http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/ 2026-04-07T11:12:21-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/ http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/lib/tpl/minima/images/favicon.ico text/html 2013-04-04T16:26:16-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=big_data&rev=1365117976&do=diff There is a lot of discussion about Big Data these days, and various ways to quantify its characteristics. I propose a simple frame of reference. Simply put, data can be big in the amount of measurements on an individual (eg, sequencing) or can be big in number of individuals on whom there are some measurements (eg, clinical notes, laboratory measurements, claims). text/html 2017-11-03T10:54:04-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=calendar&rev=1509731644&do=diff Send a meeting request via Doodle. text/html 2009-12-14T09:29:39-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=clench&rev=1260811779&do=diff CLENCH: A program for calculating cluster enrichment using the Gene Ontology Analysis of microarray data most often produces lists of genes with similar expression patterns, which are then subdivided into functional categories for biological interpretation. Such functional categorization is most commonly accomplished using Gene Ontology (GO) categories. Although there are several programs that identify and analyze functional categories for human, mouse and yeast genes none, of them accept Arabi… text/html 2011-03-06T12:14:37-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=download&rev=1299442477&do=diff Handout for AMIA-TBI 2011 Tutorial * [T02 Ontology Services i2b2-NCBO] Download the file here .. * [ C.diff Rx from Up-to-date] * [Text-extracted_relationships.zip] * [Slides for HCLS talk Oct 15th] For BMI 210 .. * [ OWL file from CLASS] * [ OWL file for BIOMEDIN 210] * [ Slides for BIOMEDIN 210] text/html 2018-11-19T15:30:18-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=home&rev=1542670218&do=diff Center for Biomedical Informatics Research X-235, Medical School Office Building 1265 Welch Road, Stanford, CA 94305-5479 Location and Parking Lab website, PubMed, NCBI Bibliography, G-Scholar, BMIR page, Research gate CAP, LinkedIn, MIDWG page text/html 2007-10-09T11:10:45-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=hybrow&rev=1191953445&do=diff The HyBrow server is moving, meanwhile try: Server 1 Server 2 Motivation: Experimental design, hypothesis-testing, and model-building in the current data-rich environment require the biologist to collect, evaluate and integrate large amounts of information of many disparate kinds. Developing a unified framework for the representation and conceptual integration of biological data and processes is a major challenge in bioinformatics because of the variety of available data and the different l… text/html 2008-10-24T10:34:10-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=hyque&rev=1224869650&do=diff Award 0849207 HyQue (for Hypothesis-based Querying of pathway models), will take as input working hypotheses about pathway models expressed in a knowledge-based formalism, evaluate their consistency using existing data in a knowledgebase, and provide as output contradictory evidence and suggestions for improving hypotheses. HyQue will incorporate formal knowledge representations based upon Semantic Web standards and an ontology to represent biological objects and relationships. text/html 2007-04-10T14:17:48-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=ontology_driven_querying&rev=1176239868&do=diff Background The Stanford Tissue Microarray Database (TMAD) is a repository of data serving a consortium of pathologists and biomedical researchers. The tissue samples in TMAD are annotated with multiple free-text fields, specifying the pathological diagnoses for each sample. These text annotations are not structured according to any ontology, making future integration of this resource with other biological and clinical data difficult. text/html 2007-04-10T14:11:03-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=pathway_knowledge_base&rev=1176239463&do=diff The role of proteins and their function in pathways is crucial to understanding complex biological processes and their failures that lead to disease. With over 200 pathway databases in existence, it is not possible for biologists to examine a pathway in all of them. The emergence and adoption of Biological Pathways Exchange (BioPAX), a standardized format for exchanging pathway information, provides a unique opportunity to integrate knowledge from multiple pathway databases. We conducted a case … text/html 2011-01-19T12:30:10-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=projects&rev=1295469010&do=diff Ontologies for data integration: Ontology-driven indexing of public datasets The volume of publicly available genomic scale data is increasing. Genomic datasets in public repositories are annotated with free-text fields that are not mapped to concepts in any ontology, making it difficult to integrate these datasets across repositories. In this project we have built a prototype system for ontology based annotation and indexing of biomedical data. The system processes the text metadata of divers… text/html 2012-08-20T21:14:52-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=ransum&rev=1345522492&do=diff Advanced statistical methods used to analyze high-throughput data (e.g. gene-expression assays) result in long lists of “significant genes.” One way to gain insight into the significance of altered expression levels is to determine whether Gene Ontology (GO) terms associated with a particular biological process, molecular function, or cellular component are over- or under-represented in the set of genes deemed significant. text/html 2007-04-10T14:05:57-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=scripts&rev=1176239157&do=diff We describe two sets of programs for expediting routine tasks in analysis of cDNA microarray data and promoter sequences. The first set permits bad data points to be flagged with respect to a number of parameters and performs normalization in 3 different ways. It allows combining of result files into comprehensive datasets, evaluation of the quality of both technical and biological replicates and row and/or column standardization of data matrices. The second set supports mapping ESTs in the geno… text/html 2007-11-08T11:22:42-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=sidebar&rev=1194549762&do=diff Sidebar text/html 2013-09-21T13:12:53-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=test&rev=1379794373&do=diff text/html 2007-08-02T15:14:10-07:00 http://web.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=umls-query&rev=1186092850&do=diff DOWNLOAD [Download UMLS-Query] Download a report describing UMLS-Query SYNOPSIS use UMLSQuery; my $U = new UMLSQuery; $U->init( u => 'username', p => 'password', h => 'hostname', dbname => 'umls'); $U->getCUI(string/aui/sui/lui, sab=>) $U->getAUI(string/cui/sui/lui, sab=>) $U->getSTR(string/cui/aui/sui/lui, sab=>) $U->getSAB(string/cui/aui/sui/lui) $U->mapToId(phrase, idtype…