High-order neural networks and kernel methods for peptide-MHC binding prediction.
PP Kuksa, MR Min, R Dugar, M Gerstein (2015). Bioinformatics 31: 3600-7.

Genomics: ENCODE leads the way on big data.
M Gerstein (2012). Nature 489: 208.

Architecture of the human regulatory network derived from ENCODE data.
MB Gerstein, A Kundaje, M Hariharan, SG Landt, KK Yan, C Cheng, XJ Mu, E Khurana, J Rozowsky, R Alexander, R Min, P Alves, A Abyzov, N Addleman, N Bhardwaj, AP Boyle, P Cayting, A Charos, DZ Chen, Y Cheng, D Clarke, C Eastman, G Euskirchen, S Frietze, Y Fu, J Gertz, F Grubert, A Harmanci, P Jain, M Kasowski, P Lacroute, JJ Leng, J Lian, H Monahan, H O'Geen, Z Ouyang, EC Partridge, D Patacsil, F Pauli, D Raha, L Ramirez, TE Reddy, B Reed, M Shi, T Slifer, J Wang, L Wu, X Yang, KY Yip, G Zilberman-Schapira, S Batzoglou, A Sidow, PJ Farnham, RM Myers, SM Weissman, M Snyder (2012). Nature 489: 91-100.

Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors.
KY Yip, C Cheng, N Bhardwaj, JB Brown, J Leng, A Kundaje, J Rozowsky, E Birney, P Bickel, M Snyder, M Gerstein (2012). Genome Biol 13: R48.

Understanding transcriptional regulation by integrative analysis of transcription factor binding data.
C Cheng, R Alexander, R Min, J Leng, KY Yip, J Rozowsky, KK Yan, X Dong, S Djebali, Y Ruan, CA Davis, P Carninci, T Lassman, TR Gingeras, R Guigo, E Birney, Z Weng, M Snyder, M Gerstein (2012). Genome Res 22: 1658-67.

TIP: a probabilistic method for identifying transcription factor target genes from ChIP-seq binding profiles.
C Cheng, R Min, M Gerstein (2011). Bioinformatics 27: 3221-7.

Detection of copy number variation from array intensity and sequencing read depth using a stepwise Bayesian model.
ZD Zhang, MB Gerstein (2010). BMC Bioinformatics 11: 539.

Genome-wide sequence-based prediction of peripheral proteins using a novel semi-supervised learning technique.
N Bhardwaj, M Gerstein, H Lu (2010). BMC Bioinformatics 11 Suppl 1: S6.

Computational analysis of membrane proteins: the largest class of drug targets.
Y Arinaminpathy, E Khurana, DM Engelman, MB Gerstein (2009). Drug Discov Today 14: 1130-5.

Multi-level learning: improving the prediction of protein, domain and residue interactions by allowing information flow between levels.
KY Yip, PM Kim, D McDermott, M Gerstein (2009). BMC Bioinformatics 10: 241.

Getting started in text mining: part two.
A Rzhetsky, M Seringhaus, MB Gerstein (2009). PLoS Comput Biol 5: e1000411.

MSB: a mean-shift-based approach for the analysis of structural variation in the genome.
LY Wang, A Abyzov, JO Korbel, M Snyder, M Gerstein (2009). Genome Res 19: 106-17.

Seeking a new biology through text mining.
A Rzhetsky, M Seringhaus, M Gerstein (2008). Cell 134: 9-13.

Toward a universal microarray: prediction of gene expression through nearest-neighbor probe sequence identification.
TE Royce, JS Rozowsky, MB Gerstein (2007). Nucleic Acids Res 35: e99.

An efficient pseudomedian filter for tiling microrrays.
TE Royce, NJ Carriero, MB Gerstein (2007). BMC Bioinformatics 8: 186.

BoCaTFBS: a boosted cascade learner to refine the binding sites suggested by ChIP-chip experiments.
LY Wang, M Snyder, M Gerstein (2006). Genome Biol 7: R102.

A supervised hidden markov model framework for efficiently segmenting tiling array data in transcriptional and chIP-chip experiments: systematically incorporating validated biological knowledge.
J Du, JS Rozowsky, JO Korbel, ZD Zhang, TE Royce, MH Schultz, M Snyder, M Gerstein (2006). Bioinformatics 22: 3016-24.

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

Global landscape of protein complexes in the yeast Saccharomyces cerevisiae.
NJ Krogan, G Cagney, H Yu, G Zhong, X Guo, A Ignatchenko, J Li, S Pu, N Datta, AP Tikuisis, T Punna, JM Peregrin-Alvarez, M Shales, X Zhang, M Davey, MD Robinson, A Paccanaro, JE Bray, A Sheung, B Beattie, DP Richards, V Canadien, A Lalev, F Mena, P Wong, A Starostine, MM Canete, J Vlasblom, S Wu, C Orsi, SR Collins, S Chandran, R Haw, JJ Rilstone, K Gandi, NJ Thompson, G Musso, P St Onge, S Ghanny, MH Lam, G Butland, AM Altaf-Ul, S Kanaya, A Shilatifard, E O'Shea, JS Weissman, CJ Ingles, TR Hughes, J Parkinson, M Gerstein, SJ Wodak, A Emili, JF Greenblatt (2006). Nature 440: 637-43.

Integrated prediction of the helical membrane protein interactome in yeast.
Y Xia, LJ Lu, M Gerstein (2006). J Mol Biol 357: 339-49.

Design optimization methods for genomic DNA tiling arrays.
P Bertone, V Trifonov, JS Rozowsky, F Schubert, O Emanuelsson, J Karro, MY Kao, M Snyder, M Gerstein (2006). Genome Res 16: 271-81.

Sequence variation in G-protein-coupled receptors: analysis of single nucleotide polymorphisms.
S Balasubramanian, Y Xia, E Freinkman, M Gerstein (2005). Nucleic Acids Res 33: 1710-21.

Fast optimal genome tiling with applications to microarray design and homology search.
P Berman, P Bertone, B Dasgupta, M Gerstein, MY Kao, M Snyder (2004). J Comput Biol 11: 766-85.

Systematic learning of gene functional classes from DNA array expression data by using multilayer perceptrons.
A Mateos, J Dopazo, R Jansen, Y Tu, M Gerstein, G Stolovitzky (2002). Genome Res 12: 1703-15.

A Bayesian system integrating expression data with sequence patterns for localizing proteins: comprehensive application to the yeast genome.
A Drawid, M Gerstein (2000). J Mol Biol 301: 1059-75.

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