We seek renewal of our NLM Training Program in Biomedical Informatics (NLMTP), which the NLM has supported for twenty years. The program, evolving as biomedical informatics (BMI) itself has evolved, has consistently produced outstanding trainees. In this renewal, we address new challenges to BMI arising from digital information streaming from innumerable sensors, instruments and simulations, which are outrunning our capacity to accumulate, organize and analyze it. Advances in BMI are needed: 1) to make sense of datasets that may be massive, heterogeneous or even contain errors, 2) to cull from them important insights into fundamental problems of biomedicine and 3) to convey information in ways readily understood by researchers and clinicians. These are fundamental BMI problems, which we will address in the NLMTP renewal. We will emphasize health care/clinical informatics (HC), translational bioinformatics (TB) and clinical research informatics (CR), areas in which our trainees will contribute to progress needed to gain the full wealth of knowledge embedded in genomic, proteomic, genetic, epidemiological, and clinical data and a full return on our substantial investments in health information technology. Our trainees will bring to each of these BMI domains what we call data-driven inference and decision making (DIDM) by which we mean the use computer programs to seek associations in databases whose complexity hides such relations from even expert humans and cognitive informatics and decision making to make such discovered patterns intelligible to humans. Our pre-doctoral trainees will be selected from graduate students who have completed one year of study at one of six participating institutions. NMLTP training will combine DIDM and BMI specific courses and research to engage them with the range of opportunities in HC, TB and CR suggested above. We also will involve pre- and postdoctoral trainees in a set of courses and activities to facilitate their integration into the BMI community. Over many years, our NLMTP has brought computation, mathematics, statistics, simulation and advanced imaging to bear on biomedical problems that were before deemed intractable. Now our researchers are deploying powerful new computational methodologies for data exploration on an unprecedented scale that offer exciting opportunities for the training to prepare a new generation of BMI professionals. We are requesting 9 pre-doctoral and 6 postdoctoral positions for this renewal.
GORRY, G.A. NLM T15 LM007093 Project Narrative - Relevance: We seek renewal of our NLM Training Program in Biomedical Informatics, which over many years has brought computation, mathematics, statistics, simulation and advanced imaging to bear on biomedical problems that were before deemed intractable. Now our program will expand to include powerful new computational methodologies of data-driven inference and decision making, and cognitive informatics, to cull the most important elements from genomic, proteomic, genetic, epidemiological, and clinical data applying to fundamental problems of biomedicine, and to enable a full return on our substantial investments in health information technology. Our 9 pre- and 6 postdoctoral trainees will prepare for careers as biomedical informatics professionals by pursuing coursework and research projects in health care/clinical informatics, translational bioinformatics, and clinical research informatics.
|Call, Lee; Stoll, Barbara; Oosterloo, Berthe et al. (2018) Metabolomic signatures distinguish the impact of formula carbohydrates on disease outcome in a preterm piglet model of NEC. Microbiome 6:111|
|Emerson, Charlene H; Lopez, Christopher R; Ribes-Zamora, Albert et al. (2018) Ku DNA End-Binding Activity Promotes Repair Fidelity and Influences End-Processing During Nonhomologous End-Joining in Saccharomyces cerevisiae. Genetics 209:115-128|
|Roh, Soung-Hun; Stam, Nicholas J; Hryc, Corey F et al. (2018) The 3.5-Å CryoEM Structure of Nanodisc-Reconstituted Yeast Vacuolar ATPase Vo Proton Channel. Mol Cell 69:993-1004.e3|
|Hendryx, Emily P; Rivière, Béatrice M; Sorensen, Danny C et al. (2018) Finding representative electrocardiogram beat morphologies with CUR. J Biomed Inform 77:97-110|
|Chen, Han; Li, Chunyan; Zhou, Zhicheng et al. (2018) Fast-Evolving Human-Specific Neural Enhancers Are Associated with Aging-Related Diseases. Cell Syst 6:604-611.e4|
|Chen, Han; Li, Chunyan; Peng, Xinxin et al. (2018) A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples. Cell 173:386-399.e12|
|Chiang, Sharon; Vankov, Emilian R; Yeh, Hsiang J et al. (2018) Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity. PLoS One 13:e0190220|
|Magnotti, John F; Beauchamp, Michael S (2017) A Causal Inference Model Explains Perception of the McGurk Effect and Other Incongruent Audiovisual Speech. PLoS Comput Biol 13:e1005229|
|Rosengarten, Rafael D; Santhanam, Balaji; Kokosar, Janez et al. (2017) The Long Noncoding RNA Transcriptome of Dictyostelium discoideum Development. G3 (Bethesda) 7:387-398|
|Wilson, Stephen J; Wilkins, Angela D; Lin, Chih-Hsu et al. (2017) DISCOVERY OF FUNCTIONAL AND DISEASE PATHWAYS BY COMMUNITY DETECTION IN PROTEIN-PROTEIN INTERACTION NETWORKS. Pac Symp Biocomput 22:336-347|
Showing the most recent 10 out of 242 publications