2007 snippet describing some of the lab's work in analyzing functional genomics and proteomics data [html] [doc]

The CRIT framework for identifying cross patterns in systems biology and application to chemogenomics.
TA Gianoulis, A Agarwal, M Snyder, MB Gerstein (2011). Genome Biol 12: R32.

Prediction and characterization of noncoding RNAs in C. elegans by integrating conservation, secondary structure, and high-throughput sequencing and array data.
ZJ Lu, KY Yip, G Wang, C Shou, LW Hillier, E Khurana, A Agarwal, R Auerbach, J Rozowsky, C Cheng, M Kato, DM Miller, F Slack, M Snyder, RH Waterston, V Reinke, MB Gerstein (2011). Genome Res 21: 276-85.

Diverse transcription factor binding features revealed by genome-wide ChIP-seq in C. elegans.
W Niu, ZJ Lu, M Zhong, M Sarov, JI Murray, CM Brdlik, J Janette, C Chen, P Alves, E Preston, C Slightham, L Jiang, AA Hyman, SK Kim, RH Waterston, M Gerstein, M Snyder, V Reinke (2011). Genome Res 21: 245-54.

Analysis of membrane proteins in metagenomics: networks of correlated environmental features and protein families.
PV Patel, TA Gianoulis, RD Bjornson, KY Yip, DM Engelman, MB Gerstein (2010). Genome Res 20: 960-71.

PARE: a tool for comparing protein abundance and mRNA expression data.
EZ Yu, AE Burba, M Gerstein (2007). BMC Bioinformatics 8: 309.

An integrative genomic approach to uncover molecular mechanisms of prokaryotic traits.
Y Liu, J Li, L Sam, CS Goh, M Gerstein, YA Lussier (2006). PLoS Comput Biol 2: e159.

Integration of curated databases to identify genotype-phenotype associations.
CS Goh, TA Gianoulis, Y Liu, J Li, A Paccanaro, YA Lussier, M Gerstein (2006). BMC Genomics 7: 257.

Predicting essential genes in fungal genomes.
M Seringhaus, A Paccanaro, A Borneman, M Snyder, M Gerstein (2006). Genome Res 16: 1126-35.

YeastHub: a semantic web use case for integrating data in the life sciences domain.
KH Cheung, KY Yip, A Smith, R Deknikker, A Masiar, M Gerstein (2005). Bioinformatics 21 Suppl 1: i85-96.

An XML-Based Approach to Integrating Heterogeneous Yeast Genome Data
KH Cheung, D Pan, A Smith, M Seringhaus, SM Douglas, M Gerstein (2004). International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences (METMBS); pp 236-242

Comparing protein abundance and mRNA expression levels on a genomic scale.
D Greenbaum, C Colangelo, K Williams, M Gerstein (2003). Genome Biol 4: 117.

Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of models.
R Jansen, HJ Bussemaker, M Gerstein (2003). Nucleic Acids Res 31: 2242-51.

Spectral biclustering of microarray data: coclustering genes and conditions.
Y Kluger, R Basri, JT Chang, M Gerstein (2003). Genome Res 13: 703-16.

Analysis of mRNA expression and protein abundance data: an approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts.
D Greenbaum, R Jansen, M Gerstein (2002). Bioinformatics 18: 585-96.

Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions.
J Qian, M Dolled-Filhart, J Lin, H Yu, M Gerstein (2001). J Mol Biol 314: 1053-66.

Genome-wide analysis relating expression level with protein subcellular localization.
A Drawid, R Jansen, M Gerstein (2000). Trends Genet 16: 426-30.

Assessing annotation transfer for genomics: quantifying the relations between protein sequence, structure and function through traditional and probabilistic scores.
CA Wilson, J Kreychman, M Gerstein (2000). J Mol Biol 297: 233-49.

Analysis of the yeast transcriptome with structural and functional categories: characterizing highly expressed proteins.
R Jansen, M Gerstein (2000). Nucleic Acids Res 28: 1481-8.

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