Author

J. S. Inda-Diaz

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Microbiology· 1 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Approach:transformer / foundation model· 2 Research Focus:benchmarking· 1 Assay:clinical cohort / trial· 1

Frequent Co-Authors

E. Kristiansson· 2 shared A. Johnning· 1 shared A. Lokrantz· 1 shared A. Sjoberg· 1 shared J. Coria· 1 shared L. Helldal· 1 shared L. Svensson· 1 shared M. Gustavsson· 1 shared

Activity by Year

2023-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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2 shared papersMicrobiology

E. Kristiansson

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2 papers

Recent·Most cited

MicrobiologybioRxivMay 9, 2023

Confidence-based Prediction of Antibiotic Resistance at the Patient Level

J. S. Inda-Diaz, A. Johnning, M. Hessel…·E. Kristiansson

This paper introduces a deep learning method that predicts antibiotic susceptibility using patient data and AST results, achieving high accuracy and low error rates across multiple antibiotics.

DOI 10.1128/mbio.03431-25

clinical cohort / trialdeep learning

𝕏 PostBluesky

Pharmacology & ToxicologybioRxivApr 17, 2023

Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms

M. Gustavsson, S. Kall, P. Svedberg…·E. Kristiansson

This paper develops a transformer-based model to predict chemical toxicity in aquatic organisms, achieving high accuracy and broader applicability than traditional QSAR methods.

DOI 10.1126/sciadv.adk6669

deep learningtransformer / foundation model

𝕏 PostBluesky