Chien-Kang Huang

Relevant term suggestion in interactive web search based on contextual information in query session logs

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 users query session to make more effective suggestions.

ProteDNA: a sequence-based predictor of sequence-specific DNA-binding residues in transcription factors

This article presents the design of a sequence-based predictor named ProteDNA for identifying the sequence-specific binding residues in a transcription factor (TF). Concerning protein–DNA interactions, there are two types of binding mechanisms involved, namely sequence-specific binding and nonspecific binding. Sequence-specific bindings occur between protein sidechains and nucleotide bases and correspond to sequence-specific recognition of genes. Therefore, sequence-specific bindings are essential for correct gene regulation. In this respect, ProteDNA is distinctive since it has been designed to identify sequence-specific binding residues. In order to accommodate users with different application needs, ProteDNA has been designed to operate under two modes, namely, the high-precision mode and the balanced mode. According to the experiments reported in this article, under the high-precision mode, ProteDNA has been able to deliver precision of 82.3%, specificity of 99.3%, sensitivity of 49.8% and accuracy of 96.5%. Meanwhile, under the balanced mode, ProteDNA has been able to deliver precision of 60.8%, specificity of 97.6%, sensitivity of 60.7% and accuracy of 95.4%. ProteDNA is available at the following websites: http://protedna.csbb.ntu.edu.tw/ http://protedna.csie.ntu.edu.tw/ http://bio222.esoe.ntu.edu.tw/ProteDNA/.

Predicting RNA-binding residues from evolutionary information and sequence conservation

BackgroundRNA-binding proteins (RBPs) play crucial roles in post-transcriptional control of RNA. RBPs are designed to efficiently recognize specific RNA sequences after it is derived from the DNA sequence. To satisfy diverse functional requirements, RNA binding proteins are composed of multiple blocks of RNA-binding domains (RBDs) presented in various structural arrangements to provide versatile functions. The ability to computationally predict RNA-binding residues in a RNA-binding protein can help biologists reveal important site-directed mutagenesis in wet-lab experiments.ResultsThe proposed prediction framework named “ProteRNA” combines a SVM-based classifier with conserved residue discovery by WildSpan to identify the residues that interact with RNA in a RNA-binding protein. Although these conserved residues can be either functionally conserved residues or structurally conserved residues, they provide clues on the important residues in a protein sequence. In the independent testing dataset, ProteRNA has been able to deliver overall accuracy of 89.78%, MCC of 0.2628, F-score of 0.3075, and F0.5-score of 0.3546.ConclusionsThis article presents the design of a sequence-based predictor aiming to identify the RNA-binding residues in a RNA-binding protein by combining machine learning and pattern mining approaches. RNA-binding proteins have diverse functions while interacting with different categories of RNAs because these proteins are composed of multiple copies of RNA-binding domains presented in various structural arrangements to expand the functional repertoire of RNA-binding proteins. Furthermore, predicting RNA-binding residues in a RNA-binding protein can help biologists reveal important site-directed mutagenesis in wet-lab experiments.

Association of primary cutaneous amyloidosis with atopic dermatitis: a nationwide population-based study in Taiwan

Background  Primary cutaneous amyloidosis (PCA) is a pruritic skin disorder most commonly seen in Southeast Asia and South America. Association of PCA with atopic dermatitis (AD) has been reported in the literature. However, no large‐scale epidemiological study of PCA and its associations with other diseases has been conducted so far.

DNA-binding residues and binding mode prediction with binding-mechanism concerned models

BackgroundProtein-DNA interactions are essential for fundamental biological activities including DNA transcription, replication, packaging, repair and rearrangement. Proteins interacting with DNA can be classified into two categories of binding mechanisms - sequence-specific and non-specific binding. Protein-DNA specific binding provides a mechanism to recognize correct nucleotide base pairs for sequence-specific identification. Protein-DNA non-specific binding shows sequence independent interaction for accelerated targeting by interacting with DNA backbone. Both sequence-specific and non-specific binding residues contribute to their roles for interaction.ResultsThe proposed framework has two stage predictors: DNA-binding residues prediction and binding mode prediction. In the first stage - DNA-binding residues prediction, the predictor for DNA specific binding residues achieves 96.45% accuracy with 50.14% sensitivity, 99.31% specificity, 81.70% precision, and 62.15% F-measure. The predictor for DNA non-specific binding residues achieves 89.14% accuracy with 53.06% sensitivity, 95.25% specificity, 65.47% precision, and 58.62% F-measure. While combining prediction results of sequence-specific and non-specific binding residues with OR operation, the predictor achieves 89.26% accuracy with 56.86% sensitivity, 95.63% specificity, 71.92% precision, and 63.51% F-measure. In the second stage, protein-DNA binding mode prediction achieves 75.83% accuracy while using support vector machine with multi-class prediction.ConclusionThis article presents the design of a sequence based predictor aiming to identify sequence-specific and non-specific binding residues in a transcription factor with DNA binding-mechanism concerned. The protein-DNA binding mode prediction was introduced to help improve DNA-binding residues prediction. In addition, the results of this study will help with the design of binding-mechanism concerned predictors for other families of proteins interacting with DNA.

A Contextual Term Suggestion Mechanism for Interactive Web Search

This paper presents a novel term suggestion mechanism for interactive web search. The main distinction of the proposed mechanism is that it exploits the contextual information among the series of query terms submitted by the user in a search process. The main objective is to facilitate identifying the exact information need of the user and therefore to make better term suggestion to the user. This paper also discusses the main issues concerning implementation of the proposed term suggestion mechanism and reports some experimental results regarding its effects.

A sequence-based hybrid predictor for identifying conformationally ambivalent regions in proteins.

BackgroundProteins are dynamic macromolecules which may undergo conformational transitions upon changes in environment. As it has been observed in laboratories that protein flexibility is correlated to essential biological functions, scientists have been designing various types of predictors for identifying structurally flexible regions in proteins. In this respect, there are two major categories of predictors. One category of predictors attempts to identify conformationally flexible regions through analysis of protein tertiary structures. Another category of predictors works completely based on analysis of the polypeptide sequences. As the availability of protein tertiary structures is generally limited, the design of predictors that work completely based on sequence information is crucial for advances of molecular biology research.ResultsIn this article, we propose a novel approach to design a sequence-based predictor for identifying conformationally ambivalent regions in proteins. The novelty in the design stems from incorporating two classifiers based on two distinctive supervised learning algorithms that provide complementary prediction powers. Experimental results show that the overall performance delivered by the hybrid predictor proposed in this article is superior to the performance delivered by the existing predictors. Furthermore, the case study presented in this article demonstrates that the proposed hybrid predictor is capable of providing the biologists with valuable clues about the functional sites in a protein chain. The proposed hybrid predictor provides the users with two optional modes, namely, the high-sensitivity mode and the high-specificity mode. The experimental results with an independent testing data set show that the proposed hybrid predictor is capable of delivering sensitivity of 0.710 and specificity of 0.608 under the high-sensitivity mode, while delivering sensitivity of 0.451 and specificity of 0.787 under the high-specificity mode.ConclusionThough experimental results show that the hybrid approach designed to exploit the complementary prediction powers of distinctive supervised learning algorithms works more effectively than conventional approaches, there exists a large room for further improvement with respect to the achieved performance. In this respect, it is of interest to investigate the effects of exploiting additional physiochemical properties that are related to conformational ambivalence. Furthermore, it is of interest to investigate the effects of incorporating lately-developed machine learning approaches, e.g. the random forest design and the multi-stage design. As conformational transition plays a key role in carrying out several essential types of biological functions, the design of more advanced predictors for identifying conformationally ambivalent regions in proteins deserves our continuous attention.

Iterative Machine-Learning Chinese Term Extraction

This paper presents an iterative approach to extracting Chinese terms. Unlike the traditional approach to extracting Chinese terms, which requires the assistance of a dictionary, the proposed approach exploits the Support Vector Machine classifier which learns the extraction rules from the occurrences of a single popular term in the corpus. Additionally, we have designed a very effective feature set and a systematic approach for selecting the positive and negative samples as the source of training. An ancient Chinese corpus, Chinese Buddhist Texts, was taken as the experiment corpus. According to our experiment results, the proposed approach can achieve a very competitive result in comparison with the Chinese Knowledge and Information Processing (CKIP) system from Academia Sinica.

Incremental Extraction of Keyterms for Classifying Multilingual Documents in the Web

With the rapid growth of the Web, there is a need of high-performance techniques for document collection and classification. The goal of our research is to develop a platform to discover English, traditional and simplified Chinese documents from the Web in the Greater China area and classify them into a large number of subject classes. Three major challenges are encountered. First, the collection (i.e., the Web) is dynamic: new documents are added in and the features of subject classes change constantly. Second, the documents should be classified in a large-scale taxonomy. Third, the collection contains documents written in different languages. A PAT-tree-based approach is developed to deal with document classification in dynamic collections. It uses PAT tree as a working structure to extract keyterms from documents in each subject class and then update the features of the class accordingly. The feedback will contribute to the classification of the incoming documents immediately. In addition, we make use of a manually-constructed keyterms to serve as the base of document classification in a large-scale taxonomy. Two sets of experiments were done to evaluate the classification performance in a dynamic collection and in a large-scale taxonomy respectively. Both of the experiments yielded encouraging results. We further suggest an approach extended from the PAT-tree-based working structure to deal with classification in multilingual documents.

Iterative Feature Selection of Translation Texts for Translator Identification

Translation is an activity to transform linguistic information to another language. Translation product is a written text in a target-language (TL), which represents the result of a translation process, has been described by a comparison with the respective source-language (SL) text. The relation between the SL text and the TL text is a kind of the numerous and highly abstract models of equivalence (Koller 1978; 21983: 95; Ladmiral 1981: 393). In most cases, these models are very limited use for the practical translator. Problems in translating are caused at least as much by discrepancies in conceptual and texts use as by discrepancies in languages. Mankind has been engaged in transform for thousands of years, it affects the development of culture and language. “No language can exist unless it is steeped in the context of culture; and no culture can exist which does not have at its center, the structure of natural language.” Translation activities can promote exchanges between different cultures and languages, is also very important in the spread and development of religion. This study works for finding the discriminative features in Buddhist translation texts and other translation texts. Using these features can set up a training model for the identification of translator. Translator Identification is a process of examining the characteristics of translation texts to distinguish who is the translator. The similar processes had been used in authorship identification, writing forensics and similarity detection, which refer to statistical analysis of literary style.