Genome-wide snp microarray analysis among Malay sub-ethnic groups in peninsular Malaysia

The use of advanced technology in the field of genetic had influenced and upgraded the dicipline and had leds to a lot of advances in the genetics of human populations. Among them, microarray of single nucleotide polymorphism (SNP) allows large coverage of the human genome. SNP microarray was used for this study to find and characterize genetic differences among Malays sub-ethnic groups in Peninsular Malaysia. The Malay sub-ethnic groups of Peninsular Malaysia consist of several sub-groups that differ in a variety of factors including language, history of migration to Malaysia, origins, customs and daily social life. One hundred and thirty five Malays participated in this study and consisted of Kelantan Malay, Minang Malay, Javanese Malay, Bugis Malay, Kedah Malay Champa Malay, Pattani Malay and Banjar Malay. From our study, more than 50,000 SNPs were successfully genotyped. The study found that there is indeed allele frequency differences among the Malay subethnic groups which absolutely show their differences. In addition, this study goes deep into Malay differences by analyzing their differences of Linkage disequilibrium (LD), haplotype and tag SNPs on three selected chromosomes that showed the highest genetic distances. More on, SNP identification for each sub-ethnic group can be produced using tag SNPs. This study further investigated the related genes which were identified. There were 31 SNPs involved in the discovery of a strong LD block which could identity each of sub-ethnic Malay based on selected tag SNPs. The end result of this study is the discovery of the SNP identity for each sub-ethnic Malay group apart from Champa Malays whichdid not have a strong LD block to be interpreted. In addition, there were six genes of interest that could be attributed to Malay sub-ethnic groups, namely FRYL,SGCB, LIG1, LSM14A, LARGE and FAM118A genes. However, further investigations need to be done to confirm these findings

Exploiting Social Annotation for Automatic Resource Discovery

Information integration applications, such as mediators or mashups, that require access to information resources currently rely on users manually discovering and integrating them in the application. Manual resource discovery is a slow process, requiring the user to sift through results obtained via keyword-based search. Although search methods have advanced to include evidence from document contents, its metadata and the contents and link structure of the referring pages, they still do not adequately cover information sources -- often called ``the hidden Web''-- that dynamically generate documents in response to a query. The recently popular social bookmarking sites, which allow users to annotate and share metadata about various information sources, provide rich evidence for resource discovery. In this paper, we describe a probabilistic model of the user annotation process in a social bookmarking system del.icio.us. We then use the model to automatically find resources relevant to a particular information domain. Our experimental results on data obtained from \emph{del.icio.us} show this approach as a promising method for helping automate the resource discovery task.Comment: 6 pages, submitted to AAAI07 workshop on Information Integration on the We

Exploratory Analysis of Highly Heterogeneous Document Collections

We present an effective multifaceted system for exploratory analysis of highly heterogeneous document collections. Our system is based on intelligently tagging individual documents in a purely automated fashion and exploiting these tags in a powerful faceted browsing framework. Tagging strategies employed include both unsupervised and supervised approaches based on machine learning and natural language processing. As one of our key tagging strategies, we introduce the KERA algorithm (Keyword Extraction for Reports and Articles). KERA extracts topic-representative terms from individual documents in a purely unsupervised fashion and is revealed to be significantly more effective than state-of-the-art methods. Finally, we evaluate our system in its ability to help users locate documents pertaining to military critical technologies buried deep in a large heterogeneous sea of information.Comment: 9 pages; KDD 2013: 19th ACM SIGKDD Conference on Knowledge Discovery and Data Minin

Student user preferences for features of next-generation OPACs: a case study of University of Sheffield international students

Purpose. The purpose of this study is to identity the features that international student users prefer for next generation OPACs. Design/ methodology/ approach. 16 international students of the University of Sheffield were interviewed in July 2008 to explore their preferences among potential features in next generation OPACs. A semi-structured interview schedule with images of mock-up screens was used. Findings. The results of the interviews were broadly consistent with previous studies. In general, students expect features in next generation OPACs should be save their time, easy to use and relevant to their search. This study found that recommender features and features that can provide better navigation of search results are desired by users. However, Web 2.0 features, such as RSS feeds and those features which involved user participation were among the most popular. Practical implications. This paper produces findings of relevance to any academic library seeking to implement a next-generation OPAC. Originality/value. There have been no previous published research studies of users’ preferences among possible features of next-generation OPACs

Folks in Folksonomies: Social Link Prediction from Shared Metadata

Web 2.0 applications have attracted a considerable amount of attention because their open-ended nature allows users to create light-weight semantic scaffolding to organize and share content. To date, the interplay of the social and semantic components of social media has been only partially explored. Here we focus on Flickr and Last.fm, two social media systems in which we can relate the tagging activity of the users with an explicit representation of their social network. We show that a substantial level of local lexical and topical alignment is observable among users who lie close to each other in the social network. We introduce a null model that preserves user activity while removing local correlations, allowing us to disentangle the actual local alignment between users from statistical effects due to the assortative mixing of user activity and centrality in the social network. This analysis suggests that users with similar topical interests are more likely to be friends, and therefore semantic similarity measures among users based solely on their annotation metadata should be predictive of social links. We test this hypothesis on the Last.fm data set, confirming that the social network constructed from semantic similarity captures actual friendship more accurately than Last.fm's suggestions based on listening patterns.Comment: http://portal.acm.org/citation.cfm?doid=1718487.171852