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The tracking of densely packed plant cells across microscopy image sequences is very challenging, because their appearance change greatly over time. A local graph matching algorithm was proposed to track such cells by exploiting the tight spatial topology of neighboring cells, and then an iterative searching strategy was used to grow the correspondence from a seed cell pair. Thus, the performance of the existing tracking approach heavily relies on the robustness of finding seed cell pair. However, the existing local graph matching algorithm cannot guarantee the correctness of the seed cell pair, especially in unregistered image sequences or image sequences with large time intervals. In this paper, we propose a DeepSeed local graph matching model to find seed cell pair robustly, by combining local graph matching and CNN-based similarity learning, which uses cells' spatial-temporal contextual information and cell pairs' similarity information. The CNN-based similarity learning is designed to learn cells' deep feature and measure cell pairs' similarity. Compared with the existing plant cell matching methods, the experimental results show that the DeepSeed local graph matching method can track most cells in unregistered image sequences. Moreover, the DeepSeed tracking algorithm can accurately track cells across image sequences with large time intervals.

The aim of this study is to develop a computer-aided diagnosis system with a deep-learning approach for distinguishing "Mild Cognitive Impairment (MCI) due to Alzheimer's Disease (AD)" patients among a list of MCI patients. In this system we are using the power of longitudinal data extracted from magnetic resonance (MR). For this work, a total of 294 MCI patients were selected from the ADNI database. Among them, 125 patients developed AD during their follow-up and the rest remained stable. The proposed computer-aided diagnosis system (CAD) attempts to identify brain regions that are significant for the prediction of developing AD. The longitudinal data were constructed using a 3D Jacobian-based method aiming to track the brain differences between two consecutive follow-ups. The proposed CAD system distinguishes MCI patients who developed AD from those who remained stable with an accuracy of 87.2 percent. Moreover, it does not depend on data acquired by invasive methods or cognitive tests. This work demonstrates that the use of data in different time periods contains information that is beneficial for prognosis prediction purposes that outperform similar methods and are slightly inferior only to those systems that use invasive methods or neuropsychological tests.

In recent years, lncRNAs (long non-coding RNAs) have been proved to be closely related to many diseases that are seriously harmful to human health. Although researches on clarifying the relationships between lncRNAs and diseases are developing rapidly, associations between the lncRNAs and diseases are still remaining largely unknown. In this manuscript, a novel Local Random Walk based prediction model called LRWHLDA is proposed for inferring potential associations between human lncRNAs and diseases. In LRWHLDA, a new heterogeneous network is established first, which allows that LRWHLDA can be implemented in the case of lacking known lncRNA-disease associations. And then, an improved local random walk method is designed for prediction of novel lncRNA-disease associations, which can help LRWHLDA achieve high prediction accuracy but with low time complexity. Finally, in order to evaluate the prediction performance of LRWHLDA, different frameworks such as LOOCV, 2-folds CV, and 5-folds CV have been implemented, simulation results indicate that LRWHLDA can achieve reliable AUCs of 0.8037, 0.8354, and 0.8556 under the frameworks of 2-fold CV, 5-fold CV, and LOOCV, respectively. Hence, it is easy to know that LRWHLDA contains the potential to be a representative of emerging methods in the field of research on potential lncRNA-disease associations prediction.

With the advancement in information technology, datasets with an enormous amount of data are available. The classification task on these datasets is more time- and memory-consuming as the number of data increases. The support vector machine (SVM), which is arguably the most popular classification technique, has disappointing performance in dealing with large datasets due to its constrained optimization problem. To deal with this challenge, the variant SVM (VSVM) has been utilized which has the fraction $({1}/{2})b^{2}$ in its primal objective function, where $b$ is the bias of the desired hyperplane. The VSVM has been solved with different optimization techniques in more time- and memory-efficient fashion. However, there is no guarantee that its optimal solution is the same as the standard SVM. In this paper, we introduce the generalized VSVM (GVSVM) which has the fraction $({1}/{2t})b^{2}$ in its primal objective function, for a fixed positive scalar $t$ . Further, we present the thorough theoretical insights that indicate the optimal solution of the GVSVM tends to the optimal solution of the standard SVM as $t \rightarrow \infty $ . One vital corollary is to derive a closed-form formula to obtain the bias term in the standard SVM. Such a formula obviates the need of approximating it, which is the modus operandi to date. An efficient neural network is then proposed to solve the GVSVM dual problem, which is asymptotically stable in the sense of Lyapunov and converges globally exponentially to the exact solution of the GVSVM. The proposed neural network has less complexity in architecture and needs fewer computations in each iteration in comparison to the existing neural solutions. Experiments confirm the efficacy of the proposed recurrent neural network and the proximity of the GVSVM and the standard SVM solutions with more significant values of $t$ .

The diversity of base classifiers and integration of multiple classifiers are two key issues in the field of ensemble learning. This paper puts forward a hybrid ensemble algorithm combining AdaBoost and genetic algorithm(GA) for cancer classification with gene expression data. The decision group is designed to increase the diversity of base classifier pool, and the GA is used to assign weight to each base classifier, thus to improve the classification performance by avoiding local extrema. The decision groups composed by using base classifiers, including K-nearest neighbor (KNN), Naïve Bayes (NB), and Decision Tree (C4.5). Experimental results show that the proposed algorithm is superior to those existing ensemble learning methods, such as Bagging, Random Forest (RF), Rotation Forest (RoF), AdaBoost, AdaBoost-BPNN, AdaBoost-SVM, and AdaBoost-RF, especially it has better performance on small samples and unbalanced gene expression data processing.

Signaling pathways describe a group of molecules in a cell that collaborate to control one or more cell functions, such as cell division or cell death. The pathways communicate by sending signals between molecules, and this process is repeated until the terminal molecule is activated and the cell function is executed. Signaling pathways are often represented as directed graphs, which does not provide enough information when modeling cell functions and reactions. Recently, directed hypergraphs have been proposed to more accurately represent reactions such as protein activation and interaction. To further improve the representation of signaling pathways, time dependency must be considered to improve the representation of cell signaling at any given time. In this paper, the importance of time dependency in modeling signaling pathways is presented. An algorithm that finds the shortest a priori path using time-dependent hypergraphs to more robustly model signaling pathways is adopted. The shortest time-dependent hyperpaths representing signaling pathways are an improvement to the recent adoption of hypergraphs representing these pathways. The results display the improved representation of signaling pathways and motivate the adoption of time-dependent signaling hypergraphs.

Pneumothorax is a common pulmonary disease that can lead to dyspnea and can be life-threatening. X-ray examination is the main means to diagnose this disease. Computer-aided diagnosis of pneumothorax on chest X-ray, as a prerequisite for a timely cure, has been widely studied, but it is still not satisfactory to achieve highly accurate results. In this paper, an image classification algorithm based on the deep convolutional neural network (DCNN) is proposed for high-resolution medical image analysis of pneumothorax X-rays, which features a Network In Network (NIN) for cleaning the data, random histogram equalization data augmentation processing, and a DCNN. The experimental results indicate that the proposed method can effectively increase the correct diagnosis rate of pneumothorax, and the Area under Curve (AUC) of the test verified in the experiment is 0.9844 on ZJU-2 test data and 0.9906 on the ChestX-ray14, respectively. In addition, a large number of atmospheric pleura samples are visualized and analyzed based on the experimental results and in-depth learning characteristics of the algorithm. The analysis results verify the validity of feature extraction for the network. Combined with the results of these two aspects, the proposed X-ray image processing algorithm can effectively improve the classification accuracy of pneumothorax photographs.

Background With the rapid development of accurate sequencing and assembly technologies, an increasing number of high-quality chromosome-level and haplotype-resolved assemblies of genomic sequences have been derived, from which there will be great opportunities for computational pangenomics. Although genome graphs are among the most useful models for pangenome representation, their structural complexity makes it difficult to present genome information intuitively, such as the linear reference genome. Thus, efficiently and accurately analyzing the genome graph spatial structure and coordinating the information remains a substantial challenge. Results We developed a new method, a colored superbubble (cSupB), that can overcome the complexity of graphs and organize a set of species- or population-specific haplotype sequences of interest. Based on this model, we propose a tri-tuple coordinate system that combines an offset value, topological structure and sample information. Additionally, cSupB provides a novel method that utilizes complete topological information and efficiently detects small indels (cSupB can adapt to the complex cycle structure. Conclusions Although the solution is made suitable for increasingly complex genome graphs by relaxing the constraint, the directed acyclic graph, the motif cSupB and the cSupB method can be extended to any colored directed acyclic graph. We anticipate that our method will facilitate the analysis of individual haplotype variants and population genomic diversity. We have developed a C + + program for implementing our method that is available at https://github.com/eggleader/cSupB.

Protein is an essential macro-nutrient for perceiving a wide range of biochemical activities and biological regulations in living cells. In this work, we have presented a novel multi-modal approach, named MultiPredGO , for predicting protein functions by utilizing two different kinds of information, namely protein sequence and the protein secondary structure. Here, our contributions are threefold; firstly, along with the protein sequence, we learn the feature representation from the protein structure. Secondly, we develop two different deep learning models after considering the characteristics of the underlying data patterns of the protein sequence and protein 3D structures. Finally, along with these two modalities, we have also utilized protein interaction information for expediting the efficiency of the proposed model in predicting the protein functions. For extracting features from different modalities, we have utilized various variations of the convolutional neural network. As the protein function classes are dependent on each other, we have used a neuro-symbolic hierarchical classification model, which resembles the structure of Gene Ontology (GO), for effectively predicting the dependent protein functions. Finally, to validate the goodness of our proposed method ( MultiPredGO ), we have compared our results with various uni-modal along with two well-known multi-modal protein function prediction approaches, namely, INGA and DeepGO. Results show that the overall performance of the proposed approach in terms of accuracy, F-measure, precision, and recall metrics are better than those by the state-of-the-art methods. MultiPredGO attains an average 13.05% and 30.87% improvements over the best existing comparing approach (DeepGO) for cellular component and molecular functions, respectively.

This paper deals with the problem of dissipativity-based filtering for switched genetic regulatory networks (GRNs) with stochastic perturbation and time-varying delays. By choosing an appropriate piecewise Lyapunov function and using the average dwell time method, we propose a new set of sufficient conditions in terms of Linear matrix inequalities (LMIs) for the existence of dissipative filter, which ensures that the resulting filtering error system is mean-square exponentially stable with dissipativity performance. The filter gains are provided by solving feasible solutions to a certain set of LMIs. A simulation example is given to demonstrate the effectiveness of the desired dissipativity-based filter design approach.

The metabolic wiring of patient cells is altered drastically in many diseases, including cancer. Understanding the nature of such changes may pave the way for new therapeutic opportunities as well as the development of personalized treatment strategies for patients. In this paper, we propose an algorithm called Metabolitics, which allows systems-level analysis of changes in the biochemical network of cells in disease states. It enables the study of a disease at both reaction- and pathway-level granularities for a detailed and summarized view of disease etiology. Metabolitics employs flux variability analysis with a dynamically built objective function based on biofluid metabolomics measurements in a personalized manner. Moreover, Metabolitics builds supervised classification models to discriminate between patients and healthy subjects based on the computed metabolic network changes. The use of Metabolitics is demonstrated for three distinct diseases, namely, breast cancer, Crohn's disease, and colorectal cancer. Our results show that the constructed supervised learning models successfully differentiate patients from healthy individuals by an average f1-score of 88 percent. Besides, in addition to the confirmation of previously reported breast cancer-associated pathways, we discovered that Biotin Metabolism along with Arginine and Proline Metabolism is subject to a significant increase in flux capacity, which have not been reported before.

Visualization of biological mechanisms by means of pathway graphs is necessary to better understand the often complex underlying system. Manual layout of such pathways or maps of knowledge is a difficult and time consuming process. Node duplication is a technique that makes layouts with improved readability possible by reducing edge crossings and shortening edge lengths in drawn diagrams. In this article, we propose an approach using Machine Learning (ML) to facilitate parts of this task by training a Support Vector Machine (SVM) with actions taken during manual biocuration. Our training input is a series of incremental snapshots of a diagram describing mechanisms of a disease, progressively curated by a human expert employing node duplication in the process. As a test of the trained SVM models, they are applied to a single large instance and 25 medium-sized instances of hand-curated biological pathways. Finally, in a user validation study, we compare the model predictions to the outcome of a node duplication questionnaire answered by users of biological pathways with varying experience. We successfully predicted nodes for duplication and emulated human choices, demonstrating that our approach can effectively learn human-like node duplication preferences to support curation of pathway diagrams in various contexts.

Retinal vessel segmentation is a critical procedure towards the accurate visualization, diagnosis, early treatment, and surgery planning of ocular diseases. Recent deep learning-based approaches have achieved impressive performance in retinal vessel segmentation. However, they usually apply global image pre-processing and take the whole retinal images as input during network training, which have two drawbacks for accurate retinal vessel segmentation. First, these methods lack the utilization of the local patch information. Second, they overlook the geometric constraint that retina only occurs in a specific area within the whole image or the extracted patch. As a consequence, these global-based methods suffer in handling details, such as recognizing the small thin vessels, discriminating the optic disk, etc. To address these drawbacks, this study proposes a G lobal and L ocal enhanced residual U -n E t ( GLUE ) for accurate retinal vessel segmentation, which benefits from both the globally and locally enhanced information inside the retinal region. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed method, which consistently improves the segmentation accuracy over a conventional U-Net and achieves competitive performance compared to the state-of-the-art.

High-risk prediction of cardiovascular disease is of great significance and impendency in medical fields with the increasing phenomenon of sub-health these years. Most existing pathological methods for the prognosis prediction are either costly or prone to misjudgement. Therefore, plenty of automated models based on machine learning have been proposed to predict the onset of cardiovascular disease with the premorbid information of patients extracted from their historical Electronic Health Records (EHRs). However, it is a tough job to select proper features from longitudinal and heterogeneous EHRs, and also a great challenge to obtain accurate and robust representations for patients. In this paper, we propose an entirely end-to-end model called DeepRisk based on attention mechanism and deep neural networks, which can not only learn high-quality features automatically from EHRs, but also efficiently integrate heterogeneous and time-ordered medical data, and finally predict patients' risk of cardiovascular diseases. Experiments are carried out on a real medical dataset and results show that DeepRisk can significantly improve the high-risk prediction accuracy for cardiovascular disease compared with state-of-the-art approaches.

RNA molecules play a significant role in cell function especially including pseudoknots. In past decades, several methods have been developed to predict RNA secondary structure with pseudoknots and the most popular one uses minimum free energy. It is a nondeterministic polynomial-time hard (NP-hard) problem. We have proposed an approach based on a metaheuristic algorithm named Chemical Reaction Optimization (CRO) to solve the RNA pseudoknotted structure prediction problem. The reaction operators of CRO algorithm have been redesigned and used on the generated population to find the structure with the minimum free energy. Besides, we have developed an additional operator called Repair operator which has a great influence on our algorithm in increasing accuracy. It helps to increase the true positive base pairs while decreasing the false positive and false negative base pairs. Four energy models have been applied to calculate the energy. To evaluate the performance, we have used four datasets containing RNA pseudoknotted sequences taken from the RNA STRAND and Pseudobase++ database. We have compared the proposed approach with some existing algorithms and shown that our CRO based model is a better prediction method in terms of accuracy and speed.

With the development of high-throughput sequencing technology, biological sequence data reflecting life information becomes increasingly accessible. Particularly on the background of the COVID-19 pandemic, biological sequence data play an important role in detecting diseases, analyzing the mechanism and discovering specific drugs. In recent years, pretraining models that have emerged in natural language processing have attracted widespread attention in many research fields not only to decrease training cost but also to improve performance on downstream tasks. Pretraining models are used for embedding biological sequence and extracting feature from large biological sequence corpus to comprehensively understand the biological sequence data. In this survey, we provide a broad review on pretraining models for biological sequence data. Moreover, we first introduce biological sequences and corresponding datasets, including brief description and accessible link. Subsequently, we systematically summarize popular pretraining models for biological sequences based on four categories: CNN, word2vec, LSTM and Transformer. Then, we present some applications with proposed pretraining models on downstream tasks to explain the role of pretraining models. Next, we provide a novel pretraining scheme for protein sequences and a multitask benchmark for protein pretraining models. Finally, we discuss the challenges and future directions in pretraining models for biological sequences.

Background Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins. Results In this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression. Conclusions We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.

Improving adverse drug event (ADE) prediction is highly critical in pharmacovigilance research. We propose a novel information component guided pharmacological network model (IC-PNM) to predict drug-ADE signals. This new method combines the pharmacological network model and information component, a Bayes statistics method. We use 33,947 drug-ADE pairs from the FDA Adverse Event Reporting System (FAERS) 2010 data as the training data, and the new 21,065 drug-ADE pairs from FAERS 2011-2015 as the validations samples. The IC-PNM data analysis suggests that both large and small sample size drug-ADE pairs are needed in training the predictive model for its prediction performance to reach an area under the receiver operating characteristic curve $(\text{AUROC})= 0.82$ ( AUROC ) = 0 . 82 . On the other hand, the IC-PNM prediction performance improved to $\text{AUROC}= 0.91$ AUROC = 0 . 91 if we removed the small sample size drug-ADE pairs from the prediction model during validation.

Structural and behavioural properties of models are very important in development of complex systems and applications. In this paper, we investigate such properties for some classes of SN P systems. First, a class of SN P systems associated to a set of routing problems are investigated through their matrix representation. This allows to make certain connections amongst some of these problems. Secondly, the behavioural properties of these SN P systems are formally verified through a natural and direct mapping of these models into kP systems which are equipped with adequate formal verification methods and tools. Some examples are used to prove the effectiveness of the verification approach.

A feedback arc set of a directed graph G is a subset of its arcs containing at least one arc of every cycle in G . Finding a feedback arc set of minimum cardinality is an NP-hard problem called the minimum feedback arc set problem . Numerically, the minimum set cover formulation of the minimum feedback arc set problem is appropriate as long as all simple cycles in G can be enumerated. Unfortunately, even those sparse graphs that are important for practical applications often have Ω (2 n ) simple cycles. Here we address precisely such situations: An exact method is proposed for sparse graphs that enumerates simple cycles in a lazy fashion and iteratively extends an incomplete cycle matrix. In all cases encountered so far, only a tractable number of cycles has to be enumerated until a minimum feedback arc set is found. The practical limits of the new method are evaluated on a test set containing computationally challenging sparse graphs, relevant for industrial applications. The 4,468 test graphs are of varying size and density and suitable for testing the scalability of exact algorithms over a wide range.

This paper explores the applications of support vector machines (SVM) technique and panel data econometric approach in innovation performance. We proposed Hybrid Fuzzy Logic with SVM based prediction analysis model to predict Innovation Performance of 3C Industry and then we construct the top management innovation awareness by text mining analysis, which differs from traditional methods, and then discusses the mediation function of financial flexibility in the relationship between innovation awareness and enterprise performance. Then, we use the financial econometric method panel regression and apply the SVM, a statistical technique that has gained special popularity in the field of AI to test the relation between innovation performance and innovation awareness of top management in 3C industry. The findings show that there exists a significant position relationship between innovation awareness and innovation performance, and that the mediation function of financial flexibility does work. With the SVM approach, the innovation performance can be predicted well by top management innovation awareness.

Recent year development in each field and area progresses rapidly. For one particular product or service, people have multiple alternative. However, to choose best alternative is most difficult task that most of person facing. VIKOR method is most used approach for choosing best alternative based on different criteria but legging in precision. In this paper, we propose modifier VIKOR with Fuzzy logic integration approach that deal with multiple criteria while making decision as well as integratestriangular fuzzy numbers (TFN) for computing criteria values and weight importance in the processing selecting best alternative. Firstly, positive best points and negative best points were demarcated in a triangular fuzzy number evaluation matrix. Secondly, we analyze the requirements and properties of the VIKOR-based triangular fuzzy number operation rule. Thirdly, an extension of VIKOR method was proposed for comprehensive evaluation of schemes to reduce the information loss arising from defuzzification of triangular fuzzy numbers; Finally, an example demonstrate the efficiency and viability of the method. In conclusion, the proposed method solves the difficulties that subtractive computation between two triple numbers violate the arithmetic regulation, and propose a reasonable procedure according to decision making problems with triple numbers.

The element extraction from network security condition is the foundation security awareness. Its excellence directly disturbsentire security system performance. In this paper we introduce fuzzy logic based rough set theory for extracting security conditional factors. The traditional extraction method of network security situation elements relies on a lot of prior knowledge. With the purpose of solving this issue, in this paper we proposed fuzzy rough set theory based featurerank matrix of neighborhood rough set. Additionally, we propose reduction based parallel algorithm that uses the concept of conditional entropy in order to constructs the feature rank matrix as well as, constructs the core attribute by using reduction rules, takes the threshold of standard deviation as the threshold, and redefines the multi threshold neighborhood of mixed data. The attack type recognition training is carried out on lib SVM, filtered classifier, j48 and random tree classifiers respectively. The results demonstrate that the proposed reduction based parallel algorithm can increase the accuracy of classification, shorten the modeling time, and show increased recall rate and decreased false alarm rate.

Antioxidant proteins perform significant functions in disease control and delaying aging which can prevent free radicals from damaging organisms. Accurate identification of antioxidant proteins has important implications for the development of new drugs and the treatment of related diseases, as they play a critical role in the control or prevention of cancer and aging-related conditions. Since experimental identification techniques are time-consuming and expensive, many computational methods have been proposed to identify antioxidant proteins. Although the accuracy of these methods is acceptable, there are still some challenges. In this study, we developed a computational model called ANPrAod to identify antioxidant proteins based on a support vector machine. In order to eliminate potential redundant features and improve prediction accuracy, 673 amino acid reduction alphabets were calculated by us to find the optimal feature representation scheme. The final model could produce an overall accuracy of 87.53% with the ROC of 0.7266 in five-fold cross-validation, which was better than the existing methods. The results of the independent dataset also demonstrated the excellent robustness and reliability of ANPrAod, which could be a promising tool for antioxidant protein identification and contribute to hypothesis-driven experimental design.

Background Drug–target interaction (DTI) plays a vital role in drug discovery. Identifying drug–target interactions related to wet-lab experiments are costly, laborious, and time-consuming. Therefore, computational methods to predict drug–target interactions are an essential task in the drug discovery process. Meanwhile, computational methods can reduce search space by proposing potential drugs already validated on wet-lab experiments. Recently, deep learning-based methods in drug-target interaction prediction have gotten more attention. Traditionally, DTI prediction methods' performance heavily depends on additional information, such as protein sequence and molecular structure of the drug, as well as deep supervised learning. Results This paper proposes a method based on deep unsupervised learning for drug-target interaction prediction called AutoDTI++. The proposed method includes three steps. The first step is to pre-process the interaction matrix. Since the interaction matrix is sparse, we solved the sparsity of the interaction matrix with drug fingerprints. Then, in the second step, the AutoDTI approach is introduced. In the third step, we post-preprocess the output of the AutoDTI model. Conclusions Experimental results have shown that we were able to improve the prediction performance. To this end, the proposed method has been compared to other algorithms using the same reference datasets. The proposed method indicates that the experimental results of running five repetitions of tenfold cross-validation on golden standard datasets (Nuclear Receptors, GPCRs, Ion channels, and Enzymes) achieve good performance with high accuracy.

Background Graphs are mathematical structures widely used for expressing relationships among elements when representing biomedical and biological information. On top of these representations, several analyses are performed. A common task is the search of one substructure within one graph, called target. The problem is referred to as one-to-one subgraph search, and it is known to be NP-complete. Heuristics and indexing techniques can be applied to facilitate the search. Indexing techniques are also exploited in the context of searching in a collection of target graphs, referred to as one-to-many subgraph problem. Filter-and-verification methods that use indexing approaches provide a fast pruning of target graphs or parts of them that do not contain the query. The expensive verification phase is then performed only on the subset of promising targets. Indexing strategies extract graph features at a sufficient granularity level for performing a powerful filtering step. Features are memorized in data structures allowing an efficient access. Indexing size, querying time and filtering power are key points for the development of efficient subgraph searching solutions. Results An existing approach, GRAPES, has been shown to have good performance in terms of speed-up for both one-to-one and one-to-many cases. However, it suffers in the size of the built index. For this reason, we propose GRAPES-DD, a modified version of GRAPES in which the indexing structure has been replaced with a Decision Diagram. Decision Diagrams are a broad class of data structures widely used to encode and manipulate functions efficiently. Experiments on biomedical structures and synthetic graphs have confirmed our expectation showing that GRAPES-DD has substantially reduced the memory utilization compared to GRAPES without worsening the searching time. Conclusion The use of Decision Diagrams for searching in biochemical and biological graphs is completely new and potentially promising thanks to their ability to encode compactly sets by exploiting their structure and regularity, and to manipulate entire sets of elements at once, instead of exploring each single element explicitly. Search strategies based on Decision Diagram makes the indexing for biochemical graphs, and not only, more affordable allowing us to potentially deal with huge and ever growing collections of biochemical and biological structures.

Background With the development of third-generation sequencing (TGS) technologies, people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long reads allow protein domain annotation without assembly, thus can produce important insights into the biological functions of the underlying data. However, the high error rate in TGS data raises a new challenge to established domain analysis pipelines. The state-of-the-art methods are not optimized for noisy reads and have shown unsatisfactory accuracy of domain classification in TGS data. New computational methods are still needed to improve the performance of domain prediction in long noisy reads. Results In this work, we introduce ProDOMA, a deep learning model that conducts domain classification for TGS reads. It uses deep neural networks with 3-frame translation encoding to learn conserved features from partially correct translations. In addition, we formulate our problem as an open-set problem and thus our model can reject reads not containing the targeted domains. In the experiments on simulated long reads of protein coding sequences and real TGS reads from the human genome, our model outperforms HMMER and DeepFam on protein domain classification. Conclusions In summary, ProDOMA is a useful end-to-end protein domain analysis tool for long noisy reads without relying on error correction.

The limited availability of shared cellular resources introduces strong coupling among seemingly unrelated components. Given the fundamental role that multistable switches play in both natural and synthetic genetic systems, here we focus on how the scarcity of resources affects the behavior of these elementary building blocks. In particular, we reveal that while competition for scarce resources pushes the dynamics towards monostability, self-activation attenuates this phenomenon, as well as perturbations from the genetic context of the switch. However, this robustness comes at a price: our analysis uncovers that strong self-activation can lead to tristable dynamics that can surprisingly and misleadingly appear as if the underlying system was monostable, especially when considering cell-to-cell heterogeneity. This letter thus exposes how self-activation and competition for scarce resources establish the stability and robustness properties of genetic switches at both single cell and population levels. Due to their analytic nature, our results provide explicit guidelines for the rational and optimal design of synthetic gene circuits and facilitate the analysis of organizing principles underlying natural systems.

One of the most recognized campaigns in the world is the fight against cancer, being the renal system one of the most affected by this pathology. Renal Cell Carcinoma (RCC), the most common form of kidney cancer in adults, is the sixth leading cause of cancer death. Due to the increased use of imaging techniques, kidney lesions can be diagnosed incidentally in approximately 50% of cases. Cuba bets on the use of technology in health and the University of Informatics Sciences (UCI) has developed a system for the storage, transmission and visualization of medical images (XAVIA PACS), which is deployed in several hospitals in the country, but does not have a component that can perform the detection of RCC in tomographic images, making the diagnosis slower, which translates into fewer possibilities for the patient. An informatic component was developed for the renal carcinomas detection in abdominal tomography images based on the Watersheds Transform. This component supports the diagnosis of RCC by specialists in the health centers where the XAVIA-PACS was deployed. An experiment was developed comparing the results with images from an international Database (KiTS 2019), in addition their diagnosis value was analyzed by leading medical specialists.

Background MicroRNAs (miRNAs) have been confirmed to have close relationship with various human complex diseases. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases. However, it is still a big challenge to identify which miRNAs are related to diseases. As experimental methods are in general expensive and time‐consuming, it is important to develop efficient computational models to discover potential miRNA-disease associations. Methods This study presents a novel prediction method called HFHLMDA, which is based on high-dimensionality features and hypergraph learning, to reveal the association between diseases and miRNAs. Firstly, the miRNA functional similarity and the disease semantic similarity are integrated to form an informative high-dimensionality feature vector. Then, a hypergraph is constructed by the K-Nearest-Neighbor (KNN) method, in which each miRNA-disease pair and its k most relevant neighbors are linked as one hyperedge to represent the complex relationships among miRNA-disease pairs. Finally, the hypergraph learning model is designed to learn the projection matrix which is used to calculate uncertain miRNA-disease association score. Result Compared with four state-of-the-art computational models, HFHLMDA achieved best results of 92.09% and 91.87% in leave-one-out cross validation and fivefold cross validation, respectively. Moreover, in case studies on Esophageal neoplasms, Hepatocellular Carcinoma, Breast Neoplasms, 90%, 98%, and 96% of the top 50 predictions have been manually confirmed by previous experimental studies. Conclusion MiRNAs have complex connections with many human diseases. In this study, we proposed a novel computational model to predict the underlying miRNA-disease associations. All results show that the proposed method is effective for miRNA–disease association predication.

Given the popularity and elegance of k-mer based tools, finding a space-efficient way to represent a set of k-mers is important for improving the scalability of bioinformatics analyses. One popular approach is to convert the set of k-mers into the more compact set of unitigs. We generalize this approach and formulate it as the problem of finding a smallest spectrum-preserving string set (SPSS) representation. We show that this problem is equivalent to finding a smallest path cover in a compacted de Bruijn graph. Using this reduction, we prove a lower bound on the size of the optimal SPSS and propose a greedy method called UST that results in a smaller representation than unitigs and is nearly optimal with respect to our lower bound. We demonstrate the usefulness of the SPSS formulation with two applications of UST. The first one is a compression algorithm, UST-Compress, which we show can store a set of k-mers using an order-of-magnitude less disk space than other lossless compression tools. The second one is an exact static k-mer membership index, UST-FM, which we show improves index size by 10-44% compared to other state-of-the-art low memory indices. Our tool is publicly available at: https://github.com/medvedevgroup/UST/.

Publisher Copyright: IEEE Copyright: Copyright 2021 Elsevier B.V., All rights reserved. Detecting predictive biomarkers from multi-omics data is important for precision medicine, to improve diagnostics of complex diseases and for better treatments. This needs substantial experimental efforts that are made difficult by the heterogeneity of cell lines and huge cost. An effective solution is to build a computational model over the diverse omics data, including genomic, molecular, and environmental information. However, choosing informative and reliable data sources from among the different types of data is a challenging problem. We propose DIVERSE, a framework of Bayesian importance-weighted tri- and bi-matrix factorization(DIVERSE3 or DIVERSE2) to predict drug responses from data of cell lines, drugs, and gene interactions. DIVERSE integrates the data sources systematically, in a step-wise manner, examining the importance of each added data set in turn. More specifically, we sequentially integrate five different data sets, which have not all been combined in earlier bioinformatic methods for predicting drug responses. Empirical experiments show that DIVERSE clearly outperformed five other methods including three state-of-the-art approaches, under cross-validation, particularly in out-of-matrix prediction, which is closer to the setting of real use cases and more challenging than simpler in-matrix prediction. Additionally, case studies for discovering new drugs further confirmed the performance advantage of DIVERSE.

The application of signal processing techniques for identification of exons in Deoxyribonucleic acid (DNA) sequence is a challenging task. The objective of this paper is to introduce a combinational window approach for locating exons in DNA sequence. In contrast to the traditional single window function for evaluation of short time Fourier transform (STFT), this work proposes a novel method for evaluating STFT coefficients using a combinational window function comprising of Gaussian, Lanczos and Chebyshev (GLC) windows. The chosen combinational window GLC has the highest relative side lobe attenuation values compared to other window functions introduced by various researchers. The proposed algorithm incorporates GLC window function for evaluating STFT coefficients and in the design of FIR bandpass filter. Simulation results revealed its effectiveness in improving the evaluation parameters like Sensitivity, Specificity, Accuracy, Area under curve (AUC), Discrimination Measure (DM). Furthermore, the proposed algorithm has been applied successfully to some universal benchmark datasets like C. elegans, Homosapiens, etc., The proposed method has shown to be an efficient approach for the prediction of protein coding regions compared to other existing methods. All the simulations are done using the MATLAB 2016a.

Millions of people suffer from bone loss and diseases each year, which give rise to bone-related medical treatments. Multi-material coatings composed of Silicon Substituted Hydroxyapatite (Si-HA) and Silver Substituted Hydroxyapatite (Ag-HA) potentiated its usage as an ideal bone implant material with favourable biological response and antibacterial properties, respectively. Each patient has different conditions (e.g. bone ages, bone density, position, and infection status) and specific bone implant requirement, thus requiring specific designs of Si-HA and Ag-HA bone implant material ratios. In this paper, a recommender system which took advantage of Wide&Deep recommendation architecture and DenseNet architecture was provided and a semi-supervised training process was utilized to recommend appropriate bone implant material ratio (Si-HA:Ag-HA) for each patient to improve the bone disease treatment and recovery. The proposed recommendation system could achieve high accuracy ( $$92.2\%$$ ) and efficiency (using only $$10\%$$ of labeled data) on test dataset, which could be potentially utilized in future clinical usages and provide doctors with decision support to make better clinical decisions.

Feature selection is a crucial stage in the design of a computer-aided classification system for breast cancer diagnosis. The main objective of the proposed research design is to discover the use of multi-objective particle swarm optimization (MOPSO) and Nondominated sorting genetic algorithm-III (NSGA-III) for feature selection in digital mammography. The Pareto-optimal fronts generated by MOPSO and NSGA-III for two conflicting objective functions are used to select optimal features. An artificial neural network (ANN) is used to compute the fitness of objective functions. The importance of features selected by MOPSO and NSGA-III are assessed using artificial neural networks. The experimental results show that MOPSO based optimization is superior to NSGA-III. MOPSO achieves high accuracy with a 55% feature reduction. MOPSO based feature selection and classification deliver an efficiency of 97.54% with 98.22% sensitivity, 96.82% specificity, 0.9508 Cohen’s kappa coefficient, and area under curve AZ= 0.983 ± 0.003.

Digitization of healthcare records contributed to a large volume of functional scientific data that can help researchers to understand the behaviour of many diseases. However, the privacy implications of this data, particularly genomics data, have surfaced recently as the collection, dissemination, and analysis of human genomics data is highly sensitive. There have been multiple privacy attacks relying on the uniqueness of the human genome that reveals a participant or a certain group’s presence in a dataset. Therefore, the current data sharing policies have ruled out any public dissemination and adopted precautionary measures prior to genomics data release, which hinders timely scientific innovation. In this article, we investigate an approach that only releases the statistics from genomic data rather than the whole dataset and propose a generalized Differentially Private mechanism for Genome-wide Association Studies (GWAS). Our method provides a quantifiable privacy guarantee that adds noise to the intermediate outputs but ensures satisfactory accuracy of the private results. Furthermore, the proposed method offers multiple adjustable parameters that the data owners can set based on the optimal privacy requirements. These variables are presented as equalizers that balance between the privacy and utility of the GWAS. The method also incorporates Online Bin Packing technique [1], which further bounds the privacy loss linearly, growing according to the number of open bins and scales with the incoming queries. Finally, we implemented and benchmarked our approach using seven different GWAS studies to test the performance of the proposed methods. The experimental results demonstrate that for 1,000 arbitrary online queries, our algorithms are more than 80% accurate with reasonable privacy loss and exceed the state-of-the-art approaches on multiple studies (i.e., EigenStrat, LMM, TDT).

The rapid development of high-throughput technology has generated a large number of biological networks. Network-based methods are able to provide rich information for inferring gene function. This is composed of analyzing the topological characteristics of genes in related networks, integrating biological information, and considering data from different data sources. To promote network biology and related biotechnology research, this article provides a survey for the state of the art of advanced methods of network-based gene function prediction and discusses the potential challenges.

Graph convolutional networks (GCNs), which are capable of effectively processing graph-structural data, have been successfully applied in text classification task. Existing studies on GCN based text classification model largely concerns with the utilization of word co-occurrence and Term Frequency-Inverse Document Frequency (TF–IDF) information for graph construction, which to some extent ignore the context information of the texts. To solve this problem, we propose a gating context-aware text classification model with Bidirectional Encoder Representations from Transformers (BERT) and graph convolutional network, named as Gating Context GCN (GC-GCN). More specifically, we integrate the graph embedding with BERT embedding by using a GCN with gating mechanism to enable the acquisition of context coding. We carry out text classification experiments to show the effectiveness of the proposed model. Experimental results shown our model has respectively obtained 0.19%, 0.57%, 1.05% and 1.17% improvements over the Text-GCN baseline on the 20NG, R8, R52, and Ohsumed benchmark datasets. Furthermore, to overcome the problem that word co-occurrence and TF–IDF are not suitable for graph construction for short texts, Euclidean distance is used to combine with word co-occurrence and TF–IDF information. We obtain an improvement by 1.38% on the MR dataset compared to Text-GCN baseline.

De novo genome assembly is the process of stitching short DNA sequences to generate longer DNA sequences, without using any reference sequence for alignment. It enables high-throughput genome sequencing and thus accelerates the discovery of new genomes. In this paper, we present a toolkit, called PPA-assembler, for de novo genome assembly in a distributed setting. The operations in our toolkit provide strong performance guarantees, and can be assembled to implement various sequencing strategies. PPA-assembler adopts the popular de Bruijn graph based approach for sequencing, and each operation is implemented as a program in Google’s Pregel framework which can be easily deployed in a generic cluster. Experiments on large real and simulated datasets demonstrate that PPA-assembler is much more efficient than the state-of-the-arts while providing comparable sequencing quality. PPA-assembler has been open-sourced at https://github.com/yaobaiwei/PPA-Assembler .

Background Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods. Results ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms. Conclusions As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

Evidence has accumulated enough to prove non-coding RNAs (ncRNAs) play important roles in cellular biological processes and disease pathogenesis. High throughput techniques have produced a large number of ncRNAs whose function remains unknown. Since the accurate identification of ncRNAs family is helpful to the research of their function, it is of necessity and urgency to predict the family of each ncRNAs. Although several traditional excellent methods are applicable to predict the family of ncRNAs, their complex procedures or inaccurate performance remain major problems confronting us. The main idea of those methods is first to predict the secondary structure, and then identify ncRNAs family according to properties of the secondary structure. Unfortunately, the multi-step error superposition, especially the imperfection of RNA secondary structure prediction tools, maybe the cause of low accuracy. In this paper, a novel end-to-end method ‘ncRFP’ was proposed to complete the prediction task based on Deep Learning. Instead of predicting the secondary structure, ncRFP predicts the ncRNAs family by automatically extracting features from ncRNAs sequences. Compared with other methods, ncRFP not only simplifies the process but also improves accuracy. The source code of ncRFP can be available at https://github.com/linyuwangPHD/ncRFP .

Disease prediction has the potential to benefit stakeholders such as the government and health insurance companies. It can identify patients at risk of disease or health conditions. Clinicians can then take appropriate measures to avoid or minimize the risk and in turn, improve quality of care and avoid potential hospital admissions. Due to the recent advancement of tools and techniques for data analytics, disease risk prediction can leverage large amounts of semantic information, such as demographics, clinical diagnosis and measurements, health behaviours, laboratory results, prescriptions and care utilisation. In this regard, electronic health data can be a potential choice for developing disease prediction models. A significant number of such disease prediction models have been proposed in the literature over time utilizing large-scale electronic health databases, different methods, and healthcare variables. The goal of this comprehensive literature review was to discuss different risk prediction models that have been proposed based on electronic health data. Search terms were designed to find relevant research articles that utilized electronic health data to predict disease risks. Online scholarly databases were searched to retrieve results, which were then reviewed and compared in terms of the method used, disease type, and prediction accuracy. This paper provides a comprehensive review of the use of electronic health data for risk prediction models. A comparison of the results from different techniques for three frequently modelled diseases using electronic health data was also discussed in this study. In addition, the advantages and disadvantages of different risk prediction models, as well as their performance, were presented. Electronic health data have been widely used for disease prediction. A few modelling approaches show very high accuracy in predicting different diseases using such data. These modelling approaches have been used to inform the clinical decision process to achieve better outcomes.

Protein phosphorylation is one of the key mechanism in prokaryotes and eukaryotes and is responsible for various biological functions such as protein degradation, intracellular localization, the multitude of cellular processes, molecular association, cytoskeletal dynamics, and enzymatic inhibition/activation. Phosphohistidine (PhosH) has a key role in a number of biological processes, including central metabolism to signalling in eukaryotes and bacteria. Thus, identification of phosphohistidine sites in a protein sequence is crucial, and experimental identification can be expensive, time-taking, and laborious. To address this problem, here, we propose a novel computational model namely iPhosH-PseAAC for prediction of phosphohistidine sites in a given protein sequence using pseudo amino acid composition (PseAAC), statistical moments, and position relative features. The results of the proposed predictor are validated through self-consistency testing, 10-fold cross-validation, and jackknife testing. The self-consistency validation gave the 100 percent accuracy, whereas, for cross-validation, the accuracy achieved is 94.26 percent. Moreover, jackknife testing gave 97.07 percent accuracy for the proposed model. Thus, the proposed model iPhosH-PseAAC for prediction of iPhosH site has the great ability to predict the PhosH sites in given proteins.

Considerable efforts have been made over the last decades to improve the robustness of clustering algorithms against noise features and outliers, known to be important sources of error in clustering. Outliers dominate the sum-of-the-squares calculations and generate cluster overlap, thus leading to unreliable clustering results. They can be particularly detrimental in computational biology, e.g., when determining the number of clusters in gene expression data related to cancer or when inferring phylogenetic trees and networks. While the issue of feature weighting has been studied in detail, no clustering methods using object weighting have been proposed yet. Here we describe a new general data partitioning method that includes an object-weighting step to assign higher weights to outliers and objects that cause cluster overlap. Different object weighting schemes, based on the Silhouette cluster validity index, the median and two intercluster distances, are defined. We compare our novel technique to a number of popular and efficient clustering algorithms, such as K-means, X-means, DAPC and Prediction Strength. In the presence of outliers and cluster overlap, our method largely outperforms X-means, DAPC and Prediction Strength as well as the K-means algorithm based on feature weighting.

Foot-and-mouth disease virus (FMDV) is an antigenic-variable RNA virus that is responsible for the recurrence of foot-and-mouth disease in livestock and can be prevented and controlled using a vaccine with broad-spectrum protection. Current anti-genicity evaluation methods, which involve animal immunity experiments and serum preparation, are unable to fulfill the needs of high-throughput antigenicity measurements. This study designed an antigenicity scoring model to rapidly predict the antigenicity of FMDV. Antigenic-dominant sites were initially determined on the VP1 protein, a position-specific scoring matrix and physical chemical indexes were integrated to generate antigenicity descriptors. Independent tests showed a high accuracy of 0.848 and an AUC value of 0.889, indicating the good performance of the model in antigenicity measurement. When applying this model to historical data, annual antigenicity coverage of widely used vaccine strains was successfully evaluated, this was also supported by previous experiments. Furthermore, the utility of this model was extended to select potential broad-spectrum vaccines among 1,201 historical non-redundant strains to recommend potential univalent, bivalent and trivalent vaccine candidates. The results suggested that the computational model designed in this study could be used for the high-throughput antigenicity measurement of FMDV and could aid in vaccine development for preventing FMDV epidemics.

Residue-residue interactions are the basis of protein thermostability. The molecular conformations of Streptomyces lividans xylanase (xyna_strli) and Thermoascus aurantiacus xylanase (xyna_theau) at 300K, 325K and 350K were obtained by Molecular Dynamics (MD) simulations. Dynamic weighted residue interaction networks were constructed and the rigid-communities were detected using the ESPRA algorithm and the Evolving Graph+Fast-Newman algorithm. The residues in the rigid-communities are primarily located in loop2, short helixes $\alpha 2^{\prime}, \alpha 3^{\prime}, \alpha 4^{\prime}$ α 2 ' , α 3 ' , α 4 ' and helixes $\alpha 3$ α 3 and $\alpha 4$ α 4 . Thus, the rigid-community is close to the N-terminus of xylanase, which is usually stabilized to increase thermostability using site-directed mutagenesis. The evolution of the rigid-community with increasing temperature shows a stable synergistic interaction between loop2, $\alpha 2^{\prime}, \alpha 3^{\prime}$ α 2 ' , α 3 ' and $\alpha 4^{\prime}$ α 4 ' in xyna_theau. In particular, the short helixes $\alpha 2^{\prime}$ α 2 ' and $\alpha 3^{\prime}$ α 3 ' form a “thermo helix” to promote thermostability. In addition, tight global interactions between loop2, $\alpha 2^{\prime}, \alpha 3^{\prime}, \alpha 3, \alpha 4^{\prime}$ α 2 ' , α 3 ' , α 3 , α 4 ' and $\alpha 4$ α 4 of xyna_theau are identified, consisting mainly of hydrogen bonds, van der Waals forces and π-π stacking. These residue interactions are more resistant to high temperatures than those in xyna_strli. Robust residue interactions within these secondary structures are key factors influencing xyna_strli and xyna_theau thermostability. Analyzing the rigid-community can elucidate the cooperation of secondary structures, which cannot be discovered from sequence and 3D structure alone.

The RSwitch is a MySQL database implemented on a PHP-based server, which also provides various useful tools for analyses of DNA, RNA, and protein sequences applying a user-friendly interface. The RSwitch database currently contains information and annotations of 215 bacterial riboswitches from 16 different types found in 50 human pathogenic bacteria. The riboswitch classes include those sensing FMN, glmS, Cobalamin, Lysine, SAH, SAM, Purine, TPP, c-di-GMPI, c-di-GMPII, Moco, PreQ1, Fluoride, Glycine, Mg2+, and Mn2+ type of bacterial riboswitches. The database provides information about the riboswitch aptamer sequences, the thermodynamic ensemble of the RNA structures on partition function and on free minimum energy function. Additionally, the database presents the centroid structure and the positional entropy for each position of the aptamer sequences. The database also provides the biochemical pathways in which the riboswitches are involved in, as well as multiple sequence alignments, multi-drug resistance bacterial strains and consensus motifs for each type of the switches. The RSwitch database is permanently available online without any restrictions. This bioinformatics database provides for the first time all information needed for assessing the suitability of the presented riboswitches as antibacterial drug targets.

Mitosis detection is one of the challenging steps in biomedical imaging research, which can be used to observe the cell behavior. Most of the already existing methods that are applied in detecting mitosis usually contain many nonmitotic events (normal cell and background) in the result (false positives, FPs). In order to address such a problem, in this study, we propose to apply 2.5-dimensional (2.5D) networks called CasDetNet_CLSTM, which can accurately detect mitotic events in 4D microscopic images. This CasDetNet_CLSTM involves a 2.5D faster region-based convolutional neural network (Faster R-CNN) as the first network, and a convolutional long short-term memory (CLSTM) network as the second network. The first network is used to select candidate cells using the information from nearby slices, whereas the second network uses temporal information to eliminate FPs and refine the result of the first network. Our experiment shows that the precision and recall of our networks yield better results than those of other state-of-the-art methods.

Computational drug repositioning, which is an efficient approach to find potential indications for drugs, has been used to increase the efficiency of drug development. The drug repositioning problem essentially is a top-K recommendation task that recommends most likely diseases to drugs based on drug and disease related information. Therefore, many recommendation methods can be adopted to drug repositioning. Collaborative metric learning (CML) algorithm can produce distance metrics that capture the important relationships among objects, and has been widely used in recommendation domains. By applying CML in drug repositioning, a joint metric space is learned to encode drug's relationships with different diseases. In this study, we propose a novel drug repositioning computational method using Collaborative Metric Learning to predict novel drug-disease associations based on known drug and disease related information. Specifically, the proposed method learns latent vectors of drugs and diseases by applying metric learning, and then predicts the association probability of one drug-disease pair based on the learned vectors. The comprehensive experimental results show that CMLDR outperforms the other state-of-the-art drug repositioning algorithms in terms of precision, recall, and AUPR.

Network motifs are subgraphs of a network which are found with significantly higher frequency than that expected in similar random networks. Motifs are small building blocks of a network and they have emerged as a way to uncover topological properties of complex networks. A special yet not much explored type of motif is the 'colored motif' where color (type) of each node, and hence the edges, in the motif is distinguishable from each other. A traditional motif is defined as a recurring structure in a network, whereas colored motif introduces detailed information about the color of the nodes. G-trie is a data structure to efficiently store a given set of subgraphs by exploiting the topological overlaps within them. In this article we have implemented a modified g-trie to store colored subgraphs and developed a method to discover colored motifs. Our method uses an approximate enumeration for counting the subgraphs to reduce the runtime. We have applied our method to find colored motifs of size three in a host pathogen protein-protein interaction network having two types of proteins namely HIV-1 and human proteins, and four types of edges. Here, we have discovered eight motifs, six of which contain both HIV-1 and human proteins, while the remaining two contain only human proteins.