ORPredict Combined with LOHS Suite: An Integrated Engineering Solution for Predicting Tumor Growth

Source abstract

Translational biomedical research is very important to the health of cancer patients. The development of novel biomedical therapies requires continuous iteration between computational prediction and experimental validation. However, the infrastructure for translational biomedical research is still under development. Currently, researchers face significant bottlenecks in two critical areas: first, the visualization of dynamic biological processes from limited experimental data, and second, the integration of diverse analytical tools required for data-driven decision-making. This project presents two complementary engineering solutions: a deep learning model for spatiotemporal biological prediction, and an integrated web-based analytics platform. The deep learning model employs a novel Convolutional LSTM (ConvLSTM) architecture trained on 10,224 microscopy images to predict bacterial infection dynamics in colorectal cancer organoids, achieving a Peak Signal-to-Noise Ratio (PSNR) of 17.3 dB and Structural Similarity Index (SSIM) of 0.59. These metrics indicate successful capture of macro-level morphological changes while identifying clear pathways for improvement. The analytics platform consolidates four machine learning modules (dose-response prediction, time-series forecasting, database querying, and polynomial regression) into a single interface that delivers results in under one second without requiring programming knowledge. In validation testing, the platform reduced experimental screening time from weeks to minutes, representing a 60-fold workflow acceleration. By reducing complex computational analysis to single-click operations, this work lowers the barrier between experimental biologists and machine learning tools.