Michael Gribskov

Michael Gribskov Profile Picture

Professor of Biological Sciences and Computer Science
Ph.D., University of Wisconsin, Madison (1985)

Contact Info:

mgribsko@purdue.edu
765-494-6933

Training Group(s):
Cancer Biology
Chemical Biology
Biomolecular Structure and Biophysics
Computational and Systems Biology

PULSe Contributor - not currently hosting students for laboratory rotations or recruiting students in the laboratory

Current Research Interests:

Genomic and transcriptomic analysis of model and non-model organisms, the application of pattern recognition and machine learning techniques to biomolecules, the design and implementation of biological databases to support molecular and systems biology, development of methods to study RNA structural patterns, and systems biology studies of cancer and human disease.

Selected Publications:

Kocher SD, Tsuruda JM, Gibson JD, Emore CM, Arechavaleta-Velasco ME, Queller,DC, Strassmann JE, Grozinger CM, Gribskov MR, San Miguel P, Westerman R, Hunt GJ. A Search for Parent-of-Origin Effects on Honey Bee Gene Expression. G3 5, 1657-1662, 2015.

McKinney GJ, Hale MC, Goetz G, Gribskov M, Thrower FP, Nichols KM. Ontogenetic changes in embryonic and brain gene expression in progeny produced from migratory and resident Oncorhynchus mykiss. Mol Ecol. 24, 1792-809, 2015.

Otsuka Y, Muto A, Takeuchi R, Okada C, Ishikawa M, Nakamura K, Yamamoto N, Dose H, Nakahigashi K, Tanishima S, Suharnan S, Nomura W, Nakayashiki T, Aref WG, Bochner BR, Conway T, Gribskov M, Kihara D, Rudd KE, Tohsato Y, Wanner BL, Mori H. GenoBase: comprehensive resource database of Escherichia coli K-12. Nucleic Acids Res. 43(Database issue):D606-17, 2015.

Jiang, B., Gleich D.F., Gribskov, M. Differential flux balance analysis of quantitative proteomic data on protein interaction networks. Symposium on Signal Processing and Mathematical Modeling of Biological Processes with Applications to Cyber-Physical Systems for Precise Medicine (GlobalSIP) 2015.

Liu, M., Gribskov M. MMC-Margin: Identification of Maximum Frequent Subgraphs by Metropolis Monte Carlo Sampling. IEEE International Conference on Big Data, 2015. DOI:10.1109/BigData.2015.7363832.

Wang S, Gribskov M, Hazbun TR, Pascuzzi PE. CellMiner Companion: an interactive web application to explore CellMiner NCI-60 data. Bioinformatics. 2016 Aug 1;32(15):2399-401.

Padmanabhan KR, Segobye K, Weller SC Schulz, B., Gribskov, M. Preliminary investigation of glyphosate resistance mechanism in giant ragweed using transcriptome. F1000Research 5:1354 (doi10.12688/f1000research.8932.1), 2016.

Huang J, Li K, Gribskov M. Accurate Classification of RNA Structures Using Topological Fingerprints. PLoS One 11, e0164726, 2016.

Andino GK, Gribskov M, Anderson DL, Evans JD, Hunt GJ. Differential gene expression in Varroa jacobsoni mites following a host shift to European honey bees (Apis mellifera). BMC Genomics 17, 926, 2016.

Wang S, Gribskov M. Comprehensive evaluation of de novo transcriptome assembly programs and their effects on differential gene expression analysis. Bioinformatics, 33, 327-333, 2017.

Jiang B, Kloster K, Gleich DF, Gribskov M. AptRank: an adaptive PageRank model for protein function prediction on bi-relational graphs. Bioinformatics. Bioinformatics 33, 1829-1836, 2017.

Peng H, Yang Y, Zhe S, Wang J, Gribskov M, Qi A. DEIsoM: A hierarchical Bayesian model for identifying differentially expressed isoforms using biological replicates. Bioinformatics, 33, 3018-3027, 2017.

Chen Y., Yang S., Crodian J., Kuang S., Wang J., Gribskov M., Plaut K., Casey T.M. Maternal high fat diet significantly impacts milk miRNA and mRNA content of lactating mice. FASEB Journal 31, S448.8, 2017.

Mershad K., Malluhi, Q.M., Ouzzani M., Tang M., Gribskov M., Aref W.G. AUDIT: approving and tracking updates with dependencies in collaborative databases. Distributed and Parallel Databases, https://doi.org/10.1007/s10619-017-7208-y, 2017.

Mershad, K., Malluhi, Q.M., Ouzzani, M., Tang M., Gribskov M., Aref W.G., Prakash D. COACT: a query interface language for collaborative databases. Distributed and Parallel Databases https://doi.org/10.1007/s10619-017-7213-1, 2017.

Chen Y., Wang J., Yang S., Utturkar S., Crodian J., Cummings S., Thimmapuram J., San Miguel P., Kuang S., Gribskov M., Plaut K., Casey T. Effect of high-fat diet on secreted milk transcriptome in midlactation mice. Physiological Genomics 49, 747-462. 2017.

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