With the efforts to prfile the spatial bioinfomation, nowadays there are large amounts of spatial trasncriptome, proteom, and HE staing of placentas. Our lab and my research work dedicated in studying placental pathology in mediating maternal condition to fetal health status. To do this, we aim to extract the knowledge about placentas from as many as the available sources. The data will be collected from research articles publishing xenium, stereoseq, codex data, and HE databases like HEST-Library. The tasks were divided into several machine learning tasks: - integrate multiple modalities of the data, specifically, into the same latent space - classify the regional data into different biological objects, say cell types - mutually infer features from different modalities - train a model to classify the observations to the known meta data In the end, we aim to deposite and publish the models. After this project (and requested 1-year duration), we plan to continue with other projects based on this project. For exampke, to use the trained model to classify the new data that we collected in our own lab.