAs. Thus, circRNA is also a key point for disease diagnosing, treating and inferring. However, traditional experimental approaches to verify the associations between the circRNA and disease are time-consuming and money-consuming. There are few computational models to predict potential circRNA-disease associations, which become our motivation to propose a new computational model. In this study, we propose a machine learning based computational model named Gradient Boosting Decision Tree with multiple biological data to predict circRNA-disease associations (GBDTCDA). The known circRNA-disease associations' data are downloaded from cricR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). The feature vector of each circRNA-disease association pair is composed of four parts, which are the statistics information of different biological networks, the graph theory information of different biological networks, circRNA-disease associations' network information and circRNA nucleotide sequence information, respectively. Therefore, we use those feature vectors to train the gradient boosting decision tree regression model. Then, the leave one out cross validation (LOOCV) is adopted to evaluate the performance of our computational model. As for predicting some common diseases related circRNAs, our method GBDTCDA also obtains the better results. The Area under the ROC Curve (AUC) values of Basal cell carcinoma, Non-small cell lung cancer and cervical cancer are 95.8%, 88.3% and 93.5%, respectively. For further illustrating the performance of GBDTCDA, a case study of breast cancer is also supplemented in this study. Thus, our proposed method GBDTCDA is a powerful tool to predict potential circRNA-disease associations based on experimental results and analyses.

Circular RNA (circRNA) is a special single-stranded, non-coding RNA molecule. A variety of circRNAs are widely distributed in organisms and can regulate gene expression by adsorbing miRNAs and proteins. Therefore, abnormal expression of circRNAs can reflect a variety of diseases. In this study, from a computational perspective, potential circRNA–disease relationships are predicted by a novel method named PDC-PGWNNM. A pair graph of circRNA–disease is designed using the interactions of circRNAs with miRNAs and regulatory relationship of miRNAs in diseases. After extracting a reliable negative set, the original observation set is expanded, and a weighted nuclear norm minimization model is used to predict the potential circRNA–disease associations. Experimental results show that PDC-PGWNNM has better performance than existing methods. At the same time, the rationality of the pair graph is explored. The regulatory information of miRNAs and the expression profiles of circRNAs are used to analyse the predicted disease–circRNAs. Special cases are discussed for glioma, colorectal cancer and coronary heart disease.

Protein complexes play a significant role in understanding cellular life in postgenomic era. Yet up to now, the existing protein complex detection algorithms are mostly applied to static PPI networks and their performance is not very ideal for the deficiency of low efficiency and sensitive to noisy data. In this paper, a novel algorithm named Fruit fly Optimization Clustering Algorithm (FOCA), is proposed to identify dynamic protein complexes by combining Fruit fly Optimization Algorithm (FOA) and gene expression profiles. Particularly, we first find the always active proteins by the stable interactions of the dynamic PPI network and detect protein complex cores from those always active proteins. Then, FOA is used to merge of the rest proteins in every dynamic sub-network to their corresponding protein complex cores. The experimental results on DIP dataset demonstrate that FOCA is very effective in detecting protein complexes than the state-of-the-art complex detection techniques.

Markov clustering (MCL) is a commonly used algorithm for clustering networks in bioinformatics. It shows good performance in clustering dynamic protein-protein interaction networks (DPINs). However, a limitation of MCL and its variants (e.g, regularized MCL and soft regularized MCL) is that the clustering results are mostly dependent on the parameters whose values are user-specified. In this study, we propose a new MCL method based on the firefly algorithm (FA) to identify protein complexes from DPIN. Based on three-sigma principle, we construct the DPIN and discuss an overall modeling process. In order to optimize parameters, we exploit a number of population-based optimization methods. A thorough comparison completed for different swarm optimization algorithms such as particle swarm optimization (PSO) and firefly algorithm (FA) has been carried out. The identified protein complexes on the DIP dataset show that the new algorithm outperforms the state-of-the-art approaches in terms of accuracy of protein complex identification.

As is well known, biological experiments are time-consuming and laborious, so there is absolutely no doubt that developing an effective computational model will help solve these problems. Most of computational models rely on the biological similarity and network-based methods that cannot consider the topological structures of metabolite-disease association graphs. We proposed a novel method based on graph convolutional networks to infer potential metabolite-disease association, named MDAGCN. We first calculated three kinds of metabolite similarities and three kinds of disease similarities. The final similarity of disease and metabolite will be obtained by integrating three kinds' similarities of each and filtering out the noise similarity values. Then metabolite similarity network, disease similarity network and known metabolite-disease association network were used to construct a heterogenous network. Finally, heterogeneous network with rich information is fed into the graph convolutional networks to obtain new features of a node through aggregation of node information so as to infer the potential associations between metabolites and diseases. Experimental results show that MDAGCN achieves more reliable results in cross validation and case studies when compared with other existing methods.

Metabolite, also known as intermediate metabolite, refers to substances produced or consumed in the metabolic processes. There are growing evidences that metabolites play an important role in the study of diseases. Due to the traditional experiments, it is time-consuming and luxurious to find the associations between metabolite and disease, we proposed a computational method, called RCNMF, to predict metabolite-disease associations. Firstly, we calculate the disease semantic similarity and the molecular fingerprint similarity of metabolite. The molecular fingerprint similarity of metabolite makes full use of the molecular structure internal information of metabolites. Then, we modify the original metabolite-disease associations matrix to replace some values of 0 with numbers between 0 and 1. Finally, we use the non-negative matrix factorization algorithm to predict potential metabolite-disease associations. We adopt the cross-validation mechanism to verify the performance of our proposed method. The AUC values of based the Leave-one-out cross validation measurement and the Five-fold cross validation measurement reach 0.9566 and 0.9430, respectively. What is more, case studies of common diseases also illustrate the effectiveness of our method. Thus, the superior experimental results show that our method can effectively predict the potential disease-metabolites associations.

Circular RNAs (circRNAs), a large group of small endogenous noncoding RNA molecules, have been proved to modulate protein-coding genes in the human genome. In recent years, many experimental studies have demonstrated that circRNAs are dysregulated in a number of diseases, and they can serve as biomarkers for disease diagnosis and prognosis. However, it is expensive and time-consuming to identify circRNA-disease associations by biological experiments and few computational models have been proposed for novel circRNA-disease association prediction. In this study, we develop a computational model based on the random walk and the logistic regression (RWLR) to predict circRNA-disease associations. Firstly, a circRNA-circRNA similarity network is constructed by calculating their functional similarity of circRNA based on circRNA-related gene ontology. Then, a random walk with restart is implemented on the circRNA similarity network, and the features of each pair of circRNA-disease are extracted based on the results of the random walk and the circRNA-disease association matrix. Finally, a logistic regression model is used to predict novel circRNA-disease associations. Leave one out validation (LOOCV), five-fold cross validation (5CV) and ten-fold cross validation (10CV) are adopted to evaluate the prediction performance of RWLR, by comparing with the latest two methods PWCDA and DWNN-RLS. The experiment results show that our RWLR has higher AUC values of LOOCV, 5CV and 10CV than the other two latest methods, which demonstrates that RWLR has a better performance than other computational methods. What's more, case studies also illustrate the reliability and effectiveness of RWLR for circRNA-disease association prediction.

With the advances in high-throughput technologies, millions of somatic mutations have been reported in the past decade. Identifying driver genes with oncogenic mutations from these data is a critical and challenging problem. Many computational methods have been proposed to predict driver genes. Among them, machine learning-based methods usually train a classifier with representations that concatenate various types of features extracted from different kinds of data. Although successful, simply concatenating different types of features may not be the best way to fuse these data. We notice that a few types of data characterize the similarities of genes, to better integrate them with other data and improve the accuracy of driver gene prediction, in this study, a deep learning-based method (deepDriver) is proposed by performing convolution on mutation-based features of genes and their neighbors in the similarity networks. The method allows the convolutional neural network to learn information within mutation data and similarity networks simultaneously, which enhances the prediction of driver genes. deepDriver achieves AUC scores of 0.984 and 0.976 on breast cancer and colorectal cancer, which are superior to the competing algorithms. Further evaluations of the top 10 predictions also demonstrate that deepDriver is valuable for predicting new driver genes.

The circular RNAs (circRNAs) have significant effects on a variety of biological processes, the dysfunction of which is closely related to the emergence and development of diseases. Therefore, identification of circRNA-disease associations will contribute to analysing the pathogenesis of diseases. Here, we present a computational model called BRWSP to predict circRNA-disease associations, which searches paths on a multiple heterogeneous network based on biased random walk. Firstly, BRWSP constructs a multiple heterogeneous network by using circRNAs, diseases, and genes. Then, the biased random walk algorithm runs on the multiple heterogeneous network to search paths between circRNAs and diseases. Finally, the performance of BRWSP is significantly better than the state-of-the-art algorithms. Furthermore, BRWSP further contributes to the discovery of novel circRNA-disease associations.

CircRNAs have particular biological structure and have proven to play important roles in diseases. It is time-consuming and costly to identify circRNA-disease associations by biological experiments. Therefore, it is appealing to develop computational methods for predicting circRNA-disease associations. In this study, we propose a new computational path weighted method for predicting circRNA-disease associations. Firstly, we calculate the functional similarity scores of diseases based on disease-related gene annotations and the semantic similarity scores of circRNAs based on circRNA-related gene ontology, respectively. To address missing similarity scores of diseases and circRNAs, we calculate the Gaussian Interaction Profile (GIP) kernel similarity scores for diseases and circRNAs, respectively, based on the circRNA-disease associations downloaded from circR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). Then, we integrate disease functional similarity scores and circRNA semantic similarity scores with their related GIP kernel similarity scores to construct a heterogeneous network made up of three sub-networks: disease similarity network, circRNA similarity network and circRNA-disease association network. Finally, we compute an association score for each circRNA-disease pair based on paths connecting them in the heterogeneous network to determine whether this circRNA-disease pair is associated. We adopt leave one out cross validation (LOOCV) and five-fold cross validations to evaluate the performance of our proposed method. In addition, three common diseases, Breast Cancer, Gastric Cancer and Colorectal Cancer, are used for case studies. Experimental results illustrate the reliability and usefulness of our computational method in terms of different validation measures, which indicates PWCDA can effectively predict potential circRNA-disease associations.

In recent years, swarm intelligence optimization algorithm is a research hotspot in the area of computational intelligence and artificial intelligence. And its application has already penetrated into many fields. In many kinds of intelligent algorithms, the firefly algorithm (FA) is a relatively novel algorithm and shows excellent performance. In this chapter, we introduced FA algorithm and its applications. First, we described the basic concepts of the FA algorithm. Then, we improved the FA by combining the characteristics of specific problems to solve various problems such as numerical optimization, clustering analysis and protein complexes discovering on protein-protein interaction network. We gave the detailed implement steps and the comparing results to show the feasibility of FA and extensive of its applications.

Proteins complexes accomplish biological functions such as transcription of DNA and translation of mRNA. Detecting protein complexes correctly and efficiently is becoming a challenging task. This paper presents a novel algorithm, core-attachment based on ant colony optimization (CA-ACO), which detects complexes in three stages. Firstly, initialize the similarity matrix. Secondly, complexes are predicted by clustering in the dynamic PPI networks. In the step, the clustering coefficient of every node is also computed. A node whose clustering coefficient is greater than the threshold is added to the core protein set. Then we mark every neighbor node of core proteins with unique core label during picking and dropping. Thirdly, filtering processes are carried out to obtain the final complex set. Experimental results show that CA-ACO algorithm had great superiority in precision, recall and f-measure compared with the state-of-the-art methods such as ClusterONE, DPClus, MCODE and so on.

Recently there are a large number of clustering methods applied to discover modules in protein-protein interaction (PPI) networks. However, due to the small-world and scale-free properties of PPI networks, most of them do not work well. This paper proposed a novel artificial bee colony (ABC) clustering model based on propagating mechanism. The ABC model based on propagating mechanism consisted of three different functions of bees which were named after queen, drone, and brood. The queen was regarded as a cluster center, and the drones stood for the sorted nodes according to the descending order of the aggregation coefficient of edge connecting these nodes with the queen node. The queen mated with the drones in order to cluster PPI data. In the end, the brood which is well-developed would be regarded as the new queen and went on a new mating-flight until all the nodes had been visited. This model could automatically obtain the cluster number during the clustering procedure, and the time complexity was greatly reduced. The simulation experiments on MIPS dataset showed that it performed well in terms of several criteria such as precision, recall and running time.

Detecting protein complexes is crucial to understand principles of cellular organization. Plenty evidences have indicated that sub-graphs with high density in protein-protein interaction (PPI) network, especially dynamic PPI network (DPIN), usually correspond to protein complexes. As a well-known density-based clustering algorithm, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) has been used in many areas due to its simplicity and the ability to detect clusters of different sizes and shapes. However, one of its limitations is that the performance of DBSCAN depends on two specified parameters $\varepsilon$ and MinPts, where $\varepsilon$ represents the maximum radius of a neighborhood from an observing point while MinPts means the minimum number of data points contained in such a neighborhood. In this article, we develop a new method named as P-DBSCAN to detect protein complexes in DPIN by using Pigeon-Inspired Optimization (PIO) Algorithm to optimize the parameters $\varepsilon$ and MinPts in DBSCAN. The experiments on DIP and MIPS datasets show that P-DBSCAN outperforms the state-of-the-art methods for protein complex detection in terms of several criteria such as precision, recall and f-measure.

Epistasis has been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for epistasis detection, genome-wide association study remains a challenging task: it makes the search space excessively huge while solution quality is excessively demanded. In this study, we introduce an ant colony optimization based algorithm, AntMiner, by incorporating heuristic information into ant-decision rules. The heuristic information is used to direct ants in the search process for improving computational efficiency and solution accuracy. During iterations, chi-squared test is conducted to measure the association between an interaction and the phenotype. At the completion of the iteration process, statistically significant epistatic interactions are ordered and then screened by a post-procedure. Experiments of AntMiner and its comparison with existing algorithms epiMODE, TEAM and AntEpiSeeker are performed on both simulation data sets and real age-related macular degeneration data set, under the criteria of detection power and sensitivity. Results demonstrate that AntMiner is promising for epistasis detection. In terms of detection power, AntMiner performs best among all the other algorithms on all cases regardless of epistasis models and single nucleotide polymorphism size; compared with AntEpiSeeker, AntMiner can obtain better detection power but with less ants and iterations. In terms of sensitivity, AntMiner is better than AntEpiSeeker in detecting epistasis models displaying marginal effects but it has moderate sensitivity on epistasis models displaying no marginal effects. The study may provide clues on heuristics for further epistasis detection. The software package is available online at https://sourceforge.net/projects/antminer/files/ .

It is well known that essential proteins play an extremely important role in controlling cellular activities in living organisms. Identifying essential proteins from protein protein interaction (PPI) networks is conducive to the understanding of cellular functions and molecular mechanisms. Hitherto, many essential proteins detection methods have been proposed. Nevertheless, those existing identification methods are not satisfactory because of low efficiency and low sensitivity to noisy data. This paper presents a novel computational approach based on artificial fish swarm optimization for essential proteins prediction in PPI networks (called AFSO_EP). In AFSO_EP, first, a part of known essential proteins are randomly chosen as artificial fishes of priori knowledge. Then, detecting essential proteins by imitating four principal biological behaviors of artificial fishes when searching for food or companions, including foraging behavior, following behavior, swarming behavior, and random behavior, in which process, the network topology, gene expression, gene ontology (GO) annotation, and subcellular localization information are utilized. To evaluate the performance of AFSO_EP, we conduct experiments on two species (Saccharomyces cerevisiae and Drosophila melanogaster), the experimental results show that our method AFSO_EP achieves a better performance for identifying essential proteins in comparison with several other well-known identification methods, which confirms the effectiveness of AFSO_EP.

Protein essentiality is fundamental to comprehend the function and evolution of genes. The prediction of protein essentiality is pivotal in identifying disease genes and potential drug targets. Since the experimental methods need many investments in time and funds, it is of great value to predict protein essentiality with high accuracy using computational methods. In this study, we present a novel feature selection named Elite Search mechanism-based Flower Pollination Algorithm (ESFPA) to determine protein essentiality. Unlike other protein essentiality prediction methods, ESFPA uses an improved swarm intelligence&ndash, based algorithm for feature selection and selects optimal features for protein essentiality prediction. The first step is to collect numerous features with the highly predictive characteristics of essentiality. The second step is to develop a feature selection strategy based on a swarm intelligence algorithm to obtain the optimal feature subset. Furthermore, an elite search mechanism is adopted to further improve the quality of feature subset. Subsequently a hybrid classifier is applied to evaluate the essentiality for each protein. Finally, the experimental results show that our method is competitive to some well-known feature selection methods. The proposed method aims to provide a new perspective for protein essentiality determination.

This paper proposed a novel method using Bacteria Foraging Optimization(BFO) algorithm to avoid the influence of cluster number on experimental result of clustering PPI networks. The initial position that the bacterium located in was considered to be the cluster center and the positions that the bacterium moved were regarded as the adjacent nodes of cluster center. The algorithm classified the nodes selected in the chemo tactic operation into cluster when executing the reproduction and elimination-dispersal operations. The procedure kept on creating new clusters until all the nodes were grouped into the clusters. The simulation result showed that the algorithm not only effectively improved the accuracy of cluster result, but also automatically determined the cluster number.

In this paper, an artificial bee colony (ABC) algorithm for the multiple sequence alignment (MSA) problem has been proposed. The ABC algorithm is a novel optimization approach inspired by a particular intelligent behaviour of honey bee swarms. Taken the discreteness of the MSA problem into consideration, a new method of ABC algorithm for determining a food source in the neighbourhood is introduced. The performance of our ABC approach is compared with other commonly used algorithms for MSA. Computational results demonstrate the superiority of the new ABC algorithm over genetic algorithm (GA) and particle swarm optimization (PSO) for many sequences with different length and identity. The new approach is more robust and obtains better mathematical and biological quality.