Medical Big Data and Artificial Intelligence for Healthcare

Zhang, Yudong; Hong, Jiman; Chen, Shuwen

doi:10.3390/app13063745

Cited by 9 publications

(2 citation statements)

References 39 publications

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“…Analyzing patients' transcriptome data provides insights into the current state of cancer and its individual variability. Machine-Learning (ML) integration in biomedical research is creating new paths for cancer diagnostics and treatment [5,6]. ML models have predicted hepatocellular carcinoma recurrence with 74.19% accuracy using whole-genome data [7].…”

Section: Introductionmentioning

confidence: 99%

MVMSGAT: Integrating Multiview, Multi-Scale Graph Convolutional Networks with Biological Prior Knowledge for Predicting Bladder Cancer Response to Neoadjuvant Therapy

Luo,

Chen,

Yao

2024

Applied Sciences

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The incidence of bladder cancer is on the rise, and its molecular heterogeneity presents significant challenges for personalized cancer therapy. Transcriptome data can characterize the variability among patients. Traditional machine-learning methods often struggle with high-dimensional genomic data, falling into the ’curse of dimensionality’. To address this challenge, we have developed MVMSGAT, an innovative predictive model tailored for forecasting responses to neoadjuvant therapy in bladder cancer patients. MVMSGAT significantly enhances model performance by incorporating multi-perspective biological prior knowledge. It initially utilizes the Boruta algorithm to select key genes from transcriptome data, subsequently constructing a comprehensive graph of gene co-expression and protein–protein interactions. MVMSGAT further employs a graph convolutional neural network to integrate this information within a multiview knowledge graph, amalgamating biological knowledge maps from various scales using an attention mechanism. For validation, MVMSGAT was tested using a five-fold cross-validation approach on two specific GEO datasets, GSE169455 and GSE69795, involving a total of 210 bladder cancer samples. MVMSGAT demonstrated superior performance, with the following metrics (mean ± standard deviation): AUC-ROC of 0.8724±0.0511, accuracy of 0.7789±0.068, F1 score of 0.8529±0.0338, and recall of 0.9231±0.0719. These results underscore the potential of MVMSGAT in advancing personalized treatment and precision medicine in bladder cancer.

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Section: Introductionmentioning

confidence: 99%

MVMSGAT: Integrating Multiview, Multi-Scale Graph Convolutional Networks with Biological Prior Knowledge for Predicting Bladder Cancer Response to Neoadjuvant Therapy

Luo,

Chen,

Yao

2024

Applied Sciences

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“…Fortunately, the technology at our disposal offers cost-effective solutions to these problems. Machine learning (ML) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], artificial intelligence (AI) [16][17][18][19][20][21][22][23][24][25][26][27][28][29] and Big Data [30][31][32][33][34][35] can provide robust and innovative answers to long-standing problems.…”

Section: Introductionmentioning

confidence: 99%