DNA mimic proteins are relatively obscure control factors that resemble DNA by mimicking its negatively charged distribution. They achieve this using negatively charged amino acids like aspartic acid (ASP/D) and glutamic acid (GLU/E). Known DNA mimic proteins control various cellular mechanisms, such as transcription, DNA repair, and gene regulation, by intervening in the binding of DNA to effector proteins. In addition to their biological functions, DNA mimic proteins may also be applicable in biotechnology, for example, by regulating CRISPR-Cas9 activity to enhance gene editing precision. Therefore, DNA mimic proteins warrant further research. However, most DNA mimic proteins cannot be identified using traditional bioinformatics methods owing to their unique amino acid sequences and structural features. We developed a new protein fingerprint, called relative distance protein fingerprint (RD-PFP), that can be used to analyze the distribution of amino acids on a protein surface. We optimized our RD-PFP by using machine learning and the characteristic feature of DNA mimic proteins (namely, their DNA-like negatively charged distribution) to more accurately predict DNA mimicry from protein structures. Our pioneering study contributes to the development of machine learning-based bioinformatics methods for screening DNA mimic proteins.

Nowadays, due to the explosive growth of web content and usage, users deal with their complex search tasks by web search engines. However, conventional search engines consider a search query corresponding only to a simple search task. In order to accomplish a complex search task, which consists of multiple subtask search goals, users usually have to issue a series of queries. For example, the complex search task “travel to Dubai” may involve several subtask search goals, including reserving hotel room, surveying Dubai landmarks, booking flights, and so forth. Therefore, a user can efficiently accomplish his or her complex search task if search engines can predict the complex search task with a variety of subtask search goals. In this work, we propose a complex search task model (CSTM) to deal with this problem. The CSTM first groups queries into complex search task clusters, and then generates subtask search goals from each complex search task cluster. To raise the performance of CSTM, we exploit four web resources including community question answering, query logs, search engine result pages, and clicked pages. Experimental results show that our CSTM is effective in identifying the comprehensive subtask search goals of a complex search task.

In this paper, we propose a two-phase fuzzy mining approach based on a tree structure to discover fuzzy frequent itemsets from a quantitative database. A simple tree structure called the upper-bound fuzzy frequent-pattern tree (abbreviated as UBFFP tree) is designed to help achieve the purpose. The two-phase fuzzy mining approach can easily derive the upper-bound fuzzy supports of itemsets through the tree and prune unpromising itemsets in the first phase, and then finds the actual frequent fuzzy itemsets in the second phase. Experimental results also show the good performance of the proposed approach.

In this paper, we design the compressed fuzzy FP-tree structure to mine fuzzy frequent itemsets from the transactions with quantitative values. It consists of two phases. In the first phase, a compressed fuzzy frequent pattern tree (CFFP tree) is constructed from the given quantitative transactions. In the second phase, a fuzzy mining approach is then proposed to mine the fuzzy frequent itemsets from the CFFP tree constructed. Experiments are also made to show the performance of the proposed approach.

In the past, we proposed a Fast Updated FP-tree (FUFP-tree) structure to efficiently handle new transactions and to make the tree update process become easier. In this paper, we attempt to modify the FUFP-tree construction based on the concept of pre-large itemsets. Pre-large itemsets are defined by a lower support threshold and an upper support threshold. The proposed approach can achieve a good execution time for tree construction especially when each time a small number of transactions are inserted. Experimental results also show that the proposed Pre-FUFP maintenance algorithm has a good performance for incrementally handling new transactions.

In this paper, we propose an enhanced approach to improving our previous method which employs syntactic structures (verb-object pairs) to identify latent user goals. Our new approach employs a supervised-learning method to learn hint verbs and considers URL information and title information to classify snippets into three coarse categories, which are resource-seeking, informational, and navigational. Also, we propose three different models to identify three different categories of specific latent user goals from the classified snippets.

With the fast growth of the Web, users often suffer from the problem of information overload since many existing search engines response lots of non-relevant documents containing query terms based on the search mechanism of keyword matching. In fact, it is eagerly expected by both users and search engine developers to reduce overloaded information by understanding user goals clearly. In this paper, we intend to utilize Web search results to identify user goals. We propose one novel probabilistic inference model which effectively employs syntactic features to discover a variety of confined user goals

Abstract Users' cross‐lingual queries to a digital library system might be short and the query terms may not be included in a common translation dictionary (unknown terms). In this article, the authors investigate the feasibility of exploiting the Web as the multilingual corpus source to translate unknown query terms for cross‐language information retrieval in digital libraries. They propose a Web‐based term translation approach to determine effective translations for unknown query terms by mining bilingual search‐result pages obtained from a real Web search engine. This approach can enhance the construction of a domain‐specific bilingual lexicon and bring multilingual support to a digital library that only has monolingual document collections. Very promising results have been obtained in generating effective translation equivalents for many unknown terms, including proper nouns, technical terms, and Web query terms, and in assisting bilingual lexicon construction for a real digital library system.

The purpose of this paper is to automatically create multilingual translation lexicons with regional variations. We propose a transitive translation approach to determine translation variations across languages that have insufficient corpora for translation via the mining of bilingual search-result pages and clues of geographic information obtained from Web search engines. The experimental results have shown the feasibility of the proposed approach in efficiently generating translation equivalents of various terms not covered by general translation dictionaries. It also revealed that the created translation lexicons can reflect different cultural aspects across regions such as Taiwan, Hong Kong and mainland China.

It is crucial for cross-language information retrieval (CLIR) systems to deal with the translation of unknown queries due to that real queries might be short. The purpose of this paper is to investigate the feasibility of exploiting the Web as the corpus source to translate unknown queries for CLIR. We propose an online translation approach to determine effective translations for unknown query terms via mining of bilingual search-result pages obtained from Web search engines. This approach can alleviate the problem of the lack of large bilingual corpora, translate many unknown query terms, provide flexible query specifications, and extract semantically-close translations to benefit CLIR tasks -- especially for cross-language Web search.

To discover translation knowledge in diverse data resources on the Web, this article proposes an effective approach to finding translation equivalents of query terms and constructing multilingual lexicons through the mining of Web anchor texts and link structures. Although Web anchor texts are wide-scoped hypertext resources, not every particular pair of languages contains sufficient anchor texts for effective extraction of translations for Web queries. For more generalized applications, the approach is designed based on a transitive translation model. The translation equivalents of a query term can be extracted via its translation in an intermediate language. To reduce interference from translation errors, the approach further integrates a competitive linking algorithm into the process of determining the most probable translation. A series of experiments has been conducted, including performance tests on term translation extraction, cross-language information retrieval, and translation suggestions for practical Web search services, respectively. The obtained experimental results have shown that the proposed approach is effective in extracting translations of unknown queries, is easy to combine with the probabilistic retrieval model to improve the cross-language retrieval performance, and is very useful when the considered language pairs lack a sufficient number of anchor texts. Based on the approach, an experimental system called LiveTrans has been developed for English--Chinese cross-language Web search.

This article presents an approach to automatically extracting translations of Web query terms through mining of Web anchor texts and link structures. One of the existing difficulties in cross-language information retrieval (CLIR) and Web search is the lack of appropriate translations of new terminology and proper names. The proposed approach successfully exploits the anchor-text resources and reduces the existing difficulties of query term translation. Many query terms that cannot be obtained in general-purpose translation dictionaries are, therefore, extracted.

The paper presents an approach to automatically extracting translations of Web query terms through mining of Web anchor texts and link structures. One of the existing difficulties in cross-language information retrieval (CLIR) and Web search is the lack of the appropriate translations of new terminology and proper names. Such a difficult problem can be effectively alleviated by our proposed approach, and the resource of anchor texts in the Web is proven a valuable corpus for this kind of term translation.

This paper presents an approach to automatically extracting the bilingual translations of many Web query terms through mining the Web anchor texts. Some preliminary experiments are conducted on using 109,416 Web pages containing both Chinese and English anchor texts in their in-links to extract Chinese translations of 200 English queries selected from popular query terms in Taiwan. It is found that the effective translations of 75% of the popular query terms can be extracted, in which 87.2% cannot be obtained in common translation dictionaries.

Since Web users ’ queries are often too short, an accurate and interactive speech interface is believed very helpful, especially for WAP-based Web search engines. To provide high accurate speech recognition and effective interactive search, a rigid and live web thesaurus that contains users ' search terms plus a set of relations between their associated terms is highly in demand. The purpose of this paper is intended to present a log-based approach for live thesaurus construction. Based on the live thesaurus, certain kinds of users ' information behaviors could be characterized and a more effective voice-based search engine could be developed.

The purpose of this paper is to present an on-going research that is intended to construct a live thesaurus directly from search term logs of real-world search engines. Such a thesaurus designed can contain representative search terms, their frequency in use, the corresponding subject categories, the associated and relevant terms, and the hot visiting Web sites/pages the search terms may reach.