Vislocas: Vision transformers for identifying protein subcellular mis-localization signatures of different cancer subtypes from immunohistochemistry images.

Autor: Wen JW; College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China. Electronic address: jingwen_wen@tju.edu.cn., Zhang HL; College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China. Electronic address: 3020001040@tju.edu.cn., Du PF; College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China. Electronic address: pdu@tju.edu.cn.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 May; Vol. 174, pp. 108392. Date of Electronic Publication: 2024 Apr 09.
DOI: 10.1016/j.compbiomed.2024.108392
Abstrakt: Proteins must be sorted to specific subcellular compartments to perform their functions. Abnormal protein subcellular localizations are related to many diseases. Although many efforts have been made in predicting protein subcellular localization from various static information, including sequences, structures and interactions, such static information cannot predict protein mis-localization events in diseases. On the contrary, the IHC (immunohistochemistry) images, which have been widely applied in clinical diagnosis, contains information that can be used to find protein mis-localization events in disease states. In this study, we create the Vislocas method, which is capable of finding mis-localized proteins from IHC images as markers of cancer subtypes. By combining CNNs and vision transformer encoders, Vislocas can automatically extract image features at both global and local level. Vislocas can be trained with full-sized IHC images from scratch. It is the first attempt to create an end-to-end IHC image-based protein subcellular location predictor. Vislocas achieved comparable or better performances than state-of-the-art methods. We applied Vislocas to find significant protein mis-localization events in different subtypes of glioma, melanoma and skin cancer. The mis-localized proteins, which were found purely from IHC images by Vislocas, are in consistency with clinical or experimental results in literatures. All codes of Vislocas have been deposited in a Github repository (https://github.com/JingwenWen99/Vislocas). All datasets of Vislocas have been deposited in Zenodo (https://zenodo.org/records/10632698).
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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