Alvis Brāzma
- Molecular Biology top 0.2%
- Gene expression and cancer classification 68
- Bioinformatics and Genomic Networks 56
- Genomics and Chromatin Dynamics 18
- Genomics and Phylogenetic Studies 15
- Biomedical Text Mining and Ontologies 15
- Gene Regulatory Network Analysis 15
- Molecular Biology Techniques and Applications 14
- Single-cell and spatial transcriptomics 10
- Cancer Research top 1%
- Aging top 2%
- Biophysics top 0.5%
- Genetics top 1%
- Co-authors
- Jaak ViloJuan Antonio VizcaínoYasset Pérez‐RiverolDeepti J KunduDavid García‐SeisdedosSelvakumar KamatchinathanMathias WalzerShengbo Wang
- Partner nations
- United KingdomUnited StatesFinland
In The Last Decade
Alvis Brāzma
129 papers receiving 14.4k citations
Hit Papers
Peers
Comparison fields: 5 of 187
- Molecular Biology 11.0k
- Cancer Research 1.4k
- Aging 150
- Biophysics 457
- Genetics 1.6k
Countries citing papers authored by Alvis Brāzma
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
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
The 25 scholars most cited alongside Alvis Brāzma, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 15 | |
| 2 | 2022 | 9 | |
| 3 | 2021 | 37 | |
| 4 | 2020 | 15 | |
| 5 | ArrayExpress update – from bulk to single-cell expression databreakdown → | 2018 | 377 |
| 6 | 2018 | 50 | |
| 7 | 2017 | 160 | |
| 8 | 2012 | 2 | |
| 9 | 2009 | 14 | |
| 10 | 2008 | 58 | |
| 11 | 2004 | 94 | |
| 12 | 2004 | 87 | |
| 13 | 2003 | 67 | |
| 14 | Predicting gene regulatory elements from their expression data in the complete yeast genome. | 1998 | 2 |
| 15 | 1998 | 238 | |
| 16 | 1998 | 183 | |
| 17 | Finding transcription factor binding site combinations in the yeast genome. | 1997 | 8 |
| 18 | Learning a subclass of regular expressions by recognizing periodic repetitions | 1993 | 2 |
| 19 | 1990 | 1 | |
| 20 | 1986 | 5 |
About Alvis Brāzma
Alvis Brāzma is a scholar working on Molecular Biology, Structural Biology and Biophysics, having authored 132 papers that have together received 14.7k indexed citations. Recurring topics across this work include Gene expression and cancer classification (68 papers), Bioinformatics and Genomic Networks (56 papers), Genomics and Chromatin Dynamics (18 papers), Genomics and Phylogenetic Studies (15 papers), Biomedical Text Mining and Ontologies (15 papers), Gene Regulatory Network Analysis (15 papers), Molecular Biology Techniques and Applications (14 papers) and Single-cell and spatial transcriptomics (10 papers). The work is most often cited by research in Molecular Biology (11.0k citations), Cancer Research (1.4k citations) and Aging (150 citations). Alvis Brāzma has collaborated with scholars based in United Kingdom, United States and Finland. Frequent co-authors include Jaak Vilo, Juan Antonio Vizcaíno, Yasset Pérez‐Riverol, Deepti J Kundu, David García‐Seisdedos, Selvakumar Kamatchinathan, Mathias Walzer, Shengbo Wang, Ananth Prakash and Chakradhar Bandla. Their work appears in journals such as Bioinformatics, Nucleic Acids Research, BMC Bioinformatics, Genome biology and Nature Communications.
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.