Understanding Breast Cancer Survival: Using Causality and Language
  Models on Multi-omics Data
Mugariya Farooq Shahad Hardan Aigerim Zhumbhayeva Yujia Zheng Preslav Nakov Kun Zhang
The need for more usable and explainable machine learning models in
healthcare increases the importance of developing and utilizing causal
discovery algorithms, which aim to discover causal relations by analyzing
observational data. Explainable approaches aid clinicians and biologists in
predicting the prognosis of diseases and suggesting proper treatments. However,
very little research has been conducted at the crossroads between causal
discovery, genomics, and breast cancer, and we aim to bridge this gap.
Moreover, evaluation of causal discovery methods on real data is in general
notoriously difficult because ground-truth causal relations are usually
unknown, and accordingly, in this paper, we also propose to address the
evaluation problem with large language models. In particular, we exploit
suitable causal discovery algorithms to investigate how various perturbations
in the genome can affect the survival of patients diagnosed with breast cancer.
We used three main causal discovery algorithms: PC, Greedy Equivalence Search
(GES), and a Generalized Precision Matrix-based one. We experiment with a
subset of The Cancer Genome Atlas, which contains information about mutations,
copy number variations, protein levels, and gene expressions for 705 breast
cancer patients. Our findings reveal important factors related to the vital
status of patients using causal discovery algorithms. However, the reliability
of these results remains a concern in the medical domain. Accordingly, as
another contribution of the work, the results are validated through language
models trained on biomedical literature, such as BlueBERT and other large
language models trained on medical corpora. Our results profess proper
utilization of causal discovery algorithms and language models for revealing
reliable causal relations for clinical applications.