Joachim Büch

488 total citations
12 papers, 341 citations indexed

About

Joachim Büch is a scholar working on Virology, Infectious Diseases and Molecular Biology. According to data from OpenAlex, Joachim Büch has authored 12 papers receiving a total of 341 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Virology, 6 papers in Infectious Diseases and 6 papers in Molecular Biology. Recurrent topics in Joachim Büch's work include HIV Research and Treatment (8 papers), HIV/AIDS drug development and treatment (6 papers) and HIV/AIDS Research and Interventions (3 papers). Joachim Büch is often cited by papers focused on HIV Research and Treatment (8 papers), HIV/AIDS drug development and treatment (6 papers) and HIV/AIDS Research and Interventions (3 papers). Joachim Büch collaborates with scholars based in Germany, Italy and Sweden. Joachim Büch's co-authors include Thomas Lengauer, Rolf Kaiser, Martin Däumer, Niko Beerenwinkel, Peter K. Cheung, Andrew Low, Francisco S. Domingues, Oliver Sander, P. Richard Harrigan and Christoph Bock and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and PLoS ONE.

In The Last Decade

Joachim Büch

12 papers receiving 336 citations

Peers

Joachim Büch

VIROL

EPIDE

IMMUN

Meixi Chen China

Alok Mulky United States

Benjamas Aiamkitsumrit United States

Joseph S. Redman United States

Gilberto Betancor United Kingdom

Jason Isaacson United States

Michael Nekorchuk United States

Vyjayanthi Krishnan United States

F Boulerice Canada

Meixi Chen China

Joachim Büch

248 ×1.1

VIROL

118 ×0.7

140 ×1.0

92 ×2.0

EPIDE

65 ×1.8

IMMUN

Citations per year, relative to Joachim Büch Joachim Büch (= 1×) peers Meixi Chen

Countries citing papers authored by Joachim Büch

Since Specialization

Citations

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

Fields of papers citing papers by Joachim Büch

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joachim Büch. 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 Joachim Büch. The network helps show where Joachim Büch may publish in the future.

Co-authorship network of co-authors of Joachim Büch

This figure shows the co-authorship network connecting the top 25 collaborators of Joachim Büch. A scholar is included among the top collaborators of Joachim Büch 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 Joachim Büch. Joachim Büch is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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12 of 12 papers shown

Büch, Joachim, Michael Böhm, Dan Turner, et al.. (2024). Geno2pheno: recombination detection for HIV-1 and HEV subtypes. PubMed. 1(1). ugae003–ugae003. 4 indexed citations

Büch, Joachim, Carole Seguin‐Devaux, Michael Böhm, et al.. (2022). Analysis of mutational history of multidrug‐resistant genotypes with a mutagenetic tree model. Journal of Medical Virology. 95(1). e28389–e28389. 2 indexed citations

Döring, Matthias, Joachim Büch, Alejandro Pironti, et al.. (2018). geno2pheno[ngs-freq]: a genotypic interpretation system for identifying viral drug resistance using next-generation sequencing data. Nucleic Acids Research. 46(W1). W271–W277. 26 indexed citations

Ramensky, Vasily, et al.. (2016). StructMAn: annotation of single-nucleotide polymorphisms in the structural context. Nucleic Acids Research. 44(W1). W463–W468. 23 indexed citations

Döring, Matthias, Pedro Borrego, Joachim Büch, et al.. (2016). A genotypic method for determining HIV-2 coreceptor usage enables epidemiological studies and clinical decision support. Retrovirology. 13(1). 85–85. 9 indexed citations

Pironti, Alejandro, Henrik Walter, Nico Pfeifer, et al.. (2016). Determination of Phenotypic Resistance Cutoffs From Routine Clinical Data. JAIDS Journal of Acquired Immune Deficiency Syndromes. 74(5). e129–e137. 4 indexed citations

Blankenburg, Hagen, Fidel Ramírez, Joachim Büch, & M. Albrecht. (2009). DASMIweb: online integration, analysis and assessment of distributed protein interaction data. Nucleic Acids Research. 37(Web Server). W122–W128. 2 indexed citations

Altmann, André, Martin Däumer, Niko Beerenwinkel, et al.. (2009). Predicting the Response to Combination Antiretroviral Therapy: Retrospective Validation of geno2pheno‐THEO on a Large Clinical Database. The Journal of Infectious Diseases. 199(7). 999–1006. 44 indexed citations

Bock, Christoph, et al.. (2009). EpiGRAPH: user-friendly software for statistical analysis and prediction of (epi)genomic data. Genome biology. 10(2). R14–R14. 41 indexed citations

10.

Blankenburg, Hagen, ROBERT FINN, Andreas Prlić, et al.. (2009). DASMI: exchanging, annotating and assessing molecular interaction data. Bioinformatics. 25(10). 1321–1328. 11 indexed citations

11.

Altmann, André, Michal Rosen‐Zvi, Mattia Prosperi, et al.. (2008). Comparison of Classifier Fusion Methods for Predicting Response to Anti HIV-1 Therapy. PLoS ONE. 3(10). e3470–e3470. 40 indexed citations

12.

Low, Andrew, Niko Beerenwinkel, Oliver Sander, et al.. (2007). Predicting HIV Coreceptor Usage on the Basis of Genetic and Clinical Covariates. Antiviral Therapy. 12(7). 1097–1106. 135 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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