SUMMARY: Identifying drugs that target intercellular communication networks represents a promising therapeutic strategy, yet linking single-cell RNA sequencing (scRNA-seq) analysis to structure-based drug screening remains technically challenging and requires substantial bioinformatics expertise. We present scDock, an integrated and user-friendly pipeline that seamlessly connects scRNA-seq data processing, cell-cell communication inference, and molecular docking-based drug discovery. Through a single configuration file, users can execute the complete workflow, from raw scRNA-seq data to ranked drug candidates, without programming skills. scDock automates the identification of disease-relevant ligand-receptor interactions from scRNA-seq data and performs structure-based virtual screening against these communication targets using Protein Data Bank (PDB) or AlphaFold-predicted protein structures. The pipeline generates comprehensive outputs at each stage, enabling users to explore intercellular signaling alterations and discover therapeutic compounds targeting specific cell-cell communications. scDock addresses a critical gap by providing an accessible end-to-end solution for communication-targeted drug discovery from single-cell data. AVAILABILITY AND IMPLEMENTATION: scDock is freely available at https://doi.org/10.6084/m9.figshare.31370368 and https://github.com/Andrewneteye4343/scDock. It is implemented in R, Python, shell scripts, and supports Linux systems, including Ubuntu and Debian.

BACKGROUND: Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer's disease (AD). RESULTS: In this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power. CONCLUSIONS: The results on simulation data and AD demonstrated that GenEpi has the ability to detect the epistasis associated with phenotypes effectively and efficiently. The released package can be generalized to largely facilitate the studies of many complex diseases in the near future.

Discriminative patterns describe significant differences between different types of subjects, and often provide insights to critical properties of the problem at hand. Pattern-based classifiers can directly utilize discriminative patterns to predict unseen samples by a majority voting or aggregation mechanism. Therefore, we are concerned with not only finding useful individual patterns, but also the effectiveness of the pattern set as a whole; and it is imperative to ensure the relevancy and non-redundancy of the discriminative patterns. Few studies have evaluated pattern redundancy via examining samples covered by the patterns; and in those that do, the focus has been mostly on the proportion of overlapping samples, suggesting that a great deal of information on non-overlapping samples was overlooked. To address this issue, we present a novel pattern selection algorithm that estimates pattern redundancy by not only the proportion of overlapping samples, but also the resemblance of non-overlapping samples. The proposed method was applied on two real microbiomic datasets, with the aim of providing new insights on the interactions between microbes, and their effects on the host. When compared with other robust classifiers and feature selection heuristics, our pattern selection algorithm led to diverse and compact sets of final patterns that demonstrated comparable or even superior predictive capabilities.

Obesity is a prevalent disease with severe complications. In recent years, interests have been geared towards exploring the relationship between gut microbial factors and the subject's degree of, or propensity to, obesity. With the relative abundance values of phylotypes as features, machine learning algorithms could be applied to identify predictive microbes that distinguish between subjects of different types, and infer the corresponding rules that describe “how” the differentiation is made. However, the relative abundance is influenced by a number of upstream factors, and the inherent information content can often times limit the validity of the inferred rules. We addressed this issue by structurally incorporating association measures into interpretable classifiers. The resulting model renders microbial abundance patterns that are both statistically significant and predictively valid. Although we concentrated on obesity in this paper, the proposed approach is applicable on 16S rRNA datasets of other domains as well. The inferred patterns are in line with current knowledge of the microbial world, while providing new insights on the interactions between microbial factors, and their effects on the host. As such, they are believed to constitute credible starting points for further research.

MOTIVATION: Gene regulation involves complicated mechanisms such as cooperativity between a set of transcription factors (TFs). Previous studies have used target genes shared by two TFs as a clue to infer TF-TF interactions. However, this task remains challenging because the target genes with low binding affinity are frequently omitted by experimental data, especially when a single strict threshold is employed. This article aims at improving the accuracy of inferring TF-TF interactions by incorporating motif discovery as a fundamental step when detecting overlapping targets of TFs based on ChIP-chip data. RESULTS: The proposed method, simTFBS, outperforms three naïve methods that adopt fixed thresholds when inferring TF-TF interactions based on ChIP-chip data. In addition, simTFBS is compared with two advanced methods and demonstrates its advantages in predicting TF-TF interactions. By comparing simTFBS with predictions based on the set of available annotated yeast TF binding motifs, we demonstrate that the good performance of simTFBS is indeed coming from the additional motifs found by the proposed procedures. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: MicroRNAs (miRNAs) are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, ab initio approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM) are extensively adopted in these ab initio approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention. RESULTS: This article reports the design of a predictor of pre-miRNAs with a novel kernel based classifier named the generalized Gaussian density estimator (G2DE) based classifier. The G2DE is a kernel based algorithm designed to provide interpretability by utilizing a few but representative kernels for constructing the classification model. The performance of the proposed predictor has been evaluated with 692 human pre-miRNAs and has been compared with two kernel based and two logic based classifiers. The experimental results show that the proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set. CONCLUSION: Software predictors that identify pre-miRNAs in genomic sequences have been exploited by biologists to facilitate molecular biology research in recent years. The G2DE employed in this study can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Furthermore, biologists can obtain valuable insights about the different characteristics of the sequences of pre-miRNAs with the models generated by the G2DE based predictor.

This article presents a comprehensive study on the main characteristics of a novel optimization algorithm specifically designed for simulation of protein-ligand interactions. Though design of optimization algorithms has been a research issue extensively studied by computer scientists for decades, the emerging applications in bioinformatics such as simulation of protein-ligand interactions and protein folding introduce additional challenges due to (1) the high dimensionality nature of the problem and (2) the highly rugged landscape of the energy function. As a result, optimization algorithms that are not carefully designed to tackle these two challenges may fail to deliver satisfactory performance. This study has been motivated by the observation that the RAME (Rank-based Adaptive Mutation Evolutionary) optimization algorithm specifically designed for simulation of protein-ligand docking has consistently outperformed the conventional optimization algorithms by a significant degree. Accordingly, it is of interest to conduct a comprehensive investigation on the characteristics of the proposed algorithm and to learn how it will perform in the more general cases.The experimental results reveal that the RAME algorithm proposed in this article is capable of delivering superior performance to several alternative versions of the genetic algorithm in handling highly-rugged functions in the high-dimensional vector space. This article also reports experiments conducted to analyze the causes of the observed performance difference. The experiences learned provide valuable clues for how the proposed algorithm can be effectively exploited to tackle other computational biology problems.

Data classification is an intensively studied machine learning problem and there are two major categories of data classification algorithms, namely the logic based and the kernel based. The logic based classifiers, such as the decision tree and the rule-based classifier, feature the advantage of presenting a good summary about the distinctive characteristics of different classes of data. On the other hand, the kernel based classifiers, such as the neural network and the support vector machine (SVM), typically can deliver higher prediction accuracy than the logic based classifiers. However, the user of a kernel based classifier normally cannot get an overall picture about the distribution of the data set. For some applications, the overall picture of the distribution of the data set can provide valuable insights about the distinctive characteristics of different classes of data and therefore is highly desirable. In this article, aiming to close the gap between the logic based classifiers and the kernel based classifiers, we propose a novel approach to carry out density estimation based on a mixture model composed of a limited number of generalized Gaussian components. One favorite feature of the classifier constructed with the proposed approach is that a user can easily obtain an overall picture of the distributions of the data set by examining the eigenvectors and eigenvalues of the covariance matrices associated with the generalized Gaussian components. Experimental results show that the classifier constructed with the proposed approach is capable of delivering superior prediction accuracy in comparison with the conventional logic based classifiers and the EM (Expectation Maximization) based classifier. On the other hand, though it cannot match the prediction accuracy delivered by the SVM, the proposed classifier enjoys one major advantage due to providing the user with an overall picture of the underlying distributions.

How to handle unbalanced datasets and how to handle high-dimensional datasets are two of the most challenging issues faced by the latest machine learning research. This article reports a study aimed at providing effective solutions to these two challenges. For handling unbalanced datasets, we proposed that a different value of the cost parameter in Support Vector Machine (SVM) is employed for each class of samples. For handling high-dimensional datasets, we resorted to Independent Components Analysis (ICA), which is a multivariate analysis algorithm, along with the conventional univariate analysis. Experimental results confirmed that the proposed approaches all together significantly improved the prediction accuracy delivered by SVM.

This paper proposes a novel method to construct a radial basis function network (RBFN) classifier. Our contribution consists of two parts. The first one is an incremental hierarchical clustering algorithm for constructing the hidden layer, and the second one is to improve the least mean square error method that calculates the weights between the hidden and the output layers of an RBFN. This paper discusses the effects of incorporating an incremental hierarchical clustering algorithm for constructing an RBFN optimized for data classification applications. The formation of clusters is controlled by the class labels of training samples and therefore the clusters identified are well adapted to the local distributions of training instances. In addition, the incremental framework largely reduces the requirement of memory space when the training data set is large. In regard to the calculation of weights, we employ the regularization theory to solve the singular matrix problem that might happen in determining the optimal weights. Experimental results show that the data classifier constructed is capable of delivering comparable classification accuracy as the support vector machine (SVM) and the kernel density estimation based classifier that we have recently proposed, while enjoying significant execution efficiency in handling data sets that contains a high percentage of redundant training instances.

In recent years, kernel density estimation has been exploited by computer scientists to model several important problems in machine learning, bioinformatics, and computer vision. However, in case the dimension of the data set is high, then the conventional kernel density estimators suffer poor convergence rates of the pointwise mean square error (MSE) and the integrated mean square error (IMSE). Therefore, design of a novel kernel density estimator that overcomes this problem has been a great challenge for many years. This paper proposes a relaxed model of the variable kernel density estimation and analyzes its performance in data classification applications. It is proved in this paper that, in terms of pointwise MSE, the convergence rate of the relaxed variable kernel density estimator can approach O(n/sup -1/) regardless of the dimension of the data set, where n is the number of sampling instances. Experiments with the data classification applications have shown that the improved convergence rate of the pointwise MSE leads to higher prediction accuracy. In fact, the experimental results have also shown that the data classifier constructed based on the relaxed variable kernel density estimator is capable of delivering the same level of prediction accuracy as the SVM with the Gaussian kernel.

This paper presents a novel learning algorithm for efficient construction of the radial basis function (RBF) networks that can deliver the same level of accuracy as the support vector machines (SVMs) in data classification applications. The proposed learning algorithm works by constructing one RBF subnetwork to approximate the probability density function of each class of objects in the training data set. With respect to algorithm design, the main distinction of the proposed learning algorithm is the novel kernel density estimation algorithm that features an average time complexity of O(n log n), where n is the number of samples in the training data set. One important advantage of the proposed learning algorithm, in comparison with the SVM, is that the proposed learning algorithm generally takes far less time to construct a data classifier with an optimized parameter setting. This feature is of significance for many contemporary applications, in particular, for those applications in which new objects are continuously added into an already large database. Another desirable feature of the proposed learning algorithm is that the RBF networks constructed are capable of carrying out data classification with more than two classes of objects in one single run. In other words, unlike with the SVM, there is no need to resort to mechanisms such as one-against-one or one-against-all for handling datasets with more than two classes of objects. The comparison with SVM is of particular interest, because it has been shown in a number of recent studies that SVM generally are able to deliver higher classification accuracy than the other existing data classification algorithms. As the proposed learning algorithm is instance-based, the data reduction issue is also addressed in this paper. One interesting observation in this regard is that, for all three data sets used in data reduction experiments, the number of training samples remaining after a naive data reduction mechanism is applied is quite close to the number of support vectors identified by the SVM software. This paper also compares the performance of the RBF networks constructed with the proposed learning algorithm and those constructed with a conventional cluster-based learning algorithm. The most interesting observation learned is that, with respect to data classification, the distributions of training samples near the boundaries between different classes of objects carry more crucial information than the distributions of samples in the inner parts of the clusters.

Analysis of protein-ligand interactions is a fundamental issue in drug design. As the detailed and accurate analysis of protein-ligand interactions involves calculation of binding free energy based on thermodynamics and even quantum mechanics, which is highly expensive in terms of computing time, conformational and structural analysis of proteins and ligands has been widely employed as a screening process in computer-aided drug design. In this paper, an efficient mechanism for identifying possible protein-ligand interactions based on analysis of protein tertiary substructures is proposed. In one experiment reported in this paper, the proposed prediction mechanism has been exploited to obtain some clues about a hypothesis that the biochemists have been speculating. The main distinction in the design of the prediction mechanism is the filtering process incorporated to expedite the analysis. The filtering process extracts the residues located in a cave of the protein tertiary structure for analysis and operates with O(nlogn) time complexity, where n is the number of residues in the protein. In comparison, the /spl alpha/hull algorithm, which is a widely used algorithm in computer graphics for identifying those instances that are on the contour of a 3-dimensional object, features O(n/sup 2/) time complexity. Experimental results show that the filtering process presented in this paper is able to speed up the analysis by a factor ranging from 3.11 to 9.79 times.

MOTIVATION: Protein sequence clustering has been widely exploited to facilitate in-depth analysis of protein functions and families. For some applications of protein sequence clustering, it is highly desirable that a hierarchical structure, also referred to as dendrogram, which shows how proteins are clustered at various levels, is generated. However, as the sizes of contemporary protein databases continue to grow at rapid rates, it is of great interest to develop some summarization mechanisms so that the users can browse the dendrogram and/or search for the desired information more effectively. RESULTS: In this paper, the design of a novel incremental clustering algorithm aimed at generating summarized dendrograms for analysis of protein databases is described. The proposed incremental clustering algorithm employs a statistics-based model to summarize the distributions of the similarity scores among the proteins in the database and to control formation of clusters. Experimental results reveal that, due to the summarization mechanism incorporated, the proposed incremental clustering algorithm offers the users highly concise dendrograms for analysis of protein clusters with biological significance. Another distinction of the proposed algorithm is its incremental nature. As the sizes of the contemporary protein databases continue to grow at fast rates, due to the concern of efficiency, it is desirable that cluster analysis of a protein database can be carried out incrementally, when the protein database is updated. Experimental results with the Swiss-Prot protein database reveal that the time complexity for carrying out incremental clustering with k new proteins added into the database containing n proteins is O(n2betalogn), where beta congruent with 0.865, provided that k << n. AVAILABILITY: The Linux executable is available on the following supplementary page.

In recent years, Support Vector Machines (SVM) have been extensively applied to deal with various data classification problems. However, in some cases, the application of SVM is limited due to the time taken to conduct model selection for SVM. This issue is of particular significant for some modern applications, such as web mining, in which the large-scale database is frequently updated. This paper proposes a data reduction based mechanism aimed at expediting the model selection process in SVM. Experimental results show that the proposed mechanism is able to greatly reduce the time taken to carry out model selection at minimum cost.

Abstract This paper proposes an effective term suggestion approach to interactive Web search. Conventional approaches to making term suggestions involve extracting co‐occurring keyterms from highly ranked retrieved documents. Such approaches must deal with term extraction difficulties and interference from irrelevant documents, and, more importantly, have difficulty extracting terms that are conceptually related but do not frequently co‐occur in documents. In this paper, we present a new, effective log‐based approach to relevant term extraction and term suggestion. Using this approach, the relevant terms suggested for a user query are those that co‐occur in similar query sessions from search engine logs, rather than in the retrieved documents. In addition, the suggested terms in each interactive search step can be organized according to its relevance to the entire query session, rather than to the most recent single query as in conventional approaches. The proposed approach was tested using a proxy server log containing about two million query transactions submitted to search engines in Taiwan. The obtained experimental results show that the proposed approach can provide organized and highly relevant terms, and can exploit the contextual information in a user's query session to make more effective suggestions.

Due to advances in network technologies, providing streaming services over the Internet has gained in popularity. Because a video stream is in compressed format, it is naturally with the variable bit rate (VBR) property and its traffic is highly burst. With the installation of a video proxy between access networks (e.g. local area networks, LAN) and backbone networks (e.g. wide area networks, WAN), video staging is proposed to cache part of the requested video into the video proxy close to clients. In this mechanism, the video can be streamed using a constant bit rate (CBR) network service across the backbone WAN and the WAN bandwidth requirement can be significantly reduced. In this paper, we propose a new approach, called "caching selected after smoothing" (CSAS), to handling video staging. The aim of the CSAS algorithm is to integrate our previous video caching and smoothing technique so as to reduce the required WAN bandwidth even more. Experiments on benchmark videos using different evaluation indices, including the proxy storage requirement, the WAN bandwidth requirement, and the WAN bandwidth utilization, show that our approach is more effective than the conventional "cut after smoothing" (CAS) algorithm.

This article discusses the design of a digital library that addresses both content and knowledge management. The design of the digital library features two major distinctions: (1) the system incorporates a two-tier repository system to facilitate content management, and (2) the system incorporates an object-oriented model to facilitate the management of temporal information and exploits information extraction and deductive inference to derive implied knowledge based on the content of the digital library. The two-tier repository system relieves the system manager from manually maintaining the hyperlinks among the Web pages, when the digital library content is updated. The task of maintaining hyperlinks among Web pages can become cumbersome to the system manager if there are a large number of Web pages and hyperlinks. With respect to knowledge management, this design aims at facilitating temporal information management and deriving implied relations among the objects in the digital library. The motivation behind developing these knowledge processing utilities is to create a system that complements the capabilities of human beings. Deriving a comprehensive list of implied relations is an exhausting task if the digital library contains a great amount of information and the number of implied relations is great. With such knowledge-processing utilities, specialists are released from performing tedious work and can, therefore, spend more time with more productive philosophical activities to derive advanced knowledge. Applying knowledge management utilities effectively can extend the applications of digital libraries to new dimensions.

No abstract available.

This paper presents a comprehensive procedure to design multimedia storage systems for on-demand playback. The design stresses effective utilization of disk bandwidth with minimal data buffer to minimize overall system costs. The design procedure is most distinctive in the following two aspects: it bases on a tight upper bound of the lumped disk seek time for the Scan disk scheduling algorithm to achieve effective utilization of disk bandwidth; it starts with a general two-level hierarchical disk array structure to derive the optimal configuration for specific requirements.>