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.

2 states with sampling on a inferred model

Installation

See 2_states_generation_inference/README.md

Figures

See generating_data_fasta.ipynb

You might also like...
Releases(v2.0)
prior-based-losses-for-medical-image-segmentation

Repository for papers: Benchmark: Effect of Prior-based Losses on Segmentation Performance: A Benchmark Midl: A Surprisingly Effective Perimeter-based

Rosana EL JURDI 9 Sep 07, 2022
EfficientNetv2 TensorRT int8

EfficientNetv2_TensorRT_int8 EfficientNetv2模型实现来自https://github.com/d-li14/efficientnetv2.pytorch 环境配置 ubuntu:18.04 cuda:11.0 cudnn:8.0 tensorrt:7

34 Apr 24, 2022
AAI supports interdisciplinary research to help better understand human, animal, and artificial cognition.

AnimalAI 3 AAI supports interdisciplinary research to help better understand human, animal, and artificial cognition. It aims to support AI research t

Matthew Crosby 58 Dec 12, 2022
A PyTorch implementation of Mugs proposed by our paper "Mugs: A Multi-Granular Self-Supervised Learning Framework".

Mugs: A Multi-Granular Self-Supervised Learning Framework This is a PyTorch implementation of Mugs proposed by our paper "Mugs: A Multi-Granular Self-

Sea AI Lab 62 Nov 08, 2022
Repository for the paper : Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data

1 Meta-FDMIxup Repository for the paper : Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data. (ACM MM 2021) paper News! the rep

Fu Yuqian 44 Nov 18, 2022
[ICCV 2021] Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification

Counterfactual Attention Learning Created by Yongming Rao*, Guangyi Chen*, Jiwen Lu, Jie Zhou This repository contains PyTorch implementation for ICCV

Yongming Rao 90 Dec 31, 2022
ICNet and PSPNet-50 in Tensorflow for real-time semantic segmentation

Real-Time Semantic Segmentation in TensorFlow Perform pixel-wise semantic segmentation on high-resolution images in real-time with Image Cascade Netwo

Oles Andrienko 219 Nov 21, 2022
Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models Benchmark and Efficient Evaluation

Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models Benchmark and Efficient Evaluation This reposi

First Person Vision @ Image Processing Laboratory - University of Catania 1 Aug 21, 2022
A hand tracking demo made with mediapipe where you can control lights with pinching your fingers and moving your hand up/down.

HandTrackingBrightnessControl A hand tracking demo made with mediapipe where you can control lights with pinching your fingers and moving your hand up

Teemu Laurila 19 Feb 12, 2022
A modular PyTorch library for optical flow estimation using neural networks

A modular PyTorch library for optical flow estimation using neural networks

neu-vig 113 Dec 20, 2022
Neural style in TensorFlow! 🎨

neural-style An implementation of neural style in TensorFlow. This implementation is a lot simpler than a lot of the other ones out there, thanks to T

Anish Athalye 5.5k Dec 29, 2022
Localizing Visual Sounds the Hard Way

Localizing-Visual-Sounds-the-Hard-Way Code and Dataset for "Localizing Visual Sounds the Hard Way". The repo contains code and our pre-trained model.

Honglie Chen 58 Dec 07, 2022
This repository contains the code for the paper Neural RGB-D Surface Reconstruction

Neural RGB-D Surface Reconstruction Paper | Project Page | Video Neural RGB-D Surface Reconstruction Dejan Azinović, Ricardo Martin-Brualla, Dan B Gol

Dejan 406 Jan 04, 2023
DeepStruc is a Conditional Variational Autoencoder which can predict the mono-metallic nanoparticle from a Pair Distribution Function.

ChemRxiv | [Paper] XXX DeepStruc Welcome to DeepStruc, a Deep Generative Model (DGM) that learns the relation between PDF and atomic structure and the

Emil Thyge Skaaning Kjær 13 Aug 01, 2022
structured-generative-modeling

This repository contains the implementation for the paper Information Theoretic StructuredGenerative Modeling, Specially thanks for the open-source co

0 Oct 11, 2021
LoL Runes Recommender With Python

LoL-Runes-Recommender Para ejecutar la aplicación se debe llamar a execute_app.p

Sebastián Salinas 1 Jan 10, 2022
Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy

lbs-data Motivation Location data is collected from the public by private firms via mobile devices. Can this data also be used to serve the public goo

Alex 11 Sep 22, 2022
Deep Learning Head Pose Estimation using PyTorch.

Hopenet is an accurate and easy to use head pose estimation network. Models have been trained on the 300W-LP dataset and have been tested on real data with good qualitative performance.

Nataniel Ruiz 1.3k Dec 26, 2022
Low-dose Digital Mammography with Deep Learning

Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography ====== This repository contains

WANG-AXIS 6 Dec 13, 2022
Code for the ECIR'22 paper "Evaluating the Robustness of Retrieval Pipelines with Query Variation Generators"

Query Variation Generators This repository contains the code and annotation data for the ECIR'22 paper "Evaluating the Robustness of Retrieval Pipelin

Gustavo Penha 12 Nov 20, 2022