Phylogeny Partners

Last update: Sep 19, 2022

Related tags

Deep Learning Phylogeny-Partners

Overview

Phylogeny-Partners

Two states models

Instalation

You may need to install the cython, networkx, numpy, scipy package:

pip install cython, networkx, numpy, scipy

and do in the folder two_states :

cd 2_states/code_two_states
cythonize -i generation_sequences.pyx
cythonize -i mutual_info.pyx

To use the last part of the jupyter notebook figures.ipynb, you should install bmDCA :

https://github.com/ranganathanlab/bmDCA

The code works on linux and Mac. (It is possible to adapt it for windows, you need to change the drand48() or random() function by one available in windows distribution)

Utilisation

You can open figures.ipynb and different_graph.ipynb to see all figures generated with the simple model.

Twenty one states models

Installation

The code present in Code_for_cluster was present in the EPFL cluster. It is helpful to generate data and to infer contact and partners. The model has been inferred with bmDCA on the cluster and arDCA on my personal computer. If you want to reproduce the data, I advise you to copy the folder Code_for_cluster and to do :

cd 21_states/Code_for_cluster/cython_code/
cythonize -i generation_sequence.pyx
cythonize -i generation_sequence_arDCA.pyx
cythonize -i analyse_sequence.pyx

Generation of data

And after, you can generate MSA (data set of aligned sequences) :

sbatch generation_sequence_bmDCA.run
sbatch generation_sequence_arDCA.run

If you are not on a cluster and cannot use the function sbatch, you can replace sbatch by bash.

Inference

When these three programmes are completed, you can infer the contact with :

sbatch inference_contact.run

And for the partners :

sbatch inference_partners_generated_data.py

If you want to see the plot from the inference, you can run the jupyter notebook inside the folder 21_states.

Phylogeny Partners

Related tags

Overview

Phylogeny-Partners

Two states models

Instalation

Utilisation

Twenty one states models

Installation

Generation of data

Inference

2 states with sampling on a inferred model

Installation

Figures

You might also like...

Releases(v2.0)

v2.0(Apr 24, 2022)

Owner

【steal piano】GitHub偷情分析工具！

Network Enhancement implementation in pytorch

An Unsupervised Detection Framework for Chinese Jargons in the Darknet

Training RNNs as Fast as CNNs

Datasets, Transforms and Models specific to Computer Vision

Airbus Ship Detection Challenge

Video Autoencoder: self-supervised disentanglement of 3D structure and motion

Repo for my Tensorflow/Keras CV experiments. Mostly revolving around the Danbooru20xx dataset

SwinIR: Image Restoration Using Swin Transformer

💡 Type hints for Numpy

Learning where to learn - Gradient sparsity in meta and continual learning

Official implementation of "Open-set Label Noise Can Improve Robustness Against Inherent Label Noise" (NeurIPS 2021)

Codebase for Image Classification Research, written in PyTorch.

Training Cifar-10 Classifier Using VGG16

[AAAI-2021] Visual Boundary Knowledge Translation for Foreground Segmentation

Synthetic Scene Text from 3D Engines

Disturbing Target Values for Neural Network regularization: attacking the loss layer to prevent overfitting

A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.

Code for paper: Towards Tokenized Human Dynamics Representation

In the AI for TSP competition we try to solve optimization problems using machine learning.