Tenenbaum JD, Avillach P, Benham-Hutchins M, Breitenstein MK, Crowgey EL, Hoffman MA, Jiang X, Madhavan S, Mattison JE, Nagarajan R, Ray B, Shin D, Visweswaran S, Zhao Z, Freimuth RR. An informatics research agenda to support precision medicine: seven key areas. J Am Med Inform Assoc. 2016 Jul;23(4):791–5.
The recent announcement of the Precision Medicine Initiative by President Obama has brought precision medicine (PM) to the forefront for healthcare providers, researchers, regulators, innovators, and funders alike. As technologies continue to evolve and datasets grow in magnitude, a strong computational infrastructure will be essential to realize PM's vision of improved healthcare derived from personal data. In addition, informatics research and innovation affords a tremendous opportunity to drive the science underlying PM. The informatics community must lead the development of technologies and methodologies that will increase the discovery and application of biomedical knowledge through close collaboration between researchers, clinicians, and patients. This perspective highlights seven key areas that are in need of further informatics research and innovation to support the realization of PM.
Kabytaev K, Durairaj A, Shin D, Rohlfing CL, Connolly S, Little RR, Stoyanov AV. Two-step ion-exchange chromatographic purification combined with reversed-phase chromatography to isolate C-peptide for mass spectrometric analysis. Schug KA, editor. J Sep Sci. 2016 Feb;39(4):676–81.
Shin D, Arthur G, Popescu M, Korkin D, Shyu C-R. Uncovering influence links in molecular knowledge networks to streamline personalized medicine. J Biomed Inform. 2014 Dec;52:394–405.
Shin D, Rogatsky E and Stoyanov A, Simultaneous monitoring of multiple transitions in mass spectrometric analysis improves limit of detection for low abundance substances in complex biological samples. J Chromatograph Separat Techniq 4: 206. doi:10.4172/2157- 7064.10002063.
Chen Z, Shin D, Chen S, Mikhail K, Hadass O, Tomlison BN, Korkin D, Shyu C-R, Cui J, Anthony DC, Gu Z. Histological quantitation of brain injury using whole slide imaging: a pilot validation study in mice. Ai J, editor. PLoS ONE. 2014;9(3):e92133.
Hadass O, Tomlinson BN, Gooyit M, Chen S, Purdy JJ, Walker JM, Zhang C, Giritharan AB, Purnell W, Robinson CR, Shin D, Schroeder VA, Suckow MA, Simonyi A, Sun GY, Mobashery S, Cui J, Chang M, Gu Z. Selective inhibition of matrix metalloproteinase-9 attenuates secondary damage resulting from severe traumatic brain injury. Fillmore H, editor. PLoS ONE. 2013;8(10):e76904.
Shin D, Arthur G, Caldwell C, Popescu M, Petruc M, Diaz-Arias A, Shyu C-R. A pathologist-in-the-loop IHC antibody test selection using the entropy-based probabilistic method. J Pathol Inform. Medknow Publications; 2012;3(1):1.
BACKGROUND: Immunohistochemistry (IHC) is an important tool to identify and quantify expression of certain proteins (antigens) to gain insights into the molecular processes in a diseased tissue. However, it is a challenge for pathologists to remember the discriminative characteristics of the growing number of such antigens across multiple diseases. The complexity of their expression patterns, fueled by continuous discoveries in molecular pathology, gives rise to a combinatorial explosion that places an unprecedented burden on a practicing pathologist and therefore increases cost and variability of IHC studies. MATERIALS AND METHODS:To tackle these issues, we have developed antibody test optimized selection method, a novel informatics tool to help pathologists in improving the IHC antibody selection process. The method uses extensions of Shannon's information entropies and Bayesian probabilities to dynamically build an efficient diagnostic tree. RESULTS:A comparative analysis of our method with the expert and World Health Organization classification guidelines showed that the proposed method brings threefold reduction in number of antibody tests required to reach a diagnostic conclusion. CONCLUSION:The developed method can significantly streamline the antibody test selection process, decrease associated costs and reduce inter- and intrapathologist variability in IHC decision-making.
- Han J, Shin DV, Arthur GL, Shyu C-R. Multi-resolution tile-based follicle detection using color and textural information of follicular lymphoma IHC slides. IEEE; 2010. pp. 866–7.
Posters and Presentations
- Kovalenko M, Hammer R, Shin D. Analysis of Influence of Additional Diagnostic Clues During Pathology Diagnosis. Pathology Informatics Summit 2017. May 2017. Pittsburgh, PA.