Alpha Tom Kodamullil

1.0k total citations
39 papers, 583 citations indexed

About

Alpha Tom Kodamullil is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Alpha Tom Kodamullil has authored 39 papers receiving a total of 583 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Molecular Biology, 10 papers in Computational Theory and Mathematics and 8 papers in Artificial Intelligence. Recurrent topics in Alpha Tom Kodamullil's work include Bioinformatics and Genomic Networks (25 papers), Biomedical Text Mining and Ontologies (15 papers) and Computational Drug Discovery Methods (10 papers). Alpha Tom Kodamullil is often cited by papers focused on Bioinformatics and Genomic Networks (25 papers), Biomedical Text Mining and Ontologies (15 papers) and Computational Drug Discovery Methods (10 papers). Alpha Tom Kodamullil collaborates with scholars based in Germany, United States and France. Alpha Tom Kodamullil's co-authors include Martin Hofmann‐Apitius, Reagon Karki, Daniel Domingo‐Fernándéz, Erfan Younesi, Christian Ebeling, Sarah Mubeen, Tamara Raschka, Yojana Gadiya, Sumit Madan and Shounak Baksi and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Alpha Tom Kodamullil

34 papers receiving 573 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alpha Tom Kodamullil Germany 15 348 129 123 108 39 39 583
Daniel Domingo‐Fernándéz Germany 15 427 1.2× 145 1.1× 64 0.5× 135 1.3× 38 1.0× 50 698
Kristina Hettne Netherlands 20 570 1.6× 195 1.5× 101 0.8× 125 1.2× 62 1.6× 52 991
Aziz M. Mezlini United States 8 1.0k 3.0× 191 1.5× 92 0.7× 132 1.2× 96 2.5× 13 1.5k
C. Paul Morrey United States 10 560 1.6× 143 1.1× 49 0.4× 110 1.0× 100 2.6× 13 775
Kalliopi Tsafou Denmark 10 876 2.5× 84 0.7× 54 0.4× 123 1.1× 117 3.0× 12 1.1k
Marek Ostaszewski Luxembourg 15 400 1.1× 42 0.3× 59 0.5× 56 0.5× 25 0.6× 48 661
Jielin Xu United States 13 362 1.0× 26 0.2× 79 0.6× 126 1.2× 35 0.9× 27 684
Soichi Ogishima Japan 15 389 1.1× 28 0.2× 110 0.9× 58 0.5× 75 1.9× 48 759
Ludwig Lausser Germany 12 228 0.7× 78 0.6× 82 0.7× 23 0.2× 17 0.4× 42 431
Daphna Laifenfeld Israel 16 339 1.0× 192 1.5× 112 0.9× 34 0.3× 57 1.5× 30 986

Countries citing papers authored by Alpha Tom Kodamullil

Since Specialization
Citations

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

Fields of papers citing papers by Alpha Tom Kodamullil

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alpha Tom Kodamullil. 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 Alpha Tom Kodamullil. The network helps show where Alpha Tom Kodamullil may publish in the future.

Co-authorship network of co-authors of Alpha Tom Kodamullil

This figure shows the co-authorship network connecting the top 25 collaborators of Alpha Tom Kodamullil. A scholar is included among the top collaborators of Alpha Tom Kodamullil 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 Alpha Tom Kodamullil. Alpha Tom Kodamullil 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.
2.
Schultz, Bruce, et al.. (2024). SynDRep: a synergistic partner prediction tool based on knowledge graph for drug repurposing. Bioinformatics Advances. 5(1). vbaf092–vbaf092.
3.
Kodamullil, Alpha Tom, et al.. (2024). A network of transcriptomic signatures identifies novel comorbidity mechanisms between schizophrenia and somatic disorders. SHILAP Revista de lepidopterología. 4(1). 11–11.
4.
Russo, Maria Francesca, Daniel Domingo‐Fernándéz, Andrea Zaliani, et al.. (2024). Curating, Collecting, and Cataloguing Global COVID-19 Datasets for the Aim of Predicting Personalized Risk. Data. 9(2). 25–25.
5.
Gebel, Stephan, Bruce Schultz, Marc Jacobs, et al.. (2023). The Epilepsy Ontology: a community-based ontology tailored for semantic interoperability and text mining. Bioinformatics Advances. 3(1). vbad033–vbad033. 2 indexed citations
6.
7.
Hoyt, Charles Tapley, Colin Birkenbihl, Benjamin M. Gyori, et al.. (2022). STonKGs: a sophisticated transformer trained on biomedical text and knowledge graphs. Bioinformatics. 38(6). 1648–1656. 17 indexed citations
8.
Lin, Yu, Stephan Gebel, Sumit Madan, et al.. (2022). CTO: A community-based clinical trial ontology and its applications in PubChemRDF and SCAIView. PubMed. 2807. 2 indexed citations
9.
Mubeen, Sarah, Alpha Tom Kodamullil, Martin Hofmann‐Apitius, & Daniel Domingo‐Fernándéz. (2022). On the influence of several factors on pathway enrichment analysis. Briefings in Bioinformatics. 23(3). 32 indexed citations
10.
Uebachs, Mischa, Daniel Domingo‐Fernándéz, Stephan Gebel, et al.. (2022). Integrative data semantics through a model-enabled data stewardship. Bioinformatics. 38(15). 3850–3852. 5 indexed citations
11.
Mubeen, Sarah, et al.. (2021). DecoPath: a web application for decoding pathway enrichment analysis. NAR Genomics and Bioinformatics. 3(3). lqab087–lqab087. 3 indexed citations
12.
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Domingo‐Fernándéz, Daniel, Sarah Mubeen, Charles Tapley Hoyt, et al.. (2021). A Systems Biology Approach for Hypothesizing the Effect of Genetic Variants on Neuroimaging Features in Alzheimer’s Disease. Journal of Alzheimer s Disease. 80(2). 831–840. 3 indexed citations
14.
Domingo‐Fernándéz, Daniel, Shounak Baksi, Bruce Schultz, et al.. (2020). COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology. Bioinformatics. 37(9). 1332–1334. 72 indexed citations
15.
Kodamullil, Alpha Tom, Shounak Baksi, Sumit Madan, et al.. (2020). The COVID-19 Ontology. Bioinformatics. 36(24). 5703–5705. 23 indexed citations
16.
Domingo‐Fernándéz, Daniel, Allison C. Provost, Alpha Tom Kodamullil, et al.. (2019). PTSD Biomarker Database: deep dive metadatabase for PTSD biomarkers, visualizations and analysis tools. Database. 2019. 13 indexed citations
17.
Kodamullil, Alpha Tom, et al.. (2017). Of Mice and Men: Comparative Analysis of Neuro-Inflammatory Mechanisms in Human and Mouse Using Cause-and-Effect Models. Journal of Alzheimer s Disease. 59(3). 1045–1055. 19 indexed citations
18.
Kodamullil, Alpha Tom, et al.. (2017). Tracing investment in drug development for Alzheimer disease. Nature Reviews Drug Discovery. 16(12). 819–819. 38 indexed citations
19.
Kodamullil, Alpha Tom, et al.. (2016). Using Drugs as Molecular Probes: A Computational Chemical Biology Approach in Neurodegenerative Diseases. Journal of Alzheimer s Disease. 56(2). 677–686. 13 indexed citations
20.
Younesi, Erfan, Phil Scordis, Alpha Tom Kodamullil, et al.. (2015). PDON: Parkinson’s disease ontology for representation and modeling of the Parkinson’s disease knowledge domain. Theoretical Biology and Medical Modelling. 12(1). 20–20. 24 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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