Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning.

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.

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.

Who cites it

8 citing papers in PubMed, 20 citations in OpenAlex.

1. Microarray-Based Genomic Profiling in Low-Dose Radiation Research: Evidence, Limitations, and Translational Perspectives.International journal of molecular sciences · 2026
   Review
2. Transcriptomics for radiation biodosimetry: progress and challenges.International journal of radiation biology · 2023
   Review
3. The transcriptomic revolution and radiation biology.International journal of radiation biology · 2022
   Article
4. Gene expression for biodosimetry and effect prediction purposes: promises, pitfalls and future directions - key session ConRad 2021.International journal of radiation biology · 2022
   Article
5. Pathway-extended gene expression signatures integrate novel biomarkers that improve predictions of patient responses to kinase inhibitors.MedComm · 2020
   Article
6. A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections.F1000Research · 2020
   Article
7. Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning.F1000Research · 2018
   Article
8. Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning.Signal transduction and targeted therapy
   Article

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

Authors and funding

3 authors at 1 institution in 2 countries.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

Indexed as