Philge Philip

Abstract Understanding how the molecules in our body respond to the co-occurrence of two diseases in an individual (comorbidity) could lead to mechanistic insights into novel treatments for comorbid conditions. Studies have shown for instance that responses of our immune system to comorbid conditions could be more complex than the union of immune responses to each disease occurring separately, but a data-driven quantification of this complexity is lacking.

Abstract Understanding how our immune system responds to the co-occurrence of two diseases in an individual (co-morbidity) could lead to mechanistic insights into and novel treatments for co-morbid conditions. Studies have shown that co-morbid immune responses could be more complex than the union of responses to each disease occurring separately, but a data-driven quantification of this complexity is lacking.

Abstract Despite the tremendous increase in omics data generated by modern sequencing technologies, their analysis can be tricky and often requires substantial expertise in bioinformatics. To address this concern, we have developed a user-friendly pipeline to analyze (cancer) genomic data that takes in raw sequencing data (FASTQ format) as input and outputs insightful statistics on the nature of the data.