# Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI
## 5 Unsupervised Learning Application in Precision Medicine
### Dimensionality Reduction Application in PM  
Dimensionality reduction plays a crucial role in PM. PCA has been employed in [69] to transform high-dimensional heart data into a lower-dimensional space, facilitating the study of genetic variations linked to anatomical traits like left ventricular morphology. However, the study also highlighted the limitations of PCA when applied to complex data, as it tends to geometric details critical for accurate medical diagnostics. To address these limitations, the study compared PCA with the Convolutional Mesh Autoencoder (CoMA), which preserves topological and geometric complexity through spectral convolutions based on the Laplace-Beltrami operator. While PCA is limited to linear transformations, CoMA's ability to handle non-linear variations enables more accurate reconstructions of cardiac shapes. This is particularly important for detecting subtle anatomical variations, which are often crucial for understanding the genetic basis of heart diseases. Another study [70] highlights PCA's value in identifying neuroimaging patterns that predict cognitive decline, compressing high-dimensional relevance maps from brain scans of patients with mild cognitive impairment. By reducing the dimensionality of these maps, key components related to the progression of dementia were identified. The study retained 64 components that were significantly associated with cognitive decline. A Cox proportional hazard model demonstrated that these PCA-derived components could predict dementia prognosis over time, with several components correlating with cognitive impairments in domains such as language and executive function. The t-SNE model has been particularly effective in visualizing and analyzing high-dimensional genomic and transcriptomic data, where traditional classification methods might struggle. In one study, t-SNE was applied to DNA methylation data to classify tumors [71]. By projecting cancer samples alongside a reference cohort, t-SNE helped to assign tumors to DNA methylation subclasses, and in cases of no classification, it revealed potential new subclasses. This approach, when integrated with additional molecular data, significantly enhanced tumor classification accuracy, contributing to more precise diagnostic and treatment plans. In another application [72], t-SNE was employed to analyze cellular diversity in multiple myeloma samples, clustering over 489,000 cells into 15 distinct groups using latent space data from a convolutional neural network. This dimensionality reduction allowed for the visualization of phenotypic landscapes, uncovering distinct bone marrow cell communities associated with different stages of the disease. The use of t-SNE, alongside proteotyping and DL, facilitated the identification of cell subpopulations, which aided in clinical decision-making and the assessment of disease progression. In colorectal cancer (CRC) research [73], t-SNE was used to downsample and visualize cells based on cluster size and marker expression, providing critical information about tumor-immune interactions and offering potential targets for therapeutic intervention. The method allowed for the differentiation of epithelial, stromal, and immune cells and identified biologically relevant subpopulations, such as budding tumor cells. Although PCA has been widely used for dimensionality reduction in biomedical research, its linear structure makes it difficult to capture non-linear correlations that are present in biological datasets. Recent advances in deep learning have provided more powerful alternatives for dimensionality reduction., with techniques highly suitable for applications in PM, where high-dimensional datasets are becoming more and more prevalent. Autoencoders have emerged as powerful tools for non-linear dimensionality reduction, particularly in non-image medical data analysis. In one study, a hybrid autoencoder (HAE) framework was developed to improve dimensionality reduction for COVID-19 prognosis prediction [74].