We have been developing computational analysis methods and algorithms to decipher complex interrelationships of tumor cells, immune cells, and microbiome, and complexity of tumor genomic data (whole exome sequencing and RNA transcriptomic sequencing).
Our new computational algorithm termed “Tumor Immune Partitioning and Clustering (TIPC)” is available at bioRxiv.
https://www.biorxiv.org/content/10.1101/2020.05.29.111542v1
This is another paper that used machine learning algorithm to decipher immune cells in routine H&E-stained histopathological sections.
https://pubmed.ncbi.nlm.nih.gov/32439699/
Our paper on biomarker correlation network analysis has been published in BMC Bioinformatics (Nishihara R et al. 2017):
bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1718-5
Ma et al. CRC transcriptomics meta-analysis in Genome Biology 2018.
I (Shuji Ogino) am an associate faculty member of Master of Science Program in Computational Biology and Quantitative Genetics (CBQ) at Harvard T.H. Chan School of Public Health (Director, Dr. John Quackenbush).
http://www.hsph.harvard.edu/sm-computational-biology/faculty/
We are interested in somatic changes in oncogenes and tumor suppressor genes (in genomic, epigenomic, transcriptomic, and proteomic analyses), alterations in metabolism (metabolomics), alterations in molecular networks and signal transduction pathways, interaction between tumor (diseased cells), host (immune and other types of cells), and environment (envirome and exposome), and eventually analyses of the interactome.
