From data disparity to data harmony: A comprehensive pan-cancer omics data collection
We present a scalable, data-driven platform for pan-cancer antigen target discovery leveraging the untapped potential of public transcriptomic data, along with extensive biological and pharmaceutical knowledge.
A machine learning-powered dashboard for the exploration of high-throughput transcriptomic datasets
We present a solution using a tagging approach to characterize GEO datasets, focusing on clinical metadata of sample transcriptomic profiles.
pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods
We introduce a Python implementation of ComBat and ComBat-Seq for batch effect correction in microarray and RNA-Seq data.
Introducing InMoose, an integrated open source Python package for multi-omic analyses
We introduce InMoose (Integrated Multi-Omics Open Source Environment), an open source Python unified framework for every -omic data type.
Integrating transcriptomics and proteomics for the discovery of novel antigen targets on the surface of malignant plasma cells amenable to CAR-T cell approach in the treatment of RRMM patients
Our translational bioinformatics approach identified novel antigen targets for CAR-T & HIT receptor therapies, promising for RRMM treatment.
