Computer Science

Unlike Western M edicine, those in Traditional Chinese M edicine (TCM ) are based on inherent rules or patterns, which can be considered as causal links. Existing approaches tend to apply computational methods on semantic ontology to do knowledge mining, but it cannot perfectly make use of internal principles in TCM . When it comes to knowledge representation, we can transform this inherent knowledge into causal graphs. In this paper, we present an approach to build a TCM knowledge model with the capability of rule reasoning using OWL 2. In particular, we focused on the causal relations among syndrome and symptoms, changes between syndromes. We evaluated our approach by giving two typical use cases and implemented them using Jena, a Java framework supporting RDF, OWL, and including a rule-based inference engine. The evaluation results suggested that our approach clearly displayed the causal relations in TCM and shows a great potential in TCM knowledge mining.

Understanding the functional mechanisms of the complex biological system as a whole is drawing more and more attention in global health care management. Traditional Chinese Medicine (TCM), essentially different from Western Medicine (WM), is gaining increasing attention due to its emphasis on individual wellness and natural herbal medicine, which satisfies the goal of integrative medicine. However, with the explosive growth of biomedical data on the Web, biomedical researchers are now confronted with the problem of large-scale data analysis and data query. Besides that, biomedical data also has a wide coverage which usually comes from multiple heterogeneous data sources and has different taxonomies, making it hard to integrate and query the big biomedical data. Embedded with domain knowledge from different disciplines all regarding human biological systems, the heterogeneous data repositories are implicitly connected by human expert knowledge. Traditional search engines cannot provide accurate and comprehensive search results for the semantically associated knowledge since they only support keywords-based searches. In this paper, we present BioTCM-SE, a semantic search engine for the information retrieval of modern biology and TCM, which provides biologists with a comprehensive and accurate associated knowledge query platform to greatly facilitate the implicit knowledge discovery between WM and TCM.

Motivation: Traditional Chinese medicine (TCM) is gaining increasing attention with the emergence of integrative medicine and personalized medicine, characterized by pattern differentiation on individual variance and treatments based on natural herbal synergism. Investigating the effectiveness and safety of the potential mechanisms of TCM and the combination principles of drug therapies will bridge the cultural gap with Western medicine and improve the development of integrative medicine. Dealing with rapidly growing amounts of biomedical data and their heterogeneous nature are two important tasks among modern biomedical communities. Bioinformatics, as an emerging interdisciplinary field of computer science and biology, has become a useful tool for easing the data deluge pressure by automating the computation processes with informatics methods. Using these methods to retrieve, store and analyze the biomedical data can effectively reveal the associated knowledge hidden in the data, and thus promote the discovery of integrated information. Recently, these techniques of bioinformatics have been used for facilitating the interactional effects of both Western medicine and TCM. The analysis of TCM data using computational technologies provides biological evidence for the basic understanding of TCM mechanisms, safety and efficacy of TCM treatments. At the same time, the carrier and targets associated with TCM remedies can inspire the rethinking of modern drug development. This review summarizes the significant achievements of applying bioinformatics techniques to many aspects of the research in TCM, such as analysis of TCM-related '-omics' data and techniques for analyzing biological processes and pharmaceutical mechanisms of TCM, which have shown certain potential of bringing new thoughts to both sides.

Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biologic...

Linked data is a decentralized space of interlinked Resource Description Framework (RDF) graphs that are published, accessed, and manipulated by a multitude of Web agents. Here, we present a multi-agent framework for mining hypothetical semantic relations from linked data, in which the discovery, management, and validation of relations can be carried out independently by different agents. These agents collaborate in relation mining by publishing and exchanging interdependent knowledge elements, e.g., hypotheses, evidence, and proofs, giving rise to an evidentiary network that connects and ranks diverse knowledge elements. Simulation results show that the framework is scalable in a multi-agent environment. Real-world applications show that the framework is suitable for interdisciplinary and collaborative relation discovery tasks in social domains.

It has become increasingly common that sponsored content (i.e., paid ads) and non-sponsored content are jointly displayed to users, especially on e-commerce platforms. Thus, both of these contents may interact together to influence their engagement behaviors. In general, sponsored content helps brands achieve their marketing goals and provides ad revenue to the platforms. In contrast, non-sponsored content contributes to the long-term health of the platform through increasing users' engagement. A key conundrum to platforms is learning how to blend both of these contents allowing their interactions to be considered and balancing these business objectives. This paper presents a system built for this purpose and applied to product detail pages of JD.COM, an ecommerce company. This system achieves three objectives: (a) Optimization of competing business objectives via Virtual Bids allowing the expressiveness of the valuation of the platform for these objectives. (b) Modeling the users' click behaviors considering explicitly the influence exerted by the sponsored and non-sponsored content displayed alongside through a deep learning approach. (c) Consideration of a Vickrey-Clarke-Groves (VCG) Auction design compatible with the allocation of ads and its induced externalities. Experiments are presented demonstrating the performance of the system. Moreover, our approach is fully deployed and serves all traffic through JD.COM's mobile application.

Background: RNA editing is a transcript-based layer of gene regulation. To date, no systemic study on RNA editing of plant nuclear genes has been reported. Here, a transcriptome-wide search for editing sites in nuclear transcripts of Arabidopsis (Arabidopsis thaliana) was performed.

The currently developed next-generation sequencing (NGS) technology has significantly enhanced our capacity of small RNA (sRNA) exploration. Several ambitious sRNA projects have been established based on the technical support of NGS. Thanks to the high-throughput feature of NGS, huge amounts of sRNA sequencing data have been generated. However, much more research efforts are needed to further exploit these valuable data. In this study, we carried out functional analyses of sRNAs from 26 angiosperms by utilizing public sRNA NGS data. We proposed that the endogenous sRNAs largely represented by the 24-nt ones had a potential role in transposable element (TE) control in both Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa), based on the comparison of sRNA locus densities within the TEs to the non-TE genes. Functional analysis was performed for the predicted targets of the conserved sRNAs that were classified into eudicot-specific, monocot-specific, and angiosperm-conserved ones. Moreover, several miRNA families were found to be highly conserved, and miR396 was suggested to be the most conserved miRNA family between the eudicots and the monocots, indicating its essential role in angiosperm development. At last, we demonstrated that it was a great challenge for researchers to fully exploit these huge sRNA NGS datasets and numerous sRNA species remained to be uncovered and functionally characterized.

Rice (Oryza sativa) feeds over half of the global population. A web-based integrated platform for rice microarray annotation and data analysis in various biological contexts is presented, which provides a convenient query for comprehensive annotation compared with similar databases. Coupled with existing rice microarray data, it provides online analysis methods from the perspective of bioinformatics. This comprehensive bioinformatics analysis platform is composed of five modules, including data retrieval, microarray annotation, sequence analysis, results visualization and data analysis. The BioChip module facilitates the retrieval of microarray data information via identifiers of “Probe Set ID”, “Locus ID” and “Analysis Name”. The BioAnno module is used to annotate the gene or probe set based on the gene function, the domain information, the KEGG biochemical and regulatory pathways and the potential microRNA which regulates the genes. The BioSeq module lists all of the related sequence information by a microarray probe set. The BioView module provides various visual results for the microarray data. The BioAnaly module is used to analyze the rice microarray’s data set.

MicroRNA (miRNA), recently recognized as a critical post-transcriptional modulator of gene expression, is involved in numerous biological processes in both animals and plants. Although eudicots and monocots, such as the model plants Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa), possess distinct root systems, several homologous miRNA families are reported to be involved in root growth control in both plants. Consistent with recent notion that numerous signaling pathways are implicated in root development, these miRNAs are implicated in auxin signaling, nutrition metabolism, or stress response and have potential role in mediating the signal interactions. However, a recapitulative representation of these results is especially desired. This review provides a global view of the involvement of miRNAs in root development focusing on the two plants, Arabidopsis and rice. Based on current research advances, several innovative mechanisms of miRNA transcription, feedback regulatory circuit between miRNAs and transcription factors (TFs), and miRNA-mediated signal interactions are also discussed.

MicroRNAs (miRNAs), one type of small RNAs (sRNAs) in plants, play an essential role in gene regulation. Several miRNA databases were established; however, successively generated new datasets need to be collected, organized and analyzed. To this end, we have constructed a plant miRNA knowledge base (PmiRKB) that provides four major functional modules. In the 'SNP' module, single nucleotide polymorphism (SNP) data of seven Arabidopsis (Arabidopsis thaliana) accessions and 21 rice (Oryza sativa) subspecies were collected to inspect the SNPs within pre-miRNAs (precursor microRNAs) and miRNA-target RNA duplexes. Depending on their locations, SNPs can affect the secondary structures of pre-miRNAs, or interactions between miRNAs and their targets. A second module, 'Pri-miR', can be used to investigate the tissue-specific, transcriptional contexts of pre-and pri-miRNAs (primary microRNAs), based on massively parallel signature sequencing data. The third module, 'MiR-Tar', was designed to validate thousands of miRNA-target pairs by using parallel analysis of RNA end (PARE) data. Correspondingly, the fourth module, 'Self-reg', also used PARE data to investigate the metabolism of miRNA precursors, including precursor processing and miRNA-or miRNA*-mediated self-regulation effects on their host precursors. PmiRKB can be freely accessed at

OsCAS (Oryza sativa Chip Annotation System) is a comprehensive web-based tool that analyzes the results of rice (Oryza sativa) microarray experiments and the potential relationships among genes. This platform is designed to facilitate the indepth exploration of microarray data with relatively high analysis efficiency. With a user-friendly web interface, OsCAS allows chip probe IDs as inputs to retrieve relevant information according to user’s designation. Public databases, such as GenBank, UniGene, Swiss-Prot, TIGR, KOME, KEGG, Gene Ontology, and miRBase, were integrated into our platform to cover gene information, protein features, metabolic pathways and regulatory factors in rice. Besides, OsCAS includes the reprocessed information from several analytical tools such as CSRDB and miRU to obtain deep insights into the primary annotations. OsCAS is time-cost-effective in annotating large sets of chip probe IDs, and can provide some precious hints for the biological experiment design. OsCAS runs on a Linux server and is accessible at http://bis.zju.edu.cn/oscas.

A uxin, known as the central hormone, plays essential roles in plant growth and development. In auxin signaling pathways, the tiny RNA molecules, i.e., microRNAs (miRNAs), show their strong potential in modulating the auxin signal transduction. Recently, we isolated a novel auxin resistant rice mutant osaxr (Oryza sativa auxin resistant) that exhibited plethoric root defects. Microarray experiments were carried out to investigate the expression patterns of both the miRNAs and the protein-coding genes in osaxr. A number of miRNAs showed reduced auxin sensitivity in osaxr compared with the wild type (WT), which may contribute to the auxin-resistant phenotype of the mutant. Auxin response elements (AuxREs) were demonstrated to be more frequently present in the promoters of auxin-related miRNAs. In our previous report, a comparative analysis of miRNA and protein-coding gene expression datasets uncovered a number of reciprocally expressed miRNA-target pairs. A feedback circuit between miRNA and auxin response factor (ARF) was then proposed. Here, we will discuss indepth some points raised in the previous report, in particular, the organ-specific expression patterns of miR164, the feedback regulatory model between miR167 and certain ARFs, and the potential signal interactions between auxin and nutrition or stress that are mediated by miRNAs in rice roots.

Based on theoretical analysis, similarity simulation tests, numerical simulation analysis and field observations, we analyzed rock collapse and rules of fraction evolution of overlying rocks and studied the rules in controlling the effect of an extremely thick igneous rock, found above a main mining coal seam in an area prone to coal mine disasters in the Haizi Coal Mine. The results show that this igneous rock, called a “main key stratum”, will not subside nor break for a long time, causing lower fractures and bed separations not to close. The presence of igneous rock plays an important role in rock bursts, mine floods, gas outburst and surface subsidence in coal mines. By analyzing the rules in controlling the effect of this igneous rock, we provide useful references for safety and high efficiency mining in coal mines under special geological conditions.