M Sriram Iyengar

Publications of M Sriram Iyengar

• Data driven linear algebraic methods for analysis of molecular pathways: Application to disease progression in shock/trauma.

  Authors: Mary F McGuire, M Sriram Iyengar, David W Mercer

  Journal of biomedical informatics. 12/2011;

  MOTIVATION: Although trauma is the leading cause of death for those below 45years of age, there is a dearth of information about the temporal behavior of the underlying biological mechanisms in thoseMOTIVATION: Although trauma is the leading cause of death for those below 45years of age, there is a dearth of information about the temporal behavior of the underlying biological mechanisms in those who survive the initial trauma only to later suffer from syndromes such as multiple organ failure. Levels of serum cytokines potentially affect the clinical outcomes of trauma; understanding how cytokine levels modulate intra-cellular signaling pathways can yield insights into molecular mechanisms of disease progression and help to identify targeted therapies. However, developing such analyses is challenging since it necessitates the integration and interpretation of large amounts of heterogeneous, quantitative and qualitative data. Here we present the Pathway Semantics Algorithm (PSA), an algebraic process of node and edge analyses of evoked biological pathways over time for in silico discovery of biomedical hypotheses, using data from a prospective controlled clinical study of the role of cytokines in multiple organ failure (MOF) at a major US trauma center. A matrix algebra approach was used in both the PSA node and PSA edge analyses with different matrix configurations and computations based on the biomedical questions to be examined. In the edge analysis, a percentage measure of crosstalk called XTALK was also developed to assess cross-pathway interference. RESULTS: In the node/molecular analysis of the first 24h from trauma, PSA uncovered seven molecules evoked computationally that differentiated outcomes of MOF or non-MOF (NMOF), of which three molecules had not been previously associated with any shock/trauma syndrome. In the edge/molecular interaction analysis, PSA examined four categories of functional molecular interaction relationships - activation, expression, inhibition, and transcription - and found that the interaction patterns and crosstalk changed over time and outcome. The PSA edge analysis suggests that a diagnosis, prognosis or therapy based on molecular interaction mechanisms may be most effective within a certain time period and for a specific functional relationship.

• MEDRank: using graph-based concept ranking to index biomedical texts.

  Authors: Jorge R Herskovic, Trevor Cohen, Devika Subramanian, M Sriram Iyengar, Jack W Smith, Elmer V Bernstam

  International journal of medical informatics. 03/2011; 80(6):431-41.

  As the volume of biomedical text increases exponentially, automatic indexing becomes increasingly important. However, existing approaches do not distinguish central (or core) concepts from conceptsAs the volume of biomedical text increases exponentially, automatic indexing becomes increasingly important. However, existing approaches do not distinguish central (or core) concepts from concepts that were mentioned in passing. We focus on the problem of indexing MEDLINE records, a process that is currently performed by highly trained humans at the National Library of Medicine (NLM). NLM indexers are assisted by a system called the Medical Text Indexer (MTI) that suggests candidate indexing terms. To improve the ability of MTI to select the core terms in MEDLINE abstracts. These core concepts are deemed to be most important and are designated as "major headings" by MEDLINE indexers. We introduce and evaluate a graph-based indexing methodology called MEDRank that generates concept graphs from biomedical text and then ranks the concepts within these graphs to identify the most important ones. We insert a MEDRank step into the MTI and compare MTI's output with and without MEDRank to the MEDLINE indexers' selected terms for a sample of 11,803 PubMed Central articles. We also tested whether human raters prefer terms generated by the MEDLINE indexers, MTI without MEDRank, and MTI with MEDRank for a sample of 36 PubMed Central articles. MEDRank improved recall of major headings designated by 30% over MTI without MEDRank (0.489 vs. 0.376). Overall recall was only slightly (6.5%) higher (0.490 vs. 0.460) as was F(2) (3%, 0.408 vs. 0.396). However, overall precision was 3.9% lower (0.268 vs. 0.279). Human raters preferred terms generated by MTI with MEDRank over terms generated by MTI without MEDRank (by an average of 1.00 more term per article), and preferred terms generated by MTI with MEDRank and the MEDLINE indexers at the same rate. The addition of MEDRank to MTI significantly improved the retrieval of core concepts in MEDLINE abstracts and more closely matched human expectations compared to MTI without MEDRank. In addition, MEDRank slightly improved overall recall and F(2).

• The promise of the CCD: challenges and opportunity for quality improvement and population health.

  Authors: John D D'Amore, Dean F Sittig, Adam Wright, M Sriram Iyengar, Roberta B Ness

  AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium. 01/2011; 2011:285-94.

  Interoperability is a requirement of recent electronic health record (EHR) adoption incentive programs in the United States. One approved structure for clinical data exchange is the continuity ofInteroperability is a requirement of recent electronic health record (EHR) adoption incentive programs in the United States. One approved structure for clinical data exchange is the continuity of care document (CCD). While primarily designed to promote communication between providers during care transitions, coded data in the CCD can be re-used to aggregate data from different EHRs. This provides an opportunity for provider networks to measure quality and improve population health from a consolidated database. To evaluate such potential, this research collected CCDs from 14 organizations and developed a computer program to parse and aggregate them. In total, 139 CCDs were parsed yielding 680 data in the core content modules of problems, medications, allergies and results. Challenges to interoperability were catalogued and potential quality metrics evaluated based on available content. This research highlights the promise of CCDs for population health and recommends changes for future interoperability standards.

• Cytoview: development of a cell modelling framework.

  Authors: Prashant Khodade, Samta Malhotra, Nirmal Kumar, M Sriram Iyengar, N Balakrishnan, Nagasuma Chandra

  Journal of biosciences. 09/2007; 32(5):965-77.

  The biological cell, a natural self-contained unit of prime biological importance, is an enormously complex machine that can be understood at many levels. A higher-level perspective of the entireThe biological cell, a natural self-contained unit of prime biological importance, is an enormously complex machine that can be understood at many levels. A higher-level perspective of the entire cell requires integration of various features into coherent, biologically meaningful descriptions. There are some efforts to model cells based on their genome, proteome or metabolome descriptions. However, there are no established methods as yet to describe cell morphologies, capture similarities and differences between different cells or between healthy and disease states. Here we report a framework to model various aspects of a cell and integrate knowledge encoded at different levels of abstraction, with cell morphologies at one end to atomic structures at the other. The different issues that have been addressed are ontologies, feature description and model building. The framework describes dotted representations and tree data structures to integrate diverse pieces of data and parametric models enabling size, shape and location descriptions. The framework serves as a first step in integrating different levels of data available for a biological cell and has the potential to lead to development of computational models in our pursuit to model cell structure and function, from which several applications can flow out.

• Using hit curves to compare search algorithm performance.

  Authors: Jorge R Herskovic, M Sriram Iyengar, Elmer V Bernstam

  Journal of biomedical informatics. 04/2007; 40(2):93-9.

  Databases continue to grow but the metrics available to evaluate information retrieval systems have not changed. Large collections such as MEDLINE and the World Wide Web contain many relevantDatabases continue to grow but the metrics available to evaluate information retrieval systems have not changed. Large collections such as MEDLINE and the World Wide Web contain many relevant documents for common queries. Ranking is therefore increasingly important and successful information retrieval systems, such as Google, have emphasized ranking. However, existing evaluation metrics such as precision and recall, do not directly account for ranking. This paper describes a novel way of measuring information retrieval performance using weighted hit curves adapted from the field of statistical detection to reflect multiple desirable characteristics such as relevance, importance, and methodologic quality. In statistical detection, hit curves have been proposed to represent occurrence of interesting events during a detection process. Similarly, hit curves can be used to study the position of relevant documents within large result sets. We describe hit curves in light of a formal model of information retrieval, show how hit curves represent system performance including ranking, and define ways to statistically compare performance of multiple systems using hit curves. We provide example scenarios where traditional measures are less suitable than hit curves and conclude that hit curves may be useful for evaluating retrieval from large collections where ranking performance is crucial.

• Hypothesis generation in signaling networks.

  Authors: Derek A Ruths, Luay Nakhleh, M Sriram Iyengar, Shrikanth A G Reddy, Prahlad T Ram

  Journal of computational biology : a journal of computational molecular cell biology. 12/2006; 13(9):1546-57.

  Biological signaling networks comprise the chemical processes by which cells detect and respond to changes in their environment. Such networks have been implicated in the regulation of importantBiological signaling networks comprise the chemical processes by which cells detect and respond to changes in their environment. Such networks have been implicated in the regulation of important cellular activities, including cellular reproduction, mobility, and death. Though technological and scientific advances have facilitated the rapid accumulation of information about signaling networks, utilizing these massive information resources has become infeasible except through computational methods and computer-based tools. To date, visualization and simulation tools have received significant emphasis. In this paper, we present a graph-theoretic formalization of biological signaling network models that are in wide but informal use, and formulate two problems on the graph: the Constrained Downstream and Minimum Knockout Problems. Solutions to these problems yield qualitative tools for generating hypotheses about the networks, which can then be experimentally tested in a laboratory setting. Using established graph algorithms, we provide a solution to the Constrained Downstream Problem. We also show that the Minimum Knockout Problem is NP-Hard, propose a heuristic, and assess its performance. In tests on the Epidermal Growth Factor Receptor (EGFR) network, we find that our heuristic reports the correct solution to the problem in seconds. Source code for the implementations of both solutions is available from the authors upon request.

• GuideView: structured multi-modal delivery of clinical guidelines.

  Authors: M Sriram Iyengar, Subhajit Sarkar, Kira Bacal, Gregory Defouw, Phyllis McCulley, Victor Hurst

  AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium. 02/2005;

  GuideView is a system designed for structured, multi-modal delivery of clinical guidelines. Clinical instructions are presented simultaneously in voice, text, pictures or video or animations. UsersGuideView is a system designed for structured, multi-modal delivery of clinical guidelines. Clinical instructions are presented simultaneously in voice, text, pictures or video or animations. Users navigate using mouse-clicks and voice commands. An evaluation study performed at a medical simulation laboratory found that voice and video instructions were rated highly.