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Salvage of bilateral renal artery occlusion after endovascular aneurysm repair with open splenorenal bypass.

Journal of vascular surgery cases and innovative techniques

Jessula S, Herman CR, Lee M, Lightfoot CB, Casey P.
PMID: 29349409
J Vasc Surg Cases Innov Tech. 2017 Sep 19;3(3):163-166. doi: 10.1016/j.jvscit.2017.04.001. eCollection 2017 Sep.

We report renal salvage maneuvers after accidental bilateral renal artery coverage during endovascular aneurysm repair of an infrarenal abdominal aortic aneurysm. A 79-year-old man with an infrarenal abdominal aortic aneurysm was treated with endovascular aneurysm repair. Completion angiography demonstrated...

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Jessula S, Herman CR, Lee M, et al. Salvage of bilateral renal artery occlusion after endovascular aneurysm repair with open splenorenal bypass. J Vasc Surg Cases Innov Tech. 2017;3(3):163-166doi: 10.1016/j.jvscit.2017.04.001.
Jessula, S., Herman, C. R., Lee, M., Lightfoot, C. B., & Casey, P. (2017). Salvage of bilateral renal artery occlusion after endovascular aneurysm repair with open splenorenal bypass. Journal of vascular surgery cases and innovative techniques, 3(3), 163-166. https://doi.org/10.1016/j.jvscit.2017.04.001
Jessula, Samuel, et al. "Salvage of bilateral renal artery occlusion after endovascular aneurysm repair with open splenorenal bypass." Journal of vascular surgery cases and innovative techniques vol. 3,3 (2017): 163-166. doi: https://doi.org/10.1016/j.jvscit.2017.04.001
Jessula S, Herman CR, Lee M, Lightfoot CB, Casey P. Salvage of bilateral renal artery occlusion after endovascular aneurysm repair with open splenorenal bypass. J Vasc Surg Cases Innov Tech. 2017 Sep 19;3(3):163-166. doi: 10.1016/j.jvscit.2017.04.001. eCollection 2017 Sep. PMID: 29349409; PMCID: PMC5764852.
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Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics.

Nature communications

Wilhelm M, Zolg DP, Graber M, Gessulat S, Schmidt T, Schnatbaum K, Schwencke-Westphal C, Seifert P, de Andrade Krätzig N, Zerweck J, Knaute T, Bräunlein E, Samaras P, Lautenbacher L, Klaeger S, Wenschuh H, Rad R, Delanghe B, Huhmer A, Carr SA, Clauser KR, Krackhardt AM, Reimer U, Kuster B.
PMID: 34162890
Nat Commun. 2021 Jun 23;12(1):4002. doi: 10.1038/s41467-021-24263-w.

No abstract available.

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Wilhelm M, Zolg DP, Graber M, et al. Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics. Nat Commun. 2021;12(1):4002doi: 10.1038/s41467-021-24263-w.
Wilhelm, M., Zolg, D. P., Graber, M., Gessulat, S., Schmidt, T., Schnatbaum, K., Schwencke-Westphal, C., Seifert, P., de Andrade Krätzig, N., Zerweck, J., Knaute, T., Bräunlein, E., Samaras, P., Lautenbacher, L., Klaeger, S., Wenschuh, H., Rad, R., Delanghe, B., Huhmer, A., Carr, S. A., Clauser, K. R., Krackhardt, A. M., Reimer, U., & Kuster, B. (2021). Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics. Nature communications, 12(1), 4002. https://doi.org/10.1038/s41467-021-24263-w
Wilhelm, Mathias, et al. "Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics." Nature communications vol. 12,1 (2021): 4002. doi: https://doi.org/10.1038/s41467-021-24263-w
Wilhelm M, Zolg DP, Graber M, Gessulat S, Schmidt T, Schnatbaum K, Schwencke-Westphal C, Seifert P, de Andrade Krätzig N, Zerweck J, Knaute T, Bräunlein E, Samaras P, Lautenbacher L, Klaeger S, Wenschuh H, Rad R, Delanghe B, Huhmer A, Carr SA, Clauser KR, Krackhardt AM, Reimer U, Kuster B. Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics. Nat Commun. 2021 Jun 23;12(1):4002. doi: 10.1038/s41467-021-24263-w. PMID: 34162890; PMCID: PMC8222294.
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Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics.

Molecular & cellular proteomics : MCP

Verbruggen S, Gessulat S, Gabriels R, Matsaroki A, Van de Voorde H, Kuster B, Degroeve S, Martens L, Van Criekinge W, Wilhelm M, Menschaert G.
PMID: 33823297
Mol Cell Proteomics. 2021;20:100076. doi: 10.1016/j.mcpro.2021.100076. Epub 2021 Apr 03.

Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for...

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Verbruggen S, Gessulat S, Gabriels R, et al. Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics. Mol Cell Proteomics. 2021;20:100076doi: 10.1016/j.mcpro.2021.100076.
Verbruggen, S., Gessulat, S., Gabriels, R., Matsaroki, A., Van de Voorde, H., Kuster, B., Degroeve, S., Martens, L., Van Criekinge, W., Wilhelm, M., & Menschaert, G. (2021). Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics. Molecular & cellular proteomics : MCP, 20100076. https://doi.org/10.1016/j.mcpro.2021.100076
Verbruggen, Steven, et al. "Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics." Molecular & cellular proteomics : MCP vol. 20 (2021): 100076. doi: https://doi.org/10.1016/j.mcpro.2021.100076
Verbruggen S, Gessulat S, Gabriels R, Matsaroki A, Van de Voorde H, Kuster B, Degroeve S, Martens L, Van Criekinge W, Wilhelm M, Menschaert G. Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics. Mol Cell Proteomics. 2021;20:100076. doi: 10.1016/j.mcpro.2021.100076. Epub 2021 Apr 03. PMID: 33823297; PMCID: PMC8214147.
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Reassuring Intraoperative Parameters Do Not Obviate the Need for Infrainguinal Bypass Completion Angiograms.

Annals of surgery

Maximilian Png CY, Kim Y, Jessula S, DeCarlo CS, Waller HD, Feldman ZM, Sumpio BJ, Lee S, Dua A, Srivastava SD, Conrad MF.
PMID: 34966067
Ann Surg. 2021 Dec 28; doi: 10.1097/SLA.0000000000005363. Epub 2021 Dec 28.

OBJECTIVE: To determine if routine completion angiography for lower extremity bypasses using vein conduit results in lower rates of postoperative bypass occlusion.SUMMARY BACKGROUND DATA: With the increasing availability of on-table angiography and significant advancements in endovascular techniques, some operators...

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Maximilian Png CY, Kim Y, Jessula S, et al. Reassuring Intraoperative Parameters Do Not Obviate the Need for Infrainguinal Bypass Completion Angiograms. Ann Surg. 2021;doi: 10.1097/SLA.0000000000005363.
Maximilian Png, C. Y., Kim, Y., Jessula, S., DeCarlo, C. S., Waller, H. D., Feldman, Z. M., Sumpio, B. J., Lee, S., Dua, A., Srivastava, S. D., & Conrad, M. F. (2021). Reassuring Intraoperative Parameters Do Not Obviate the Need for Infrainguinal Bypass Completion Angiograms. Annals of surgery, . https://doi.org/10.1097/SLA.0000000000005363
Maximilian Png, C Y, et al. "Reassuring Intraoperative Parameters Do Not Obviate the Need for Infrainguinal Bypass Completion Angiograms." Annals of surgery vol. (2021). doi: https://doi.org/10.1097/SLA.0000000000005363
Maximilian Png CY, Kim Y, Jessula S, DeCarlo CS, Waller HD, Feldman ZM, Sumpio BJ, Lee S, Dua A, Srivastava SD, Conrad MF. Reassuring Intraoperative Parameters Do Not Obviate the Need for Infrainguinal Bypass Completion Angiograms. Ann Surg. 2021 Dec 28; doi: 10.1097/SLA.0000000000005363. Epub 2021 Dec 28. PMID: 34966067.
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Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics.

Molecular & cellular proteomics : MCP

Verbruggen S, Gessulat S, Gabriels R, Matsaroki A, Van de Voorde H, Kuster B, Degroeve S, Martens L, Van Criekinge W, Wilhelm M, Menschaert G.
PMID: 33823297
Mol Cell Proteomics. 2021;20:100076. doi: 10.1016/j.mcpro.2021.100076. Epub 2021 Apr 03.

Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for...

Cite
Verbruggen S, Gessulat S, Gabriels R, et al. Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics. Mol Cell Proteomics. 2021;20:100076doi: 10.1016/j.mcpro.2021.100076.
Verbruggen, S., Gessulat, S., Gabriels, R., Matsaroki, A., Van de Voorde, H., Kuster, B., Degroeve, S., Martens, L., Van Criekinge, W., Wilhelm, M., & Menschaert, G. (2021). Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics. Molecular & cellular proteomics : MCP, 20100076. https://doi.org/10.1016/j.mcpro.2021.100076
Verbruggen, Steven, et al. "Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics." Molecular & cellular proteomics : MCP vol. 20 (2021): 100076. doi: https://doi.org/10.1016/j.mcpro.2021.100076
Verbruggen S, Gessulat S, Gabriels R, Matsaroki A, Van de Voorde H, Kuster B, Degroeve S, Martens L, Van Criekinge W, Wilhelm M, Menschaert G. Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics. Mol Cell Proteomics. 2021;20:100076. doi: 10.1016/j.mcpro.2021.100076. Epub 2021 Apr 03. PMID: 33823297; PMCID: PMC8214147.
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A Smartphone app for intensive care unit rotation Orientation.

Canadian medical education journal

Bednarek O, Loubani O, Jessula S, Minor S.
PMID: 33349767
Can Med Educ J. 2020 Dec 07;11(6):e156-e158. doi: 10.36834/cmej.70650. eCollection 2020 Dec.

No abstract available.

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Bednarek O, Loubani O, Jessula S, et al. A Smartphone app for intensive care unit rotation Orientation. Can Med Educ J. 2020;11(6):e156-e158doi: 10.36834/cmej.70650.
Bednarek, O., Loubani, O., Jessula, S., & Minor, S. (2020). A Smartphone app for intensive care unit rotation Orientation. Canadian medical education journal, 11(6), e156-e158. https://doi.org/10.36834/cmej.70650
Bednarek, Olga, et al. "A Smartphone app for intensive care unit rotation Orientation." Canadian medical education journal vol. 11,6 (2020): e156-e158. doi: https://doi.org/10.36834/cmej.70650
Bednarek O, Loubani O, Jessula S, Minor S. A Smartphone app for intensive care unit rotation Orientation. Can Med Educ J. 2020 Dec 07;11(6):e156-e158. doi: 10.36834/cmej.70650. eCollection 2020 Dec. PMID: 33349767; PMCID: PMC7749671.
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Recent advances in protein-protein interaction prediction: experimental and computational methods.

Expert opinion on drug discovery

Jessulat M, Pitre S, Gui Y, Hooshyar M, Omidi K, Samanfar B, Tan le H, Alamgir M, Green J, Dehne F, Golshani A.
PMID: 22646215
Expert Opin Drug Discov. 2011 Sep;6(9):921-35. doi: 10.1517/17460441.2011.603722. Epub 2011 Jul 29.

INTRODUCTION: Proteins within the cell act as part of complex networks, which allow pathways and processes to function. Therefore, understanding how proteins interact is a significant area of current research.AREAS COVERED: This review aims to present an overview of...

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Jessulat M, Pitre S, Gui Y, et al. Recent advances in protein-protein interaction prediction: experimental and computational methods. Expert Opin Drug Discov. 2011;6(9):921-35doi: 10.1517/17460441.2011.603722.
Jessulat, M., Pitre, S., Gui, Y., Hooshyar, M., Omidi, K., Samanfar, B., Tan, l. e. . H., Alamgir, M., Green, J., Dehne, F., & Golshani, A. (2011). Recent advances in protein-protein interaction prediction: experimental and computational methods. Expert opinion on drug discovery, 6(9), 921-35. https://doi.org/10.1517/17460441.2011.603722
Jessulat, Matthew, et al. "Recent advances in protein-protein interaction prediction: experimental and computational methods." Expert opinion on drug discovery vol. 6,9 (2011): 921-35. doi: https://doi.org/10.1517/17460441.2011.603722
Jessulat M, Pitre S, Gui Y, Hooshyar M, Omidi K, Samanfar B, Tan le H, Alamgir M, Green J, Dehne F, Golshani A. Recent advances in protein-protein interaction prediction: experimental and computational methods. Expert Opin Drug Discov. 2011 Sep;6(9):921-35. doi: 10.1517/17460441.2011.603722. Epub 2011 Jul 29. PMID: 22646215.
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Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis.

iScience

Moutaoufik MT, Malty R, Amin S, Zhang Q, Phanse S, Gagarinova A, Zilocchi M, Hoell L, Minic Z, Gagarinova M, Aoki H, Stockwell J, Jessulat M, Goebels F, Broderick K, Scott NE, Vlasblom J, Musso G, Prasad B, Lamantea E, Garavaglia B, Rajput A, Murayama K, Okazaki Y, Foster LJ, Bader GD, Cayabyab FS, Babu M.
PMID: 31536960
iScience. 2019 Sep 27;19:1114-1132. doi: 10.1016/j.isci.2019.08.057. Epub 2019 Sep 04.

Mitochondrial protein (MP) assemblies undergo alterations during neurogenesis, a complex process vital in brain homeostasis and disease. Yet which MP assemblies remodel during differentiation remains unclear. Here, using mass spectrometry-based co-fractionation profiles and phosphoproteomics, we generated mitochondrial interaction maps...

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Moutaoufik MT, Malty R, Amin S, et al. Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis. iScience. 2019;19:1114-1132doi: 10.1016/j.isci.2019.08.057.
Moutaoufik, M. T., Malty, R., Amin, S., Zhang, Q., Phanse, S., Gagarinova, A., Zilocchi, M., Hoell, L., Minic, Z., Gagarinova, M., Aoki, H., Stockwell, J., Jessulat, M., Goebels, F., Broderick, K., Scott, N. E., Vlasblom, J., Musso, G., Prasad, B., Lamantea, E., Garavaglia, B., Rajput, A., Murayama, K., Okazaki, Y., Foster, L. J., Bader, G. D., Cayabyab, F. S., & Babu, M. (2019). Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis. iScience, 191114-1132. https://doi.org/10.1016/j.isci.2019.08.057
Moutaoufik, Mohamed Taha, et al. "Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis." iScience vol. 19 (2019): 1114-1132. doi: https://doi.org/10.1016/j.isci.2019.08.057
Moutaoufik MT, Malty R, Amin S, Zhang Q, Phanse S, Gagarinova A, Zilocchi M, Hoell L, Minic Z, Gagarinova M, Aoki H, Stockwell J, Jessulat M, Goebels F, Broderick K, Scott NE, Vlasblom J, Musso G, Prasad B, Lamantea E, Garavaglia B, Rajput A, Murayama K, Okazaki Y, Foster LJ, Bader GD, Cayabyab FS, Babu M. Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis. iScience. 2019 Sep 27;19:1114-1132. doi: 10.1016/j.isci.2019.08.057. Epub 2019 Sep 04. PMID: 31536960; PMCID: PMC6831851.
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In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences.

iScience

Burnside D, Schoenrock A, Moteshareie H, Hooshyar M, Basra P, Hajikarimlou M, Dick K, Barnes B, Kazmirchuk T, Jessulat M, Pitre S, Samanfar B, Babu M, Green JR, Wong A, Dehne F, Biggar KK, Golshani A.
PMID: 30660105
iScience. 2019 Jan 25;11:375-387. doi: 10.1016/j.isci.2018.11.038. Epub 2018 Dec 04.

Synthetic proteins with high affinity and selectivity for a protein target can be used as research tools, biomarkers, and pharmacological agents, but few methods exist to design such proteins de novo. To this end, the In-Silico Protein Synthesizer (InSiPS)...

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Burnside D, Schoenrock A, Moteshareie H, et al. In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences. iScience. 2018;11:375-387doi: 10.1016/j.isci.2018.11.038.
Burnside, D., Schoenrock, A., Moteshareie, H., Hooshyar, M., Basra, P., Hajikarimlou, M., Dick, K., Barnes, B., Kazmirchuk, T., Jessulat, M., Pitre, S., Samanfar, B., Babu, M., Green, J. R., Wong, A., Dehne, F., Biggar, K. K., & Golshani, A. (2019). In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences. iScience, 11375-387. https://doi.org/10.1016/j.isci.2018.11.038
Burnside, Daniel, et al. "In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences." iScience vol. 11 (2019): 375-387. doi: https://doi.org/10.1016/j.isci.2018.11.038
Burnside D, Schoenrock A, Moteshareie H, Hooshyar M, Basra P, Hajikarimlou M, Dick K, Barnes B, Kazmirchuk T, Jessulat M, Pitre S, Samanfar B, Babu M, Green JR, Wong A, Dehne F, Biggar KK, Golshani A. In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences. iScience. 2019 Jan 25;11:375-387. doi: 10.1016/j.isci.2018.11.038. Epub 2018 Dec 04. PMID: 30660105; PMCID: PMC6348295.
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INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results.

Rapid communications in mass spectrometry : RCM

Zolg DP, Gessulat S, Paschke C, Graber M, Rathke-Kuhnert M, Seefried F, Fitzemeier K, Berg F, Lopez-Ferrer D, Horn D, Henrich C, Huhmer A, Delanghe B, Frejno M.
PMID: 34015160
Rapid Commun Mass Spectrom. 2021 May 20;e9128. doi: 10.1002/rcm.9128. Epub 2021 May 20.

Database search engines for bottom-up proteomics largely ignore peptide fragment ion intensities during the automated scoring of tandem mass spectra against protein databases. Recent advances in deep learning allow the accurate prediction of peptide fragment ion intensities. Using these...

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Zolg DP, Gessulat S, Paschke C, et al. INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results. Rapid Commun Mass Spectrom. 2021;e9128doi: 10.1002/rcm.9128.
Zolg, D. P., Gessulat, S., Paschke, C., Graber, M., Rathke-Kuhnert, M., Seefried, F., Fitzemeier, K., Berg, F., Lopez-Ferrer, D., Horn, D., Henrich, C., Huhmer, A., Delanghe, B., & Frejno, M. (2021). INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results. Rapid communications in mass spectrometry : RCM, e9128. https://doi.org/10.1002/rcm.9128
Zolg, Daniel P, et al. "INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results." Rapid communications in mass spectrometry : RCM vol. (2021): e9128. doi: https://doi.org/10.1002/rcm.9128
Zolg DP, Gessulat S, Paschke C, Graber M, Rathke-Kuhnert M, Seefried F, Fitzemeier K, Berg F, Lopez-Ferrer D, Horn D, Henrich C, Huhmer A, Delanghe B, Frejno M. INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results. Rapid Commun Mass Spectrom. 2021 May 20;e9128. doi: 10.1002/rcm.9128. Epub 2021 May 20. PMID: 34015160.
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Chemical-genetic profile analysis of five inhibitory compounds in yeast.

BMC chemical biology

Alamgir M, Erukova V, Jessulat M, Azizi A, Golshani A.
PMID: 20691087
BMC Chem Biol. 2010 Aug 06;10:6. doi: 10.1186/1472-6769-10-6.

BACKGROUND: Chemical-genetic profiling of inhibitory compounds can lead to identification of their modes of action. These profiles can help elucidate the complex interactions between small bioactive compounds and the cell machinery, and explain putative gene function(s).RESULTS: Colony size reduction...

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Alamgir M, Erukova V, Jessulat M, et al. Chemical-genetic profile analysis of five inhibitory compounds in yeast. BMC Chem Biol. 2010;10:6doi: 10.1186/1472-6769-10-6.
Alamgir, M., Erukova, V., Jessulat, M., Azizi, A., & Golshani, A. (2010). Chemical-genetic profile analysis of five inhibitory compounds in yeast. BMC chemical biology, 106. https://doi.org/10.1186/1472-6769-10-6
Alamgir, Md, et al. "Chemical-genetic profile analysis of five inhibitory compounds in yeast." BMC chemical biology vol. 10 (2010): 6. doi: https://doi.org/10.1186/1472-6769-10-6
Alamgir M, Erukova V, Jessulat M, Azizi A, Golshani A. Chemical-genetic profile analysis of five inhibitory compounds in yeast. BMC Chem Biol. 2010 Aug 06;10:6. doi: 10.1186/1472-6769-10-6. PMID: 20691087; PMCID: PMC2925817.
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Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics.

Molecular & cellular proteomics : MCP

Verbruggen S, Gessulat S, Gabriels R, Matsaroki A, Van de Voorde H, Kuster B, Degroeve S, Martens L, Van Criekinge W, Wilhelm M, Menschaert G.
PMID: 33823297
Mol Cell Proteomics. 2021 Apr 03;20:100076. doi: 10.1016/j.mcpro.2021.100076. Epub 2021 Apr 03.

Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for...

Cite
Verbruggen S, Gessulat S, Gabriels R, et al. Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics. Mol Cell Proteomics. 2021;20:100076doi: 10.1016/j.mcpro.2021.100076.
Verbruggen, S., Gessulat, S., Gabriels, R., Matsaroki, A., Van de Voorde, H., Kuster, B., Degroeve, S., Martens, L., Van Criekinge, W., Wilhelm, M., & Menschaert, G. (2021). Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics. Molecular & cellular proteomics : MCP, 20100076. https://doi.org/10.1016/j.mcpro.2021.100076
Verbruggen, Steven, et al. "Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics." Molecular & cellular proteomics : MCP vol. 20 (2021): 100076. doi: https://doi.org/10.1016/j.mcpro.2021.100076
Verbruggen S, Gessulat S, Gabriels R, Matsaroki A, Van de Voorde H, Kuster B, Degroeve S, Martens L, Van Criekinge W, Wilhelm M, Menschaert G. Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics. Mol Cell Proteomics. 2021 Apr 03;20:100076. doi: 10.1016/j.mcpro.2021.100076. Epub 2021 Apr 03. PMID: 33823297; PMCID: PMC8214147.
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