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Protein-network modeling of prostate cancer gene signatures reveals essential pathways in disease recurrence

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第4期18卷 392-402页

作者： Chen, James L. Li, Jianrong Stadler, Walter M. Lussier, Yves A. Univ Chicago Dept Med Ctr Biomed Informat Chicago IL 60637 USA Univ Chicago Inst Genom & Syst Biol Chicago IL 60637 USA Univ Chicago Computat Inst Chicago IL 60637 USA Univ Chicago Dept Med Hematol Oncol Sect Chicago IL 60637 USA

Objective Uncovering the dominant molecular deregulation among the multitude of pathways implicated in aggressive prostate cancer is essential to intelligently developing targeted therapies. Paradoxically, published prostate cancer gene expression signatures of poor prognosis share little overlap and thus do not reveal shared mechanisms. The authors hypothesize that, by analyzing gene signatures with quantitative models of protein-protein interactions, key pathways will be elucidated and shown to be shared. Design The authors statistically prioritized common interactors between established cancer genes and genes from each prostate cancer signature of poor prognosis independently via a previously validated single protein analysis of network (SPAN) methodology. Additionally, they computationally identified pathways among the aggregated interactors across signatures and validated them using a similarity metric and patient survival. Measurement Using an information-theoretic metric, the authors assessed the mechanistic similarity of the interactor signature. Its prognostic ability was assessed in an independent cohort of 198 patients with high-Gleason prostate cancer using Kaplan Meier analysis. Results Of the 13 prostate cancer signatures that were evaluated, eight interacted significantly with established cancer genes (false discovery rate <5%) and generated a 42-gene interactor signature that showed the highest mechanistic similarity (p<0.0001). Via parameter-free unsupervised classification, the interactor signature dichotomized the independent prostate cancer cohort with a significant survival difference (p=0.009). Interpretation of the network not only recapitulated phosphatidylinositol-3 kinase/NF-kappa B signaling, but also highlighted less well established relevant pathways such as the Janus kinase 2 cascade. Conclusions SPAN methodolgy provides a robust means of abstracting disparate prostate cancer gene expression signatures into clinically useful, prioritize

关键词： Prostate cancer protein networks systems biology information theory network modeling Simulation of complex systems (at all levels: molecules to work groups to organizations) knowledge representations Uncertain reasoning and decision theory languages and computational methods statistical analysis of large datasets advanced algorithms discovery and text and data mining methods Natural-language processing Automated learning Ontologies

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The Yale cTAKES extensions for document classification: architecture and application

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第5期18卷 614-620页

作者： Garla, Vijay Lo Re, Vincent, III Dorey-Stein, Zachariah Kidwai, Farah Scotch, Matthew Womack, Julie Justice, Amy Brandt, Cynthia Yale Univ Ctr Med Informat Interdept Program Computat Biol & Bioinformat New Haven CT 06520 USA Univ Penn Sch Med Ctr Clin Epidemiol & Biostat Philadelphia PA 19104 USA Connecticut VA Healthcare Syst West Haven CT USA Arizona State Univ Dept Biomed Informat Tempe AZ USA Yale Univ Sch Nursing New Haven CT 06520 USA Yale Univ Sch Med Gen Internal Med New Haven CT 06520 USA Yale Univ Ctr Med Informat Sch Med New Haven CT 06520 USA

Background Open-source clinical natural-language-processing (NLP) systems have lowered the barrier to the development of effective clinical document classification systems. Clinical natural-language-processing systems annotate the syntax and semantics of clinical text;however, feature extraction and representation for document classification pose technical challenges. Methods The authors developed extensions to the clinical Text Analysis and Knowledge Extraction System (cTAKES) that simplify feature extraction, experimentation with various feature representations, and the development of both rule and machine-learning based document classifiers. The authors describe and evaluate their system, the Yale cTAKES Extensions (YTEX), on the classification of radiology reports that contain findings suggestive of hepatic decompensation. Results and discussion The F-1-Score of the system for the retrieval of abdominal radiology reports was 96%, and was 79%, 91%, and 95% for the presence of liver masses, ascites, and varices, respectively. The authors released YTEX as open source, available at http://***/p/ytex.

关键词： Hepatitis visualization of data and knowledge languages computational methods advanced algorithms natural-language processing distributed systems agents software engineering: architecture developing and refining EHR data standards (including image standards) data models data exchange communication integration across care settings (inter- and intra-enterprise) detecting disease outbreaks and biological threats translational research—application of biological knowledge to clinical care linking the genotype and phenotype natural-language processing delivering health information and knowledge to the public monitoring the health of populations

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Exploiting time in electronic health record correlations

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第Sup1期18卷 I109-I115页

作者： Hripcsak, George Albers, David J. Perotte, Adler Columbia Univ Med Ctr Dept Biomed Informat New York NY 10032 USA

Objective To demonstrate that a large, heterogeneous clinical database can reveal fine temporal patterns in clinical associations;to illustrate several types of associations;and to ascertain the value of exploiting time. Materials and methods Lagged linear correlation was calculated between seven clinical laboratory values and 30 clinical concepts extracted from resident signout notes from a 22-year, 3-million-patient database of electronic health records. Time points were interpolated, and patients were normalized to reduce inter-patient effects. Results The method revealed several types of associations with detailed temporal patterns. Definitional associations included low blood potassium preceding 'hypokalemia.' Low potassium preceding the drug spironolactone with high potassium following spironolactone exemplified intentional and physiologic associations, respectively. Counterintuitive results such as the fact that diseases appeared to follow their effects may be due to the workflow of healthcare, in which clinical findings precede the clinician's diagnosis of a disease even though the disease actually preceded the findings. Fully exploiting time by interpolating time points produced less noisy results. Discussion Electronic health records are not direct reflections of the patient state, but rather reflections of the healthcare process and the recording process. With proper techniques and understanding, and with proper incorporation of time, interpretable associations can be derived from a large clinical database. Conclusion A large, heterogeneous clinical database can reveal clinical associations, time is an important feature, and care must be taken to interpret the results.

关键词： Electronic health records data mining time series associations modeling physiologic and disease processes linking the genotype and phenotype languages and computational methods statistical analysis of large datasets advanced algorithms high-performance and large-scale computing detecting disease outbreaks and biological threats simulation of complex systems (at all levels: molecules to work groups to organizations)

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A secure protocol for protecting the identity of providers when disclosing data for disease surveillance

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第3期18卷 212-217页

作者： El Emam, Khaled Hu, Jun Mercer, Jay Peyton, Liam Kantarcioglu, Murat Malin, Bradley Buckeridge, David Samet, Saeed Earle, Craig Childrens Hosp Eastern Ontario Res Inst Ottawa ON K1H 8L1 Canada Univ Ottawa Sch Informat Technol & Engn Ottawa ON Canada Univ Texas Dallas Dallas TX 75230 USA Vanderbilt Univ Nashville TN USA McGill Univ Dept Epidemiol & Biostat Montreal PQ Canada Ontario Inst Canc Res Toronto ON Canada Inst Clin Evaluat Sci Toronto ON Canada

Background Providers have been reluctant to disclose patient data for public-health purposes. Even if patient privacy is ensured, the desire to protect provider confidentiality has been an important driver of this reluctance. Methods Six requirements for a surveillance protocol were defined that satisfy the confidentiality needs of providers and ensure utility to public health. The authors developed a secure multi-party computation protocol using the Paillier cryptosystem to allow the disclosure of stratified case counts and denominators to meet these requirements. The authors evaluated the protocol in a simulated environment on its computation performance and ability to detect disease outbreak clusters. Results Theoretical and empirical assessments demonstrate that all requirements are met by the protocol. A system implementing the protocol scales linearly in terms of computation time as the number of providers is increased. The absolute time to perform the computations was 12.5 s for data from 3000 practices. This is acceptable performance, given that the reporting would normally be done at 24 h intervals. The accuracy of detection disease outbreak cluster was unchanged compared with a non-secure distributed surveillance protocol, with an F-score higher than 0.92 for outbreaks involving 500 or more cases. Conclusion The protocol and associated software provide a practical method for providers to disclose patient data for sentinel, syndromic or other indicator-based surveillance while protecting patient privacy and the identity of individual providers.

关键词： Privacy reidentification de-identification consent ethics integration across care settings (inter- and intraenterprise) languages computational methods advanced algorithms personal health records and self-care systems assuring information system security and personal privacy anonymization other methods for security and policy enforcement cryptography data exchange communication ethical study methods statistical analysis of large datasets methods for integration of information from disparate sources distributed systems agents software engineering: architecture detecting disease outbreaks and biological threats policy legal historical simulation of complex systems (at all levels: molecules to work groups to organizations) monitoring the health of populations privacy and security machine learning health data standards vocabulary ontology scientific information and health data policy consumer health/patient education information information retrieval NLP public-health informatics clinical trials syndromic surveillance secure computation

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Targeted screening for pediatric conditions with the CHICA system

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第4期18卷 485-490页

作者： Carroll, Aaron E. Biondich, Paul G. Anand, Vibha Dugan, Tamara M. Sheley, Meena E. Xu, Shawn Z. Downs, Stephen M. Indiana Univ Sch Med Childrens Hlth Serv Res Indianapolis IN 46202 USA Indiana Univ Sch Med Regenstrief Inst Hlth Care Indianapolis IN 46202 USA

Objective The Child Health Improvement through Computer Automation (CHICA) system is a decision-support and electronic-medical-record system for pediatric health maintenance and disease management. The purpose of this study was to explore CHICA's ability to screen patients for disorders that have validated screening criteria specifically tuberculosis (TB) and iron-deficiency anemia. Design Children between 0 and 11 years were randomized by the CHICA system. In the intervention group, parents were asked about TB and iron-deficiency risk, and physicians received a tailored prompt. In the control group, no screens were performed, and the physician received a generic prompt about these disorders. Results 1123 participants were randomized to the control group and 1116 participants to the intervention group. Significantly more people reported positive risk factors for iron-deficiency anemia in the intervention group (17.5% vs 3.1%, OR 6.6, 95% CI 4.5 to 9.5). In general, far fewer parents reported risk factors for TB than for iron-deficiency anemia. Again, there were significantly higher detection rates of positive risk factors in the intervention group (1.8% vs 0.8%, OR 2.3, 95% CI 1.0 to 5.0). Limitations It is possible that there may be more positive screens without improving outcomes. However, the guidelines are based on studies that have evaluated the questions the authors used as sensitive and specific, and there is no reason to believe that parents misunderstood them. Conclusions Many screening tests are risk-based, not universal, leaving physicians to determine who should have a further workup. This can be a time-consuming process. The authors demonstrated that the CHICA system performs well in assessing risk automatically for TB and iron-deficiency anemia.

关键词： Tuberculosis iron-deficiency anemia CHICA screening computer decision-support system visualization of data and knowledge knowledge representations communications networking methods mobility uncertain reasoning and decision theory languages computational methods advanced algorithms methods for integration of information from disparate sources distributed systems agents software engineering: architecture data models automated learning developing/using clinical decision support (other than diagnostic) and guideline systems discovery text and data mining methods

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2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第5期18卷 552-556页

作者： Uzuner, Oezlem South, Brett R. Shen, Shuying DuVall, Scott L. SUNY Albany Coll Comp & Informat Dept Informat Studies Albany NY 12222 USA VA Salt Lake City Hlth Care Syst Salt Lake City UT USA Univ Utah Dept Internal Med Salt Lake City UT 84112 USA Univ Utah Dept Biomed Informat Salt Lake City UT USA

The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks: a concept extraction task focused on the extraction of medical concepts from patient reports;an assertion classification task focused on assigning assertion types for medical problem concepts;and a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. i2b2 and the VA provided an annotated reference standard corpus for the three tasks. Using this reference standard, 22 systems were developed for concept extraction, 21 for assertion classification, and 16 for relation classification. These systems showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations. Depending on the task, the rule-based systems can either provide input for machine learning or post-process the output of machine learning. Ensembles of classifiers, information from unlabeled data, and external knowledge sources can help when the training data are inadequate.

关键词： Information storage and retrieval (text and images) discovery and text and data mining methods Other methods of information extraction Natural-language processing Automated learning visualization of data and knowledge uncertain reasoning and decision theory languages and computational methods statistical analysis of large datasets advanced algorithms discovery other methods of information extraction automated learning human-computer interaction and human-centered computing NLP machine learning Informatics

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Translational bioinformatics: linking knowledge across biological and clinical realms

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第4期18卷 354-357页

作者： Sarkar, Indra Neil Butte, Atul J. Lussier, Yves A. Tarczy-Hornoch, Peter Ohno-Machado, Lucila Univ Vermont Ctr Clin & Translat Sci Burlington VT 05405 USA Univ Vermont Coll Med Dept Microbiol & Mol Genet Burlington VT 05405 USA Univ Vermont Coll Engn & Math Sci Dept Comp Sci Burlington VT 05405 USA Stanford Univ Sch Med Dept Pediat Div Syst Med Stanford CA 94305 USA Univ Chicago Dept Med Med Genet Sect Chicago IL 60637 USA Univ Chicago Ludwig Ctr Metastasis Res UC Comprehens Canc Ctr Chicago IL 60637 USA Univ Chicago Inst Translat Med Computat Inst Inst Genom & Syst Biol Chicago IL 60637 USA Univ Washington Div Biomed & Hlth Informat Seattle WA 98195 USA Univ Washington Inst Translat Hlth Sci Seattle WA 98195 USA Univ Washington Inst Genom Med Seattle WA 98195 USA Univ Washington Dept Comp Sci Seattle WA 98195 USA Univ Calif San Diego Div Biomed Informat La Jolla CA 92093 USA

Nearly a decade since the completion of the first draft of the human genome, the biomedical community is positioned to usher in a new era of scientific inquiry that links fundamental biological insights with clinical knowledge. Accordingly, holistic approaches are needed to develop and assess hypotheses that incorporate genotypic, phenotypic, and environmental knowledge. This perspective presents translational bioinformatics as a discipline that builds on the successes of bioinformatics and health informatics for the study of complex diseases. The early successes of translational bioinformatics are indicative of the potential to achieve the promise of the Human Genome Project for gaining deeper insights to the genetic underpinnings of disease and progress toward the development of a new generation of therapies.

关键词： Translational bioinformatics systems medicine systems biology bioinformatics biomedical informatics knowledge representation information retrieval phylogenetics modeling physiologic and disease processes linking the genotype and phenotype identifying genome and protein structure and function visualization of data and knowledge simulation of complex systems (at all levels: molecules to work groups to organizations) knowledge representations uncertain reasoning and decision theory languages computational methods statistical analysis of large datasets advanced algorithms discovery text and data mining methods natural-language processing automated learning ontologies

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PIV evaluation algorithms for industrial applications

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MEASUREMENT SCIENCE AND TECHNOLOGY 2004年第6期15卷 1027-1038页

作者： Lecuona, A Nogueira, J Rodríguez, PA Acosta, A Univ Carlos III Madrid Dept Thermal & Fluids Engn Madrid 28911 Spain

Particle image velocimetry (PIV) is now applied with confidence in industrial facilities such as large wind tunnels, yielding data not possible before. Despite the difficulties that arise in such environments, the conventional PIV methods can provide high quality data, especially when dealing with spatial and temporal slowly varying values of the flow magnitudes. Obtaining highly spatially resolved velocity fields is still challenging due to the inherent difficulties in these industrial facilities, such as large velocity gradients, background light and reflections. This work has focused on: (i) the behaviour of conventional PIV when the conventional limits of the processing algorithm are approached or surpassed and (ii) which kind of advanced methods can reduce the main sources of error that are characterized in this paper. The focus is on the description of vortex flows. Group locking, a major source of error, is introduced, modelled and metrologically characterized. The part of the study devoted to advanced methods deals with one that has already shown to be of profit in images from industrial facilities, local field correction PIV (LFC-PIV). This is a robust and an accurate method, able to obtain a high yield of measurements in these environments, without the need of external adjustment to each particular situation. To illustrate this point, some examples of processing of real images are given. The conclusions of this work suggest guidelines about the error figures when measuring flows with embedded vortex using conventional and advanced methods in industrial facilities.

关键词： PIV advanced algorithms LFC-PIV error vortex group locking wind tunnels spatial resolution

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Low-temperature long-time simulations of Ising ferromagnets using the Monte Carlo with absorbing Markov chains method

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COMPUTER PHYSICS COMMUNICATIONS 2002年第1-2期147卷 659-664页

作者： Novotny, MA Mississippi State Univ Dept Phys & Astron Mississippi State MS 39762 USA

The Monte Carlo with Absorbing Markov Chains (MCAMC) method is introduced. This method is a generalization of the rejection-free method known as the n-fold way. The MCAMC algorithm is applied to the study of the very low-temperature properties of the lifetime of the metastable state of Ising ferromagnets. This is done both for square-lattice and cubic-lattice nearest-neighbor models. Comparison is made with exact low-temperature predictions, in particular the low-temperature predictions that the metastable lifetime is discontinuous at particular values of the field. This discontinuity for the square lattice is not seen in finite-temperatures studies. For the cubic lattice, it is shown that these 'exact predictions' are incorrect near the fields where there are discontinuities. The low-temperature formula must be modified and the corrected low-temperature predictions are not discontinuous in the energy of the nucleating droplet. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： advanced algorithms dynamic Monte Carlo lifetimes metastable prefactor

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Low-temperature long-time simulations of Ising ferromagnets using the Monte Carlo with absorbing Markov chains method

Low-temperature long-time simulations of Ising ferromagnets ...

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Europhysics Conference on Computational Physics (CCP 2001)

作者： Novotny, MA Mississippi State Univ Dept Phys & Astron Mississippi State MS 39762 USA

关键词： advanced algorithms dynamic Monte Carlo lifetimes metastable prefactor

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