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Showing 1 to 12 of 16 entries
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LC-MS Based Detection of Differential Protein Expression.

Journal of proteomics & bioinformatics

Tuli L, Ressom HW.
PMID: 20473349
J Proteomics Bioinform. 2009 Oct 02;2:416-438. doi: 10.4172/jpb.1000102.

While several techniques are available in proteomics, LC-MS based analysis of complex protein/peptide mixtures has turned out to be a mainstream analytical technique for quantitative proteomics. Significant technical advances at both sample preparation/separation and mass spectrometry levels have revolutionized...

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Tuli L, Ressom HW. LC-MS Based Detection of Differential Protein Expression. J Proteomics Bioinform. 2009;2:416-438doi: 10.4172/jpb.1000102.
Tuli, L., & Ressom, H. W. (2009). LC-MS Based Detection of Differential Protein Expression. Journal of proteomics & bioinformatics, 2416-438. https://doi.org/10.4172/jpb.1000102
Tuli, Leepika, and Ressom, Habtom W. "LC-MS Based Detection of Differential Protein Expression." Journal of proteomics & bioinformatics vol. 2 (2009): 416-438. doi: https://doi.org/10.4172/jpb.1000102
Tuli L, Ressom HW. LC-MS Based Detection of Differential Protein Expression. J Proteomics Bioinform. 2009 Oct 02;2:416-438. doi: 10.4172/jpb.1000102. PMID: 20473349; PMCID: PMC2867618.
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Ion annotation-assisted analysis of LC-MS based metabolomic experiment.

Proteome science

Varghese RS, Zhou B, Nezami Ranjbar MR, Zhao Y, Ressom HW.
PMID: 22759585
Proteome Sci. 2012 Jun 21;10:S8. doi: 10.1186/1477-5956-10-S1-S8.

BACKGROUND: Analysis of multiple LC-MS based metabolomic studies is carried out to determine overlaps and differences among various experiments. For example, in large metabolic biomarker discovery studies involving hundreds of samples, it may be necessary to conduct multiple experiments,...

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Varghese RS, Zhou B, Nezami Ranjbar MR, et al. Ion annotation-assisted analysis of LC-MS based metabolomic experiment. Proteome Sci. 2012;10:S8doi: 10.1186/1477-5956-10-S1-S8.
Varghese, R. S., Zhou, B., Nezami Ranjbar, M. R., Zhao, Y., & Ressom, H. W. (2012). Ion annotation-assisted analysis of LC-MS based metabolomic experiment. Proteome science, 10S8. https://doi.org/10.1186/1477-5956-10-S1-S8
Varghese, Rency S, et al. "Ion annotation-assisted analysis of LC-MS based metabolomic experiment." Proteome science vol. 10 (2012): S8. doi: https://doi.org/10.1186/1477-5956-10-S1-S8
Varghese RS, Zhou B, Nezami Ranjbar MR, Zhao Y, Ressom HW. Ion annotation-assisted analysis of LC-MS based metabolomic experiment. Proteome Sci. 2012 Jun 21;10:S8. doi: 10.1186/1477-5956-10-S1-S8. PMID: 22759585; PMCID: PMC3380757.
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Gaussian process regression model for normalization of LC-MS data using scan-level information.

Proteome science

Nezami Ranjbar MR, Zhao Y, Tadesse MG, Wang Y, Ressom HW.
PMID: 24564985
Proteome Sci. 2013 Nov 07;11:S13. doi: 10.1186/1477-5956-11-S1-S13. Epub 2013 Nov 07.

BACKGROUND: Differences in sample collection, biomolecule extraction, and instrument variability introduce bias to data generated by liquid chromatography coupled with mass spectrometry (LC-MS). Normalization is used to address these issues. In this paper, we introduce a new normalization method...

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Nezami Ranjbar MR, Zhao Y, Tadesse MG, et al. Gaussian process regression model for normalization of LC-MS data using scan-level information. Proteome Sci. 2013;11:S13doi: 10.1186/1477-5956-11-S1-S13.
Nezami Ranjbar, M. R., Zhao, Y., Tadesse, M. G., Wang, Y., & Ressom, H. W. (2013). Gaussian process regression model for normalization of LC-MS data using scan-level information. Proteome science, 11S13. https://doi.org/10.1186/1477-5956-11-S1-S13
Nezami Ranjbar, Mohammad R, et al. "Gaussian process regression model for normalization of LC-MS data using scan-level information." Proteome science vol. 11 (2013): S13. doi: https://doi.org/10.1186/1477-5956-11-S1-S13
Nezami Ranjbar MR, Zhao Y, Tadesse MG, Wang Y, Ressom HW. Gaussian process regression model for normalization of LC-MS data using scan-level information. Proteome Sci. 2013 Nov 07;11:S13. doi: 10.1186/1477-5956-11-S1-S13. Epub 2013 Nov 07. PMID: 24564985; PMCID: PMC3908948.
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Assessment of GC-MS in Detecting Changes in the Levels of Metabolites Using a Spike-in Experiment in Human Plasma.

Metabolomics : open access

Varghese RS, Poto CD, Tu C, Ranjbar MRN, Luo Y, Choi J, Tadesse MG, Ressom HW.
PMID: 31218095
Metabolomics (Los Angel). 2016;6(2). doi: 10.4172/2153-0769.1000175. Epub 2016 Jun 03.

Gas Chromatography coupled with Mass Spectrometry (GC-MS) has been broadly used for the detection of changes in metabolite levels in complex samples. Internal Standards (IS) spiked into a complex background at different concentrations help assess the capability of GC-MS...

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Varghese RS, Poto CD, Tu C, et al. Assessment of GC-MS in Detecting Changes in the Levels of Metabolites Using a Spike-in Experiment in Human Plasma. Metabolomics (Los Angel). 2016;6(2)doi: 10.4172/2153-0769.1000175.
Varghese, R. S., Poto, C. D., Tu, C., Ranjbar, M. R. N., Luo, Y., Choi, J., Tadesse, M. G., & Ressom, H. W. (2016). Assessment of GC-MS in Detecting Changes in the Levels of Metabolites Using a Spike-in Experiment in Human Plasma. Metabolomics : open access, 6(2), . https://doi.org/10.4172/2153-0769.1000175
Varghese, Rency S, et al. "Assessment of GC-MS in Detecting Changes in the Levels of Metabolites Using a Spike-in Experiment in Human Plasma." Metabolomics : open access vol. 6,2 (2016). doi: https://doi.org/10.4172/2153-0769.1000175
Varghese RS, Poto CD, Tu C, Ranjbar MRN, Luo Y, Choi J, Tadesse MG, Ressom HW. Assessment of GC-MS in Detecting Changes in the Levels of Metabolites Using a Spike-in Experiment in Human Plasma. Metabolomics (Los Angel). 2016;6(2). doi: 10.4172/2153-0769.1000175. Epub 2016 Jun 03. PMID: 31218095; PMCID: PMC6583805.
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LC-MS-based metabolomics.

Molecular bioSystems

Zhou B, Xiao JF, Tuli L, Ressom HW.
PMID: 22041788
Mol Biosyst. 2012 Feb;8(2):470-81. doi: 10.1039/c1mb05350g. Epub 2011 Nov 01.

Metabolomics aims at identification and quantitation of small molecules involved in metabolic reactions. LC-MS has enjoyed a growing popularity as the platform for metabolomic studies due to its high throughput, soft ionization, and good coverage of metabolites. The success...

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Zhou B, Xiao JF, Tuli L, et al. LC-MS-based metabolomics. Mol Biosyst. 2011;8(2):470-81doi: 10.1039/c1mb05350g.
Zhou, B., Xiao, J. F., Tuli, L., & Ressom, H. W. (2012). LC-MS-based metabolomics. Molecular bioSystems, 8(2), 470-81. https://doi.org/10.1039/c1mb05350g
Zhou, Bin, et al. "LC-MS-based metabolomics." Molecular bioSystems vol. 8,2 (2012): 470-81. doi: https://doi.org/10.1039/c1mb05350g
Zhou B, Xiao JF, Tuli L, Ressom HW. LC-MS-based metabolomics. Mol Biosyst. 2012 Feb;8(2):470-81. doi: 10.1039/c1mb05350g. Epub 2011 Nov 01. PMID: 22041788; PMCID: PMC3699692.
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A new method for alignment of LC-MALDI-TOF data.

Proteome science

Tang Z, Zhang L, Cheema AK, Ressom HW.
PMID: 22166061
Proteome Sci. 2011 Oct 14;9:S10. doi: 10.1186/1477-5956-9-S1-S10.

BACKGROUND: In proteomics studies, liquid chromatography coupled to mass spectrometry (LC-MS) has proven to be a powerful technology to investigate differential expression of proteins/peptides that are characterized by their peak intensities, mass-to-charge ratio (m/z), and retention time (RT). The...

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Tang Z, Zhang L, Cheema AK, et al. A new method for alignment of LC-MALDI-TOF data. Proteome Sci. 2011;9:S10doi: 10.1186/1477-5956-9-S1-S10.
Tang, Z., Zhang, L., Cheema, A. K., & Ressom, H. W. (2011). A new method for alignment of LC-MALDI-TOF data. Proteome science, 9S10. https://doi.org/10.1186/1477-5956-9-S1-S10
Tang, Zhiqun, et al. "A new method for alignment of LC-MALDI-TOF data." Proteome science vol. 9 (2011): S10. doi: https://doi.org/10.1186/1477-5956-9-S1-S10
Tang Z, Zhang L, Cheema AK, Ressom HW. A new method for alignment of LC-MALDI-TOF data. Proteome Sci. 2011 Oct 14;9:S10. doi: 10.1186/1477-5956-9-S1-S10. PMID: 22166061; PMCID: PMC3289071.
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INDEED: R package for network based differential expression analysis.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine

Li Z, Zuo Y, Xu C, Varghese RS, Ressom HW.
PMID: 31179159
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2018 Dec;2018:2709-2712. doi: 10.1109/BIBM.2018.8621426. Epub 2019 Jan 24.

With recent advancement of omics technologies, fueled by decreased cost and increased number of available datasets, computational methods for differential expression analysis are sought to identify disease-associated biomolecules. Conventional differential expression analysis methods (e.g. student's t-test, ANOVA) focus on...

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Li Z, Zuo Y, Xu C, et al. INDEED: R package for network based differential expression analysis. Proceedings (IEEE Int Conf Bioinformatics Biomed). 2019;2018:2709-2712doi: 10.1109/BIBM.2018.8621426.
Li, Z., Zuo, Y., Xu, C., Varghese, R. S., & Ressom, H. W. (2018). INDEED: R package for network based differential expression analysis. Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 20182709-2712. https://doi.org/10.1109/BIBM.2018.8621426
Li, Zhenzhi, et al. "INDEED: R package for network based differential expression analysis." Proceedings. IEEE International Conference on Bioinformatics and Biomedicine vol. 2018 (2018): 2709-2712. doi: https://doi.org/10.1109/BIBM.2018.8621426
Li Z, Zuo Y, Xu C, Varghese RS, Ressom HW. INDEED: R package for network based differential expression analysis. Proceedings (IEEE Int Conf Bioinformatics Biomed). 2018 Dec;2018:2709-2712. doi: 10.1109/BIBM.2018.8621426. Epub 2019 Jan 24. PMID: 31179159; PMCID: PMC6549230.
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Erratum to: Discoidin Domain Receptor 1 is a therapeutic target for neurodegenerative diseases.

Human molecular genetics

Fowler AJ, Hebron M, Balaraman K, Shi W, Missner AA, Greenzaid JD, Chiu TL, Ullman C, Weatherdon E, Duka V, Torres-Yaghi Y, Pagan FL, Liu X, Ressom H, Ahn J, Wolf C, Moussa C.
PMID: 34028526
Hum Mol Genet. 2021 Jun 17;30(13):1271-1272. doi: 10.1093/hmg/ddab082.

No abstract available.

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Fowler AJ, Hebron M, Balaraman K, et al. Erratum to: Discoidin Domain Receptor 1 is a therapeutic target for neurodegenerative diseases. Hum Mol Genet. 2021;30(13):1271-1272doi: 10.1093/hmg/ddab082.
Fowler, A. J., Hebron, M., Balaraman, K., Shi, W., Missner, A. A., Greenzaid, J. D., Chiu, T. L., Ullman, C., Weatherdon, E., Duka, V., Torres-Yaghi, Y., Pagan, F. L., Liu, X., Ressom, H., Ahn, J., Wolf, C., & Moussa, C. (2021). Erratum to: Discoidin Domain Receptor 1 is a therapeutic target for neurodegenerative diseases. Human molecular genetics, 30(13), 1271-1272. https://doi.org/10.1093/hmg/ddab082
Fowler, Alan J, et al. "Erratum to: Discoidin Domain Receptor 1 is a therapeutic target for neurodegenerative diseases." Human molecular genetics vol. 30,13 (2021): 1271-1272. doi: https://doi.org/10.1093/hmg/ddab082
Fowler AJ, Hebron M, Balaraman K, Shi W, Missner AA, Greenzaid JD, Chiu TL, Ullman C, Weatherdon E, Duka V, Torres-Yaghi Y, Pagan FL, Liu X, Ressom H, Ahn J, Wolf C, Moussa C. Erratum to: Discoidin Domain Receptor 1 is a therapeutic target for neurodegenerative diseases. Hum Mol Genet. 2021 Jun 17;30(13):1271-1272. doi: 10.1093/hmg/ddab082. PMID: 34028526; PMCID: PMC8212769.
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Iminodiacetic acid (IDA)-generated mesoporous nanopolymer: a template to relate surface area, hydrophilicity, and glycopeptides enrichment.

Mikrochimica acta

Sajid MS, Saleem S, Jabeen F, Fatima B, Zulfikar M, Ashiq MN, Ressom HW, Pukala TL, Najam-Ul-Haq M.
PMID: 34762162
Mikrochim Acta. 2021 Nov 11;188(12):417. doi: 10.1007/s00604-021-05074-8.

A three-step strategy is introduced to develop inherent iminodiacetic (IDA)-functionalized nanopolymer. SEM micrographs show homogenous spherical beads with a particle size of 500 nm. Further modification to COOH-functionalized 1,2-epoxy-5-hexene/DVB mesoporous nanopolymer enriches glycopeptides via hydrophilic interactions followed by their...

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Sajid MS, Saleem S, Jabeen F, et al. Iminodiacetic acid (IDA)-generated mesoporous nanopolymer: a template to relate surface area, hydrophilicity, and glycopeptides enrichment. Mikrochim Acta. 2021;188(12):417doi: 10.1007/s00604-021-05074-8.
Sajid, M. S., Saleem, S., Jabeen, F., Fatima, B., Zulfikar, M., Ashiq, M. N., Ressom, H. W., Pukala, T. L., & Najam-Ul-Haq, M. (2021). Iminodiacetic acid (IDA)-generated mesoporous nanopolymer: a template to relate surface area, hydrophilicity, and glycopeptides enrichment. Mikrochimica acta, 188(12), 417. https://doi.org/10.1007/s00604-021-05074-8
Sajid, Muhammad Salman, et al. "Iminodiacetic acid (IDA)-generated mesoporous nanopolymer: a template to relate surface area, hydrophilicity, and glycopeptides enrichment." Mikrochimica acta vol. 188,12 (2021): 417. doi: https://doi.org/10.1007/s00604-021-05074-8
Sajid MS, Saleem S, Jabeen F, Fatima B, Zulfikar M, Ashiq MN, Ressom HW, Pukala TL, Najam-Ul-Haq M. Iminodiacetic acid (IDA)-generated mesoporous nanopolymer: a template to relate surface area, hydrophilicity, and glycopeptides enrichment. Mikrochim Acta. 2021 Nov 11;188(12):417. doi: 10.1007/s00604-021-05074-8. PMID: 34762162.
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Pathway and network approaches for identification of cancer signature markers from omics data.

Journal of Cancer

Wang J, Zuo Y, Man YG, Avital I, Stojadinovic A, Liu M, Yang X, Varghese RS, Tadesse MG, Ressom HW.
PMID: 25553089
J Cancer. 2015 Jan 01;6(1):54-65. doi: 10.7150/jca.10631. eCollection 2015.

The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data...

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Wang J, Zuo Y, Man YG, et al. Pathway and network approaches for identification of cancer signature markers from omics data. J Cancer. 2015;6(1):54-65doi: 10.7150/jca.10631.
Wang, J., Zuo, Y., Man, Y. G., Avital, I., Stojadinovic, A., Liu, M., Yang, X., Varghese, R. S., Tadesse, M. G., & Ressom, H. W. (2015). Pathway and network approaches for identification of cancer signature markers from omics data. Journal of Cancer, 6(1), 54-65. https://doi.org/10.7150/jca.10631
Wang, Jinlian, et al. "Pathway and network approaches for identification of cancer signature markers from omics data." Journal of Cancer vol. 6,1 (2015): 54-65. doi: https://doi.org/10.7150/jca.10631
Wang J, Zuo Y, Man YG, Avital I, Stojadinovic A, Liu M, Yang X, Varghese RS, Tadesse MG, Ressom HW. Pathway and network approaches for identification of cancer signature markers from omics data. J Cancer. 2015 Jan 01;6(1):54-65. doi: 10.7150/jca.10631. eCollection 2015. PMID: 25553089; PMCID: PMC4278915.
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Integrative Analysis of DNA Methylation and microRNA Expression Reveals Mechanisms of Racial Heterogeneity in Hepatocellular Carcinoma.

Frontiers in genetics

Varghese RS, Barefoot ME, Jain S, Chen Y, Zhang Y, Alley A, Kroemer AH, Tadesse MG, Kumar D, Sherif ZA, Ressom HW.
PMID: 34557219
Front Genet. 2021 Sep 07;12:708326. doi: 10.3389/fgene.2021.708326. eCollection 2021.

Pathologic alterations in epigenetic regulation have long been considered a hallmark of many cancers, including hepatocellular carcinoma (HCC). In a healthy individual, the relationship between DNA methylation and microRNA (miRNA) expression maintains a fine balance; however, disruptions in this...

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Varghese RS, Barefoot ME, Jain S, et al. Integrative Analysis of DNA Methylation and microRNA Expression Reveals Mechanisms of Racial Heterogeneity in Hepatocellular Carcinoma. Front Genet. 2021;12:708326doi: 10.3389/fgene.2021.708326.
Varghese, R. S., Barefoot, M. E., Jain, S., Chen, Y., Zhang, Y., Alley, A., Kroemer, A. H., Tadesse, M. G., Kumar, D., Sherif, Z. A., & Ressom, H. W. (2021). Integrative Analysis of DNA Methylation and microRNA Expression Reveals Mechanisms of Racial Heterogeneity in Hepatocellular Carcinoma. Frontiers in genetics, 12708326. https://doi.org/10.3389/fgene.2021.708326
Varghese, Rency S, et al. "Integrative Analysis of DNA Methylation and microRNA Expression Reveals Mechanisms of Racial Heterogeneity in Hepatocellular Carcinoma." Frontiers in genetics vol. 12 (2021): 708326. doi: https://doi.org/10.3389/fgene.2021.708326
Varghese RS, Barefoot ME, Jain S, Chen Y, Zhang Y, Alley A, Kroemer AH, Tadesse MG, Kumar D, Sherif ZA, Ressom HW. Integrative Analysis of DNA Methylation and microRNA Expression Reveals Mechanisms of Racial Heterogeneity in Hepatocellular Carcinoma. Front Genet. 2021 Sep 07;12:708326. doi: 10.3389/fgene.2021.708326. eCollection 2021. PMID: 34557219; PMCID: PMC8453167.
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[No title available]

PeerJ

Azizan KA, Ressom HW, Mendoza ER, Baharum SN.
PMID: 28695065
PeerJ. 2017 Jul 05;5:e3451. doi: 10.7717/peerj.3451. eCollection 2017.

No abstract available.

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Azizan KA, Ressom HW, Mendoza ER, et al. . PeerJ. 2017;5:e3451doi: 10.7717/peerj.3451.
Azizan, K. A., Ressom, H. W., Mendoza, E. R., & Baharum, S. N. (2017). . PeerJ, 5e3451. https://doi.org/10.7717/peerj.3451
Azizan, Kamalrul Azlan, et al. "." PeerJ vol. 5 (2017): e3451. doi: https://doi.org/10.7717/peerj.3451
Azizan KA, Ressom HW, Mendoza ER, Baharum SN. . PeerJ. 2017 Jul 05;5:e3451. doi: 10.7717/peerj.3451. eCollection 2017. PMID: 28695065; PMCID: PMC5501154.
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