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Shounak Baksi

dblp:190/7002 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2021
0000-0001-7225-5377ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › knowledge representation in biology
biomedical ontology
0.512021
The COVID-19 Ontology · Bioinform. 2021

Methods — techniques the papers use, named apart from their topics

ontology engineering · 0.5literature mining · 0.5
YearPublicationVenuePosition
2021 COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology
abstract
SUMMARY: The COVID-19 crisis has elicited a global response by the scientific community that has led to a burst of publications on the pathophysiology of the virus. However, without coordinated efforts to organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing this knowledge in a structured and computable form, as in the form of a knowledge graph, researchers can readily reason and analyze this information on a much larger scale. Here, we present the COVID-19 Knowledge Graph, an expansive cause-and-effect network constructed from scientific literature on the new coronavirus that aims to provide a comprehensive view of its pathophysiology. To make this resource available to the research community and facilitate its exploration and analysis, we also implemented a web application and released the KG in multiple standard formats. AVAILABILITY AND IMPLEMENTATION: The COVID-19 Knowledge Graph is publicly available under CC-0 license at https://github.com/covid19kg and https://bikmi.covid19-knowledgespace.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Daniel Domingo-Fernández, Shounak Baksi, Bruce Schultz, Yojana Gadiya, Reagon Karki, Tamara Raschka, Christian Ebeling, Martin Hofmann-Apitius, Alpha Tom Kodamullil
Bioinform.2
2021 The COVID-19 Ontology
abstract
MOTIVATION: The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel coronavirus (SARS-CoV-2) entities. The ontology has a strong scope on chemical entities suited for drug repurposing, as this is a major target of ongoing COVID-19 therapeutic development. RESULTS: The ontology comprises 2270 classes of concepts and 38 987 axioms (2622 logical axioms and 2434 declaration axioms). It depicts the roles of molecular and cellular entities in virus-host interactions and in the virus life cycle, as well as a wide spectrum of medical and epidemiological concepts linked to COVID-19. The performance of the ontology has been tested on Medline and the COVID-19 corpus provided by the Allen Institute. AVAILABILITYAND IMPLEMENTATION: COVID-19 Ontology is released under a Creative Commons 4.0 License and shared via https://github.com/covid-19-ontology/covid-19. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/COVID-19. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Astghik Sargsyan, Alpha Tom Kodamullil, Shounak Baksi, Johannes Darms, Sumit Madan, Stephan Gebel, Oliver Keminer, Geena Mariya Jose, Helena Balabin, Lauren Nicole Delong, Manfred Kohler, Marc Jacobs 0001, Martin Hofmann-Apitius
Bioinform.3