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Items 1 to 10 of about 430
1. Hillenmeyer ME, Fung E, Wildenhain J, Pierce SE, Hoon S, Lee W, Proctor M, St Onge RP, Tyers M, Koller D, Altman RB, Davis RW, Nislow C, Giaever G: The chemical genomic portrait of yeast: uncovering a phenotype for all genes. Science; 2008 Apr 18;320(5874):362-5
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] The chemical genomic portrait of yeast: uncovering a phenotype for all genes.
  • Genetics aims to understand the relation between genotype and phenotype.
  • However, because complete deletion of most yeast genes ( approximately 80%) has no obvious phenotypic consequence in rich medium, it is difficult to study their functions.
  • To uncover phenotypes for this nonessential fraction of the genome, we performed 1144 chemical genomic assays on the yeast whole-genome heterozygous and homozygous deletion collections and quantified the growth fitness of each deletion strain in the presence of chemical or environmental stress conditions.
  • We found that 97% of gene deletions exhibited a measurable growth phenotype, suggesting that nearly all genes are essential for optimal growth in at least one condition.

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  • (PMID = 18420932.001).
  • [ISSN] 1095-9203
  • [Journal-full-title] Science (New York, N.Y.)
  • [ISO-abbreviation] Science
  • [Language] ENG
  • [Grant] United States / NIGMS NIH HHS / GM / GM061374-09; United States / NIGMS NIH HHS / GM / U01 GM061374; United States / NIGMS NIH HHS / GM / U01 GM061374-09
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.
  • [Publication-country] United States
  • [Chemical-registry-number] 0 / Culture Media; 0 / Saccharomyces cerevisiae Proteins; 0 / Small Molecule Libraries
  • [Other-IDs] NLM/ NIHMS118447; NLM/ PMC2794835
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2. Kivi M, Liu X, Raychaudhuri S, Altman RB, Small PM: Determining the genomic locations of repetitive DNA sequences with a whole-genome microarray: IS6110 in Mycobacterium tuberculosis. J Clin Microbiol; 2002 Jun;40(6):2192-8
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  • [Title] Determining the genomic locations of repetitive DNA sequences with a whole-genome microarray: IS6110 in Mycobacterium tuberculosis.
  • The mycobacterial insertion sequence IS6110 has been exploited extensively as a clonal marker in molecular epidemiologic studies of tuberculosis.
  • In addition, it has been hypothesized that this element is an important driving force behind genotypic variability that may have phenotypic consequences.
  • We present here a novel, DNA microarray-based methodology, designated SiteMapping, that simultaneously maps the locations and orientations of multiple copies of IS6110 within the genome.
  • To investigate the sensitivity, accuracy, and limitations of the technique, it was applied to eight Mycobacterium tuberculosis strains for which complete or partial IS6110 insertion site information had been determined previously.
  • SiteMapping correctly located 64% (38 of 59) of the IS6110 copies predicted by restriction fragment length polymorphism analysis.
  • The technique is highly specific; 97% of the predicted insertion sites were true insertions.
  • Eight previously unknown insertions were identified and confirmed by PCR or sequencing.
  • The performance could be improved by modifications in the experimental protocol and in the approach to data analysis.
  • SiteMapping has general applicability and demonstrates an expansion in the applications of microarrays that complements conventional approaches in the study of genome architecture.

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  • (PMID = 12037086.001).
  • [ISSN] 0095-1137
  • [Journal-full-title] Journal of clinical microbiology
  • [ISO-abbreviation] J. Clin. Microbiol.
  • [Language] ENG
  • [Databank-accession-numbers] GENBANK/ AF404410/ AF404411/ AF404412/ AF404413/ AF404414
  • [Grant] United States / NIGMS NIH HHS / GM / R24 GM061374; United States / NIGMS NIH HHS / GM / T32 GM007365; United States / NIAID NIH HHS / AI / AI35969; United States / NIGMS NIH HHS / GM / GM-07365; United States / NIAID NIH HHS / AI / U01 AI035969; United States / NIGMS NIH HHS / GM / U01 GM061374; United States / NLM NIH HHS / LM / LM-06422; United States / NIGMS NIH HHS / GM / GM-61374
  • [Publication-type] Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S.
  • [Publication-country] United States
  • [Chemical-registry-number] 0 / DNA Transposable Elements; 0 / DNA, Bacterial
  • [Other-IDs] NLM/ PMC130717
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3. Rubin DL, Shafa F, Oliver DE, Hewett M, Altman RB: Representing genetic sequence data for pharmacogenomics: an evolutionary approach using ontological and relational models. Bioinformatics; 2002;18 Suppl 1:S207-15
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  • [Title] Representing genetic sequence data for pharmacogenomics: an evolutionary approach using ontological and relational models.
  • MOTIVATION: The information model chosen to store biological data affects the types of queries possible, database performance, and difficulty in updating that information model.
  • Genetic sequence data for pharmacogenetics studies can be complex, and the best information model to use may change over time.
  • As experimental and analytical methods change, and as biological knowledge advances, the data storage requirements and types of queries needed may also change.
  • RESULTS: We developed a model for genetic sequence and polymorphism data, and used XML Schema to specify the elements and attributes required for this model.
  • We implemented this model as an ontology in a frame-based representation and as a relational model in a database system.
  • We collected genetic data from two pharmacogenetics resequencing studies, and formulated queries useful for analysing these data.
  • We compared the ontology and relational models in terms of query complexity, performance, and difficulty in changing the information model.
  • Our results demonstrate benefits of evolving the schema for storing pharmacogenetics data: ontologies perform well in early design stages as the information model changes rapidly and simplify query formulation, while relational models offer improved query speed once the information model and types of queries needed stabilize.
  • [MeSH-major] Database Management Systems. Databases, Genetic. Gene Expression Profiling / methods. Information Storage and Retrieval / methods. Models, Genetic. Pharmacogenetics / methods. Sequence Analysis, DNA / methods
  • [MeSH-minor] Algorithms. Hypermedia. Sequence Alignment / methods

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  • (PMID = 12169549.001).
  • [ISSN] 1367-4803
  • [Journal-full-title] Bioinformatics (Oxford, England)
  • [ISO-abbreviation] Bioinformatics
  • [Language] eng
  • [Grant] United States / NIGMS NIH HHS / GM / U01GM61374
  • [Publication-type] Comparative Study; Evaluation Studies; Journal Article; Research Support, U.S. Gov't, P.H.S.; Validation Studies
  • [Publication-country] England
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4. Raychaudhuri S, Chang JT, Sutphin PD, Altman RB: Associating genes with gene ontology codes using a maximum entropy analysis of biomedical literature. Genome Res; 2002 Jan;12(1):203-14
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  • [Title] Associating genes with gene ontology codes using a maximum entropy analysis of biomedical literature.
  • Functional characterizations of thousands of gene products from many species are described in the published literature.
  • These discussions are extremely valuable for characterizing the functions not only of these gene products, but also of their homologs in other organisms.
  • The Gene Ontology (GO) is an effort to create a controlled terminology for labeling gene functions in a more precise, reliable, computer-readable manner.
  • Currently, the best annotations of gene function with the GO are performed by highly trained biologists who read the literature and select appropriate codes.
  • In this study, we explored the possibility that statistical natural language processing techniques can be used to assign GO codes.
  • We compared three document classification methods (maximum entropy modeling, naïve Bayes classification, and nearest-neighbor classification) to the problem of associating a set of GO codes (for biological process) to literature abstracts and thus to the genes associated with the abstracts.
  • We showed that maximum entropy modeling outperforms the other methods and achieves an accuracy of 72% when ascertaining the function discussed within an abstract.
  • The maximum entropy method provides confidence measures that correlate well with performance.
  • We conclude that statistical methods may be used to assign GO codes and may be useful for the difficult task of reassignment as terminology standards evolve over time.

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  • (PMID = 11779846.001).
  • [ISSN] 1088-9051
  • [Journal-full-title] Genome research
  • [ISO-abbreviation] Genome Res.
  • [Language] ENG
  • [Grant] United States / NIGMS NIH HHS / GM / R24 GM061374; United States / NCI NIH HHS / CA / CA-88480; United States / NIGMS NIH HHS / GM / T32 GM007365; United States / NLM NIH HHS / LM / LM06244; United States / NIGMS NIH HHS / GM / GM61374; United States / NIGMS NIH HHS / GM / GM-07365; United States / NIGMS NIH HHS / GM / U01 GM061374; United States / NCI NIH HHS / CA / R01 CA088480; United States / NCI NIH HHS / CA / R37 CA088480
  • [Publication-type] Comparative Study; Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S.
  • [Publication-country] United States
  • [Other-IDs] NLM/ PMC155261
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5. Lahti JL, Tang GW, Capriotti E, Liu T, Altman RB: Bioinformatics and variability in drug response: a protein structural perspective. J R Soc Interface; 2012 Jul 7;9(72):1409-37
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  • [Title] Bioinformatics and variability in drug response: a protein structural perspective.
  • Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based.
  • Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated.
  • The unacceptable risk-benefit profile for many drugs mandates a paradigm shift towards personalized medicine.
  • However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations.
  • Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes.
  • Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein-drug interactions and, ultimately, drug response.
  • In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions.
  • Next, we describe the impact of coding mutations on protein structures and drug response.
  • Finally, we highlight tools for analysing protein structures and protein-drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants.

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  • [ISSN] 1742-5662
  • [Journal-full-title] Journal of the Royal Society, Interface
  • [ISO-abbreviation] J R Soc Interface
  • [Language] ENG
  • [Grant] United States / NIGMS NIH HHS / GM / R24 GM061374; United States / NLM NIH HHS / LM / R01 LM005652; United States / NLM NIH HHS / LM / LM-05652; United States / NIGMS NIH HHS / GM / U01 GM061374; United States / NIGMS NIH HHS / GM / R01 GM102365; United States / NIGMS NIH HHS / GM / U54 GM072970; United States / NIGMS NIH HHS / GM / GM-61374
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Review
  • [Publication-country] England
  • [Chemical-registry-number] 0 / Pharmaceutical Preparations; 0 / Proteins
  • [Other-IDs] NLM/ PMC3367825
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6. Engreitz JM, Daigle BJ Jr, Marshall JJ, Altman RB: Independent component analysis: mining microarray data for fundamental human gene expression modules. J Biomed Inform; 2010 Dec;43(6):932-44
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  • [Title] Independent component analysis: mining microarray data for fundamental human gene expression modules.
  • As public microarray repositories rapidly accumulate gene expression data, these resources contain increasingly valuable information about cellular processes in human biology.
  • This presents a unique opportunity for intelligent data mining methods to extract information about the transcriptional modules underlying these biological processes.
  • Modeling cellular gene expression as a combination of functional modules, we use independent component analysis (ICA) to derive 423 fundamental components of human biology from a 9395-array compendium of heterogeneous expression data.
  • Annotation using the Gene Ontology (GO) suggests that while some of these components represent known biological modules, others may describe biology not well characterized by existing manually-curated ontologies.
  • In order to understand the biological functions represented by these modules, we investigate the mechanism of the preclinical anti-cancer drug parthenolide (PTL) by analyzing the differential expression of our fundamental components.
  • Our method correctly identifies known pathways and predicts that N-glycan biosynthesis and T-cell receptor signaling may contribute to PTL response.
  • The fundamental gene modules we describe have the potential to provide pathway-level insight into new gene expression datasets.

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  • [Copyright] Copyright © 2010 Elsevier Inc. All rights reserved.
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  • (PMID = 20619355.001).
  • [ISSN] 1532-0480
  • [Journal-full-title] Journal of biomedical informatics
  • [ISO-abbreviation] J Biomed Inform
  • [Language] ENG
  • [Grant] United States / NIGMS NIH HHS / GM / GM061374-10; United States / NIGMS NIH HHS / GM / R01 GM102365; United States / NIGMS NIH HHS / GM / U01 GM061374; United States / NIGMS NIH HHS / GM / U01 GM061374-10
  • [Publication-type] Journal Article
  • [Publication-country] United States
  • [Chemical-registry-number] 0 / Polysaccharides; 0 / Receptors, Antigen, T-Cell
  • [Other-IDs] NLM/ NIHMS226012; NLM/ PMC2991480
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7. Gottlieb A, Stein GY, Ruppin E, Altman RB, Sharan R: A method for inferring medical diagnoses from patient similarities. BMC Med; 2013 Sep 02;11:194
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] A method for inferring medical diagnoses from patient similarities.
  • BACKGROUND: Clinical decision support systems assist physicians in interpreting complex patient data.
  • However, they typically operate on a per-patient basis and do not exploit the extensive latent medical knowledge in electronic health records (EHRs).
  • The emergence of large EHR systems offers the opportunity to integrate population information actively into these tools.
  • METHODS: Here, we assess the ability of a large corpus of electronic records to predict individual discharge diagnoses.
  • We present a method that exploits similarities between patients along multiple dimensions to predict the eventual discharge diagnoses.
  • RESULTS: Using demographic, initial blood and electrocardiography measurements, as well as medical history of hospitalized patients from two independent hospitals, we obtained high performance in cross-validation (area under the curve >0.88) and correctly predicted at least one diagnosis among the top ten predictions for more than 84% of the patients tested.
  • Importantly, our method provides accurate predictions (>0.86 precision in cross validation) for major disease categories, including infectious and parasitic diseases, endocrine and metabolic diseases and diseases of the circulatory systems.
  • Our performance applies to both chronic and acute diagnoses.
  • CONCLUSIONS: Our results suggest that one can harness the wealth of population-based information embedded in electronic health records for patient-specific predictive tasks.
  • [MeSH-major] Diagnosis, Computer-Assisted. Electronic Health Records. Models, Statistical
  • [MeSH-minor] Humans

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  • [Journal-full-title] BMC medicine
  • [ISO-abbreviation] BMC Med
  • [Language] eng
  • [Grant] United States / NIGMS NIH HHS / GM / R01 GM102365; United States / NLM NIH HHS / LM / R01 LM005652; United States / NIGMS NIH HHS / GM / GM102365; United States / NLM NIH HHS / LM / LM05652
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC3844462
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8. Bagley SC, Sirota M, Chen R, Butte AJ, Altman RB: Constraints on Biological Mechanism from Disease Comorbidity Using Electronic Medical Records and Database of Genetic Variants. PLoS Comput Biol; 2016 Apr;12(4):e1004885
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Constraints on Biological Mechanism from Disease Comorbidity Using Electronic Medical Records and Database of Genetic Variants.
  • Patterns of disease co-occurrence that deviate from statistical independence may represent important constraints on biological mechanism, which sometimes can be explained by shared genetics.
  • In this work we study the relationship between disease co-occurrence and commonly shared genetic architecture of disease.
  • Records of pairs of diseases were combined from two different electronic medical systems (Columbia, Stanford), and compared to a large database of published disease-associated genetic variants (VARIMED); data on 35 disorders were available across all three sources, which include medical records for over 1.2 million patients and variants from over 17,000 publications.
  • Based on the sources in which they appeared, disease pairs were categorized as having predominant clinical, genetic, or both kinds of manifestations.
  • Confounding effects of age on disease incidence were controlled for by only comparing diseases when they fall in the same cluster of similarly shaped incidence patterns.
  • We find that disease pairs that are overrepresented in both electronic medical record systems and in VARIMED come from two main disease classes, autoimmune and neuropsychiatric.
  • We furthermore identify specific genes that are shared within these disease groups.
  • [MeSH-major] Comorbidity. Databases, Genetic. Electronic Health Records. Genetic Variation
  • [MeSH-minor] Age Factors. Cluster Analysis. Computational Biology. Humans. Models, Statistical

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  • (PMID = 27115429.001).
  • [ISSN] 1553-7358
  • [Journal-full-title] PLoS computational biology
  • [ISO-abbreviation] PLoS Comput. Biol.
  • [Language] eng
  • [Grant] United States / NHGRI NIH HHS / HG / 5-U01HG007436-03; United States / NIGMS NIH HHS / GM / GM102365; United States / NLM NIH HHS / LM / LM05652; United States / NIMH NIH HHS / MH / 5P50MH094267; United States / NCRR NIH HHS / RR / UL1 RR025744; United States / NIGMS NIH HHS / GM / R01 GM102365; United States / NCATS NIH HHS / TR / UL1 TR001085; United States / NIGMS NIH HHS / GM / R01 GM079719
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
  • [Publication-country] United States
  • [Other-IDs] NLM/ PMC4846031
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9. Karczewski KJ, Snyder M, Altman RB, Tatonetti NP: Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association. PLoS Genet; 2014 Feb;10(2):e1004122
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association.
  • Transcription factors (TFs) are fundamental controllers of cellular regulation that function in a complex and combinatorial manner.
  • Accurate identification of a transcription factor's targets is essential to understanding the role that factors play in disease biology.
  • However, due to a high false positive rate, identifying coherent functional target sets is difficult.
  • We have created an improved mapping of targets by integrating ChIP-Seq data with 423 functional modules derived from 9,395 human expression experiments.
  • We identified 5,002 TF-module relationships, significantly improved TF target prediction, and found 30 high-confidence TF-TF associations, of which 14 are known.
  • Importantly, we also connected TFs to diseases through these functional modules and identified 3,859 significant TF-disease relationships.
  • As an example, we found a link between MEF2A and Crohn's disease, which we validated in an independent expression dataset.
  • These results show the power of combining expression data and ChIP-Seq data to remove noise and better extract the associations between TFs, functional modules, and disease.
  • [MeSH-major] Computational Biology. Gene Expression Regulation / genetics. Protein Interaction Maps / genetics. Transcription Factors / metabolism
  • [MeSH-minor] Cell Cycle / genetics. Gene Expression Profiling. Humans. Molecular Sequence Annotation

  • NCI CPTAC Assay Portal. NCI CPTAC Assay Portal .
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  • (PMID = 24516403.001).
  • [ISSN] 1553-7404
  • [Journal-full-title] PLoS genetics
  • [ISO-abbreviation] PLoS Genet.
  • [Language] eng
  • [Grant] United States / NLM NIH HHS / LM / R01 LM005652; United States / AHRQ HHS / HS / R01 HS022961; United States / NIGMS NIH HHS / GM / GM102365; United States / NLM NIH HHS / LM / LM05652; United States / AHRQ HHS / HS / R01HS022961; United States / NIGMS NIH HHS / GM / GM61374; United States / NLM NIH HHS / LM / T15 LM007033; United States / NIGMS NIH HHS / GM / R01 GM102365; United States / NCI NIH HHS / CA / P30 CA124435
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
  • [Publication-country] United States
  • [Chemical-registry-number] 0 / Transcription Factors
  • [Other-IDs] NLM/ PMC3916285
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10. Delp SL, Ku JP, Pande VS, Sherman MA, Altman RB: Simbios: an NIH national center for physics-based simulation of biological structures. J Am Med Inform Assoc; 2012 Mar-Apr;19(2):186-9
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Simbios: an NIH national center for physics-based simulation of biological structures.
  • Physics-based simulation provides a powerful framework for understanding biological form and function.
  • Simulations can be used by biologists to study macromolecular assemblies and by clinicians to design treatments for diseases.
  • Simulations help biomedical researchers understand the physical constraints on biological systems as they engineer novel drugs, synthetic tissues, medical devices, and surgical interventions.
  • Although individual biomedical investigators make outstanding contributions to physics-based simulation, the field has been fragmented.
  • Applications are typically limited to a single physical scale, and individual investigators usually must create their own software.
  • These conditions created a major barrier to advancing simulation capabilities.
  • In 2004, we established a National Center for Physics-Based Simulation of Biological Structures (Simbios) to help integrate the field and accelerate biomedical research.
  • In 6 years, Simbios has become a vibrant national center, with collaborators in 16 states and eight countries.
  • Simbios focuses on problems at both the molecular scale and the organismal level, with a long-term goal of uniting these in accurate multiscale simulations.
  • [MeSH-major] Computer Simulation. Physics. Software
  • [MeSH-minor] Access to Information. Forecasting. Humans. Models, Biological. National Institutes of Health (U.S.). United States

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  • (PMID = 22081222.001).
  • [ISSN] 1527-974X
  • [Journal-full-title] Journal of the American Medical Informatics Association : JAMIA
  • [ISO-abbreviation] J Am Med Inform Assoc
  • [Language] eng
  • [Grant] United States / NIGMS NIH HHS / GM / U54 GM072970; United States / NIGMS NIH HHS / GM / U54 GM072970
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural
  • [Publication-country] United States
  • [Other-IDs] NLM/ PMC3277621
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