Alvis Brāzma

68.6k total citations · 7 hit papers
132 papers, 14.7k citations indexed

About

Alvis Brāzma is a scholar working on Molecular Biology, Artificial Intelligence and Genetics. According to data from OpenAlex, Alvis Brāzma has authored 132 papers receiving a total of 14.7k indexed citations (citations by other indexed papers that have themselves been cited), including 117 papers in Molecular Biology, 13 papers in Artificial Intelligence and 10 papers in Genetics. Recurrent topics in Alvis Brāzma's work include Gene expression and cancer classification (68 papers), Bioinformatics and Genomic Networks (56 papers) and Genomics and Chromatin Dynamics (18 papers). Alvis Brāzma is often cited by papers focused on Gene expression and cancer classification (68 papers), Bioinformatics and Genomic Networks (56 papers) and Genomics and Chromatin Dynamics (18 papers). Alvis Brāzma collaborates with scholars based in United Kingdom, United States and Finland. Alvis Brāzma's co-authors include Jaak Vilo, Deepti J Kundu, Juan Antonio Vizcaíno, Yasset Pérez‐Riverol, Mathias Walzer, Shengbo Wang, Martin Eisenacher, Chakradhar Bandla, Suresh Hewapathirana and Selvakumar Kamatchinathan and has published in prestigious journals such as Nature, Cell and The Lancet.

In The Last Decade

Alvis Brāzma

129 papers receiving 14.4k citations

Hit Papers

The PRIDE databas... 2003 2026 2010 2018 2021 2005 2003 2003 2006 1000 2.0k 3.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences
    2021 3.9k citations Alvis Brāzma et al. Nucleic Acids Research profile →
  2. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis
    2005 1.4k citations Steffen Durinck, Yves Moreau et al. Computer applications in the biosciences profile →
  3. Global Transcriptional Responses of Fission Yeast to Environmental Stress
    2003 653 citations Alvis Brāzma et al. profile →
  4. ArrayExpress--a public repository for microarray gene expression data at the EBI
    2003 572 citations Alvis Brāzma Nucleic Acids Research profile →
  5. ArrayExpress--a public database of microarray experiments and gene expression profiles
    2006 539 citations Helen Parkinson, Misha Kapushesky et al. Nucleic Acids Research profile →
  6. ArrayExpress update—simplifying data submissions
    2014 509 citations Eleanor Williams, Natalja Kurbatova et al. Nucleic Acids Research profile →
  7. ArrayExpress update – from bulk to single-cell expression data
    2018 377 citations Awais Athar, Anja Füllgrabe et al. Nucleic Acids Research profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alvis Brāzma United Kingdom 50 11.0k 1.6k 1.4k 1.1k 1.0k 132 14.7k
Miguel A. Andrade‐Navarro Germany 60 11.9k 1.1× 1.3k 0.8× 1.9k 1.4× 822 0.8× 849 0.8× 268 15.3k
Olga G. Troyanskaya United States 54 11.3k 1.0× 2.1k 1.3× 1.3k 1.0× 802 0.7× 538 0.5× 153 15.8k
Dari Shalon United States 11 10.5k 1.0× 1.5k 0.9× 1.3k 0.9× 760 0.7× 723 0.7× 13 13.6k
Vishwanath R. Iyer United States 45 15.8k 1.4× 1.9k 1.2× 1.2k 0.9× 801 0.7× 1.4k 1.4× 90 19.4k
Jaak Vilo Estonia 36 8.1k 0.7× 1.7k 1.1× 2.0k 1.5× 1.5k 1.4× 1.2k 1.2× 91 13.4k
Ioannis Xénarios Switzerland 52 10.3k 0.9× 1.2k 0.8× 650 0.5× 1.1k 1.0× 2.0k 2.0× 171 14.3k
Jean Yang Australia 52 8.2k 0.7× 1.2k 0.8× 1.6k 1.1× 1.6k 1.4× 661 0.6× 257 13.8k
Sandrine Dudoit United States 50 11.4k 1.0× 1.6k 1.0× 2.2k 1.6× 1.4k 1.3× 790 0.8× 99 17.4k
Andrew I. Su United States 51 8.7k 0.8× 2.1k 1.3× 1.5k 1.1× 1.8k 1.6× 1.0k 1.0× 146 16.1k
Steven E. Brenner United States 49 19.2k 1.8× 2.7k 1.6× 808 0.6× 1.3k 1.2× 2.6k 2.6× 141 23.7k

Countries citing papers authored by Alvis Brāzma

Since Specialization
Citations

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

Fields of papers citing papers by Alvis Brāzma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alvis Brāzma

This figure shows the co-authorship network connecting the top 25 collaborators of Alvis Brāzma. A scholar is included among the top collaborators of Alvis Brāzma 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 Alvis Brāzma. Alvis Brāzma 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.
Li, Xu, Manik Garg, Tingting Jia, et al.. (2022). Single-Cell Analysis Reveals the Immune Characteristics of Myeloid Cells and Memory T Cells in Recovered COVID-19 Patients With Different Severities. Frontiers in Immunology. 12. 781432–781432. 15 indexed citations
2.
Papatheodorou, Irene, et al.. (2022). Brain Regeneration Resembles Brain Cancer at Its Early Wound Healing Stage and Diverges From Cancer Later at Its Proliferation and Differentiation Stages. Frontiers in Cell and Developmental Biology. 10. 813314–813314. 9 indexed citations
3.
Cavadas, Bruno, Rui Camacho, Rui M. Ferreira, et al.. (2020). Gastric Microbiome Diversities in Gastric Cancer Patients from Europe and Asia Mimic the Human Population Structure and Are Partly Driven by Microbiome Quantitative Trait Loci. Microorganisms. 8(8). 1196–1196. 15 indexed citations
4.
Athar, Awais, Anja Füllgrabe, Nancy George, et al.. (2018). ArrayExpress update – from bulk to single-cell expression data. Nucleic Acids Research. 47(D1). D711–D715. 377 indexed citations breakdown →
5.
Williams, Eleanor, Josh Moore, Simon Li, et al.. (2017). Image Data Resource: a bioimage data integration and publication platform. Nature Methods. 14(8). 775–781. 160 indexed citations
6.
Torrente, Aurora & Alvis Brāzma. (2017). clustComp, a bioconductor package for the comparison of clustering results. Bioinformatics. 33(24). 4001–4003.
7.
Blomstedt, Paul, Ritabrata Dutta, Sohan Seth, Alvis Brāzma, & Samuel Kaski. (2016). Modelling-based experiment retrieval: a case study with gene expression clustering. Bioinformatics. 32(9). 1388–1394. 8 indexed citations
8.
Faulconbridge, Adam, Marco Brandizi, Julio Fernandez-Banet, et al.. (2011). The BioSample Database (BioSD) at the European Bioinformatics Institute. Nucleic Acids Research. 40(D1). D64–D70. 42 indexed citations
9.
Vingron, Martin, Alvis Brāzma, Richard Coulson, et al.. (2009). Integrating sequence, evolution and functional genomics in regulatory genomics. Genome Biology. 10(1). 202–202. 14 indexed citations
10.
Krestyaninova, Maria, Juris Vīksna, Natalja Kurbatova, et al.. (2009). A System for Information Management in BioMedical Studies—SIMBioMS. Bioinformatics. 25(20). 2768–2769. 14 indexed citations
11.
Sansone, Susanna‐Assunta, Philippe Rocca‐Serra, Marco Brandizi, et al.. (2008). The First RSBI (ISA-TAB) Workshop: “Can a Simple Format Work for Complex Studies?”. OMICS A Journal of Integrative Biology. 12(2). 143–149. 58 indexed citations
12.
Durinck, Steffen, Yves Moreau, A. Kasprzyk, et al.. (2005). BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Computer applications in the biosciences. 21(16). 3439–3440. 1376 indexed citations breakdown →
13.
Mukherjee, Gaurab, Niran Abeygunawardena, Helen Parkinson, et al.. (2005). Plant-Based Microarray Data at the European Bioinformatics Institute. Introducing AtMIAMExpress, a Submission Tool for Arabidopsis Gene Expression Data to ArrayExpress. PLANT PHYSIOLOGY. 139(2). 632–636. 5 indexed citations
14.
Kapushesky, Misha, Patrick Kemmeren, Aedín C. Culhane, et al.. (2004). Expression Profiler: next generation--an online platform for analysis of microarray data. Nucleic Acids Research. 32(Web Server). W465–W470. 94 indexed citations
15.
Brāzma, Alvis, Inge Jonassen, Jaak Vilo, & Esko Ukkonen. (1998). Predicting gene regulatory elements from their expression data in the complete yeast genome.. 2 indexed citations
16.
Brāzma, Alvis, Inge Jonassen, Jaak Vilo, & Esko Ukkonen. (1998). Predicting Gene Regulatory Elements in Silico on a Genomic Scale. Genome Research. 8(11). 1202–1215. 238 indexed citations
17.
Brāzma, Alvis, Jaak Vilo, & Esko Ukkonen. (1997). Finding transcription factor binding site combinations in the yeast genome.. 57–59. 8 indexed citations
18.
Brāzma, Alvis. (1993). Learning a subclass of regular expressions by recognizing periodic repetitions. 137–146. 2 indexed citations
19.
Kinber, Efim & Alvis Brāzma. (1990). Models of inductive synthesis. The Journal of Logic Programming. 9(2-3). 221–233. 1 indexed citations
20.
Brāzma, Alvis & Efim Kinber. (1986). Generalized regular expressions—A language for synthesis of programs with branching in loops. Theoretical Computer Science. 46. 175–195. 5 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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