Michael B. Hall

3.8k total citations
22 papers, 545 citations indexed

About

Michael B. Hall is a scholar working on Molecular Biology, Infectious Diseases and Epidemiology. According to data from OpenAlex, Michael B. Hall has authored 22 papers receiving a total of 545 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 8 papers in Infectious Diseases and 7 papers in Epidemiology. Recurrent topics in Michael B. Hall's work include Genomics and Phylogenetic Studies (10 papers), Tuberculosis Research and Epidemiology (7 papers) and Mycobacterium research and diagnosis (7 papers). Michael B. Hall is often cited by papers focused on Genomics and Phylogenetic Studies (10 papers), Tuberculosis Research and Epidemiology (7 papers) and Mycobacterium research and diagnosis (7 papers). Michael B. Hall collaborates with scholars based in United Kingdom, Australia and United States. Michael B. Hall's co-authors include Lachlan Coin, Minh Duc Cao, Haotian Teng, Tânia Duarte, Sheng Wang, John Wilson, Sudeep Roy, Zamin Iqbal, Todd A. MacKenzie and Sophie George and has published in prestigious journals such as Nature Communications, Bioinformatics and Journal of Clinical Microbiology.

In The Last Decade

Michael B. Hall

22 papers receiving 532 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael B. Hall United Kingdom 12 285 88 85 83 75 22 545
Noah Alexander United States 10 345 1.2× 116 1.3× 46 0.5× 55 0.7× 40 0.5× 15 575
Blair J. Rossetti United States 8 360 1.3× 94 1.1× 37 0.4× 53 0.6× 53 0.7× 9 765
Federico Bonsembiante Italy 13 126 0.4× 37 0.4× 28 0.3× 63 0.8× 87 1.2× 49 550
Simeone Dal Monego Italy 11 336 1.2× 35 0.4× 51 0.6× 27 0.3× 302 4.0× 29 700
Daniel Strauss United States 14 309 1.1× 41 0.5× 36 0.4× 80 1.0× 135 1.8× 37 656
Wim Trypsteen Belgium 16 266 0.9× 58 0.7× 116 1.4× 93 1.1× 362 4.8× 48 839
Shi L China 5 133 0.5× 48 0.5× 75 0.9× 57 0.7× 77 1.0× 8 333
Chen Nadler Israel 13 172 0.6× 52 0.6× 47 0.6× 30 0.4× 189 2.5× 36 786
D. Tsakogiannis Greece 13 173 0.6× 21 0.2× 50 0.6× 283 3.4× 125 1.7× 48 489
Elizabeth B. Burgener United States 11 231 0.8× 417 4.7× 51 0.6× 105 1.3× 69 0.9× 29 698

Countries citing papers authored by Michael B. Hall

Since Specialization
Citations

This map shows the geographic impact of Michael B. Hall'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 Michael B. Hall with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael B. Hall more than expected).

Fields of papers citing papers by Michael B. Hall

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael B. Hall. 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 Michael B. Hall. The network helps show where Michael B. Hall may publish in the future.

Co-authorship network of co-authors of Michael B. Hall

This figure shows the co-authorship network connecting the top 25 collaborators of Michael B. Hall. A scholar is included among the top collaborators of Michael B. Hall 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 Michael B. Hall. Michael B. Hall 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.
Hall, Michael B., Chenxi Zhou, & Lachlan Coin. (2025). Genome size estimation from long read overlaps. Bioinformatics. 41(11). 1 indexed citations
2.
Hall, Michael B., Xuling Chang, Linda T. Viberg, et al.. (2025). Genome graphs reveal the importance of structural variation in Mycobacterium tuberculosis evolution and drug resistance. Nature Communications. 16(1). 10746–10746. 1 indexed citations
3.
Hall, Michael B., Ryan R. Wick, Louise M. Judd, et al.. (2024). Benchmarking reveals superiority of deep learning variant callers on bacterial nanopore sequence data. eLife. 13. 7 indexed citations
4.
Hall, Michael B. & Lachlan Coin. (2024). Pangenome databases improve host removal and mycobacteria classification from clinical metagenomic data. GigaScience. 13. 7 indexed citations
5.
Hall, Michael B., Ryan R. Wick, Louise M. Judd, et al.. (2024). Benchmarking reveals superiority of deep learning variant callers on bacterial nanopore sequence data. eLife. 13. 12 indexed citations
6.
Rabodoarivelo, Marie Sylvianne, Astrid M. Knoblauch, Michael B. Hall, et al.. (2024). Concordance of targeted and whole genome sequencing for Mycobacterium tuberculosis genotypic drug susceptibility testing. Diagnostic Microbiology and Infectious Disease. 109(2). 116249–116249. 3 indexed citations
7.
Hall, Michael B., Leandro Lima, Lachlan Coin, & Zamin Iqbal. (2023). Drug resistance prediction for Mycobacterium tuberculosis with reference graphs. Microbial Genomics. 9(8). 4 indexed citations
8.
Nilgiriwala, Kayzad, Marie Sylvianne Rabodoarivelo, Michael B. Hall, et al.. (2023). Genomic Sequencing from Sputum for Tuberculosis Disease Diagnosis, Lineage Determination, and Drug Susceptibility Prediction. Journal of Clinical Microbiology. 61(3). e0157822–e0157822. 12 indexed citations
9.
Hall, Michael B., Marie Sylvianne Rabodoarivelo, Anastasia Koch, et al.. (2022). Evaluation of Nanopore sequencing for Mycobacterium tuberculosis drug susceptibility testing and outbreak investigation: a genomic analysis. The Lancet Microbe. 4(2). e84–e92. 47 indexed citations
10.
Hunt, Martin, Brice Letcher, Kerri M. Malone, et al.. (2022). Minos: variant adjudication and joint genotyping of cohorts of bacterial genomes. Genome biology. 23(1). 147–147. 14 indexed citations
11.
Ganesamoorthy, Devika, Alan J. Robertson, Wenhan Chen, et al.. (2022). Whole genome deep sequencing analysis of cell-free DNA in samples with low tumour content. BMC Cancer. 22(1). 85–85. 18 indexed citations
12.
Urban, Lara, Andre Holzer, J. Jotautas Baronas, et al.. (2021). Freshwater monitoring by nanopore sequencing. eLife. 10. 75 indexed citations
13.
Colquhoun, Rachel, Michael B. Hall, Leandro Lima, et al.. (2021). Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs. Genome biology. 22(1). 267–267. 39 indexed citations
14.
LaFleur, Marni, Kim E. Reuter, Michael B. Hall, et al.. (2021). Drug-Resistant Tuberculosis in Pet Ring-Tailed Lemur, Madagascar. Emerging infectious diseases. 27(3). 977–979. 5 indexed citations
15.
Teng, Haotian, Minh Duc Cao, Michael B. Hall, et al.. (2018). Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning. GigaScience. 7(5). 112 indexed citations
16.
Hall, Michael B., et al.. (2018). Novel Use of a Volumizing Hyaluronic Acid Filler for Treatment of Infraorbital Hollows. JAMA Facial Plastic Surgery. 20(5). 367–372. 33 indexed citations
17.
Hall, Michael B. & Ryan Heffelfinger. (2015). Autologous Fat Transfer as a Facial Filler: Current and Future Applications. Current Otorhinolaryngology Reports. 3(1). 33–41. 1 indexed citations
18.
Hall, Michael B., Carolina V. Guimaraes, & Heather C. Nardone. (2014). Facial Mass in an Infant. JAMA Otolaryngology–Head & Neck Surgery. 140(5). 475–475. 1 indexed citations
19.
MacKenzie, Todd A., et al.. (2007). Patients use an internet technology to report when things go wrong. BMJ Quality & Safety. 16(3). 213–215. 25 indexed citations
20.
Wilson, John, et al.. (1993). Tissue response to Bioglass endosseous ridge maintenance implants.. PubMed. 19(4). 295–302. 54 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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