# Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI
## 5 Unsupervised Learning Application in Precision Medicine
### Anomaly Detection Application in PM  
In PM, anomaly detection algorithms can identify rare conditions or atypical responses to treatments, which is essential for developing personalized therapies. An excellent example is the use of autoencoders, commonly applied to detect anomalies in high-dimensional biological data. In a recent work [78], the context-aware deconfounding autoencoder (CODE-AE) was designed to address the out-of-distribution problem in PM. CODE-AE extracts meaningful biological signals and distinguishes confounding variables, effectively identifying anomalies in gene expression data. This enables more accurate prediction of patient-specific drug responses and improves the robustness of clinical outcome predictions from in vitro data. Another application focuses on developing a framework named Detect for anomaly detection in the brain microstructure using deep diffusion MRI (dMRI) tractometry [79]. The method uses autoencoders to learn normative patterns from healthy brain data and then detect deviations in individual patients without needing prior diagnostic labels. By comparing reconstructed dMRI tract profiles to the original input, Detect identifies where the deviations occur along white matter pathways. The tool has been successfully applied in identifying microstructural anomalies in various conditions, including epilepsy and schizophrenia. For example, in epilepsy, it detected subtle changes in the white matter beyond areas visible in standard MRI, offering insights into seizure networks. In schizophrenia, Detect identified outliers with higher sensitivity and specificity compared to conventional methods, providing more accurate individual-level diagnosis.  
Another significant application of anomaly detection is the work by [PERSON] et al. [80] which discusses the role of OSVMs in medical formulation recognition (MFR). The authors propose a new Medical Formulation Engine that enables intelligent searches across pharmaceutical archives to enhance the efficiency of formulation recognition and development. It faces the challenge of distinguishing between formulations and non-formulation texts as an anomaly detection problem. In this context, pharmaceutical formulations are considered the \"normal\" class, while non-formulation texts are treated as anomalies. To address this, the study employs OSVM, highlighting the suitability of the model for identifying these rare instances within large, unbalanced datasets. The proposed MFR system achieved promising accuracy, with a mean recognition rate of 75.2% across experiments and a maximum accuracy of 82%. There are also examples of how this type of UL is applied to images [81]. VasNet, a vasculature-aware UL framework, is specifically designed to address the challenges posed by complex medical imaging data. It uses domain adversarial neural networks (DANNs) to identify and extract relevant vascular structures from noisy, unlabeled imaging data. VasNet distinguishes confounding elements, such as background noise and scattering, from critical features of the vasculature. It then reconstructs vascular images with high fidelity, enabling the detection of structural abnormalities such as thrombosis, internal hemorrhage, and other vascular pathologies.