Pathology informatics fellowship retreats: The use of interactive scenarios and case studies as pathology informatics teaching tools
Lee RE, McClintock DS, Balis UJ, Baron JM, Becich MJ, Beckwith BA, Brodsky VB, Carter AB, Dighe AS, Haghighi M, Hipp JD, Henricks WH, Kim JY, Klepseis VE, Kuo FC, Lane WJ, Levy BP, Onozato ML, Park SL, Sinard JH, Tuthill MJ, Gilbertson JR. Pathology informatics fellowship retreats: The use of interactive scenarios and case studies as pathology informatics teaching tools. J Pathol Inform. 2012;3:41. doi: 10.4103/2153-3539.103995. Epub 2012 Nov 28. PMID: 23248762
Rasteger-Mojarad M, Boyce RD, Prasad R. UWM-TRIADS: Classifying Drug-Drug Interactions with Two-Stage SVM and Post-Processing. Proceedings of the 2013 International Workshop on Semantic Evaluation (SemEval), Task 9 - Extraction of Drug-drug Interactions from BioMedical Texts. Atlanta Georgia, June 2013.
Implementing Research Findings And Evidence-Based Interventions Into Real-World Dental Practice Settings
Orii N, Ganapathiraju MK (2012) Wiki-Pi: a web resource to aid in the discovery of gene function via protein-protein interactions. PLoS ONE. 7(11): e49029. doi:10.1371/journal.pone.0049029
Protein-protein interactions (PPIs) are the basis of biological functions. Knowledge of the interactions of a protein can help understand its molecular function and its association with different biological processes and pathways. Several publicly available databases provide comprehensive information about individual proteins, such as their sequence, structure, and function. There also exist databases that are built exclusively to provide PPIs by curating them from published literature. The information provided in these web resources is protein-centric, and not PPI-centric. The PPIs are typically provided as lists of interactions of a given gene with links to interacting partners; they do not present a comprehensive view of the nature of both the proteins involved in the interactions. A web database that allows search and retrieval based on biomedical characteristics of PPIs is lacking, and is needed. We present Wiki-Pi (read Wiki-π), a web-based interface to a database of human PPIs, which allows users to retrieve interactions by their biomedical attributes such as their association to diseases, pathways, drugs and biological functions. Each retrieved PPI is shown with annotations of both of the participant proteins side-by-side, creating a basis to hypothesize the biological function facilitated by the interaction. Conceptually, it is a search engine for PPIs analogous to PubMed for scientific literature. Its usefulness in generating novel scientific hypotheses is demonstrated through the study of IGSF21, a little-known gene that was recently identified to be associated with diabetic retinopathy. Using Wiki-Pi, we infer that its association to diabetic retinopathy may be mediated through its interactions with the genes HSPB1, KRAS, TMSB4X and DGKD, and that it may be involved in cellular response to external stimuli, cytoskeletal organization and regulation of molecular activity. The website also provides a wiki-like capability allowing users to describe or discuss an interaction. Wiki-Pi is available publicly and freely at http://severus.dbmi.pitt.edu/wiki-pi/.
Osmanbeyoglu HU, Wehner JA, Carbonell JG, Ganapathiraju MK. Active machine learning for transmembrane helix prediction. BMC Bioinformatics. 2010; 11:S58. PMCID: PMC3009531. PMID: 20122233.
About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited in the Protein Data Bank due to the difficulty in crystallizing membrane proteins. Algorithms that can identify proteins whose high-resolution structure can aid in predicting the structure of many previously unresolved proteins are therefore of potentially high value. Active machine learning is a supervised machine learning approach which is suitable for this domain where there are a large number of sequences but only very few have known corresponding structures. In essence, active learning seeks to identify proteins whose structure, if revealed experimentally, is maximally predictive of others.
An active learning approach is presented for selection of a minimal set of proteins whose structures can aid in the determination of transmembrane helices for the remaining proteins. TMpro, an algorithm for high accuracy TM helix prediction we previously developed, is coupled with active learning. We show that with a well-designed selection procedure, high accuracy can be achieved with only few proteins. TMpro, trained with a single protein achieved an F-score of 94% on benchmark evaluation and 91% on MPtopo dataset, which correspond to the state-of-the-art accuracies on TM helix prediction that are achieved usually by training with over 100 training proteins.
Active learning is suitable for bioinformatics applications, where manually characterized data are not a comprehensive representation of all possible data, and in fact can be a very sparse subset thereof. It aids in selection of data instances which when characterized experimentally can improve the accuracy of computational characterization of remaining raw data. The results presented here also demonstrate that the feature extraction method of TMpro is well designed, achieving a very good separation between TM and non TM segments.
Bekhuis T, Demner-Fushman D, Crowley RS. Comparative effectiveness research designs: an analysis of terms and coverage in MeSH and Emtree. J Med Libr Assoc. 2013 Apr;101(2):92-100. doi: 10.3163/1536-5050.101.2.004.
We analyzed the extent to which comparative effectiveness research (CER) organizations share terms for designs, analyzed coverage of CER designs in Medical Subject Headings (MeSH) and Emtree, and explored whether scientists use CER design terms.
We developed local terminologies (LTs) and a CER design terminology by extracting terms in documents from five organizations. We defined coverage as the distribution over match type in MeSH and Emtree. We created a crosswalk by recording terms to which design terms mapped in both controlled vocabularies. We analyzed the hits for queries restricted to titles and abstracts to explore scientists' language.
Pairwise LT overlap ranged from 22.64% (12/53) to 75.61% (31/41). The CER design terminology (n = 78 terms) consisted of terms for primary study designs and a few terms useful for evaluating evidence, such as opinion paper and systematic review. Patterns of coverage were similar in MeSH and Emtree (gamma = 0.581, P = 0.002).
Stakeholder terminologies vary, and terms are inconsistently covered in MeSH and Emtree. The CER design terminology and crosswalk may be useful for expert searchers. For partially mapped terms, queries could consist of free text for modifiers such as nonrandomized or interrupted added to broad or related controlled terms.
Inhibitory Metabolic Drug Interactions with Newer Psychotropic Drugs: Inclusion in Package Inserts and Influences of Concurrence in Drug Interaction Screening Software.
Boyce RD, Collins C, Clayton M, Kloke J, Horn J. Inhibitory Metabolic Drug Interactions with Newer Psychotropic Drugs: Inclusion in Package Inserts and Influences of Concurrence in Drug Interaction Screening Software. Annals of Pharmacotherapy. Volume 46. Epub 2012 Oct 2. DOI 10.1345/aph.1R150. PMID: 23032655
Food and Drug Administration (FDA) regulations mandate that package inserts (PIs) include observed or predicted clinically significant drug-drug interactions (DDIs), as well as the results of pharmacokinetic studies that establish the absence of effect.
To quantify how frequently observed metabolic inhibition DDIs affecting US-marketed psychotropics are present in FDA-approved PIs and what influence the source of DDI information has on agreement between 3 DDI screening programs.
The scientific literature and PIs were reviewed to determine all drug pairs for which there was rigorous evidence of a metabolic inhibition interaction or noninteraction. The DDIs were tabulated noting the source of evidence and the strength of agreement over chance. Descriptive statistics were used to examine the influence of source of DDI information on agreement among 3 DDI screening tools. Logistic regression was used to assess the influence of drug class, indication, generic status, regulatory approval date, and magnitude of effect on agreement between the literature and PI as well as agreement among the DDI screening tools.
Thirty percent (13/44) of the metabolic inhibition DDIs affecting newer psychotropics were not mentioned in PIs. Drug class, indication, regulatory approval date, generic status, or magnitude of effect did not appear to be associated with more complete DDI information in PIs. DDIs found exclusively in PIs were 3.25 times more likely to be agreed upon by all 3 DDI screening tools than were those found exclusively in the literature. Generic status was inversely associated with agreement among the DDI screening tools (odds ratio 0.11; 95% CI 0.01 to 0.89).
The presence in PIs of DDI information for newer psychotropics appears to have a strong influence on agreement among DDI screening tools. Users of DDI screening software should consult more than 1 source when considering interactions involving generic psychotropics.
Apollo: Increasing Access and Use of Epidemic Models Through the Development and Adoption of a Standard Ontology
The major goals of this project are to (1) develop a standard vocabulary for the field of epidemic modeling using a tool called Protégé; (2) create two extensions to Protégé that are needed by the project; (3) develop a standard syntax using the vocabulary for representing the inputs (e.g., disease control measures) and outputs of epidemic models and to use this syntax in an existing system called the Apollo Web Services that makes it possible for other computer programs to access epidemic models; and (4) to increase the capacity to run epidemic models on supercomputers so as to demonstrate
Funded Competitive Renewal Project Title: Transfer Rule Learning with Functional Mapping for Integrative Modeling of Panomics Data
The major goal of this project is develop, apply, and evaluate novel Transfer Rule Learning (TRL) methods for integrative biomarker discovery from related biomedical data sets.