Version 1
: Received: 13 August 2024 / Approved: 14 August 2024 / Online: 14 August 2024 (14:37:35 CEST)
How to cite:
Ai, D.; Du, Y.; Duan, H.; Qi, J.; Wang, Y. Tumor Heterogeneity in Gastrointestinal Cancer based on Multimodal Data Analysis. Preprints2024, 2024081076. https://doi.org/10.20944/preprints202408.1076.v1
Ai, D.; Du, Y.; Duan, H.; Qi, J.; Wang, Y. Tumor Heterogeneity in Gastrointestinal Cancer based on Multimodal Data Analysis. Preprints 2024, 2024081076. https://doi.org/10.20944/preprints202408.1076.v1
Ai, D.; Du, Y.; Duan, H.; Qi, J.; Wang, Y. Tumor Heterogeneity in Gastrointestinal Cancer based on Multimodal Data Analysis. Preprints2024, 2024081076. https://doi.org/10.20944/preprints202408.1076.v1
APA Style
Ai, D., Du, Y., Duan, H., Qi, J., & Wang, Y. (2024). Tumor Heterogeneity in Gastrointestinal Cancer based on Multimodal Data Analysis. Preprints. https://doi.org/10.20944/preprints202408.1076.v1
Chicago/Turabian Style
Ai, D., Juan Qi and Yuduo Wang. 2024 "Tumor Heterogeneity in Gastrointestinal Cancer based on Multimodal Data Analysis" Preprints. https://doi.org/10.20944/preprints202408.1076.v1
Abstract
The abstract should be a total of about 250 words and structured to contain the following headings: Background/Objectives, Methods, Results, Conclusions. Background/Objectives: Place the question addressed in a broad context and highlight the purpose of the study; Methods: Describe briefly the main methods or treatments applied. Include any relevant preregistration numbers, and species and strains of any animals used; Results: Summarize the article’s main findings; Conclusions: Indicate the main conclusions or interpretations. The abstract should be an objective representation of the article: it must not contain results which are not presented and substantiated in the main text and should not exaggerate the main conclusions. Clinical trial abstracts should include items that the CONSORT group has identified as essential.
Keywords
tumor heterogeneity; transcriptome profile; cancer classification; multiomics; cancer imaging
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
