Weijian Xuan

SNP Function Portal

MOTIVATION Finding the potential functional significance of SNPs is a major bottleneck in understanding genome-wide SNP scanning results, as the related functional data are distributed across many different databases. The SNP Function Portal is designed to be a clearing house for all public domain SNP functional annotation data, as well as in-house functional annotations derived from different data sources. It currently contains SNP functional annotations in six major categories including genomic elements, transcription regulation, protein function, pathway, disease and population genetics. Besides extensive SNP functional annotations, the SNP Function Portal includes a powerful search engine that accepts different types of genetic markers as input and identifies all genetically related SNPs based on the HapMap Phase II data as well as the relationship of different markers to known genes. As a result, our system allows users to identify the potential biological impact of genetic markers and complex relationships among genetic markers and genes, and it greatly facilitates knowledge discovery in genome-wide SNP scanning experiments. AVAILABILITY http://brainarray.mbni.med.umich.edu/Brainarray/Database/SearchSNP/snpfunc.aspx.

Medline search engine for finding genetic markers with biological significance

MOTIVATION Genome-wide high density SNP association studies are expected to identify various SNP alleles associated with different complex disorders. Understanding the biological significance of these SNP alleles in the context of existing literature is a major challenge since existing search engines are not designed to search literature for SNPs or other genetic markers. The literature mining of gene and protein functions has received significant attention and effort while similar work on genetic markers and their related diseases is still in its infancy. Our goal is to develop a web-based tool that facilitates the mining of Medline literature related to genetic studies and gene/protein function studies. Our solution consists of four main function modules for (1) identification of different types of genetic markers or genetic variations in Medline records (2) distinguishing positive versus negative linkage or association between genetic markers and diseases (3) integrating marker genomic location data from different databases to enable the retrieval of Medline records related to markers in the same linkage disequilibrium region (4) and a web interface called MarkerInfoFinder to search, display, sort and download Medline citation results. Tests using published data suggest MarkerInfoFinder can significantly increase the efficiency of finding genetic disorders and their underlying molecular mechanisms. The functions we developed will also be used to build a knowledge base for genetic markers and diseases. AVAILABILITY The MarkerInfoFinder is publicly available at: http://brainarray.mbni.med.umich.edu/brainarray/datamining/MarkerInfoFinder.

Open Biomedical Ontology-based Medline exploration

BackgroundEffective Medline database exploration is critical for the understanding of high throughput experimental results and the development of novel hypotheses about the mechanisms underlying the targeted biological processes. While existing solutions enhance Medline exploration through different approaches such as document clustering, network presentations of underlying conceptual relationships and the mapping of search results to MeSH and Gene Ontology trees, we believe the use of multiple ontologies from the Open Biomedical Ontology can greatly help researchers to explore literature from different perspectives as well as to quickly locate the most relevant Medline records for further investigation.ResultsWe developed an ontology-based interactive Medline exploration solution called PubOnto to enable the interactive exploration and filtering of search results through the use of multiple ontologies from the OBO foundry. The PubOnto program is a rich internet application based on the FLEX platform. It contains a number of interactive tools, visualization capabilities, an open service architecture, and a customizable user interface. It is freely accessible at: http://brainarray.mbni.med.umich.edu/brainarray/prototype/pubonto.

Cross-domain neurobiology data integration and exploration.

BackgroundUnderstanding the biomedical implications of data from high throughput experiments requires solutions for effective cross-scale and cross-domain data exploration. However, existing solutions do not provide sufficient support for linking molecular level data to neuroanatomical structures, which is critical for understanding high level neurobiological functions.ResultsOur work integrates molecular level data with high level biological functions and we present results using anatomical structure as a scaffold. Our solution also allows the sharing of intermediate data exploration results with other web applications, greatly increasing the power of cross-domain data exploration and mining.ConclusionsThe Flex-based PubAnatomy web application we developed enables highly interactive visual exploration of literature and experimental data for understanding the relationships between molecular level changes, pathways, brain circuits and pathophysiological processes. The prototype of PubAnatomy is freely accessible at:[http://brainarray.mbni.med.umich.edu/Brainarray/prototype/PubAnatomy]

GeneInfoMiner—a web server for exploring biomedical literature using batch sequence ID

GeneInfoMiner is a web-based system for searching Medline abstracts using sequence ID lists such as GenBank accession numbers derived from high-throughput experiments. It will map query results to MeSH topics to facilitate the exploration of the biological significance of the sequence ID lists. GeneInfoMiner is based on a custom gene and protein name identification engine that can map gene and protein names to important molecular biology databases.

Identifying gene and protein names from biological texts

Extracting and identifying gene and protein names from literature is a critical step for mining functional information of genes and proteins. While extensive efforts have been devoted to this important task, most of them were aiming at extracting gene/protein name per se without paying much attention to associate the extracted name with existing gene and protein database entries. We developed a simple and efficient method to identify gene and protein names in literature using a combination of heuristic and statistical strategies. Our approach will map the extracted names to individual LocusLink entries thus enable the seamless integration of literature information with existing gene/protein databases. Evaluation on a test corpus shows that our method can achieve both high recall and precision. Our method exhibits good performance and can be used as a building block for large biomedical literature mining systems.

An active visual search interface for Medline.

Searching the Medline database is almost a daily necessity for many biomedical researchers. However, available Medline search solutions are mainly designed for the quick retrieval of a small set of most relevant documents. Because of this search model, they are not suitable for the large-scale exploration of literature and the underlying biomedical conceptual relationships, which are common tasks in the age of high throughput experimental data analysis and cross-discipline research. We try to develop a new Medline exploration approach by incorporating interactive visualization together with powerful grouping, summary, sorting and active external content retrieval functions. Our solution, PubViz, is based on the FLEX platform designed for interactive web applications and its prototype is publicly available at: http://brainarray.mbni.med.umich.edu/Brainarray/DataMining/PubViz.

Tagging Sentence Boundaries in Biomedical Literature

Identifying sentence boundaries is an indispensable task for most natural language processing (NLP) systems. While extensive efforts have been devoted to mine biomedical text using NLP techniques, few attempts are specifically targeted at disambiguating sentence boundaries in biomedical literature, which has a number of unique features that can reduce the accuracy of algorithms designed for general English genre significantly. In order to increase the accuracy of sentence boundary identification for biomedical literature, we developed a method using a combination of heuristic and statistical strategies. Our approach does not require part-of-speech taggers or training procedures. Experiments with biomedical test corpora show our system significantly outperforms existing sentence boundary determination algorithms, particularly for full text biomedical literature. Our system is very fast and it should also be easily adaptable for sentence boundary determination in scientific literature from non-biomedical fields.

Cross-Domain Neurobiology Data Integration and Exploration

Understanding the biomedical implications of data from high throughput experiments requires solutions for effective cross-scale and cross-domain data exploration. Our work integrates molecular level data with high level biological functions using anatomical structure as a scaffold. The Flex-based PubAnatomy web application we developed enables highly interactive visual exploration of literature and experimental data for understanding the relationships between molecular level changes, pathways, brain circuits and pathophysiological processes. PubAnatomy also allows the sharing of intermediate data exploration results with other web applications, greatly increasing the power of cross-domain data exploration and mining. The prototype of PubAnatomy is freely accessible at: http://brainarray.mbni.med.umich.edu/ Brainarray/prototype/PubAnatomy