CiteULike: jyuhs Butte

Clinical arrays of laboratory measures, or "clinarrays", built from an electronic health record enable disease subtyping by severity.

DP Chen — 2008-08-19T03:06:36-00:00

AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium (2007), pp. 115-119.

The severity of diseases has often been assigned by direct observation of a patient and by pathological examination after symptoms have appeared. As we move into the genomic era, the ability to predict disease severity prior to manifestation has improved dramatically due to genomic sequencing and analysis of gene expression microarrays. However, as the severity of diseases can be exacerbated by non genetic factors, the ability to predict disease severity by examining gene expression alone may be inadequate. We propose the creation of a "clinarray" to examine phenotypic expression in the form of clinical laboratory measurements. We demonstrate that the clinarray can be used to distinguish between the severities of patients with cystic fibrosis and those with Crohns disease by applying unsupervised clustering methods that have been previously applied to microarrays.

Evaluation and integration of 49 genome-wide experiments and the prediction of previously unknown obesity-related genes

Sangeeta English — 2007-11-17T18:29:58-00:00

Bioinformatics, Vol. 23, No. 21. (1 November 2007), pp. 2910-2917.

Motivation: Genome-wide experiments only rarely show resounding success in yielding genes associated with complex polygenic disorders. We evaluate 49 obesity-related genome-wide experiments with publicly available findings including microarray, genetics, proteomics and gene knock-down from human, mouse, rat and worm, in terms of their ability to rediscover a comprehensive set of genes previously found to be causally associated or having variants associated with obesity. Results: Individual experiments show poor predictive ability for rediscovering known obesity-associated genes. We show that intersecting the results of experiments significantly improves the sensitivity, specificity and precision of the prediction of obesity-associated genes. We create an integrative model that statistically significantly outperforms all 49 individual genome-wide experiments. We find that genes known to be associated with obesity are significantly implicated in more obesity-related experiments and use this to provide a list of genes that we predict to have the highest likelihood of association for obesity. The approach described here can include any number and type of genome-wide experiments and might be useful for other complex polygenic disorders as well. Contact: abutte@stanford.edu Supplementary information: Available online and at http://buttelab.stanford.edu/doku.php?id=public:obesityintegration 10.1093/bioinformatics/btm483

Methodologies for extracting functional pharmacogenomic experiments from international repository.

YA Lin — 2008-08-19T02:57:57-00:00

AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium (2007), pp. 463-467.

Pharmacogenomic studies are studies designed to elucidate the relationships between drugs and genes on the genomic scale. Given the rapidly increasing amount of microarray data in international repositories, and the implicit drug information contained in PubMed, MeSH and UMLS, we propose automatic methods for identifying drug related microarray experiments from NCBI GEO by the semantic connections between these data resources. In our study, we find that 51.5% of microarray experiments are associated with at least one PubMed identifier, 22.1% of these contain a MeSH term that relates to the UMLS Pharmacologic Substances semantic sub-tree. Our work shows an abundance of publicly available gene expression data available to enable the discovery of novel drug indications, drug classifications and other pharmacogenomic studies.

Finding disease-related genomic experiments within an international repository: first steps in translational bioinformatics.

AJ Butte — 2008-03-03T16:08:09-00:00

AMIA Annu Symp Proc (2006), pp. 106-110.

The amount of gene expression data in international repositories has grown exponentially. An important first step in translating the results of genomic experiments into medicine is to relate these genomic experiments to the human diseases they have studied. Unfortunately, repositories for expression data store the crucial annotative details only as free-text, making it manually intractable to link these with human disease. In this study, we sought to find experiments in NCBI GEO that are related to human diseases by making use of annotations relating these experiments with PUBMED identifiers representing the publication in which each experiment was published. In this manner, we find that 35% of PUBMED-associated genomic experiments can be related to a human disease, and that publicly-available data from these genomic experiments can already be related to over 270 human diseases and conditions. This represents an important first step in bridging the world of nucleotides, transcripts and expression with the afflications of us all.

Novel integration of hospital electronic medical records and gene expression measurements to identify genetic markers of maturation.

DP Chen — 2008-05-16T12:58:50-00:00

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing (2008), pp. 243-254.

Traditionally, the elucidation of genes involved in maturation and aging has been studied in a temporal fashion by examining gene expression at different time points in an organism's life as well as by knocking out, knocking in, and mutating genes thought to be involved. Here, we propose an in silico method to combine clinical electronic medical record (EMR) data and gene expression measurements in the context of disease to identify genes that may be involved in the process of human maturation and aging. First we show that absolute lymphocyte count may serve as a biomarker for maturation by using statistical methods to compare trends among different clinical laboratory tests in response to an increase in age. We then propose using the rate of decay for absolute lymphocyte count across 12 diseases as a proxy for differences in aging. We correlate the differing rates with gene expression across the same diseases to find maturation/aging related genes. Among the 53 genes with strongest correlations between expression profile and change in rate of decay, we found genes previously implicated in the process of aging, including MGMT (DNA repair), TERF2 (telomere stability), POLD1 (DNA replication and repair), and POLG (mtDNA replication).

Enabling integrative genomic analysis of high-impact human diseases through text mining.

J Dudley — 2008-05-14T20:17:55-00:00

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing (2008), pp. 580-591.

Our limited ability to perform large-scale translational discovery and analysis of disease characterizations from public genomic data repositories remains a major bottleneck in efforts to translate genomics experiments to medicine. Through comprehensive, integrative genomic analysis of all available human disease characterizations we gain crucial insight into the molecular phenomena underlying pathogenesis as well as intra- and inter-disease differentiation. Such knowledge is crucial in the development of improved clinical diagnostics and the identification of molecular targets for novel therapeutics. In this study we build on our previous work to realize the next important step in large-scale translational discovery and analysis, which is to automatically identify those genomic experiments in which a disease state is compared to a normal control state. We present an automated text mining method that employs Natural Language Processing (NLP) techniques to automatically identify disease-related experiments in the NCBI Gene Expression Omnibus (GEO) that include measurements for both disease and normal control states. In this manner, we find that 62% of disease-related experiments contain sample subsets that can be automatically identified as normal controls. Furthermore, we calculate that the identified experiments characterize diseases that contribute to 30% of all human disease-related mortality in the United States. This work demonstrates that we now have the necessary tools and methods to initiate large-scale translational bioinformatics inquiry across the broad spectrum of high-impact human disease.

Creation and implications of a phenome-genome network

Atul Butte — 2006-01-11T03:42:50-00:00

Nature Biotechnology, Vol. 24, No. 1. (10 January 2006), pp. 55-62.

MEDICINE: The Ultimate Model Organism

Atul Butte — 2008-04-18T14:04:49-00:00

Science, Vol. 320, No. 5874. (18 April 2008), pp. 325-327.

10.1126/science.1158343

AILUN: reannotating gene expression data automatically

Rong Chen — 2007-10-31T18:40:34-00:00

Nat Meth, Vol. 4, No. 11. (November 2007), pp. 879-879.

TIM-1 and TIM-4 glycoproteins bind phosphatidylserine and mediate uptake of apoptotic cells.

N Kobayashi — 2008-03-05T03:08:11-00:00

Immunity, Vol. 27, No. 6. (December 2007), pp. 927-940.

The T cell immunoglobulin mucin (TIM) proteins regulate T cell activation and tolerance. Here we showed that TIM-4 is expressed on human and mouse macrophages and dendritic cells, and both TIM-4 and TIM-1 specifically bound phosphatidylserine (PS) on the surface of apoptotic cells but not any other phospholipid tested. TIM-4(+) peritoneal macrophages, TIM-1(+) kidney cells, and TIM-4- or TIM-1-transfected cells efficiently phagocytosed apoptotic cells, and phagocytosis could be blocked by TIM-4 or TIM-1 monoclonal antibodies. Mutations in the unique cavity of TIM-4 eliminated PS binding and phagocytosis. TIM-4 mAbs that blocked PS binding and phagocytosis mapped to epitopes in this binding cavity. These results show that TIM-4 and TIM-1 are immunologically restricted members of the group of receptors whose recognition of PS is critical for the efficient clearance of apoptotic cells and prevention of autoimmunity.