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arXiv Machine Learning By Ali Anaissi, Yuanzhe Jia, Qishun Peng, Weidong Huang, Ali Braytee, Junaid Akram

Tensor-based Fusion of Multi-modal Data for Predicting Cancer Risk Levels

Read the original on arXiv Machine Learning →

The paper introduces an unsupervised machine learning framework that fuses heterogeneous genomic and clinical data using tensor decomposition and clustering to predict cancer risk levels. By extracting latent, biologically meaningful features through tensor decomposition, the approach outperforms unimodal feature sets. Experiments on LinkedOmics data of 11,158 patients across 32 cancer types show that the tSVD+GMM configuration consistently achieves superior performance on clinical benchmarks such as tumor purity, pathological stage, and survival outcomes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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