NNR conformation conditional and global probabilities estimation and analysis in peptides or proteins fragments

Overview

NNR and global probabilities estimation and analysis in peptides or protein fragments

This module calculates global and NNR conformation dependent probabilites in each peptide residue.

First, workflow.sh bash script creates a workspace where results folder and dihedral angles .dat file are generated.

After that, Python module obtains bidimensional arrays and probabilities about conformation population and structure combinations in global and NNR Conformation conditional methods.

Specifically, global_arrays.py and conditional_arrays.py generate the 2d arrays where dihedral angles value in each point is contained. Later, this arrays are used to calculate alpha, ppII, beta and remaining probabilities. In the first place, combinations probabilities are obtained by global_probabilities.py, directe_conditional_probabilities.py, inverse_conditional_probabilities.py. The programm it is called comparition.py joins this probabilities in a single file. On the other hand, population.py calculates global and conditional probabilites for each residue. This scripts create a csv file for each residue.

Finally, R Markdown file (data_representation.Rmd) contains code chunks to represent graphically combination probabilites, population data and Ramachandran plots. In particular, this scripts export each graphic in a png file.

In this case, the data used for the example output files was from penta-alanine simulation.

Ramachandran plot

Conformation populations

Combination probabilities

Raster Vision is an open source Python framework for building computer vision models on satellite, aerial, and other large imagery sets

Raster Vision is an open source Python framework for building computer vision models on satellite, aerial, and other large imagery sets (including obl

Azavea 1.7k Dec 22, 2022
Pytorch implementation of DeepMind's differentiable neural computer paper.

DNC pytorch This is a Pytorch implementation of DeepMind's Differentiable Neural Computer (DNC) architecture introduced in their recent Nature paper:

Yuanpu Xie 91 Nov 21, 2022
DFFNet: An IoT-perceptive Dual Feature Fusion Network for General Real-time Semantic Segmentation

DFFNet Paper DFFNet: An IoT-perceptive Dual Feature Fusion Network for General Real-time Semantic Segmentation. Xiangyan Tang, Wenxuan Tu, Keqiu Li, J

4 Sep 23, 2022
KITTI-360 Annotation Tool is a framework that developed based on python(cherrypy + jinja2 + sqlite3) as the server end and javascript + WebGL as the front end.

KITTI-360 Annotation Tool is a framework that developed based on python(cherrypy + jinja2 + sqlite3) as the server end and javascript + WebGL as the front end.

86 Dec 12, 2022
The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"

Swin-Unet The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"(https://arxiv.org/abs/2105.05537). A validatio

869 Jan 07, 2023
Jaxtorch (a jax nn library)

Jaxtorch (a jax nn library) This is my jax based nn library. I created this because I was annoyed by the complexity and 'magic'-ness of the popular ja

nshepperd 17 Dec 08, 2022
CTC segmentation python package

CTC segmentation CTC segmentation can be used to find utterances alignments within large audio files. This repository contains the ctc-segmentation py

Ludwig Kürzinger 217 Jan 04, 2023
Diverse Image Captioning with Context-Object Split Latent Spaces (NeurIPS 2020)

Diverse Image Captioning with Context-Object Split Latent Spaces This repository is the PyTorch implementation of the paper: Diverse Image Captioning

Visual Inference Lab @TU Darmstadt 34 Nov 21, 2022
Official implement of Paper:A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sening images

A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images 深度监督影像融合网络DSIFN用于高分辨率双时相遥感影像变化检测 Of

Chenxiao Zhang 135 Dec 19, 2022
Totally Versatile Miscellanea for Pytorch

Totally Versatile Miscellania for PyTorch Thomas Viehmann [email protected] Thi

Thomas Viehmann 428 Dec 28, 2022
Omniverse sample scripts - A guide for developing with Python scripts on NVIDIA Ominverse

Omniverse sample scripts ここでは、NVIDIA Omniverse ( https://www.nvidia.com/ja-jp/om

ft-lab (Yutaka Yoshisaka) 37 Nov 17, 2022
A Parameter-free Deep Embedded Clustering Method for Single-cell RNA-seq Data

A Parameter-free Deep Embedded Clustering Method for Single-cell RNA-seq Data Overview Clustering analysis is widely utilized in single-cell RNA-seque

AI-Biomed @NSCC-gz 3 May 08, 2022
Self-Supervised Learning of Event-based Optical Flow with Spiking Neural Networks

Self-Supervised Learning of Event-based Optical Flow with Spiking Neural Networks Work accepted at NeurIPS'21 [paper, video]. If you use this code in

TU Delft 43 Dec 07, 2022
A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

Emma 1 Jan 18, 2022
New approach to benchmark VQA models

VQA Benchmarking This repository contains the web application & the python interface to evaluate VQA models. Documentation Please see the documentatio

4 Jul 25, 2022
Elevation Mapping on GPU.

Elevation Mapping cupy Overview This is a ros package of elevation mapping on GPU. Code are written in python and uses cupy for GPU calculation. * pla

Robotic Systems Lab - Legged Robotics at ETH Zürich 183 Dec 19, 2022
CS_Final_Metal_surface_detection - This is a final project for CoderSchool Machine Learning bootcamp on 29/12/2021.

CS_Final_Metal_surface_detection This is a final project for CoderSchool Machine Learning bootcamp on 29/12/2021. The project is based on the dataset

Cuong Vo 1 Dec 29, 2021
[ICCV 2021] Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation

EPCDepth EPCDepth is a self-supervised monocular depth estimation model, whose supervision is coming from the other image in a stereo pair. Details ar

Rui Peng 110 Dec 23, 2022
Learning with Subset Stacking

Learning with Subset Stacking (LESS) LESS is a new supervised learning algorithm that is based on training many local estimators on subsets of a given

S. Ilker Birbil 19 Oct 04, 2022
LocUNet is a deep learning method to localize a UE based solely on the reported signal strengths from a set of BSs.

LocUNet LocUNet is a deep learning method to localize a UE based solely on the reported signal strengths from a set of BSs. The method utilizes accura

4 Oct 05, 2022