Data Science in Biomedical Industry

I am asked quite often how I see Data Science in the biomedical industry. I have, of course, many answers each of which is context dependent. However one theme which I find frequently recurring is a sort of straw-man debate which seems to inherently attract technical practitioners.

The debate is usually structured as follows:
How do you see the validation of medical AI products working in practice?
Answer: clinical trials, test-validation sets, blah, blah
But doesn’t this lead to enormous overheads?
Answer: yes, but there are shortcuts
But if you take these shortcuts then don’t you run the risk of running into costly failures when you finally run the clinical trials?
It goes on….

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ML Embeddings and the Neuronal Code

I had the opportunity to talk recently with a relatively advanced researcher in machine learning methods. The conversation turned briefly to the study of embeddings when he mentioned that most of his work involves things that can be embedded in Euclidean space. Since I’ve been spending a bit of time thinking about embeddings recently, I asked him some questions to get the official ML take on the subject. I was resonably gratified to learn that – although most ML engineers don’t think much about embeddings – the research on this topic considers the embedding to be tightly bound to the network architecture. It is not possible to study abstract embeddings, divorced from applications. I fully agree with this point-of-view.

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RCTs vs Real-World Evidence for medical AI

Randomised controlled trials (RCTs) have been the gold standard for statistical evidence, of treatment effect, for over 100 years. Their strength is in their attempt to avoid major sources of bias in a comparison of the evidence. However, they are costly to run, particularly in the domain of personalised medicine, to which medical AI products typically belong.

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Invited Speaker: Dynamics of Immune Responses

I have been invited to speak at the Dynamics of Immune Responses workshop/seminar/conference in May-June 2020. The invitation arose through my previous efforts to found a company in this space.

There is a growing awareness in the field of immunology of the potential for using mathematical techniques. The wedge-issue here is the cascade of data appearing via new cytometry techniques; large-data looks like a math issue to most people. I of course come from the other side of a spectrum – everything looks like a math issue to me – I wanted to stimulate drug development which engages with immune system dynamics by founding my company.

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Causality and the Scientific Method

I have a short thought, stemming from a combination of projects that I’m working on at the moment, and I want to share it.

The current trend towards Causality in AI is very attractive to people like me. It matches our personal biases and views of the world. However, it is lacking a natural heuristic. How do we decide how much resources to devote to alternative models of the world, as we gather evidence as to their accuracy?

Like I say, I have a number of parallel projects, many of which address exactly this question on technical and biological levels.

Effectual entrepreneurship takes place in situations of high uncertainty and low knowledge. As uncertainty decreases, planning and management take over.

There is something from the world of business, studying entrepreneurship, which might be a better heuristic than any normative model I can come up with. Effectual entrepreneurship is a perspective on entrepreneurship, studying highly successful repeat entrepreneurs (eg. Elon Musk), which establishes control, rather than planning, at the core of entrepreneurial activities.

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Build – Test – Move

First a mea culpa, I have a huge backlog of relatively heavy articles that I really want to add to the blog. But I’ve been busy getting married – congratulations to me – and I didn’t have enough time. I strongly believe in following relatively strict guidelines on writing and editing articles, where I set myself deadlines and avoid over-writing on topics – it is just a blog after all – but for deep insights I do also have a minimum standard that I want to be able to produce before I’m willing to hit the Publish button.

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Why do Trees work better than DNNs on genome data?

This topic occurred to me following my recent talk at a dental conference at Charité Berlin. Upon hearing that I have a strong interest in inference, my fellow keynote mentioned that it drives him crazy that random forests, and similar algorithms, work so much better than DNNs on genomic data. He challenged me to come up with a reason for why this is the case.

I think that I know why. The problem I have is that I suspect that I can never prove it. That issue of not being able to prove things in machine learning is probably an equally interesting topic, for a future article, but here I want to address my theory of why random forests work better than DNNs for analysing genome data.

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