Applications of machine learning-assisted extracellular vesicles analysis technology in tumor diagnosis

Figures

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  Fig. 1. Application of ML-assisted EV analysis technology in tumor diagnosis (PSDs come from FigDraw.com).

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  Fig. 2. (A) Schematic diagram of LDA-based tumor-derived EV-miRNA cancer diagnosis. (B) Schematic diagram of cancer diagnosis of tumor-derived EV-membrane proteins of different sizes based on LDA.

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  Fig. 3. (A) Schematic diagram of RF-based identification of mixed tumor biological samples. (B) Schematic diagram of RF-based colorectal cancer marker screening and individual tumor identification. (C) Schematic diagram of RF-based tumor marker screening and differentiation of different tumors.

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  Fig. 4. (A) Schematic diagram of classifying different subtypes of lung cancer based on RF. (B) Schematic diagram of identifying different cell lines of breast cancer based on LDA. (C) Schematic diagram of identifying different cell lines of breast cancer based on PCA-SERS. (D) Breast cancer based on PCA-SERS schematic diagram of identification of different cancer cell lines.

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  Fig. 5. (A) Schematic diagram of distinguishing pancreatic cancer in different stages based on LDA. (B) Schematic diagram of distinguishing lung cancer in different stages based on deep learning. (C) Schematic diagram of glioblastoma gene mutation site monitoring based on CNN.