Murray Patterson

2.0k total citations
46 papers, 513 citations indexed

About

Murray Patterson is a scholar working on Molecular Biology, Genetics and Artificial Intelligence. According to data from OpenAlex, Murray Patterson has authored 46 papers receiving a total of 513 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Molecular Biology, 13 papers in Genetics and 9 papers in Artificial Intelligence. Recurrent topics in Murray Patterson's work include Machine Learning in Bioinformatics (17 papers), Genomics and Phylogenetic Studies (17 papers) and Genome Rearrangement Algorithms (10 papers). Murray Patterson is often cited by papers focused on Machine Learning in Bioinformatics (17 papers), Genomics and Phylogenetic Studies (17 papers) and Genome Rearrangement Algorithms (10 papers). Murray Patterson collaborates with scholars based in United States, Italy and Canada. Murray Patterson's co-authors include Sarwan Ali, Tobias Marschall, Nadia Pisanti, Alexander Schönhuth, Gunnar W. Klau, Leo van Iersel, Leen Stougie, Gianluca Della Vedova, Ján Maňuch and Paola Bonizzoni and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Scientific Reports.

In The Last Decade

Murray Patterson

42 papers receiving 506 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Murray Patterson United States 10 376 190 108 62 51 46 513
Deepak Unni United States 11 357 0.9× 326 1.7× 78 0.7× 59 1.0× 54 1.1× 15 751
Loretta Auvil United States 16 387 1.0× 225 1.2× 250 2.3× 167 2.7× 27 0.5× 38 832
German Tischler United Kingdom 11 271 0.7× 96 0.5× 84 0.8× 79 1.3× 48 0.9× 21 486
Broňa Brejová Slovakia 16 504 1.3× 70 0.4× 133 1.2× 104 1.7× 32 0.6× 52 704
A. D. Baxevanis United States 9 520 1.4× 94 0.5× 61 0.6× 41 0.7× 30 0.6× 10 684
Adriano Barbosa-Silva Germany 15 473 1.3× 127 0.7× 70 0.6× 79 1.3× 29 0.6× 26 708
Xosé M. Fernández United Kingdom 19 637 1.7× 203 1.1× 59 0.5× 84 1.4× 74 1.5× 41 1.0k
Andrew Hollinger Canada 2 433 1.2× 151 0.8× 71 0.7× 42 0.7× 97 1.9× 3 577
Pawan K. Dhar India 16 701 1.9× 86 0.5× 76 0.7× 18 0.3× 113 2.2× 57 915
Sebastian Meier‐Ewert Germany 16 823 2.2× 175 0.9× 176 1.6× 53 0.9× 41 0.8× 28 996

Countries citing papers authored by Murray Patterson

Since Specialization
Citations

This map shows the geographic impact of Murray Patterson's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Murray Patterson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Murray Patterson more than expected).

Fields of papers citing papers by Murray Patterson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Murray Patterson. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Murray Patterson. The network helps show where Murray Patterson may publish in the future.

Co-authorship network of co-authors of Murray Patterson

This figure shows the co-authorship network connecting the top 25 collaborators of Murray Patterson. A scholar is included among the top collaborators of Murray Patterson based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Murray Patterson. Murray Patterson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ali, Sarwan, et al.. (2025). Hist2Vec: A histogram and kernel-based embedding method for molecular sequence analysis. Expert Systems with Applications. 273. 126859–126859. 1 indexed citations
2.
Ali, Sarwan, et al.. (2024). Elliptic geometry-based kernel matrix for improved biological sequence classification. Knowledge-Based Systems. 304. 112479–112479. 1 indexed citations
3.
Ali, Sarwan, et al.. (2024). Molecular sequence classification using efficient kernel based embedding. Information Sciences. 679. 121100–121100. 3 indexed citations
4.
Ali, Sarwan, et al.. (2023). When Protein Structure Embedding Meets Large Language Models. Genes. 15(1). 25–25. 4 indexed citations
5.
Patterson, Murray, et al.. (2023). Identifying Distinguishing Acoustic Features in Felid Vocalizations Based on Call Type and Species Classification. Acoustics Australia. 51(3). 345–357. 1 indexed citations
6.
Ali, Sarwan, et al.. (2023). Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location. Journal of Computational Biology. 30(4). 432–445. 1 indexed citations
7.
Ali, Sarwan, et al.. (2023). Spike2CGR: an efficient method for spike sequence classification using chaos game representation. Machine Learning. 112(10). 3633–3658. 2 indexed citations
8.
Ali, Sarwan, et al.. (2023). Reads2Vec: Efficient Embedding of Raw High-Throughput Sequencing Reads Data. Journal of Computational Biology. 30(4). 469–491. 1 indexed citations
9.
Ali, Sarwan & Murray Patterson. (2023). Improving ISOMAP Efficiency with RKS: A Comparative Study with t-Distributed Stochastic Neighbor Embedding on Protein Sequences. SHILAP Revista de lepidopterología. 6(4). 579–591. 1 indexed citations
10.
11.
Ali, Sarwan, et al.. (2023). ViralVectors: compact and scalable alignment-free virome feature generation. Medical & Biological Engineering & Computing. 61(10). 2607–2626. 2 indexed citations
12.
Ali, Sarwan, et al.. (2022). Efficient analysis of COVID-19 clinical data using machine learning models. Medical & Biological Engineering & Computing. 60(7). 1881–1896. 19 indexed citations
13.
Ali, Sarwan, et al.. (2021). Simpler and Faster Development of Tumor Phylogeny Pipelines. Journal of Computational Biology. 28(11). 1142–1155. 5 indexed citations
14.
Knyazev, Sergey, et al.. (2021). From Alpha to Zeta: Identifying Variants and Subtypes of SARS-CoV-2 Via Clustering. Journal of Computational Biology. 28(11). 1113–1129. 8 indexed citations
15.
Ciccolella, Simone, Murray Patterson, Paola Bonizzoni, & Gianluca Della Vedova. (2021). Effective Clustering for Single Cell Sequencing Cancer Data. IEEE Journal of Biomedical and Health Informatics. 25(11). 4068–4078. 7 indexed citations
16.
Ciccolella, Simone, Camir Ricketts, Mauricio Soto, et al.. (2020). Inferring cancer progression from Single-Cell Sequencing while allowing mutation losses. Bioinformatics. 37(3). 326–333. 20 indexed citations
17.
Ciccolella, Simone, Mauricio Soto, Murray Patterson, et al.. (2020). gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data. BMC Bioinformatics. 21(S1). 413–413. 9 indexed citations
18.
Beretta, Stefano, Murray Patterson, Simone Zaccaria, Gianluca Della Vedova, & Paola Bonizzoni. (2018). HapCHAT: adaptive haplotype assembly for efficiently leveraging high coverage in long reads. BMC Bioinformatics. 19(1). 252–252. 6 indexed citations
19.
Bracciali, Andrea, Marco Aldinucci, Murray Patterson, et al.. (2016). PWHATSHAP: efficient haplotyping for future generation sequencing. BMC Bioinformatics. 17(S11). 342–342. 7 indexed citations
20.
Maňuch, Ján, Murray Patterson, Roland Wittler, Cédric Chauve, & Éric Tannier. (2012). Linearization of ancestral multichromosomal genomes. BMC Bioinformatics. 13(S19). S11–S11. 16 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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