Towards an interpretable deep learning model of cancer
Artikel i vetenskaplig tidskrift, 2025

Cancer is a manifestation of dysfunctional cell states. It emerges from an interplay of intrinsic and extrinsic factors that disrupt cellular dynamics, including genetic and epigenetic alterations, as well as the tumor microenvironment. This complexity can make it challenging to infer molecular causes for treating the disease. This may be addressed by system-wide computer models of cells, as they allow rapid generation and testing of hypotheses that would be too slow or impossible to perform in the laboratory and clinic. However, so far, such models have been impeded by both experimental and computational limitations. In this perspective, we argue that they can now be achieved using deep learning algorithms to integrate omics data and prior knowledge of molecular networks. Such models would have many applications in precision oncology, e.g., for identifying drug targets and biomarkers, predicting resistance mechanisms and toxicity effects of drugs, or simulating cell-cell interactions in the microenvironment.

Författare

Avlant Nilsson

Karolinska universitetssjukhuset

Chalmers, Life sciences, Systembiologi

Massachusetts Institute of Technology (MIT)

Nikolaos Meimetis

Massachusetts Institute of Technology (MIT)

Douglas A. Lauffenburger

Massachusetts Institute of Technology (MIT)

npj Precision Oncology

2397-768X (eISSN)

Vol. 9 1 46

Maskininlärning av immunsystemet

Vetenskapsrådet (VR) (2019-06349), 2020-01-01 -- 2023-12-31.

Ämneskategorier (SSIF 2025)

Bioinformatik och beräkningsbiologi

Cancer och onkologi

DOI

10.1038/s41698-025-00822-y

PubMed

39948231

Mer information

Senast uppdaterat

2025-03-19