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Be very very good at differential equations. Systems/synthetic biology primarily rely on dynamics to model behavior and side effects. AlphaFold is actually pre

by melony 4y ago

Be very very good at differential equations. Systems/synthetic biology primarily rely on dynamics to model behavior and side effects. AlphaFold is actually pretty different from the biomodel type of modeling. This is a good intro to (non deep learning based) computational/synthetic biology. ML in biology is somewhat orthogonal but it is rapidly growing. http://be150.caltech.edu/2020/content/index.html http://be150.caltech.edu/2020/content/index.html Traditional bioinformatics is mostly text mining and string processing. Modern bioinformatics is heavily dominated by ML.

dr_kiszonka 4y ago
Great course - thanks for sharing! Regarding being very good at differential equations: if I remember correctly, one way to approach solving them is to convert them to difference equations and solve them numerically (and not analytically). Is this true? As you can probably tell, I am not good at differential equations at all, so I was thinking about using difference equations as a crutch.
- jmuhlich 4y ago
  The systems of differential equations produced by these types of models tend to be analytically intractable, so "numerical methods" are absolutely essential here. The phrase you want to look for is "initial value problem" (IVP): you know the initial values of the quantities described by the equations and you want to find out how those quantities evolve over time. https://en.wikipedia.org/wiki/Numerical_methods_for_ordinary_differential_equations https://en.wikipedia.org/wiki/Numerical_methods_for_ordinary...
wardedVibe 4y ago
Any tips for breaking into the field? Been doing ml/signal processing type stuff towards the end of grad school, but did some diffeq/dynamical systems stuff at the beginning and was considering pivoting towards biological applications of ML. Interviewed with pumas.ai last summer but they eventually turned me down.
- melony 4y ago
  Go into industry not academia. Academia won’t pay well and they do not appreciated good quality software engineering. For most labs, ML is considered a tool, not a primary driver of innovation. Look towards places like Isomorphic labs and similar companies that are cash rich and has a strong engineering heritage.

by melony 4y ago

dr_kiszonka 4y ago

Great course - thanks for sharing! Regarding being very good at differential equations: if I remember correctly, one way to approach solving them is to convert them to difference equations and solve them numerically (and not analytically). Is this true? As you can probably tell, I am not good at differential equations at all, so I was thinking about using difference equations as a crutch.

jmuhlich 4y ago
The systems of differential equations produced by these types of models tend to be analytically intractable, so "numerical methods" are absolutely essential here. The phrase you want to look for is "initial value problem" (IVP): you know the initial values of the quantities described by the equations and you want to find out how those quantities evolve over time. https://en.wikipedia.org/wiki/Numerical_methods_for_ordinary_differential_equations https://en.wikipedia.org/wiki/Numerical_methods_for_ordinary...

wardedVibe 4y ago

Any tips for breaking into the field? Been doing ml/signal processing type stuff towards the end of grad school, but did some diffeq/dynamical systems stuff at the beginning and was considering pivoting towards biological applications of ML. Interviewed with pumas.ai last summer but they eventually turned me down.

melony 4y ago
Go into industry not academia. Academia won’t pay well and they do not appreciated good quality software engineering. For most labs, ML is considered a tool, not a primary driver of innovation. Look towards places like Isomorphic labs and similar companies that are cash rich and has a strong engineering heritage.