ArticleBioinformatics (Oxford, England)2021
Generalized Read-Across prediction using genra-py.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Building a compendium of expert driven read-across cases to facilitate an analysis of the contribution that different similarity contexts play in read-across performance.Computational toxicology (Amsterdam, Netherlands) · 2025Article
- Guidance on the use of read-across for chemical safety assessment in food and feed.EFSA journal. European Food Safety Authority · 2025Article
- Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning.Critical reviews in toxicology · 2024Review
- A systematic analysis of read-across within REACH registration dossiers.Computational toxicology (Amsterdam, Netherlands) · 2024Article
- Systematic Approaches for the Encoding of Chemical Groups: A Case Study.Chemical research in toxicology · 2024Article
- A Comparison of Machine Learning Approaches for predicting Hepatotoxicity potential using Chemical Structure and Targeted Transcriptomic Data.Computational toxicology (Amsterdam, Netherlands) · 2024Article
- Towards systematic read-across using Generalised Read-Across (GenRA).Computational toxicology (Amsterdam, Netherlands) · 2023Article
- Development of a CSRML version of the Analog Identification Methodology (AIM) fragments and their evaluation within the Generalised Read-Across (GenRA) approach.Computational toxicology (Amsterdam, Netherlands) · 2023Article
- Advances in computational methods along the exposure to toxicological response paradigm.Toxicology and applied pharmacology · 2022Article
- Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research.Frontiers in toxicology · 2022Article
- Scientific Opinion of the Scientific Panel on Plant Protection Products and their Residues (PPR Panel) on testing and interpretation of comparativeEFSA journal. European Food Safety Authority · 2021Article
- Repeat-dose toxicity prediction with Generalized Read-Across (GenRA) using targeted transcriptomic data: A proof-of-concept case study.Computational toxicology (Amsterdam, Netherlands) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
motivationGeneralized Read-Across (GenRA) is a data-driven approach to estimate physico-chemical, biological or eco-toxicological properties of chemicals by inference from analogues. GenRA attempts to mimic a human expert's manual read-across reasoning for filling data gaps about new chemicals from known chemicals with an interpretable and automated approach based on nearest-neighbors. A key objective of GenRA is to systematically explore different choices of input data selection and neighborhood definition to objectively evaluate predictive performance of automated read-across estimates of chemical properties.
resultsWe have implemented genra-py as a python package that can be freely used for chemical safety analysis and risk assessment applications. Automated read-across prediction in genra-py conforms to the scikit-learn machine learning library's estimator design pattern, making it easy to use and integrate in computational pipelines. We demonstrate the data-driven application of genra-py to address two key human health risk assessment problems namely: hazard identification and point of departure estimation. AVAILABILITY AND IMPLEMENTATION: The package is available from github.com/i-shah/genra-py.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.