Murray Patterson
Murray Patterson
Assistant Professor, Georgia State University
Adresă de e-mail confirmată pe cs.gsu.edu - Pagina de pornire
Citat de
Citat de
WhatsHap: Weighted Haplotype Assembly for Future-Generation Sequencing Reads
M Patterson, T Marschall, N Pisanti, L Van Iersel, L Stougie, GW Klau, ...
Journal of Computational Biology 22 (6), 498-509, 2015
WhatsHap: fast and accurate read-based phasing
M Martin, M Patterson, S Garg, S O Fischer, N Pisanti, GW Klau, ...
BioRxiv, 085050, 2016
Spike2vec: An efficient and scalable embedding approach for covid-19 spike sequences
S Ali, M Patterson
2021 IEEE International Conference on Big Data (Big Data), 1533-1540, 2021
A k-mer Based Approach for SARS-CoV-2 Variant Identification
S Ali, B Sahoo, N Ullah, A Zelikovskiy, M Patterson, I Khan
Bioinformatics Research and Applications: 17th International Symposium …, 2021
DeCoSTAR: reconstructing the ancestral organization of genes or genomes using reconciled phylogenies
W Duchemin, Y Anselmetti, M Patterson, Y Ponty, S Bérard, C Chauve, ...
Genome biology and evolution 9 (5), 1312-1319, 2017
Inferring cancer progression from single-cell sequencing while allowing mutation losses
S Ciccolella, C Ricketts, M Soto Gomez, M Patterson, D Silverbush, ...
Bioinformatics 37 (3), 326-333, 2021
WhatsHap: Haplotype Assembly for Future-Generation Sequencing Reads
M Patterson, T Marschall, N Pisanti, L van Iersel, L Stougie, GW Klau, ...
Research in Computational Molecular Biology: 18th Annual International …, 2014
PWM2Vec: An efficient embedding approach for viral host specification from coronavirus spike sequences
S Ali, B Bello, P Chourasia, RT Punathil, Y Zhou, M Patterson
Biology 11 (3), 418, 2022
Overlap graphs and de Bruijn graphs: data structures for de novo genome assembly in the big data era
R Rizzi, S Beretta, M Patterson, Y Pirola, M Previtali, G Della Vedova, ...
Quantitative Biology 7, 278-292, 2019
Efficient analysis of COVID-19 clinical data using machine learning models
S Ali, Y Zhou, M Patterson
Medical & Biological Engineering & Computing 60 (7), 1881-1896, 2022
Linearization of ancestral multichromosomal genomes
J Maňuch, M Patterson, R Wittler, C Chauve, E Tannier
BMC bioinformatics 13, 1-11, 2012
Effective and scalable clustering of SARS-CoV-2 sequences
S Ali, TE Ali, MA Khan, I Khan, M Patterson
Proceedings of the 5th international conference on big data research, 42-49, 2021
Lateral gene transfer, rearrangement, reconciliation
M Patterson, G Szöllősi, V Daubin, E Tannier
BMC bioinformatics 14, 1-7, 2013
Grounding for model expansion in k-guarded formulas with inductive definitions
MD Patterson
Simon Fraser University, 2006
Robust representation and efficient feature selection allows for effective clustering of sars-cov-2 variants
Z Tayebi, S Ali, M Patterson
Algorithms 14 (12), 348, 2021
O Fischer
M Martin, M Patterson, S Garg
S., Pisanti, N., Klau, GW, Schöenhuth, A., & Marschall, 2016
From alpha to zeta: Identifying variants and subtypes of sars-cov-2 via clustering
A Melnyk, F Mohebbi, S Knyazev, B Sahoo, R Hosseini, P Skums, ...
Journal of Computational Biology 28 (11), 1113-1129, 2021
O Fischer S, Pisanti N, Klau GW, et al
M Martin, M Patterson, S Garg
WhatsHap: fast and accurate read-based phasing. bioRxiv 85050 (10.1101), 085050, 2016
Efficient approximate kernel based spike sequence classification
S Ali, B Sahoo, MA Khan, A Zelikovsky, IU Khan, M Patterson
IEEE/ACM Transactions on Computational Biology and Bioinformatics 20 (6 …, 2022
On the gapped consecutive-ones property
C Chauve, J Maňuch, M Patterson
Electronic Notes in Discrete Mathematics 34, 121-125, 2009
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