Track genomics, neuroscience and molecular biology.
Search papers, labs, and topics across Lattice.
This paper introduces an unsupervised framework for fusing heterogeneous genomic and clinical data using tensor decomposition to predict cancer risk levels, addressing limitations of conventional cross-modal feature extraction. By employing tensor decomposition (specifically tSVD) followed by Gaussian Mixture Model (GMM) clustering, the method extracts latent, biologically meaningful features that surpass unimodal approaches in predictive accuracy. Validation on LinkedOmics data across 32 cancer types shows tSVD+GMM consistently outperforms other configurations in patient risk stratification against clinical benchmarks.
Tensor decomposition unlocks latent, biologically meaningful cross-modal features that significantly improve cancer risk prediction over unimodal data.
Cancer diagnosis and treatment increasingly rely on multi-modal diagnostic technologies that generate high-dimensional datasets. Although these datasets are rich in pathological information, they pose considerable challenges in interpretation, dimensionality reduction, and clinical applicability. Conventional machine learning approaches often fail to extract meaningful cross-modal features, thereby limiting predictive accuracy in patient risk stratification. To address these limitations, this paper proposes an unsupervised machine learning framework that integrates feature extraction, tensor decomposition, and clustering to improve cancer risk prediction. The framework employs tensor decomposition to fuse heterogeneous genomic and clinical data, enabling the extraction of latent, biologically meaningful features that outperform unimodal feature sets. Multiple feature extraction strategies are investigated, and the resulting features are subsequently clustered using a range of clustering algorithms. Comparative evaluations based on internal and external metrics are then used to identify the most effective configurations. The framework is validated on LinkedOmics data comprising 11,158 patients across 32 cancer types. For patient risk stratification, clinically relevant attributes such as tumor purity, pathological stage, and survival outcomes serve as ground-truth indicators. Experimental results demonstrate the superiority of tSVD+GMM, which consistently outperforms other configurations across clinical benchmarks.