Evidence map›Paper›PMID 38895385›Full record

ArticlebioRxiv : the preprint server for biology2024

Transfer learning predicts species-specific drug interactions in emerging pathogens.

Carolina H Chung, David C Chang, Nicole M Rhoads, Madeline R Shay, Karthik Srinivasan, Mercy A Okezue, Ashlee D Brunaugh, Sriram Chandrasekaran

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Carolina H ChungDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.ORCID 0000-0003-2490-1842
David C ChangDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
Nicole M RhoadsDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
Madeline R ShayCellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
Karthik SrinivasanDepartment of Ophthalmology and Visual Sciences, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
Mercy A OkezueDepartment of Pharmaceutical Sciences, University of Michigan College of Pharmacy, Ann Arbor, MI, 48109, USA.
Ashlee D BrunaughDepartment of Pharmaceutical Sciences, University of Michigan College of Pharmacy, Ann Arbor, MI, 48109, USA.
Sriram ChandrasekaranDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.ORCID 0000-0002-8405-5708

Funding

A multifactorial pipeline to dissect combinatorial drug efficacy in TuberculosisR01AI150826 · NIAID · UNIVERSITY OF WASHINGTON · PI SHERMAN, DAVID R · 2021 to 2024
$2.9M
Linking metabolic activity with drug sensitivity using metabolic influence networksR35GM137795 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sriram Chandrasekaran · 2020 to 2026
$2.6M
A multifactorial pipeline to dissect combinatorial drug efficacy in TuberculosisR56AI150826 · NIAID · UNIVERSITY OF WASHINGTON · PI SHERMAN, DAVID R · 2020 to 2020
$733k
A pipeline for prioritizing and evaluating multidrug regimens for Mycobacterium abscessusR21AI144536 · NIAID · SEATTLE CHILDREN'S HOSPITAL · PI HERNANDEZ, RAFAEL E · 2019 to 2020
$494k
NIAID NIH HHS R01 AI150826NIAID NIH HHS R21 AI144536NIAID NIH HHS R56 AI150826NIGMS NIH HHS R35 GM137795
6 · The paper itself

Abstract

Machine learning (ML) algorithms are necessary to efficiently identify potent drug combinations within a large candidate space to combat drug resistance. However, existing ML approaches cannot be applied to emerging and under-studied pathogens with limited training data. To address this, we developed a transfer learning and crowdsourcing framework (TACTIC) to train ML models on data from multiple bacteria. TACTIC was built using 2,965 drug interactions from 12 bacterial strains and outperformed traditional ML models in predicting drug interaction outcomes for species that lack training data. Top TACTIC model features revealed genetic and metabolic factors that influence cross-species and species-specific drug interaction outcomes. Upon analyzing ~600,000 predicted drug interactions across 9 metabolic environments and 18 bacterial strains, we identified a small set of drug interactions that are selectively synergistic against Gram-negative (e.g.,

Identifiers

PMID38895385
PMCPMC11185605

What OpenQuestion holds

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Registered trials

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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.