Introduction In recent years, a group of people in the scientific community starts to look into the application of graph to represent genomes. Graph is an extremely powerful abstraction for processing various kind of data. Meanwhile, mathematically, a graph is simple and complicated at the same time. To define a graph…

Some Background (A slide deck for this work can be found https://speakerdeck.com/jchin/decomposing-dynamics-from-different-time-scale-for-time-lapse-image-sequences-with-a-deep-cnn) I left my job as a Scientific Fellow in PacBio after 9-year venture helping to make single-molecule sequencing becoming useful for the scientific community (see my story about the first couple year in PacBio there). Most of my technical/scientific work…

Can we construct an error free consensus sequence from a set of noisy sequences using a neural network? (associated Jupyter notebooks can be found at https://github.com/cschin/DCNet) Using Neural Network to Remove Noise Using a deep convolutional neural network (CNN) for denoising images or constructing super resolution images has generating some quite amazing results. The basic idea…

AI/Machine Learning in Biotech Startups No doubt that the fast development of recent progress using deep neural network has change the way that we can solve various problem from image recognition to genomics. Many startups (Viome/CZ Biohub/Deep Genomics) has emphasize using “AI/machine learning” for their research and product. Other relative “big” biotech startups in Silicon…

“In 1904 at a physics conference in St. Louis most physicists seemed to reject atoms and he was not even invited to the physics section.” — Wikipedia page about Ludwig Boltzmann Lucky for us, we live in an era that we can see “see” atoms with atomic force microscope. And…

I have downloaded this paper “Deep Unsupervised Learning using Nonequilibrium Thermodynamics” for while but only read it through today. The typical example of direct connection between neural network and statistical physics is some spin-magnetic system like Ising model. This paper is interesting in the sense that it connected deep learning…

“Information Theory and Statistical Mechanics” is the title of a paper that E.T. Jaynes published about 60 years ago. (Official journal link Information Theory and Statistical Mechanics, you can download PDF from https://journals.aps.org/pr/abstract/10.1103/PhysRev.106.620 ) For people who knows both information theory and statistical mechanics, one can recognize the same form…

Statistical Physics and Machine Learning When I was young, I was attracted to physics for two things (1) elegant math that connects the abstract beauty and the physical world and (2) the possibility to explain complex behaviors in physics worlds starting from simple elements. Eventual, I did my graduate research…