WorldCat Identities

Butte, Atul J.

Works: 72 works in 100 publications in 2 languages and 1,718 library holdings
Genres: Academic theses 
Roles: Contributor, Thesis advisor, Other, Author
Publication Timeline
Most widely held works by Atul J Butte
Microarrays for an integrative genomics by Isaac S Kohane( )

27 editions published between 2001 and 2005 in English and held by 1,587 WorldCat member libraries worldwide

Functional genomics--the deconstruction of the genome to determine the biological function of genes and gene interactions--is one of the most fruitful new areas of biology. The growing use of DNA microarrays allows researchers to assess the expression of tens of thousands of genes at a time. This quantitative change has led to qualitative progress in our ability to understand regulatory processes at the cellular level.This book provides a systematic introduction to the use of DNA microarrays as an investigative tool for functional genomics. The presentation is appropriate for readers from biology or bioinformatics. After presenting a framework for the design of microarray-driven functional genomics experiments, the book discusses the foundations for analyzing microarray data sets, genomic data-mining, the creation of standardized nomenclature and data models, clinical applications of functional genomics research, and the future of functional genomics
Tōgō genomikusu no tameno maikuroarei dēta anarishisu( Book )

3 editions published between 2004 and 2012 in Japanese and held by 3 WorldCat member libraries worldwide

GeneChaser: Identifying all biological and clinical conditions in which genes of interest are differentially expressed by Rong Chen( )

1 edition published in 2008 in English and held by 2 WorldCat member libraries worldwide

An integrative method for scoring candidate genes from association studies: application to warfarin dosing by Nicholas P Tatonetti( )

1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide

Content-based microarray search using differential expression profiles by Jesse M Engreitz( )

1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide

Data-driven detection, prediction, and validation of drug-drug interactions by Nicholas Pierino Tatonetti( )

1 edition published in 2012 in English and held by 2 WorldCat member libraries worldwide

Small molecule drugs continue to be an important part of medical therapy. However, their use is plagued by the onset of unexpected side effects, often seen only in late-stage clinical trials or after release to the market. As a result, there have been a number of high profile drug withdrawals because of side effects. More worrisome, however, are side effects that result from drug-drug interactions (DDIs). It is very difficult to empirically study DDIs before drugs enter the market because of the small samples of co-prescribed drugs in most late stage clinical drug (Phase III) studies. Some DDIs can be predicted based on knowledge of shared pathways of metabolism--such as when two drugs share a metabolizing enzyme and so the effective levels of one or both drugs are affected by saturation of the enzyme. But many DDIs are more idiosyncratic and difficult to predict. The most difficult cases are those in which two drugs produce a synergistic effect not seen with either drug alone. Thus, I created surveillance methods to detect unexpected DDIs, relying on clinical databases--both electronic medical records and spontaneous adverse event reporting systems. Understanding DDIs has an additional benefit for drug discovery. If two drugs have a synergistic effect, they may shed new light on the molecular mechanisms of their action or of the diseases they treat. If we use a model not of "one drug-one target" but of multiple interacting cellular pathways that respond to drugs ("the network is the target"), then we can leverage DDIs for the study of disease. However, to do this we need new ways to probe and understand these pathways, such as studying the unexpected synergies between drugs in observational reporting systems. Thus it would be extremely valuable to have computational methods that link adverse events to molecular events. The emergence of large databases linking drugs, diseases, drug effects, demographics and genes offers a new opportunity to create informatics methods for greater understanding small molecule effects at the clinical and biological level. I describe studies in which I have shown the great power of integrating these databases. In particular, I used the FDA Adverse Events Reporting System (FDA-AERS) to discover a signal for abnormal glucose in patients taking both paroxetine and pravastatin. Paroxetine is a selective serotonin reuptake inhibitor antidepressant. Pravastatin is an HMG CoA reductase inhibitor cholesterol-lowering drug. Neither is typically associated with hyperglycemia. Based on my analysis of the FDA-AERS, I examined patient electronic medical records in three separate hospitals (Stanford, Harvard, Vanderbilt), and demonstrated a striking increase in glucose levels on patients on both drugs, compared to their glucose levels on only one of the drugs. I also showed that mice on these two drugs have increased glucose. I am working with FDA to consider a potential update to the drug labels. Although this discovery illustrates the power of clinical data mining, the databases I used are filled with biases that make their use treacherous. I believe there are many similarly valuable discoveries to be made in these databases. However, only with careful attention to systematic biases can I ensure that the predictions I make are valid. In this thesis I describe methods to address the major informatics challenges to detecting and understanding the effects of taking multiple drugs at once. In particular, I have shown that I can (1) remove the bias introduced by unmeasured confounding variables, (2) improve the detection of drug interactions in cases of low or even non-reporting, (3) link drug effects to genes through chemical informatics methods, and (4) validate new drug effects using novel retrospective and prospective studies. The work forms an infrastructure that is useful to (1) pharmacogenomics scientists wishing to understand drug action at the molecular level, (2) pharmacologists wishing to better understand the effects of drugs singly and in combination, and (3) regulatory agencies wishing to understand the efficacy and safety of drugs and drug-interactions at a population level
Integration of electronic health records and public biological repositories illuminates human pathophysiology and underlying molecular relationships by David Pei-Ann Chen( )

1 edition published in 2011 in English and held by 2 WorldCat member libraries worldwide

Secondary use of electronic health record (EHR) data has the potential to unlock novel insight into human pathophysiology. While EHR data has often been used in retrospective studies, management of public health, and to improve patient safety, its use in discovering underlying molecular mechanisms of human disease and pathophysiology has been limited. Much of this can be attributed to the differing priorities between healthcare providers and basic biological researchers. The advent of biobanks that collect physiological measurements as well as tissue samples and molecular measurements promises to address this issue. However, the sheer number of different biological and clinical measurement modalities hinders the generation of a truly complete view of the human organism. The increased adoption of EHRs as well as growing biological data repositories enables researchers to answer biological questions applicable to the human population. The goal is not to treat humans as experimental organisms, but rather to gain as much knowledge as possible from every patient seen. By viewing EHRs as a repository of perturbations and their associated physiological consequences we can begin to design experiments that leverage EHR data to generate hypotheses that can be further evaluated. This thesis aims to describe methods to summarize EHR biomarker data in a systematic way to enable downstream analysis as well as methods for integrating EHR data and disparate biological data. I will describe the creation of the "clinarray" and its application to specific disease populations to differentiate patients by severity and to discover latent physiological factors associated with disease. I will also describe how to aggregate and analyze clinarrays from across the EHR to build models of aging. Finally I will discuss the use of diseases to integrate EHR data with gene expression data from a disparate biological data source to discover genes related to aging and to generate hypotheses for relationships between biomarkers and genes. The integration of readily available clinical and biological data promises to improve our understanding of phenomics without impacting patient care and adding an unnecessary burden to the healthcare system. It is important for biological research to leverage the increased amount of molecular and environmental data stored in EHRs to build a more complete view of the human organism
Selected proceedings of the First Summit on Translational Bioinformatics 2008 by Atul J Butte( )

1 edition published in 2009 in English and held by 2 WorldCat member libraries worldwide

Computational pathology for genomic medicine by Andrew Hanno Beck( )

1 edition published in 2013 in English and held by 2 WorldCat member libraries worldwide

The medical specialty of pathology is focused on the transformation of information extracted from patient tissue samples into biologically informative and clinically useful diagnoses to guide research and clinical care. Since the mid-19th century, the primary data type used by surgical pathologists has been microscopic images of hematoxylin and eosin stained tissue sections. Over the past several decades, molecular data have been increasingly incorporated into pathological diagnoses. There is now a need for the development of new computational methods to systematically model and integrate these complex data to support the development of data-driven diagnostics for pathology. The overall goal of this dissertation is to develop and apply methods in this new field of Computational Pathology, which is aimed at: 1) The extraction of comprehensive integrated sets of data characterizing disease from a patient's tissue sample; and 2) The application of machine learning-based methods to inform the interpretation of a patient's disease state. The dissertation is centered on three projects, aimed at the development and application of methods in Computational Pathology for the analysis of three primary data types used in cancer diagnostics: 1) morphology; 2) biomarker expression; and 3) genomic signatures. First, we developed the Computational Pathologist (C-Path) system for the quantitative analysis of cancer morphology from microscopic images. We used the system to build a microscopic image-based prognostic model in breast cancer. The C-Path prognostic model outperformed competing approaches and uncovered the prognostic significance of several novel characteristics of breast cancer morphology. Second, to systematically evaluate the biological informativeness and clinical utility of the two most commonly used protein biomarkers (estrogen receptor (ER) and progesterone receptor (PR)) in breast cancer diagnostics, we performed an integrative analysis over publically available expression profiling data, clinical data, and immunohistochemistry data collected from over 4,000 breast cancer patients, extracted from 20 published studies. We validated our findings on an independent integrated breast cancer dataset from over 2,000 breast cancer patients in the Nurses' Health Study. Our analyses demonstrated that the ER-/PR+ disease subtype is rare and non-reproducible. Further, in our genomewide study we identified hundreds of biomarkers more informative than PR for the stratification of both ER+ and ER- disease. Third, we developed a new computational method, Significance Analysis of Prognostic Signatures (SAPS), for the identification of robust prognostic signatures from clinically annotated Omics data. We applied SAPS to publically available clinically annotated gene expression data obtained from over 3,800 breast cancer patients from 19 published studies and over 1,700 ovarian cancer patients from 11 published studies. Using these two large meta-datasets, we applied SAPS and performed the largest analysis of subtype-specific prognostic pathways ever performed in breast or ovarian cancer. Our analyses led to the identification of a core set of prognostic biological signatures in breast and ovarian cancer and their molecular subtypes. Further, the SAPS method should be generally useful for future studies aimed at the identification of biologically informative and clinically useful signatures from clinically annotated Omics data. Taken together, these studies provide new insights into the biological factors driving cancer progression, and our methods and models will support the continuing development of the field of Computational Pathology
Personalized Medicine and Cardiovascular Disease: From Genome to Bedside by Stephen Chao Ying Pan( )

1 edition published in 2011 in English and held by 2 WorldCat member libraries worldwide

Scalable and accurate deep learning with electronic health records by Alvin Rajkomar( )

1 edition published in 2018 in English and held by 2 WorldCat member libraries worldwide

Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage by Dvir Aran( )

1 edition published in 2019 in English and held by 2 WorldCat member libraries worldwide

A longitudinal big data approach for precision health by Sophia Miryam Schüssler-Fiorenza Rose( )

1 edition published in 2019 in English and held by 2 WorldCat member libraries worldwide

The receptor CD44 is associated with systemic insulin resistance and proinflammatory macrophages in human adipose tissue by Li Fen Liu( )

1 edition published in 2015 in English and held by 2 WorldCat member libraries worldwide

Environment-wide associations to disease and disease-related phenotypes by Chirag Jagdish Patel( )

1 edition published in 2011 in English and held by 2 WorldCat member libraries worldwide

Common diseases arise out of combination of both genetic and environmental influences. Advances in genomic technology have enabled investigators to create hypotheses regarding the contribution of genetic factors at a breathtaking pace. However, the assessment of multiple and specific environmental factors--and their interactions with the genome-- has not. We lack high-throughput analytic methodologies to comprehensively and systematically associate multiple physical and specific environmental factors, or the "envirome", to disease and human health. We claim that the creation of hypotheses regarding the environmental contribution to disease is practicable through high-throughput analytic methods that have been well established in genomics. In the following dissertation, we develop and apply methods to systematically and comprehensively associate specific factors of the envirome with disease states, prioritizing factors for in-depth future study. The current disciplines of studying the environmental determinants of health include toxicology and epidemiology, which operate on molecular and population scales, respectively. This dissertation proposes approaches in both of these disciplines. For example, we have developed a framework to conduct the first "Environment-wide Association Study" (EWAS), systematically associating environmental factors to disease on a population scale. We have applied this framework to investigate type 2 diabetes and heart disease on cohorts that are representative United States population, finding novel and robust associations in diverse and independent cohorts. Given the lack of explained risk resulting from current day genome-wide studies, the time is ripe to usher in a more comprehensive study of the environment, or "enviromics", toward better understanding of multifactorial diseases and their prevention
Infection in the intensive care unit alters physiological networks by Adam D Grossman( )

1 edition published in 2009 in English and held by 2 WorldCat member libraries worldwide

Methods and applications for position-specific evolutionary features in clinical genomics by Joel Dudley( )

1 edition published in 2011 in English and held by 2 WorldCat member libraries worldwide

One of the grand challenges in genomic medicine is to translate fundamental scientific discoveries regarding the structure, variation, and function of the genomes of individuals and populations towards improved health outcomes. The main hypothesis of this thesis is that all forms of human genetic variation contributing to the etiology and pathophysiology of modern human diseases have distinct and quantifiable evolutionary histories, which can be computed for every position in the human genome independent of human population characteristics, and used as informative quantitative priors in the discovery and assessment of variants of clinical importance in modern human populations. To enable robust evaluation of the specific questions posed by this thesis, I first explore the necessary properties and theoretical basis for a null evolutionary hypothesis for Evolutionary Genomic Medicine, and conclude that the well-established Neutral Theory of Molecular Evolution provides a sound theoretical and methodological basis for evaluating alternative hypothesis in Evolutionary Genomic Medicine. Due to advances in multiplex genotyping technologies, genome-wide associations studies (GWAS), have emerged as the premier modality for discovery and assessment clinical genomic variation. Although these efforts have been successful in revealing thousands variants robustly associated with a broad spectrum of clinical phenotypes, the variants established by the GWAS approach have so far failed to explain large proportions of the known genetic variance associated with important clinical traits such as Type 2 Diabetes and Hypertension. Because disease-associated variation is linked with genomic loci of functional importance which have undergone evolutionary selection, and even the proxy loci (e.g. tagging SNPs) used to probe for disease associated loci themselves have quantifiable evolutionary histories, I evaluate a compendium of disease-associated variants to evaluate the effect of long-term evolutionary histories on the discovery of disease-associated variants. Through this work I demonstrate that disease-associated variants have distinct evolutionary properties, and that evolutionary features of positions can be incorporated as priors to improve discovery of disease-associated variants. A similar approach is applied to evaluate pharmacogenomics variants associated with warfarin, demonstrating that evolutionary features of genomic positions improve clinical assessment of pharmacogenomics variation. Through the findings and insights gained from efforts in pursuit of my thesis which are reported here, my collaborators and I clearly demonstrate that quantitative evolutionary features can be estimated for each position in the human genome across species, and then applied to modern human population data to improve discovery and assessment of genomic variation associated with clinical phenotypes
Translational bioinformatics applications in genome medicine by Atul J Butte( )

1 edition published in 2009 in English and held by 2 WorldCat member libraries worldwide

Evidence for benefit of statins to modify cognitive decline and risk in Alzheimer's disease by Nophar Geifman( )

1 edition published in 2017 in English and held by 2 WorldCat member libraries worldwide

Altering physiological networks using drugs: steps towards personalized physiology by Adam D Grossman( )

1 edition published in 2013 in English and held by 2 WorldCat member libraries worldwide

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WorldCat IdentitiesRelated Identities
Microarrays for an integrative genomics
Alternative Names
Atul Butte ahli biologi asal Amerika Serikat

Atul Butte American medical researcher

Atul Butte Amerikaans bioloog

ビュート, A. J