Project description:We developed a semi-supervised deep learning framework for the identification of doublets in scRNA-seq analysis called Solo. To validate our method, we used MULTI-seq, cholesterol modified oligos (CMOs), to experimentally identify doublets in a solid tissue with diverse cell types, mouse kidney, and showed Solo recapitulated experimentally identified doublets.
Project description:Data and results for paper, "Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data," found at:
https://doi.org/10.1101/2020.11.12.380881
Deep learning software for PSM recalibration, called ProteoTorch-DNN, available at:
https://github.com/proteoTorch/proteoTorch
with documentation:
https://proteotorch.readthedocs.io/en/latest/
Project description:With the current clinical available technologies, not all cancers are staged accurately. Because of this a large percentage of patients are misclassified before treatment, leading to under or over treatment. Therefore, a classification system based on the molecular feature is required to deter-mine the tumour behavior and metastatic potential. Here, we have shown the diagnostic potential of MALDI MSI using supervised machine learning approach in distinguishing cancerous colorectal tissue from normal with an overall accuracy of 98%. Also, shown is the capability of the technique in predicting the presence of metastasis in endometrial cancer with an overall accuracy of 80%. The development of such a model can help in determining the optimum treatment for cancerous pa-tients, reduce morbidity and better patient outcome.