Having said that, there are absolutely cases where someone in dry lab is mining data for whatever purpose, finds something new, then gets a wet lab colleague to help investigate it. You don't just want to be the guy they toss NGS at and say, "Hey analyze this." There will always be a bit of that, but your real utility is novel application of tools, creating pipelines, etc. It doesn't have to be algorithm development or anything, but the strength of dry lab is thinking of new ways to analyze data. There will always, in my experience, be a bit of method development in a pure dry lab setting. Part of the reason the big places are big is because they've put the research guys in spitting distance of the hospitals and clinicians.įurther, just be upfront with any PI in telling them what you want to do. So then try to find places that have ties to medical schools. So my question is rather simple (the answer not so much, I guess): where computational biology can really make a difference for cancer research? Finding new targets? Repurposing existing drugs? De novo design of drugs with better properties? Predictive modeling for discovering carcinogenic agents? Machine learning in health-care? Moreover, I easily get bored so I'm really trying to express my interest in using different types of data (I'd simply give up after a few months if I were to use only gene expression data.). This is very difficult for a prospective PhD student: I genuinely don't know where to start from. Others are just not working on drug discovery, and trying to repurpose old drugs. While some topics are very common, each group is approaching the problem differently (for instance, different stages of the drug discovery pipeline). I tried to look at the literature and the websites of several research groups (in Europe). My main (and only) interest is (data-driven) cancer research. I recently started trying to come up with some form of a PhD project proposal.
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