# Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI
## 5 Unsupervised Learning Application in Precision Medicine  
### Clustering Application in PM  
K-means clustering plays a crucial role in medical research by facilitating the identification of distinct patient subgroups, which can lead to more personalized treatment strategies and improved clinical outcomes. A recent study [62] employed K-means clustering to assess the immune signature of juvenile-onset systemic lupus erythematosus(SLE), aiming to enhance patient stratification. By selecting 8 subsets from 28 immune cell subsets, K-means categorizes patients into four distinct groups, revealing significant variations in T-cell frequencies. Correlation network analysis identified extensive immune correlations with clinical features, suggesting that K-means clustering can illuminate the multifactorial and heterogeneous nature of juvenile-onset SLE. The paper by [PERSON] et al. [63] describes the application of spherical K-means clustering for identifying distinct biological phenotypes in patients with acetylcholine receptor-antibody positive myasthenia gravis (MG). This UL method classified samples into different subgroups, using proteomics data. A modification of classical K-means was used, specifically spherical K-means, which employed cosine similarity as the distance metric. This was considered more suitable for high-dimensional data like proteomic profiles and offered better robustness against noise. Clustering revealed four distinct patient subgroups, with protein signature 3 (PS3) identified as having high disease severity, marked by complement activation, thus suggesting that PS3 patients might benefit from complement-inhibiting therapies. In the management of type 2 diabetes, K-means was used to classify patients based on genetic profiles, clinical biomarkers, and drug responses, allowing for more precise treatment strategies. One cluster, for example, showed higher insulin resistance, while another responded better to drugs like metformin, helping to reduce adverse reactions and long-term complications such as cardiovascular risks [64]. In acute ischemic stroke (AIS), over 1400 patients were K-means clustered into groups based on metabolic and inflammatory markers, such as lipid levels and ion concentrations. One group, with high lipid levels, responded better to lipid-lowering drugs, while another cluster with elevated inflammation benefited from anti-inflammatory treatments. This approach personalized stroke care, improving recovery outcomes by predicting patient-specific trajectories [65]. In medical imaging, K-means was used to enhance MRI segmentation, grouping pixels by intensity and spatial data to detect subtle brain abnormalities, such as lesions in neurodegenerative diseases like multiple sclerosis. This method improved diagnostic accuracy, allowing for earlier intervention and more tailored treatment plans, significantly advancing patient outcomes compared to traditional techniques [66]. Hierarchical clustering offers flexibility in capturing data structure, making it ideal for exploratory analysis in complex, high-dimensional datasets. In a recent work of [PERSON] et al. [67], a hierarchical clustering algorithm was employed to develop the BANDIT tool, enabling it to uncover complex drug-target relationships and latent patterns within the global drug landscape that may be hidden in flat data representations. The efficacy of BANDIT was validated through extensive testing, where it successfully replicated known shared-target relationships, individual drug-target interactions, and established mechanisms of action within the test set. Furthermore, BANDIT's predictions aligned with outcomes from large-scale experimental screens, demonstrating its robustness and reliability. Experimentally, several novel predictions made by BANDIT were confirmed using various bioassays and model systems, highlighting its potential to uncover previously unrecognized small molecules with therapeutic relevance, particularly in refractory tumors.