XAI in Genomics with LLM Generated Explanations in Oncology.

Isabelle Bichindaritz, Shazveen Saleem, Leszek Kotula

Abstract read

In one paragraph

Article in CEUR workshop proceedings., 2026. 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

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

5 · Who and what money

Authors and funding

3 authors.

Isabelle BichindaritzBiomedical and Health Informatics, State University of New York at Oswego, NY, USA.ORCID 0000-0003-1712-490X

Shazveen SaleemBiomedical and Health Informatics, State University of New York at Oswego, NY, USA.

Leszek KotulaUpstate Medical University, State University pf New York, Syracuse, NY, USA.ORCID 1111-0002-7977-7124

Funding

Explainable Analogical Learning for Oncology Diagnosis and Prediction based on Deep PathophysiologyR15CA297754 · NCI · COLLEGE AT OSWEGO · PI BICHINDARITZ, ISABELLE · 2025 to 2025

$591k

NCI NIH HHS R15 CA297754

6 · The paper itself

Abstract

The amount of genetic data available for analysis is rapidly growing, and the importance of analyzing this type of data is paramount to the field of precision medicine, in which diagnostic and prognostic tasks rely on genetic data analysis in many fields, including in oncology. Given the high dimensionality of genomic data, AI or machine learning in genomics requires more sophisticated methods to help clinicians interpret their results, as gene-level explanations may not be sufficient to make sense of these results. Given the growing importance of XAI, a rapidly growing number of AI papers in genomics refer to XAI methods. In this paper, we present general methods for XAI in genomics and focus on how LLMs can contribute to explanations at the case level in the domain of oncology. The preliminary results obtained in this study are encouraging about the role LLM chatbots can play in the user interface to help explain machine learning results to users at the case level in conversational or visual interaction modalities.

Indexed as

case-based reasoningGenAILLMsoncologysurvival analysisXAI

Identifiers

PMID42836250

PMCPMC13638230

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.