Welcome to Ressom Lab

     Ressom Lab is a research group at Georgetown Lombardi Comprehensive Cancer Center, Georgetown University Medical Center. Its goals are to develop analytical tools for mass spectrometry-based proteomics, metabolomics, and glycomics studies and to apply the tools for biomarker discovery and systems biology research.
    Our research group is composed of two components (a wet lab and a computational lab). The wet lab is focused on collection, storage, and processing of human serum and plasma samples prior to mass spectrometric data acquisition by LC/GC-MS. In addition to a variety of LC-MS systems accessible to us at Georgetown Lombardi's Proteomics and Metabolomics Shared Resource, the following instruments are available in our wet lab:

Agilent 1260 Infinity HPLC system

LECO Pegasus HT GC-TOF MS system

Agilent GC-MS with both EI and CI sources

The combination of GC/MS and LC/MS technologies will provide complementary approaches to analyze a broad class of metabolites. The computational lab develops signal processing and machine learning methods for analysis of omics data generated in our wet lab or in other collaborating labs.
    Our research is focused on building a pipeline for mass spectrometry-based proteomic, metabolomic, and glycomic biomarker discovery studies. Our long-term goal is to find and validate candidate cancer biomarkers in human serum and plasma samples. Our pipeline involves experimental design, sample collection, sample storage, sample preparation, mass spectrometric data acquisition, data preprocessing, statistical analysis, biomarker identification and validation. Our computational group is engaged in developing signal processing and machine learning methods needed for the pipeline, while our wet lab conducts method development and sample preparation needed for mass spectrometric experiments.
    Our projects are funded by grants from the National Institutes of Health (NIH) to develop analytical tools for mass spectrometric data analysis and to find and validate metabolic, peptide, and glycan biomarkers for early detection of hepatocellular carcinoma. We collaborate with clinicians, basic scientists, computational scientists, and experts in mass spectrometry, proteomics, metabolomics, and glycomics. In particular, we collaborate with Computational Bioinformatics and Bioimaging Laboratory of Virginia Tech; Proteomics and Mass Spectrometry Laboratory of University of South Alabama; Texas Tech; and Molecular Genetics/Proteomics Core, Children National Medical Center.

©2010-2013 Ressom Lab   Suite 173, Building D 4000 Reservoir Rd,N.W., Washington, D.C. 20057-1484

Current Projects

• Analysis of LC-MS Data to Identify Peptide and Glycan biomarkers for Hepatocellular Carcinoma. National Cancer Institute (NCI) R01CA143420 (PI: Ressom)

The goal of this project is to find and validate peptide and glycan biomarkers for early detection of hepatocellular carcinoma (HCC) in a high risk population of cirrhotic patients. The specific aims are to: (1) develop a Bayesian hierarchical model for alignment and normalization of liquid chromatography mass spectrometry (LC-MS) data; the model will be used for label-free quantification and comparison of peptides and glycans by LC-MS systems. (2) identify and validate an integrated set of candidate peptide and glycan biomarkers for early detection of hepatocellular carcinoma; a novel machine learning method will integrate peptide and glycan biomarkers to enhance their diagnostic capability.

• Quantitative analysis of LC-MS data for peptide and glycan biomarker discovery. National Institute of General Medicine (NIGMS) R01GM086746 (PI: Ressom)

The goal of this project is to develop computational methods for quantitative comparison of peptides and glycans in serum and plasma samples by label-free LC-ESI-MS and LC-MALDI-TOF methods. The specific aims are to: (1) develop a probabilistic-based mixture regression model for alignment of LC-ESI-MS data, (2) develop a clustering-based method for alignment of LC-MALDI-TOF data, (3) identify differentially abundant peptides and glycans between distinct biological groups, and (4) identify key pathways enriched by multiple candidate biomarkers.

• Metabolomic and Integromic Approaches to Identify Fingerprints for Early Detection and Treatment of Liver Cancer. NCI R21 CA153176 (PI: Ressom)

The specific aims of this project are the following: (1) to evaluate metabolic changes in the progression of chronic liver disease (CLD) to hepatocellular carcinoma (HCC) in serum and plasma samples by an ultra-performance liquid chromatography coupled with a quadrupole time of flight mass spectrometry (UPLC-QTOF MS). Serum and plasma samples collected from newly diagnosed HCC cases and matched cirrhotic controls will be utilized. The identified metabolic biomarkers will be verified by comparing their tandem mass spectrometry data with those generated from commercially available standard compounds. (2) Investigate key metabolic and signaling pathways that may be altered in the progression of CLD to HCC.

Previous Projects

• III-CXT-Small: Analysis of Mass Spectrometry Data for Biomarker Discovery. National Science Foundation (NSF) IIS-0812246 (PI: Ressom)


• Peptide Biomarker Discovery by Mass Spectrometry for Early Detection of Liver Cancer. NCI R21 CA130837 (PI: Ressom)


• Novel analytical tools for biomarker discovery from mass spectrometry data. Associate Membership to NCI's Early Detection Research Network (PI: Ressom)


• Identification of serum biomarkers for early detection of liver cancer. Prevent Cancer Foundation (PI: Ressom)


• Novel machine learning methods for analysis of MALDI-TOF mass spectrometry data. NCI R03 CA119313 (PI: Ressom).


• A pattern recognition system for identifying organic compounds from XMF EEMs. National Aeronautics and Space Administration (NASA) (PI: Ressom)


• Integrated fluorocarbon microsensor system using catalytic modification. NSF CBET-0428341 (PI: Wheeler, Co-PIs: Ressom and Pereira da Cunha)


• Acquisition of an x-band satellite data groundstation for regional multidisciplinary research. NSF EIA-0131889 (PI: Thomas, Co-PIs: Beard-Tisdale, Sader, and Ressom)


• Biodiversity and ecosystems informatics. NSF OCE-0420393 (PI: Musavi, Co-PIs: Markowsky, Hinkle, Stefanidis, and Ressom)


• Accurate DNA base calling. NSF DBI-0090738 (PI: Musavi, Co-PIs: Van Beneden, Ressom, and Domnisoru)


• Analysis of gene expression data from Rhizoctonia solani using a novel clustering algorithm. University of Maine Faculty Research Fund (PI: Ressom, Co-PI: Tavantzis)


• Seagrass health estimation using neural networks. Maine Space Grant Consortium (PI: Ressom)


• Neural network-based estimation of chlorophyll-a concentration in coastal waters. NASA and the Maine Space Grant Consortium (PI: Ressom)

©2010-2013 Ressom Lab   Suite 173, Building D 4000 Reservoir Rd,N.W., Washington, D.C. 20057-1484

Publications

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2013

• Zhou B, Xiao JF, Ressom HW. Prioritization of putative metabolite identifications in LC-MS/MS experiments using a computational pipeline. Proteomics. 2013 Jan;13(2):248-60. PMID: 23307777
• Wu FX, Ressom H, Dunn MJ. Focus on bioinformatics in proteomics. Proteomics. 2013 Jan;13(2):219-20. PMID: 23341066
• Hsu PC, Zhou B, Zhao Y, Ressom HW, Cheema AK, Pickworth W, Shields PG. Feasibility of Identifying the Tobacco-related Global Metabolome in Blood by UPLC-QTOF-MS. J Proteome Res. 2013 Feb 1;12(2):679-91.PMID: 23240883

2012

• Xiao JF, Varghese RS, Zhou B, Nezami Ranjbar MR; Zhao Y, Tsai T-H,Di Poto C, Wang J, Goerlitz D, Luo Y, Cheema A, Sarhan N, Soliman H,Tadesse MG, Ziada DH, Ressom HW (2012). LC-MS based serum metabo-lomics for identification of hepatocellular carcinoma biomarkers in Egyptian cohort. J Proteome Res. In Press. PMID:23078175
• Kaur P, Rizk NM, Ibrahim S, Younes N, Uppal A, Dennis K, Karve T,Blakeslee K, Kwagyan J, Zirie M, Ressom HW, Cheema AK (2012). iTRAQ-based quantitative protein expression profiling and MRM verification of markers in Type 2 diabetes. J Proteome Res. 2012 Oct10. PMID: 23050762 .
• Nezami Ranjbar MR, Zhao Y, Tadesse MG, Wang Y, Ressom HW (2012). Evaluation of normalization methods for analysis of LC-MS data. Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshop (BIBMW), Philadelphia, PA, October 2012, pp. 610-617.
• Zhao Y, Tsai T-H, Di Poto C, Pannell L, Tadesse MG, Ressom HW (2012). Variability assessment of LC-MS experiments and its application to experimental design and difference detection.
• Nezami Ranjbar MR, Tadesse MG, Wang Y, Ressom HW (2012). Normalization of LC-MS data using Gaussian process. Proceedings of the 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Washington, DC, December 2012.
• Wang J, Yuan H, Tadesse MG, Ressom HW (2012). A Bayesian network model for omics data integration. Proceedings of the 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Washington DC, December 2012.
• Ressom HW, Xiao JF, Tuli L, Varghese RS, Zhou B, Tsai T-H, Nezami Ranjbar MR, Zhao Y, Wang J, Di Poto C, Cheema AK, Tadesse MG, Goldman R, Shetty K (2012). Utilization of metabolomics to identify serum biomarkers for hepatocellular carcinoma in patients with liver cirrhosis. Anal Chim Acta. 2012 Sep 19;743C:90-100.PMID:22882828
• Zhou B, Wang J, Ressom HW (2012). MetaboSearch: Tool for mass-based metabolite identification using multiple databases. PLoS One 7(6): e40096. doi:10.1371/journal.pone.0040096.PMID:22768229
• Tuli L, Tsai T-H, Varghese R, Xiao JF, Cheema A, and Ressom HW (2012). Using a spike-in experiment to evaluate analysis of LC-MS data. Proteome Science. Proteome Science 10(1):13.
• Zhang Z, Xuan J, Clarke R, Ressom HW (2012). Module-based breast cancer classification. International Journal of Data Mining and Bioinformatics. In Press.
• Varghese RS, Zhou B, Nezami Ranjbar MR, Zhao Y, Ressom HW (2012). Ion annotation-assisted analysis of LC-MS based metabolomic experiment. Proteome Sci. 2012 Jun 21;10 Suppl 1:S8.
• Scholz SW, Mhyre T, Ressom H, Shah S, Federoff HJ (2012). Genomics and bioinformatics of Parkinson's disease. Cold Spring Harb Perspect Med. 2012 Jul;2(7):a009449.
• Wang J, Zhang y, Marian C, Ressom HW (2012). Identification of aberrant pathways and network activities from high-throughput data. Briefings in Bioinformatics. In Press. DOI: 10.1093/bib/BBS001
• Xiao JF, Zhou B, Ressom HW (2012). Metabolite identification and quantitation in LC-MS/MS-based metabolomics. Trends Analyt Chem 32:1-14.
• Zhou B, Xiao JF, Tuli L, Ressom HW (2012). LC-MS-based metabolomics. LC-MS-based metabolomics. Mol. BioSyst. 8(2) 470-481.
• Kaur P, Sheikh K, Kirilyuk A, Kirilyuk K, Kallakury B, Ressom HW, Cheema A (2012). Metabolomic profiling for biomarker discovery in pancreatic cancer. International Journal of Mass Spectrometry 310: 44-51.
• Kamalru AA, Baharum SN, Ressom HW, Noor NM (2012). GC-MS analysis and PLS-DA validation of the trimethyl silyl-derivatization techniques. American Journal of Applied Sciences. American Journal of Applied Sciences 9 (7): 1124-1136, 2012.
• Patacsil D, Tran AT, Cho YS, Suy S, Saenz F, Malyukova I, Ressom H, Collins SP, Clarke R, Kumar D (2012). Gamma-tocotrienol induced apoptosis is associated with unfolded protein response in human breast cancer cells. J Nutr Biochem. 2012 Jan;23(1):93-100. Epub 2011 Mar 22.

2011

• Befekadu GK, Tadesse MG, Tsai T-H, Ressom HW (2011). Probabilistic mixture regression models for alignment of LC-MS data. IEEE/ACM Trans Comput Biol Bioinform 8(5):1417-24.
• Tang Z, Zhang L, Cheema A, Ressom HW (2011). A new method for alignment of LC-MALDI-TOF data. Proteome Science 9 (Suppl 1):S1.
• Cheema AK, Timofeeva O, Varghese R, Dimtchev A, Shiekh K, Shulaev V, Suy S, Collins S, Ressom H, Jung M, Dritschilo A* (2011). Integrated analysis of ATM mediated gene and protein expression impacting cellular metabolism. J Proteome Res. 2011 May 6;10(5):2651-7. Epub 2011 Mar 7.
• Ochs MF, Karchin R, Ressom H, Gentleman R (2011). Identification of aberrant pathway and network activity from high-througput data - workshop introduction. Pac Symp Biocomput. 2011:364-8.
• Tsai T-H, Tadesse MG, Wang Y, and Ressom HW (2011). Bayesian alignment model for LC-MS data. Proceedings of the IEEE International Conference on Bioinformatics & Biomedicine, Atlanta, GA, November 12-15, 2011, pp. 261-264.
• Zhou B, Xiao JF, Ressom HW (2011). A computational pipeline for LC-MS/MS based metabolite identification. Proceedings of the IEEE International Conference on Bioinformatics & Biomedicine, Atlanta, GA, November 12-15, 2011, pp. 247-251.
• Varghese RS, Zhou B, Ressom HW (2011). Meta-analysis of LC-MS based metabolomic experiments. Proceedings of the Third International Workshop on Data Mining for Biomarker Discovery (DMBD 2011), Atlanta, GA, November 12-15, 2011, pp. 157-164.
• Patacsil D, Tran AT, Cho YS, Suy S, Saenz F, Malyukova I, Ressom H, Collins SP, Clarke R, Kumar D (2011). Gamma-tocotrienol induced apoptosis is associated with unfolded protein response in human breast cancer cells. J Nutr Biochem. 2011 Mar 21. [Epub ahead of print] PMID:21429729
• Cheema AK, Timofeeva O, Varghese R, Dimtchev A, Shiekh K, Shulaev V, Suy S, Collins S, Ressom H, Jung M, Dritschilo A (2011). Integrated analysis of ATM mediated gene and protein expression impacting cellular metabolism. J Proteome Res. 2011 Mar 7. [Epub ahead of print]
• Ochs MF, Karchin R, Ressom H, Gentleman R (2011). Identification of aberrant pathway and network activity from high-througput data - workshop introduction. Pac Symp Biocomput. 2011:364-8.
• Varghese RS and Ressom HW (2011). LC-MS data analysis for differential protein expression detection. Methods Mol Biol. 2011;694:139-50.

2010

• Varghese RS, Cheema A, Cheema P, Bourbeau M, Tuli L, Zhou B, Jung M, Dritschilo A, Ressom HW (2010). Analysis of LC-MS data for characterizing the metabolic changes in response to radiation. J Proteome Res. 9(5):2786-93.
• Zhang Y, Xuan J, de los Reyes BG, Clarke R, Ressom HW (2010). Reconstruction of gene regulatory modules in cancer cell cycle by multi-source data integration. PLoS One 5(4):e10268.
• Tang Z, Varghese RS, Bekesova S, Loffredo CA, Abdel-Hamid M, Kyselova Z, Mechref Y, Novotny M, Goldman R, Ressom HW (2010). Identification of N-glycan serum markers associated with hepatocellular carcinoma from mass spectrometry data. J Proteome Res. 9(1):104-12.
• Prahalad P, Dakshanamurthy S, Ressom H, Byers SW (2010). Retinoic acid mediates regulation of network formation by COUP-TFII and VE-cadherin expression by TGFbeta receptor kinase in breast cancer cells. PLoS One 5(4):e1002.
• Befekadu GK, Tadesse MG, Tsai T-H, Ressom HW. Probabilistic mixture regression models for alignment of LC-MS data. IEEE/ACM Trans Comput Biol Bioinform. 2010 Sep 8. [Epub ahead of print] PMID: 20837998
• Zhou B, Cheema A, Ressom HW (2010). SVM-based spectral matching for metabolite identification. Proceedings of the 32nd Annual Conference of the IEEE Engineering in Medicine and Biology Society, Buenos Aires, Argentina, August 31 - September 4, 2010, pp. 756-759.
• Tang Z, Zhang L, Cheema A, Ressom HW (2010). A new method for alignment of LC-MALDI-TOF data. Proceedings of the IEEE International Conference on Bioinformatics & Biomedicine, December 18-21, Hong Kong. 2010, pp.346-351.
• Zhang Y, Xuan J, Clarke R, Ressom HW (2010). Module-based biomarker discovery in breast cancer. Proceedings of the IEEE International Conference on Bioinformatics & Biomedicine, December 18-21, Hong Kong. 2010, pp.352-356.
• Tuli L, Tsai T-H, Varghese R, Cheema A, and Ressom HW (2010). Using a spike-in experiment to evaluate analysis of LC-MS data. Proceedings of the IEEE International Workshop on Computational Proteomics, December 18-21, Hong Kong. 2010, pp.67-72.
• Kaur P, Sheikh K, Kirilyuk A, Kirilyuk K, Kallakury B, Ressom HW, Cheema A (2010). Metabolomic profiling for biomarker discovery in pancreatic cancer. Proceedings of the IEEE International Conference on Bioinformatics & Biomedicine, December 18-21, Hong Kong. 2010. pp. 505-509.

2009

• Tuli L and Ressom HW (2009). LCS based detection of differential protein expression. Journal of Proteomics and Bioinformatics 2(10); 416-438.
• Goldman R, Ressom HW, Varghese RS, Goldman L, Bascug G, Loffredo CA, Abdel-Hamid M, Gouda I, Ezzat S, Kyselova Z, Mechref Y, Novotny MV (2009). Detection of hepatocellular carcinoma using glycomic analysis. Clin Cancer Res. 15(5):1808-13.
• Zhang Y, Xuan J, de Los Reyes BG, Clarke R, Ressom HW (2009). Reverse engineering module networks by PSO-RNN hybrid modeling. BMC Genomics 10(Suppl 1):S15.
• Solomon DA, Kim JS, Ressom HW, Sibenaller Z, Ryken T, Jean W, Bigner D, Yan H, Waldman T (2009). Sample type bias in the analysis of cancer genomes. Cancer Res. 69(14):5630-3.
• Timofeeva OA, Zhang X, Ressom HW, Varghese RS, Kallakury BV, Wang K, Ji Y, Cheema A, Jung M, Brown ML, Rhim JS, Dritschilo A (2009). Enhanced expression of SOS1 is detected in prostate cancer epithelial cells from African-American men. Int J Oncol. 35(4):751-60.
• Ressom HW, Varghese RS, Goldman R (2009). Computational methods for analysis of MALDI-TOF spectra to discover peptide serum biomarkers. Protein Protocols Handbook 3rd Ed. (J.M. Walker, Ed.), Humana Press. 2009.
• Ressom HW, Befekadu GK, Tadesse MG (2009). Analysis of LC-MS data using probabilistic-based mixture regression models. at ?Automatisierungstechnik 57(9): 453-465.
• Befekadu GK, Tadesse MG, Hathout Y, Ressom HW (2009). A Bayesian based functional mixed-effects model for analysis of LC-MS data. Conf Proc IEEE Eng Med Biol Soc. 2009; 6743-6746.
• An, Yanming, Ressom, HW, Goldman R (2009). Enrichment of serum peptides and analysis by MALDI-TOF mass spectrometry. Protein Protocols Handbook 3rd Ed. (J.M. Walker, Ed.), Humana Press. 2009.

2008

• Zhang Y, Xuan J, de Los Reyes BG, Clarke R, Ressom HW (2008). Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data. BMC Bioinformatics 9(1):203.
• Solomon DA, Kim JS, Cronin JC, Sibenaller Z, Ryken T, Rosenberg SA, Ressom H, Jean W, Bigner D, Yan H, Samuels Y, Waldman T (2008). Mutational Inactivation of PTPRD in Glioblastoma Multiforme and Malignant Melanoma. Cancer Res. 68(24):10300-6.
• Ressom HW, Varghese RS, Goldman L, Loffredo CA, Abdel-Hamid M, Kyselova Z, Mechref Y, Novotny M, Goldman R (2008). Analysis of MALDI-TOF mass spectrometry data for detection of glycan biomarkers. Pacific Symposium on Biocomputing 13:216-227.
• Ressom HW, Varghese RS, Zhang Y, Xuan J, Clarke R (2008). Classification algorithms for phenotype prediction in genomics and proteomics. Frontiers in Biosciences 13:691-708.
• Ressom HW, Varghese RS, Goldman L, Yanming A, Loffredo CA, Abdel-Hamid M, Kyselova Z, Mechref Y, Novotny M, Drake, S, Goldman R (2008). Analysis of MALDI-TOF mass spectrometry data for discovery of peptide and glycan biomarkers of hepatocellular carcinoma. Journal of Proteome Research 7(2) 603-610.
• Solomon DA, Kim JS, Jenkins S, Ressom H, Huang M, Coppa N, Mabanta L, Bigner D, Yan H, Jean W, Waldman T (2008). Identification of p18 INK4c as a tumor suppressor gene in glioblastoma multiforme. Cancer Res 68(8):2564-9.
• Song J., Liu H., Ressom H., Tiwari S., Ecelbarger CA (2008). Chronic rosiglitazone therapy normalizes expression of ACE1, SCD1 and other genes in the kidney of obese Zucker rats as determined by microarray analysis. Experimental and Clinical Endocrinology & Diabetes 116:315-325.
• Clarke R, Ressom HW, Wang A, Xuan J, Liu MC, Gehan EA, Wang Y (2008). The properties of very high dimensional data spaces: implications for exploring gene and protein expression data. Nature Reviews Cancer 8(1):37-49.
• Tang Y, Kitisin K, Jogunoori W, Li C, Deng CX, Mueller SC, Ressom HW, Rashid A, He AR, Mendelson JS, Jessup JM, Shetty K, Zasloff M, Mishra B, Reddy EP, Johnson L, Mishra L (2008). Progenitor/stem cells give rise to liver cancer due to aberrant TGF-beta and IL-6 signaling. PNAS 105(7): 2445-2450.
• Varghese RS, Goldman L, An Y, Loffredo CA, Abdel-Hamid M, Kyselova Z, Mechref Y, Novotny M, Drake SK, Goldman R, Ressom HW (2008). Integrated peptide and glycan biomarker discovery using MALDI-TOF mass spectrometry. Conf Proc IEEE Eng Med Biol Soc. 2008;1:3791-3794.
• Befekadu GK, Tadesse MG, Hathout Y, Ressom HW (2008). Multi-class alignment of LC-MS data using probabilistic-based mixture regression models. Conf Proc IEEE Eng Med Biol Soc. 2008;1:4094-4097.
• Zhang Y, Xuan J, de los Reyes BG, Clarke R, Ressom HW (2008). Network motif-based identification of breast cancer susceptibility genes. Conf Proc IEEE Eng Med Biol Soc. 2008;1:5696-5699.
• Befekadu GK, Tadesse MG, Ressom HW (2008). Probabilistic-based mixture regression models for multi-class alignment of LC-MS data. Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology (BIOCOMP'08), Las Vegas, NV, July 14-17, 2008, 368-373.
• Zhang Y, Xuan J, de los Reyes BG, Clarke R, Ressom HW (2008). Reverse engineering module networks by PSO-RNN hybrid modeling. Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology (BIOCOMP'08), Las Vegas, NV, July 14-17, 2008, 401-407.
• De Los Reyes BG, Lakshman D, Yun SJ, Ressom H (2008). Agricultural genomic. In: Textbook of Agricultural Biotechnology, (A. Nag, Ed.), PHI Learning, New Delhi, India, pp. 55-88.

2007

• Ressom HW, Varghese RS, Drake SK, Hortin GL, Abdel-Hamid M, Loffredo CA, and Goldman R (2007). Peak selection from MALDI-TOF mass spectra using ant colony optimization. Bioinformatics 23(5):619-626.
• Goldman R, Ressom HW, Abdel-Hamid M, Goldman L, Wang A, Varghese RS, An Y, Loffredo CA, Drake SK, Eissa SA, Gouda I, Ezzat S, Seillier-Moiseiwitsch F (2007). Candidate markers for the detection of hepatocellular carcinoma in low molecular weight fraction of serum. Carcinogenesis 28(10):2149-53.
• Kim JS, Lee C, Bonifant CL, Ressom H, Waldman T (2007). Activation of p53-Dependent Growth Suppression in Human Cells by Mutations in PTEN or PIK3CA.Mol Cell Biol 27(2): 662-677.
• Xu J, Gao M, Fan S, Meng Q, Goldberg ID, Abounader R, Ressom H, Laterra JJ, Rosen EM (2007). Effect of Akt inhibition on scatter factor-regulated gene expression in DU-145 human prostate cancer cells. Oncogene 26(20): 2925-2938.
• Cheng C, Yun K-Y, Ressom HW, Mohanty B, Bajic VB, Jia Y, Yun SJ, de los Reyes BG (2007). An early response regulatory cluster induced by low temperature and hydrogen peroxide in seedlings of chilling-tolerant japonica rice. BMC Genomics 8: 175.
• Musavi MT, Ressom H, Srirangam S, Natarajan P, Virnstein RW, Morris LJ, and Tweedale W (2007). Neural network-based light attenuation model for monitoring seagrass population in the Indian river lagoon. J Intell Inf Syst. 29: 63-77.
• Ressom HW, Varghese RS, Goldman L, Loffredo CA, Abdel-Hamid M, Kyselova Z, Mechref Y, Novotny M, Goldman R (2007). Analysis of mass spectrometry data for serum biomarker discovery. Proceedings The third IEEE-NIH LIfe Science Systems and Applications (LISSA'07) Workshop, Nov 2007, Bethesda, MD.
• Ressom HW, Zhang Y, Xuan J, Wang J, Clarke R (2007). Integrating multi-source biological data for transcriptional regulatory module discovery. Proceedings The third IEEE-NIH LIfe Science Systems and Applications (LISSA'07) Workshop, Nov 2007, Bethesda, MD.

2006

• Ressom HW, Fyfe SK, Srirangam S, Natarajan P, and Musavi MT (2006). Neural network-based estimation of photosynthetic efficiency. International Journal of Ocean and Oceanography 1(3), Sep. 06. http://www.ripublication.com/Volume/ijoov1n3.htm.
• Orvisky E, Drake SK, Martin BM, Ressom H, Varghese RS, Saha D, Hortin GL, Loffredo CA, Goldman R (2006). Enrichment of low molecular weight fraction of serum for mass spectrometric analysis of peptides associated with hepatocellular carcinoma. Proteomics 6(9) 2895-2902.
• Ressom HW, Zhang Y, Xuan J, Wang J, Clarke R (2006). Inferring network interactions using recurrent neural networks and swarm intelligence. Proceedings of the 28th IEEE Engineering in Medicine and Biology Society Annual International Conference, New York, NY, 4241-4244.
• Ressom HW, Varghese RS, Orvisky E, Drake SK, Hortin GL, Abdel-Hamid M, Loffredo CA, and Goldman R (2006). Ant colony optimization for biomarker identification from MALDI-TOF mass spectra. Proceedings of the 28th IEEE Engineering in Medicine and Biology Society Annual International Conference, New York, NY, 4560-4563.
• Ressom HW, Varghese RS, Orvisky E, Drake SK, Hortin GL, Abdel-Hamid M, Loffredo CA, and Goldman R (2006). Biomarker identification and rule extraction from mass spectral serum profiles. Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, Toronto, ON, 164-170.
• Ressom HW, Zhang Y, Xuan J, Wang J, Clarke R (2006). Inference of gene regulatory networks from time course gene expression data using neural networks and swarm intelligence. Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, Toronto, ON, 435-442.
• Ressom HW, Turner K, and Musavi MT (2006). Estimation of ocean water chlorophyll-a concentration using computational intelligence. Proceedings of Oceans '06 MTS/IEEE, Boston, MA, 1-6.
• Ressom HW, Turner K, and Musavi MT (2006). Estimation of ocean water chlorophyll-a concentration using fuzzy c-means clustering and artificial neural networks. Proceedings of the International Joint Conference on Neural Networks, Vancouver, BC, Canada, 4118-4125.
• Ressom HW, Zhang Y, Xuan J, Wang J, Clarke R (2006). Inferring network interactions using recurrent neural networks and particle swarm optimization. Proceedings of the First International Conference on Computational Systems Biology, Shanghai, China.

2005

• Ressom HW, Varghese RS, Abdel-Hamid M, Abdel-Latif Eissa S, Saha D, Goldman L, Petricoin EF, Conrads TP, Veenstra TD, Loffredo CA, Goldman R (2005). Analysis of mass spectral serum profiles for biomarker selection. Bioinformatics 21(21):4039-4045.
• Ressom H, Natarjan P, Varghese RS, and Musavi MT (2005). Applications of fuzzy logic in genomics. Journal of Fuzzy Sets and Systems 152: 125-138.
• Ressom H, Miller RL, Natarajan P, and Slade WH (2005). Computational intelligence and its application in remote sensing. In: Remote Sensing of Coastal Aquatic Environments: Technologies, Techniques and Applications. Series: Remote Sensing and Digital Image Processing, Vol. 7, Miller, Richard L.; Del Castillo, Carlos E.; McKee, Brent A. (Eds.), XVIII, 347 p., ISBN: 1-4020-3099-1, Springer, Berlin.
• Ressom HW and Natarajan P (2005). Gene expression clustering using computational intelligence. In: Bioinformatics: New Research, Editor: Yan, Peter V., pp. 1-25, ISBN 1-59454-242-2, Nova Science Publishers Inc., New York.
• Ressom HW, Varghese RS, Orvisky E, Drake SK, Hortin GL, Abdel-Hamid M, Loffredo CA, and Goldman R (2005). Analysis of MALDI-TOF serum profiles for biomarker selection and sample classification. Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, La Jolla, CA, pp. 378-384.
• Ressom H, Varghese RS, Saha D, Orvisky E, Goldman L, Petricoin EF, Conrads TP, Veenstra TD, Abdel-Hamid M, Loffredo CA, Goldman R (2005). Particle swarm optimization for analysis of mass spectral serum profiles. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2005, Beyer, H.G. et al. (Editors), ACM Press, New York, vol. 1, pp. 431-438.

2004

• Slade WH, Ressom H, Musavi MT, and Miller RL (2004). Inversion of ocean color observations using particle swarm optimization. IEEE Transactions on Geoscience and Remote Sensing 42(9):1915-1923.
• Slade WH, Ressom H, M.T, Musavi, and Miller RL (2004). Ocean color inversion by particle swarm optimization. Proceedings of 2004 IEEE Congress on Evolutionary Computation (CEC 2004), Portland, Oregon, June 19-23, 2004, pp. 971-977.
• Varghese RS, Musavi MT, and Ressom H (2004). Fuzzy confidence value for DNA bases. Proceedings of IEEE International conference on Fuzzy Systems (FUZZ-IEEE 2004), Budapest, Hungary, July 25-29, 2004. IEEE Catalog # 04CH37542C, ISBN 0-7803-8354-0, CD-ROM Edition.
• Ressom H, Natarjan P, Srirangam S, Musavi MT, Virnstein RW, Morris LJ, and Tweedale W (2004). Neural network based light attenuation model for monitoring seagrass health. Proceedings of the International Joint Conference on Neural Networks (IJCNN 2004), Budapest, Hungary, July 25-29, 2004, IEEE Catalog # 04CH37541C, ISBN 0-7803-8360-5, CD-ROM Edition.
• Ressom H, Fyfe SK, Srirangam S, Musavi MT, and Natarjan P (2004). Estimation of photosynthetic efficiency for monitoring seagrass health using neural networks. Proceedings of IASTED International Conference on Neural Networks and Computational Intelligence (NCI 2004), Grindelwald, Switzerland, Feb. 23-25, 2004, pp. 232-237.
• Slade WH, Miller RL, Ressom H, and Natarjan P (2004). Neural network retrieval of phytoplankton abundance from remotely-sensed ocean radiance. Proceedings of IASTED International Conference on Neural Networks and Computational Intelligence (NCI 2004), Grindelwald, Switzerland, Feb. 23-25, 2004, pp. 226-231.

2003

• Ressom H, Wang D, and Natarjan P (2003). Adaptive double self-organizing maps for clustering gene expression profiles. Neural Networks 16(5-6):633-640.
• Ressom H, Reynolds R, and Varghese RS (2003). Increasing the efficiency of fuzzy logic-based gene expression data analysis. Physiological Genomics 13:107-117.
• Ressom H, Wang D, and Natarjan P (2003). Clustering gene expression data using adaptive double self-organizing map. Physiological Genomics 14:35-46.
• Ressom H, Wang D, and Natarjan P (2003). Adaptive double self-organizing map and its application in clustering gene expression data. Proceedings of International Joint Conference on Neural Networks (IJCNN), Portland, OR, July 20-24, 2003, IEEE Catalog # 03CH37464C, ISBN 0-7803-7899-7, CD-ROM Edition, pp. 39-44.
• Ressom H, Fyfe SK, Natarjan P, and Srirangam S (2003). Monitoring seagrass health using neural networks. Proceedings of International Joint Conference on Neural Networks (IJCNN), Portland, OR, July 20-24, 2003, IEEE Catalog # 03CH37464C, ISBN 0-7803-7899-7, CD-ROM Edition, pp. 1019-1024.
• Srirangam S, Ressom H, Natarjan P, Musavi MT, Virnstein RW, Morris LJ, and Tweedale W (2003). Neural network-based estimation of light attenuation coefficient. Proceedings of International Joint Conference on Neural Networks (IJCNN), Portland, OR, July 20-24, 2003, IEEE Catalog # 03CH37464C, ISBN 0-7803-7899-7, CD-ROM Edition, pp. 590-595.
• Slade WH, Miller RL, Ressom H, and Natarjan P (2003). Ensemble neural network methods for satellite-derived estimation of chlorophyll a. Proceedings of International Joint Conference on Neural Networks (IJCNN), Portland, OR, July 20-24, 2003, IEEE Catalog # 03CH37464C, ISBN 0-7803-7899-7, CD-ROM Edition, pp. 547-552.
• Ressom H, Wang D, Varghese RS, and Reynolds R (2003). Fuzzy logic-based gene regulatory network. Proceedings of IEEE International Conference on Fuzzy Systems, St. Louis, MO, May 25-28, 2003, pp. 1210-1215.
• Ressom H, Wang D, and Natarjan P (2003). Computational methods for identifying number of clusters in gene expression data. Proceedings of IASTED International Conference on Neural Networks and Computational Intelligence (NCI 2003), Cancun, Mexico, May 19-21, 2003, pp. 233-238.

2002

• Xiong N, Litz L, and Ressom H (2002). Learning premises of fuzzy rules for knowledge acquisition in classification problems. Knowledge and Information Systems 4(1): 96-111.
• Slade WH, Miller RL, Ressom H, and Natarjan P (2002). Improved modeling of satellite-derived chlorophyll a concentration using neural networks. Ocean Optics XVI CD from ONR, Ocean Optics XVI, Santa Fe, NM, November 18-22, 2002.
• French BD, Domnisoru C, Ressom H, and Musavi MT (2002). Confidence value prediction of called genetic bases using a fuzzy predication system," Proceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences (METMBS '02), Las Vegas, NV, June 24-27, 2002, CSREA Press, ISBN 1-892512-39-4, H.R. Arabnia (Ed.), pp. 203-209.
• Wang D, Ressom H, Musavi MT, and Domnisoru C (2002). Double self-organizing maps to cluster gene expression data. Proceedings of 10th European Symposium on Artificial Neural Networks (ESANN 2002), Bruges, Belgium, April 2002, d-side publi., ISBN 2-930307-02-1, pp. 45-50.
• Reynolds R, Ressom H, Musavi MT, and Domnisoru C (2002). Improving robustness of fuzzy gene modeling. Proceedings of 10th European Symposium on Artificial Neural Networks (ESANN 2002), Bruges, Belgium, April 2002, d-side publi., ISBN 2-930307-02-1, pp. 51-56.

2001

• Ressom H, Musavi MT, Natarjan P (2001). Neural network-based estimation of phytoplankton primary production. Proceedings of SPIE, San Diego, CA, August 2001, vol. 4488, pp. 213-220.
• Musavi MT, R. Miller, Ressom H, Natarjan P. (2001) Neural network-based estimation of chlorophyll-a concentration in coastal waters," Proceedings of SPIE, San Diego, CA, August 2001, vol. 4488, pp. 176-183.
• Reynolds R, Ressom H, Musavi MT, and Domnisoru C (2001). Use of clustering to improve performance in fuzzy gene expression analysis. Proceedings of International Joint Conference on Neural Networks, Washington, DC, July 2001, pp. 2738-2743.
• Ressom H, Musavi MT, and Khan S (2001). Intelligent control of critical dimension in photolithography process," Proceedings of International Joint Conference on Neural Networks, Washington, DC, July 2001, pp. 486-490.

2000

• Khan S, Musavi MT, and Ressom H (2000). Critical dimension control in semiconductor manufacturing. Intelligent Engineering Systems Through Artificial Neural Networks, vol. 10, editors: C.H. Dagli et al, ASME Press Series, pp. 995-1000.
• Ressom H (2000). Neural network-based on-line quality prediction in nonwoven production. Intelligent Engineering Systems Through Artificial Neural Networks, vol. 10, editors: C.H. Dagli et al, ASME Press Series, pp. 1031-1036.
• Ressom H, Voos H, Litz L, and Schmitt P (2000). On-line estimation of key quality parameters in nonwoven production. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Nashville, TN, October 2000, pp. 1745-1749.

1999

• Martin Becker, Andreas Diehl, and Habtom Ressom (1999). Modeling a refrigeration system using recurrent neural networks. atp-Automatisierungstechnische Praxis 41(9):22-31. (in German)
• Habtom Ressom, Holger Voos, and Lothar Litz, NeuroIdentifier: A software tool for neural network-based identification of dynamic systems,?at - Automatisierungstechnik, Oldenbourg-Verlag, vol. 47, No. 4, pp. 181-182, 1999. (in German)
• Habtom Ressom (1999) Dynamic system and virtual sensor modeling using neural networks, VDI Fortschritt-Berichte, Series 8, No. 771, VDI-Verlag, Duesseldorf.
• Habtom Ressom (1999). Modeling a compression plant using recurrent neural networks. Proceedings of International Joint Conference on Neural Networks (IJCNN), Washington DC, July 10-16, 1999, IEEE Catalog # 99CH36339C, ISBN 0-7803-5532-6, CD-ROM Edition
• Holger Voos, Habtom Ressom, and Lothar Litz (1999). Development of an engineering tool for the design and optimization of knowledge-based control systems: a perspective. Proceedings of the 11th International Conference on Software Engineering and Knowledge Engineering, Kaiserslautern, Germany, 1999, pp. 327-331.
• Habtom Ressom (1999). Soft-sensing using recurrent neural networks. Proceedings of IEEE International Symposium on Intelligent Control, Gaithersburg, MD, 1999. pp. 342-347.
• Habtom Ressom (1999). Modeling a refrigeration system using recurrent neural networks. In: Lecture notes in computer science 1625: Editor: B. Reusch, Springer-Verlag, vol. 1625, pp. 47-52.

1998

• Habtom Ressom and Lothar Litz (1998). Neural network-based inferential sensing. In: Perspective in Neural Computing, Editors: L. Niklasson et al., Springer-Verlag, pp. 785-790.
• Habtom Ressom and Lothar Litz (1998). Virtual sensors based on recurrent neural networks and the extended Kalman filter. Proceedings of IEEE International Conference on Control Applications, Trieste, Italy, 1998. pp. 163-167.
• Lothar Litz, Klaus-Peter Klaan, and Habtom Ressom (1998). Methodik und Einsatzpotential von Fuzzy Control, Neuronalen Netzen und Neuro-Fuzzy. In: Fuzzy Control und Neuronale Netze zur Optimierung der Stickstoff- und Phosphorelimination, Schriftenreihe des Fachgebietes Siedlungswasserwirtschaft, Universit Kaiserslautern, Editors: T. G. Schmitt and J. Hansen, vol. 11, pp. 23-38.

1997

• Ressom Habtom and Lothar Litz (1997). Neurocontrol of nonlinear dynamic systems subject to unmeasured disturbance inputs. Lecture notes in computer science, Editor: Gerstner, vol. 1327, Springer-Verlag, pp. 855-860.
• Ressom Habtom and Lothar Litz, (1997) Estimation of unmeasured inputs using recurrent neural networks and the extended Kalman filter. Proceedings of IEEE International Conference on Neural Networks, Houston, TX, 1997, pp. 2067-2071.
• Habtom Ressom and Lothar Litz (1997). Neural network-based control of nonlinear dynamic systems with unmeasured inputs. Proceedings of 12th International Conference on Systems Engineering, Coventry, U.K., 1997, pp. 296-300.

©2010-2013  Ressom Lab Suite 173, Building D 4000 Reservoir Rd,N.W., Washington, D.C. 20057-1484

Courses (Georgetown University)

• TBIO 530 - Systems Biology & Bioinformatics


• BCHB 581 - Transcriptomics: Microarray Data Analysis


• BIST 531 - Pattern Recognition

Previous Courses (University of Maine)

• ECE 414 - Feedback Control Systems


• ECE 590 - Neural Networks


• ECE 512 - Linear Systems Analysis


• ECE 314 - Linear Circuits and Systems

©2010-2013  Ressom Lab Suite 173, Building D 4000 Reservoir Rd,N.W., Washington, D.C. 20057-1484

Habtom W. Ressom, Ph.D.       Associate Professor

    Dr. Ressom received a Ph.D. in Electrical Engineering from the University of Kaiserslautern, Germany in 1999. Prior to joining Georgetown University in 2004, he was an Assistant Professor of Electrical and Computer Engineering at the University of Maine, where he applied artificial neural networks, fuzzy logic, and evolutionary computing for microarray gene expression data analysis, DNA base calling, ocean color remote sensing, and industrial process control. His research at Georgetown University focuses on cancer biomarker discovery and systems biology by analysis of omics data. Specifically, he uses label-free LC-MS methods to search for candidate peptide, glycan, and metabolic biomarkers in serum and plasma. His laboratory develops signal processing, statistical, and machine learning methods to analyze LC-MS data and to integrate omics data for biomarker discovery and systems biology research. His laboratory is funded by grants from the National Science Foundation and the National Institutes of Health. Dr. Ressom is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and a co-director of the Lombardi Comprehensive Cancer Center's Genomics and Epigenomics Shared Resource, which provides services for various studies including DNA sequencing, fragment analysis, gene expression, microRNA, methylation, and SNP genotyping.

E-mail: hwr@georgetown.edu        Office: 175 Building D     Phone:202-687-2283

Rency S. Varghese, M.S.       Research Associate

    Ms. Varghese obtained her M.S. degree in Electrical Engineering from University of Maine, Orono in 2004 and her Bachelors degree in Electrical and Electronics Engineering from College of Engineering, Trivandrum, India in 1999. She worked as software engineer/consultant for two years in India. Before joining LCCC in 2004, she worked as Research Assistant during her study at University of Maine, where she applied methods such as artificial neural networks, fuzzy logic, and evolutionary computing for microarray data analysis and DNA base calling. She is currently working on the development of machine learning methods for analysis of mass spectrometry data.

E-mail: rsv4@georgetown.edu        Office: 174 Building D     Phone:202-687-7369

Cristina Di Poto, Ph.D.       Postdoctoral Research Fellow

    Dr. Di Poto received her Ph.D. in Biochemistry from University of Pavia, Italy in 2008. She is currently focused on developing optimal sample preparation, fractionation, mass spectrometry data analysis and interpretation in human proteomics.

E-mail: cd329@georgetown.edu        Office: W325 Research Building     Phone:202-687-2926

Jinlian Wang, Ph.D.       Postdoctoral Research Fellow

    Dr. Wang received her Ph.D. in Artificial Intelligence from College of Electrical Information and Control Engineering, Beijing University of Technology, China in 2008.?She was a post doctoral fellow at Lombardi Comprehensive Cancer Center, Georgetown University Medical Center in 2009, where she built a web system for predicting O-GlcNAcylated proteins and sites using machine learning methods. She also developed a biomedical event annotation tool, applied to biomedical literature mining. She is interested in cancer biomarker discovery and integration of multiple omics data for network and pathaway analysis.

E-mail: jw462@georgetown.edu        Office: 174 Building D     Phone:202-687-3045

Bin Zhou, M.S.       Research Assistant

    Bin Zhou received his B.S. degree from Wuhan University, China and M.S. degree from Zhejiang University, China both in Electrical Engineering. Currently a PhD student at Virginia Tech, his research interest focuses on developing signal processing and machine learning methods for image processing and analysis of metabolomic data.

E-mail: bz37@georgetown.edu        Office: 173 Building D      Phone:202-687-3381

Tsung-Heng Tsai, M.S.       Research Assistant

    Tsung-Heng Tsai received his B.S. in Power Mechanical Engineering from the National Tsing Hua University in 2003, and his M.S. in Electrical and Control Engineering from the National Chiao Tung University in 2005. During 2007-2009, he was a research assistant at the Institute of Information Science, Academia Sinica. He is currently a Ph.D. student at Virginia Tech. His research interest is proteomics data analysis with statistical and machine learning approaches.

E-mail: tt287@georgetown.edu        Office: 176 Building D     Phone:202-687-9396

Mohammad R. Nezami Ranjbar, M.S.       Research Assistant

    Mohammad R. Nezami Ranjbar received his BSc and MSc in Electrical Engineering from Sharif University of Technology, Iran. He is currently a PhD student at Virginia Tech. His research interest is proteomics data modeling and analysis.

E-mail: mn345@georgetown.edu        Office: 176 Building D     Phone:202-687-1387

Yi Zhao, M.S.       Research Assistant

    Yi Zhao received her B.S. in statistics from School of Mathematical Sciences, Nankai University, China. She got her M.S. in biostatistics from Georgetown University, USA in 2012. Her research interest is non-parametric statistics and high-dimensional data.

E-mail: yz266@hoyamail.georgetown.edu        Office: 174 Building D     Phone:202-687-3045/202-687-7369

Minkun (Kevin) Wang, B.S.       Research Assistant

     Kevin (Minkun) Wang received his B.S. degree in Electrical Engineering from University of Science and Technology of China. He is currently a PhD candidate at Virginia Tech in Dept. ECE. His research interest is signal/image processing, sparse representation via dictionary learning, and machine learning methods for image processing.

E-mail: minkunw@gmail.com        Office: 176 Building D     Phone:Phone: 202-687-9369/202-687-1387

Yiming (Mike) Zuo, B.S.       Research Assistant

     Yiming Zuo received his B.S. degree in the Information Science & Electronic Engineering from Zhejiang University, China in 2012. He is currently a Ph.D. student of Virginia Tech. He is a research assistant at Department of Oncology, Georgetown University. His research interest is developing methods for metabolic data processing.

E-mail: yz335@hoyamail.georgetown.edu        Office: 176 Building D     Phone: 202-687-3381

Yue Luo (Karen), M.S.       Research Associate

    Yue Luo (Karen) received her M.S. degree in analytical chemistry from University of Vermont, and her B.S. degree from Zhejiang University, China. Prior joining in Ressom Lab, she worked in Metabolomics Core Facility for two years. She is interested in applying mass spectrometry in metabolomics, lipidomics and proteomics research.

E-mail: yl436@georgetown.edu        Office: W325 Research Building     Phone:202-687-4453

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©2010-2011  Ressom Lab Suite 173, Building D 4000 Reservoir Rd, N.W., Washington, D.C. 20057-1484

Scripts & Data Download Matlab Scripts (Ressom et al. Bioinformatics 2005) Matlab Scripts & MALDI-TOF proteomics data (Ressom et al. Bioinformatics 2007) MALDI-TOF proteomics data (Goldman et al. Carcinogenesis 2007) MALDI-TOF glycomics data (Ressom et al. Pacific Symposium on Biocomputing 2008)

©2010-2013  Ressom Lab Suite 173, Building D 4000 Reservoir Rd,N.W., Washington, D.C. 20057-1484

Collaborating Labs

Computational Bioinformatics and Bioimaging Laboratory, Virginia Tech Proteomics and Mass Spectrometry Laboratory, University of South Alabama Texas Tech Molecular Genetics/Proteomics Core, Children National Medical Center