Name: Rakesh Ramachandran
Type: User
Company: Sali Lab, UCSF
Bio: Data scientist with expertise in molecular modeling, structural bioinformatics, machine learning and cryo-electron microscopy (cryo-EM)
Location: San Francisco, USA
Blog: https://www.linkedin.com/in/rakeshrucsf/
Rakesh Ramachandran's Projects
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Files related to the CoE workshop
This repository is about prediction of tertiary structure of proteins from the primary structure of proteins (sequences of amino acids). So here, I'm trying to build a neural network that can input sequence of amino acids and output 3D structure of proteins (coordinates of major atoms in proteins that define the overall shape of the protein).
2019 - Open source project designed during the QBI/UCSF Hackathon with the objective of building a computationally efficient way of tagging vesicles events.
Contains test trajectory for GROMACS based Adaptive MD
guacomole recipes
Hidden Markov Random Field Model and its Expectation-Maximization
Implementation of the Hidden Markov Random Field Model and its Expectation-Maximization Algorithm
Experimenting with an interface to run OpenMM within IMP mainly to use OpenMM's MD engine
Scripts, inputs and outputs used in the Integrative Structure Modeling of Spliceosomal Complexes
Lab Manual for the Aly Lab at Columbia University
This project has two parts. In part one, we use markov random field to denoise an image. In Part two, we use similar model for image segmentation.
Image denoising using Markov random fields.
Meld plugin for OpenMM
A Python implementation of Markov Random Field (MRF) for image segmentation
Probabilistic Graphical Models final project
An R package for analysis of Markov Random Fields on 2-dimensional lattices.
Markov Random Field
Scripts for calculating simulated transmission electron microscopy images based upon PDB files
Integrative modeling of nanobody binding modes to the SARS-CoV-2 Spike protein
A collection of code snippets for doing molecular simulations with OpenMM.
Presentations folder for all my files
D-Lab's 6 hour introduction to machine learning in Python. Learn how to perform classification, regression, clustering, and do model selection using scikit-learn and TPOT.
Experimenting with Pytorch for deeplearning
Config files for my GitHub profile.