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Pruthvi Prakash Navada

dblp:414/8309 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 1 · 1 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
1 paper
Bioinformatics and computational biology · 67% Computational social science and digital humanities · 33%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational social science and digital humanities
causal inference		 0.812024
<tt>Eliater</tt>: a Python package for estimating outcomes of perturbations in biomolecular networks · Bioinform. 2024
Bioinformatics and computational biology › network bioinformatics › biological network analysis
molecular network analysis		 0.812024
<tt>Eliater</tt>: a Python package for estimating outcomes of perturbations in biomolecular networks · Bioinform. 2024
Bioinformatics and computational biology › functional genomics
perturbation effect estimation		 0.812024
<tt>Eliater</tt>: a Python package for estimating outcomes of perturbations in biomolecular networks · Bioinform. 2024

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

causal inference · 0.8
YearPublicationVenuePosition
2024 <tt>Eliater</tt>: a Python package for estimating outcomes of perturbations in biomolecular networks
abstract
SUMMARY: We introduce Eliater, a Python package for estimating the effect of perturbation of an upstream molecule on a downstream molecule in a biomolecular network. The estimation takes as input a biomolecular network, observational biomolecular data, and a perturbation of interest, and outputs an estimated quantitative effect of the perturbation. We showcase the functionalities of Eliater in a case study of Escherichia coli transcriptional regulatory network. AVAILABILITY AND IMPLEMENTATION: The code, the documentation, and several case studies are available open source at https://github.com/y0-causal-inference/eliater.
Sara Mohammad Taheri, Pruthvi Prakash Navada, Charles Tapley Hoyt, Jeremy Zucker, Karen Sachs, Benjamin M. Gyori, Olga Vitek
 Bioinform.2