maraxen

Graduate student at MIT researching evolution using liquid-handling robots

MITCambridge, MA

Pinned Repositories

eVOLVERCLI
A Command-Line Interface for the eVOLVER Continuous Culture Framework
Language:Shell20
alphaflow
AlphaFold Meets Flow Matching for Generating Protein Ensembles
Language:Python00
dpu
Data Processing Unit for eVOLVER
Language:HTML00
eagerpy
PyTorch, TensorFlow, JAX and NumPy — all of them natively using the same code
Language:Python00
evolver
Language:Python00
evolver-arduino
Arduino code for the eVOLVER continuous cell culture framework
Language:C++00
evolver-electron
Language:JavaScript00
Fast-NW-and-SW-Pairwise-alignment-using-numba-JIT
This project includes Needleman-Wunsch and Smith-Waterman algorithms and their afine gap variations (Gotoh) written to work with Cython, PyPy and Numba. Numba JIT shows greater performance. For Best performance use gotoh_jit.py to get only the best score and use gotoh_jit_traceback to get the best alignment
Language:Python00
hardware
eVOLVER Hardware (PCB, 3D printed parts)
Language:G-code00
Iterative_masking
Iterative masking algorithm on MSA Transformer to generate synthetic sequences
Language:Jupyter Notebook00

maraxen/pylabrobot
A hardware agnostic interface and developer ecosystem for lab automation
Language:Python
maraxen/alphaflow
AlphaFold Meets Flow Matching for Generating Protein Ensembles
maraxen/protein_scoring
Generating and scoring novel enzyme sequences with a variety of models and metrics
maraxen/Iterative_masking
Iterative masking algorithm on MSA Transformer to generate synthetic sequences
maraxen/hardware
eVOLVER Hardware (PCB, 3D printed parts)
Language:G-code
maraxen/dpu
Data Processing Unit for eVOLVER
Language:HTML
maraxen/eagerpy
PyTorch, TensorFlow, JAX and NumPy — all of them natively using the same code
maraxen/evolver-arduino
Arduino code for the eVOLVER continuous cell culture framework
Language:C++
maraxen/evolver
Language:Python
maraxen/eVOLVERCLI
A Command-Line Interface for the eVOLVER Continuous Culture Framework
Language:Shell2
maraxen/MLDE
A machine-learning package for navigating combinatorial protein fitness landscapes.
maraxen/evolver-electron
maraxen/Fast-NW-and-SW-Pairwise-alignment-using-numba-JIT
This project includes Needleman-Wunsch and Smith-Waterman algorithms and their afine gap variations (Gotoh) written to work with Cython, PyPy and Numba. Numba JIT shows greater performance. For Best performance use gotoh_jit.py to get only the best score and use gotoh_jit_traceback to get the best alignment