Search

Background DNA methylation is a crucial epigenomic mechanism in various biological processes. Using whole-genome bisulfite sequencing (WGBS) technology, methylated cytosine sites can be revealed at the single nucleotide level. However, the WGBS data analysis process is usually complicated and challenging. Results To alleviate the associated difficulties, we integrated the WGBS data processing steps and downstream analysis into a two-phase approach. First, we set up the required tools in Galaxy and developed workflows to calculate the methylation level from raw WGBS data and generate a methylation status summary, the mtable. This computation environment is wrapped into the Docker container image DocMethyl, which allows users to rapidly deploy an executable environment without tedious software installation and library dependency problems. Next, the mtable files were uploaded to the web server EpiMOLAS_web to link with the gene annotation databases that enable rapid data retrieval and analyses. Conclusion To our knowledge, the EpiMOLAS framework, consisting of DocMethyl and EpiMOLAS_web, is the first approach to include containerization technology and a web-based system for WGBS data analysis from raw data processing to downstream analysis. EpiMOLAS will help users cope with their WGBS data and also conduct reproducible analyses of publicly available data, thereby gaining insights into the mechanisms underlying complex biological phenomenon. The Galaxy Docker image DocMethyl is available at https://hub.docker.com/r/lsbnb/docmethyl/. EpiMOLAS_web is publicly accessible at http://symbiosis.iis.sinica.edu.tw/epimolas/.

Framing is a phenomenon that is studied and debated widely in sociology and political science. It refers to the manner in which audiences interpret information and justify their claims or activities. The subconscious influence of framing might lead to opinion changes and social movements. However, multi-frame classification on microblogging data has not yet been investigated. In this study, we aim to classify a large number of posts into frames. We describe in detail the implementation of a new algorithm for multi-frame classification tasks called Frame Dispatcher, which aims to classify microblogging data into frames. In our experiments, we extracted over 15,000 posts from approximately 200 Facebook fan pages concerning an anti-curriculum student movement. The experimental results show that Frame Dispatcher can classify microblogging data into frames efficiently and effectively.

Members of the academic community have increasingly turned to digital libraries to search for the latest work of their peers. On account of their role in the academic community, it is very important that these digital libraries collect citations in a consistent, accurate, and up-to-date manner, yet they do not correctly compile citations for myriads of authors for various reasons including authors with the same name, a problem known as the ''name ambiguity problem.'' This problem occurs when multiple authors share the same name and particularly when names are simplified as in cases where names merely contain the first initial and the last name. This paper proposes a reliable and accurate pair-wise similarities approach to disambiguate names using supervised classification on Web correlations and authorship correlations. This approach makes use of Web correlations among citations assuming citations that co-refer on publication lists on the Web should to refer to the same author. This approach also makes use of authorship correlations assuming citations with the same rare author name refer to the same author, and furthermore, citations with the same full names of authors or e-mail addresses likely refer to the same author. These two types of correlations are measured in our approach using pair-wise similarity metrics. In addition, a binary classifier, as part of supervised classification, is applied to label matching pairs of citations using pair-wise similarity metrics, and these labels are then used to group citations into different clusters such that each cluster represents an individual author. Results show our approach greatly improves upon the name disambiguation accuracy and performance of other proposed approaches, especially in some name clusters with high degree of ambiguity.

Dramatic increase in the number of academic publications has led to growing demand for efficient organization of the resources to meet researchers' needs. As a result, a number of network services have compiled databases from the public resources scattered over the Internet. However, publications by different conferences and journals adopt different citation styles. It is an interesting problem to accurately extract metadata from a citation string which is formatted in one of thousands of different styles. It has attracted a great deal of attention in research in recent years. In this paper, based on the notion of sequence alignment, we present a citation parser called BibPro that extracts components of a citation string. To demonstrate the efficacy of BibPro, we conducted experiments on three benchmark data sets. The results show that BibPro achieved over 90 percent accuracy on each benchmark. Even with citations and associated metadata retrieved from the web as training data, our experiments show that BibPro still achieves a reasonable performance.

In this paper, we propose a new concept called subject-keyphrase and also introduce a method to extract subject-keyphrases from a document. Subject-keyphrases refer to the words or phrases used to represent the sentence subjects of a document, i.e., the content of a document is organized around the subjects of the document. It can be expected that each sentence of a document is composed of a subject and an object, where the subject is defined in relation to its object. Using "definition" and "use" relations, we may thus identify the subjects in a given document by looking for keyphrases which appear frequently as subjects of sentences in the document. We thus present a subject-keyphrase extraction (SKE) algorithm based on the notion of definition-use chain (DU Chain) to identify subject-keyphrases. Experimental results show that SKE can successfully identify the subject-keyphrases to effectively capture the main idea of a document.

In this paper we focus on building a large scale keyword search service over structured Peer-to-Peer (P2P) networks. Current state-of-the-art keyword search approaches for structured P2P systems are based on inverted list intersection. However, the biggest challenge in those approaches is that when the indices are distributed over peers, a simple query may cause a large amount of data to be transmitted over the network. We propose in this paper a new P2P keyword search scheme, called "Proof," which aims to reduce the network traffic generated during the intersection process. We applied three main ideas in Proof to reduce network traffic, including (1) using a sorted query flow, (2) storing content summaries in the inverted lists, and (3) setting a stop condition for the checking of content summaries. We also discuss the advantages and limitations of Proof, and conducted extensive experiments to evaluate the search performance and the quality of search results. Our simulation results showed that, compared with previous solutions, Proof can dramatically reduce network traffic while providing 100% precision and high recall of search results, at some additional storage overhead.

To increase the commercial value and accessibility of pages, most content sites tend to publish their pages with intrasite redundant information, such as navigation panels, advertisements, and copyright announcements. Such redundant information increases the index size of general search engines and causes page topics to drift. In this paper, we study the problem of mining intrapage informative structure in news Web sites in order to find and eliminate redundant information. Note that intrapage informative structure is a subset of the original Web page and is composed of a set of fine-grained and informative blocks. The intrapage informative structures of pages in a news Web site contain only anchors linking to news pages or bodies of news articles. We propose an intrapage informative structure mining system called WISDOM (Web intrapage informative structure mining based on the document object model) which applies Information Theory to DOM tree knowledge in order to build the structure. WISDOM splits a DOM tree into many small subtrees and applies a top-down informative block searching algorithm to select a set of candidate informative blocks. The structure is built by expanding the set using proposed merging methods. Experiments on several real news Web sites show high precision and recall rates which validates WISDOM'S practical applicability.

We study the problem of mining the informative structure of a news Web site that consists of thousands of hyperlinked documents. We define the informative structure of a news Web site as a set of index pages (or referred to as TOC, i.e., table of contents, pages) and a set of article pages linked by these TOC pages. Based on the Hyperlink Induced Topics Search (HITS) algorithm, we propose an entropy-based analysis (LAMIS) mechanism for analyzing the entropy of anchor texts and links to eliminate the redundancy of the hyperlinked structure so that the complex structure of a Web site can be distilled. However, to increase the value and the accessibility of pages, most of the content sites tend to publish their pages with intrasite redundant information, such as navigation panels, advertisements, copy announcements, etc. To further eliminate such redundancy, we propose another mechanism, called InfoDiscoverer, which applies the distilled structure to identify sets of article pages. InfoDiscoverer also employs the entropy information to analyze the information measures of article sets and to extract informative content blocks from these sets. Our result is useful for search engines, information agents, and crawlers to index, extract, and navigate significant information from a Web site. Experiments on several real news Web sites show that the precision and the recall of our approaches are much superior to those obtained by conventional methods in mining the informative structures of news Web sites. On the average, the augmented LAMIS leads to prominent performance improvement and increases the precision by a factor ranging from 122 to 257 percent when the desired recall falls between 0.5 and 1. In comparison with manual heuristics, the precision and the recall of InfoDiscoverer are greater than 0.956.

This paper presents an intelligent Internet information system, Automatic Classifier for the Internet Resource Discovery (ACIRD), which uses machine learning techniques to organize and retrieve Internet documents. ACIRD consists of a knowledge acquisition process, document classifier, and two-phase search engine. The knowledge acquisition process of ACIRD automatically learns classification knowledge from classified Internet documents. The document classifier applies learned classification knowledge to classify newly collected Internet documents into one or more classes. Experimental results indicate that ACIRD performs as well or better than human experts in both knowledge acquisition and document classification. By using the learned classification knowledge and the given class lattice, the ACIRD two-phase search engine responds to user queries with hierarchically structured navigable results (instead of a conventional flat ranked document list), which greatly aids users in locating information from numerous, diversified Internet documents.

In this postgenomic era, a huge volume of information derived from expressed sequence tags (ESTs) has been constructed for functional description of gene expression profiles. Comparative studies have become more and more important to researchers of biology. In order to facilitate these comparative studies, we have constructed a user-friendly EST annotation pipeline with comparison tools on an integrated EST service website, Bio301. Bio301 includes regular EST preprocessing, BLAST similarity search, gene ontology (GO) annotation, statistics reporting, a graphical GO browsing interface, and microarray probe selection tools. In addition, Bio301 is equipped with statistical library comparison functions using multiple EST libraries based on GO annotations for mining meaningful biological information.

Survey of academic literature or papers should be considered with both relevance and importance of references. Authors cite related references by considering integrity and novelty. However, the state-of-art publicly academic search engines and services can only recommend related papers of a certain topic. It shows to manually evaluate the novelty of the recommended papers is necessary. In this paper, we propose a citation-network-based methodology, namely Citation Authority Diffusion (CAD), to rapidly mine the limited key papers of a topic, and measure the novelty on literature survey. A defined Authority Matrix ($AM$) is used to standardize duplication rate of authors and to describe the authority relation between the citing and the cited papers. Based on $AM$, our $CAD$ methodology leverages the Belief Propagation to diffuse the authority among the citation network. Therefore, CAD transforms the converged citation network to a novelty paper list to researchers. The experimental results show CAD can mine more novelty papers by using real-world cases.

Scholars usually spend great deal of time on searching and reading papers of key researchers. However, to objectively determine key researcher of a topic relies on several measurements, such as publication, citation, recent academic activities. In this paper, a prototype of scholars searching and recommendation system based on a web mining approach in expert finding system is proposed. The system gives and recommends the ranking of scholars and turns out top-k scholars. A new ranking measure is designed, namely p-index, to reveal the scholar ranking of a certain field. We use a real-world dataset to test the robustness, the experiment results show our approach outperforms other existing approaches and users are highly interested in using the system again.

Ontology is essential in the formalization of domain knowledge for effective human-computer interactions (i.e., expert-finding). Many researchers have proposed approaches to measure the similarity between concepts by accessing fuzzy domain ontology. However, engineering of the construction of domain ontologies turns out to be labor intensive and tedious. In this paper, we propose an approach to mine domain concepts from Wikipedia Category Network, and to generate the fuzzy relation based on a concept vector extraction method to measure the relatedness between a single term and a concept. Our methodology can conceptualize domain knowledge by mining Wikipedia Category Network. An empirical experiment is conducted to evaluate the robustness by using TREC dataset. Experiment results show the constructed fuzzy domain ontology derived by proposed approach can discover robust fuzzy domain ontology with satisfactory accuracy in information retrieval tasks.

Researchers usually present their publication records (we call citation records in this paper) on publication lists on the Web, which could be an important data source for many applications to collect more publication records than from some digital libraries, such as DBLP. However, it is still not easy to design an algorithm to extract citation records from publication lists because of the diversity of page layouts and citation formats. In this paper, we propose an automatic approach to extract citation records from publication list pages by utilizing two properties. First, citation records are usually represented as nodes at the same level in the DOM tree. Second, citation records in the same page are presented by similar HTML tags. Extensive experiments are conducted to measure the effects of all parameters and system performance. Experiment results show that our approach performs stable and well (with 86.2% of F-measure on average).

Building an expert finding system is very important for many applications especially in the academic environment. Previous work uses e-mails or Web pages as corpus to analyze the expertise for each expert. In this paper, we present an Expert Finding System, abbreviated as EFS to build experts' profiles by using their journal publications. For a given proposal, the EFS first looks up the Wikipedia Web site to get relative link information, and then list and rank all associated experts by using those information. In our experiments, we use a real-world dataset which comprises of 882 people and 13,654 papers, and are categorized into 9 expertise domains. Our experimental results show that the EFS works well on several expertise domains like ldquoArtificial Intelligencerdquo and ldquoImage & Pattern Recognitionrdquo etc.

Today, bibliographic digital libraries play an important role in helping members of academic community search for novel research. In particular, author disambiguation for citations is a major problem during the data integration and cleaning process, since author names are usually very ambiguous. For solving this problem, we proposed two kinds of correlations between citations, namely, Topic Correlationand Web Correlation, to exploit relationships between citations, in order to identify whether two citations with the same author name refer to the same individual.The topic correlation measures the similarity between research topics of two citations; while the Web correlation measures the number of co-occurrence in web pages. We employ a pair-wise grouping algorithm to group citations into clusters. The results of experiments show that the disambiguation accuracy has great improvement when using topic correlation and Web correlation, and Web correlation provides stronger evidences about the authors of citations.

The dramatic increase in the number of academic publications has led to a growing demand for efficient organization of the resources to meet researchers' specific needs. As a result, a number of network services have compiled databases from the public resources scattered over the Internet. However, publications in different conferences and journals follow different citation formats, so the problem of accurately extracting metadata from a publication string has also attracted a great deal of attention in recent years. In this paper, we extend our previous work to propose a new tool called BibPro for extracting metadata from citation strings by using a gene sequence alignment tool. The main enhancement of BibPro to our previously tool is that BibPro does not need knowledge databases (e.g., an author name database) to generate feature indices for citation strings. Instead, only the order of punctuation marks in a citation string is used to represent its format. Second, BibPro employs the basic local alignment search tool (BLAST) to find the most similar citation formats in database and then uses the Needleman-Wunsch algorithm to choose the best-fit citation format as the extraction template. Our experimental results show that, in terms of precision and recall, BibPro outperforms other existent systems (e.g., INFOMAP and ParaCite), and BibPro can scale well.

Finding and keeping track of other researchers' publication lists is an essential activity for every researcher, because they often contain citations not found elsewhere and may provide access to information, such as slides and talks, which can help other researchers keep abreast of state-of- the-art knowledge and technology. There are many different ways to generate publication list web pages, and a researcher may have several different versions of a publication list on the Web because he holds different positions. So it is difficult to find the correct publication list web page from the top results retrieved from search engines, especially when we only know the name of the researcher. Very few works have addressed the problem. In this paper, we propose a system called the "publication list Web page finder" (PLF), which can automatically find the publication list Web pages for a given researcher's name. The PLF system is an automatic and language-independent system, and its main idea is that publication list Web pages often contain many citations about a specific researcher, so the system uses those citations as clues to find out publication list web pages. Our experimental results show that the PLF system outperforms other approaches, especially when a researcher has multiple publication list Web pages.

Expressed sequence tags (ESTs) provides a scope viewer of transcribed gene in organisms in depth. In this work, we establish a web-based system, called Bio301. Bio301 is an integrated system for computational analysis of expressed sequence tags (EST) and microarray design decision support. It provides EST cleaning processes, BLAST similarity analysis, gene ontology (GO) retrieval, GO browsing, statistics and recommend cDNA/EST clone list for microarray design. Furthermore, the Web interface of Bio301 is easy for users to manipulate and manage EST data like a virtual lab on Internet.

In this paper we focus on building a large scale keyword search service over structured Peer-to-Peer (P2P) networks. Current state-of-the-art keyword search approaches for structured P2P systems are based on inverted list intersection. However, the biggest challenge in those approaches is that when the indices are distributed over peers, a simple query may cause a large amount of data to be transmitted over the network. We propose a new P2P keyword search scheme, called "Proof', to reduce network traffic for queries. The key idea is storing a content summary for each web page in the inverted list, so that a query can be processed by only transmitting a small size of candidate results. Our simulation results showed that, compared with previous DHT-based P2P systems, Proof can dramatically reduce network traffic and computation time. It provides 100% precision and 90.09% recall of search results, at an acceptable cost of storage overhead, even when the number of peers and documents increases continually.

Searching for information about a particular person is a common activity on search engines. However, current search engines do not provide any special function for search a person. Previous research has solved the problem by using additional background knowledge, such as a friend list, to cluster the searched web pages. However, it is still difficult to retrieve and choose suitable background knowledge. In this paper, we propose a Web Appearance Disambiguation (WAD) system to solve the problem by only using the hyperlink structures between web pages. The key idea of the WAD system is to find out smaller node motifs as evidences of close relationship between pages for clustering searched web pages. Our experimental results show that, under no background knowledge, the performance of the WAD system achieves 70% for the F-measure.

Laboratories worldwide are working on EST cDNA library and microarray projects, each project involving hundreds to hundreds of thousands of clones. To support the needs of individual laboratories, an efficient EST sequence management system is needed. Here, we describe BIO101, a centralized approach to laboratory EST sequence management and functional annotation. The system includes a) laboratory-based authorization, project-focused and custom database management features; b) a pipeline with integrated tools for producing and clustering high-quality ESTs and for identifying homologues; c) a pipeline scheduling mechanism that emphasizes fairness; and d) collaborative manual and automatic functional annotation features. BIO101 is accessible to registered members at http://bio101.iis.sinica.edu.tw/

We address the issue of providing highly informative and comprehensive annotations using information revealed by the structured vocabularies of gene ontology (GO). For a target, a set of candidate terms for inferring target properties is collected and form a unique distribution on the GO directed acyclic graph (DAG). We propose a novel ontology-based clustering algorithm $CLUGO, which considers GO hierarchical characteristics and the clustering of term distributions. By identifying significant groups in the distributions, CLUGO assigns comprehensive and correct annotations for a target. According to the results of experiments with automated sequence functional annotations, CLUGO represents a considerable improvement over our previous work - GOMIT in terms of recall while maintaining a similar level of precision. We conclude that given a GO candidate term distribution, CLUGO is an efficient ontology-based clustering algorithm for selecting comprehensive and correct annotations.

The Chinese financial news titles has only few words so that it is hard for measuring the similarity between titles if compare all their keywords only. In this study, we proposed a method of semantic similarity measurement for Chinese financial news titles based on constructing the Event Frame structure as the template of a Chinese financial news title. It concerns the relation between the basic meanings of two news titles for similarity measurement. In addition, a semantic similarity function is used to integrate both the relation of Event Frames of the financial news titles and the relation between the keywords of these titles. In this matter, the proposed method can differentiate the Chinese financial news that mention the same event from all other Chinese financial news by the Event Frame, since it concerns the relation between the basic meanings of two news titles and reduces the comparing time. The result of this approach shows that the Event Frame extracting has high precision and the provided semantic similarity measurement can emphasize the relation between the connotations of two news titles.

Due to the growth of dynamic page generation techniques, the amount and the complexity of Web pages has been increasing explosively, as has the information contained within Web pages. Redundant and irrelevant information is distributed and mixed throughout a page, making it difficult to automatically identify the useful information in that page. Consequently, we propose an information hierarchy in this paper, and, from that hierarchy, we can extract the significance and the relationship value of information contained within a Web page. We can then use this hierarchical structure to create a new browsing process. Our DOM-based Information Space Adsorption (DOMISA) system applies information theory to map information in a page into an information space, and our gradient tree adsorption (GTA) process uses the document object model (DOM) trees of pages to build information hierarchies. Experiments on several commercial news Web sites show high precision and recall rates achieved by DOMISA in determining information clusters of pages which validates its practical applicability to Web sites.

The authors address the issue of providing highly representative descriptions in automated functional annotations. For an uncharacterized sequence, a common strategy is to infer such annotations from those of well-characterized sequences that contain its homologues. However, under many circumstances, this strategy fails to produce meaningful annotations. Using information revealed by the structured vocabularies of Gene ontology, we propose a quantitative algorithm to assign representative annotations. We established a confidence function that reflects both the precision and coverage of a candidate annotation, and reasoned the function's parameters form analyses of significant forms of candidate distributions on the GO graph. We tested the algorithm with our self-designed BIO101 (http: //BIO101.iis.sinica.edu.tw), an automated annotation system that supports the workflows of functional annotations for expressed sequence tags (ESTs). According to our experimental results, the algorithm is capable of producing representative and meaningful functional annotations.

Scientific research reports usually contain a list of citations on previous related works. Therefore an automatic citation tool is an essential component of a digital library of scientific literatures. Due to variations in formats, it is difficult to automatically transform semi-structured citation data into structured citations. Some digital library institutes, like ResearchIndex (CiteSeer) or OpCit, have attempted automatic citation parsing. In order to recognize metadata, e.g., authors, title, journal, etc., of a citation string, we present a new methodology based on protein sequence alignment tool. We also develop a template generating system to transform known semi-structured citation strings into protein sequences. These protein sequences are then saved as templates in a database. A new semi-structured citation string is also translated it into a protein sequence. We then use BLAST (Basic Local Alignment Search Tool), a sequence alignment tool, to match for the most similar template to the new protein sequence from the template database previously constructed. We then parse metadata of the citation string according to the template. In our experiment, 2,500 templates are generated by our template generating system. By parsing all of these 2,500 citations using our parsing system, we obtain 89% precision rate. However, using the same template database to train ParaTools, 79% precision rate is obtained. Note that the original ParaTools using its default template database, which contains about 400 templates, only obtains 30% precision rate.

In this paper, we study the problem of mining the informative structure of a news Web site which consists of thousands of hyperlinked documents. We define the informative structure of a news Web site as a set of index pages (or referred to as TOC, i.e., table of contents, pages) and a set of article pages linked by TOC pages through informative links. It is noted that the Hyperlink Induced Topics Search (HITS) algorithm has been employed to provide a solution to analyzing authorities and hubs of pages. However, most of the content sites tend to contain some extra hyperlinks, such as navigation panels, advertisements and banners, so as to increase the add-on values of their Web pages. Therefore, due to the structure induced by these extra hyperlinks, HITS is found to be insufficient to provide a good precision in solving the problem. To remedy this, we develop an algorithm to utilize entropy-based Link Analysis on Mining Web Informative Structures. This algorithm is referred to as LAMIS. The key idea of LAMIS is to utilize information entropy for representing the knowledge that corresponds to the amount of information in a link or a page in the link analysis. Experiments on several real news Web sites show that the precision and the recall of LAMIS are much superior to those obtained by heuristic methods and conventional ink analysis methods.