Pan-Cancer Metastasis Prediction Based on Graph Deep Learning Method

Machine-learning-based risk stratification for probability of dying in patients with basal ganglia hemorrhage

To confirm whether machine learning algorithms (MLA) can achieve an effective risk stratification of dying within 7 days after basal ganglia hemorrhage (BGH). We collected patients with BGH admitted to Sichuan Provincial People's Hospital between August 2005 and August 2021. We developed standard ML-supervised models and fusion models to assess the prognostic risk of patients with BGH and compared them with the classical logistic regression model. We also use the SHAP algorithm to provide clinical interpretability. 1383 patients with BGH were included and divided into the conservative treatment group (CTG) and surgical treatment group (STG). In CTG, the Stack model has the highest sensitivity (78.5%). In STG, Weight-Stack model achieves 58.6% sensitivity and 85.1% specificity, and XGBoost achieves 61.4% sensitivity and 82.4% specificity. The SHAP algorithm shows that the predicted preferred characteristics of the CTG are consciousness, hemorrhage volume, prehospital time, break into ventricles, brain herniation, intraoperative blood loss, and hsCRP were also added to the STG. XGBoost, Stack, and Weight-Stack models combined with easily available clinical data enable risk stratification of BGH patients with high performance. These ML classifiers could assist clinicians and families to identify risk states timely when emergency admission and offer medical care and nursing information.

MetastaSite: Predicting metastasis to different sites using deep learning with gene expression data

Deep learning has massive potential in predicting phenotype from different omics profiles. However, deep neural networks are viewed as black boxes, providing predictions without explanation. Therefore, the requirements for these models to become interpretable are increasing, especially in the medical field. Here we propose a computational framework that takes the gene expression profile of any primary cancer sample and predicts whether patients' samples are primary (localized) or metastasized to the brain, bone, lung, or liver based on deep learning architecture. Specifically, we first constructed an AutoEncoder framework to learn the non-linear relationship between genes, and then DeepLIFT was applied to calculate genes' importance scores. Next, to mine the top essential genes that can distinguish the primary and metastasized tumors, we iteratively added ten top-ranked genes based upon their importance score to train a DNN model. Then we trained a final multi-class DNN that uses the output from the previous part as an input and predicts whether samples are primary or metastasized to the brain, bone, lung, or liver. The prediction performances ranged from AUC of 0.93-0.82. We further designed the model's workflow to provide a second functionality beyond metastasis site prediction, i.e., to identify the biological functions that the DL model uses to perform the prediction. To our knowledge, this is the first multi-class DNN model developed for the generic prediction of metastasis to various sites.