Unlocking Cancer’s Secrets: Machine Learning-Powered Quantification of Cell Invasion and Discovery of Novel Targets

Traditional methods for studying cancer invasion are limited in their ability to process and analyse large, complex datasets. By integrating advanced machine learning algorithms with high-content imaging of 3D cancer models, we can automate the quantification of invasion patterns and extract meaningful insights.

We will use convolutional neural networks (CNNs) for image recognition to automatically detect and quantify cancer cell movement and matrix degradation. Additionally, unsupervised clustering methods such as k-means clustering and principal component analysis (PCA) will be employed to classify invasion patterns based on the extracted features from imaging data. These methods will help reveal subpopulations of invasive cells and associated biomarkers.

For target discovery, we will implement random forest and support vector machines (SVM) to identify key invasion-related genes and pathways from multi-omics data. The machine learning models will be trained on multiple cancer types to ensure generalizability and robustness across various tumors.

By combining these ML methods with biological data, we aim to uncover new therapeutic targets and develop personalized treatments that specifically inhibit invasive cancer cell behavior, ultimately contributing to more effective cancer therapies.