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Systems and methods for measuring cross-modal similarity between mass spectra and peptides are provided.
Recommended citation: Saeed, F., & Tariq, M. U. (2022). "Systems and methods for measuring similarity between mass spectra and peptides." IU.S. Patent No. 11,251,031. Washington, DC: U.S. Patent and Trademark Office.. https://patents.google.com/patent/US11251031B1/en
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Published in IEEE, 2017
This paper presents an OpenCL implementation of parallel graph sampling on FPGAs
Recommended citation: Tariq, U., Cheema, U. I., & Saeed, F. (2017, December). "Power-efficient and highly scalable parallel graph sampling using fpgas." International Conference on ReConFigurable Computing and FPGAs (ReConFig) (pp. 1-6). IEEE. https://ieeexplore.ieee.org/abstract/document/8279806
Published in IEEE Access, 2020
This paper surveys the computational tools for proteogenomic analysis using mass-spectrometry data
Recommended citation: Tariq, M. U., Haseeb, M., Aledhari, M., Razzak, R., Parizi, R. M., & Saeed, F. (2020). "Methods for proteogenomics data analysis, challenges, and scalability bottlenecks: a survey." IEEE Access. 9, 5497-5516 https://ieeexplore.ieee.org/abstract/document/9309010
Published in PloS one, 2021
SpeCollate is the first of its kind deep learning based peptide database search engine. By embedding both the query spectra and the database peptides using a dual encoder network, SpeCollate can efficiently rank high quality peptides for each query spectrum providing up to 95% search accuracy.
Recommended citation: Tariq, M. U., & Saeed, F. (2021). "SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions." PloS one. 16(10), e0259349. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0259349
Published in IEEE, 2021
This paper presents a graph based algorithm for generating all possible fragmentation pathways or organic molecules from a given mass-spectrum
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Published in Environmental Science & Technology, 2022
Structural clustering of isomeric families of organic molecules.
Recommended citation: Leyva, D., Tariq, M. U., Jaffé, R., Saeed, F., & Lima, F. F. (2022). "Unsupervised structural classification of dissolved organic matter based on fragmentation pathways.." Environmental Science & Technology. 56(2), 1458-1468. https://pubs.acs.org/doi/abs/10.1021/acs.est.1c04726
Published in Aquatic Sciences, 2022
Understanding the dissolved organic matter (DOM) export to ocean and compositional variability
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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