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

Official Implement of CVPR 2021 paper “Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting”

RGBT Crowd Counting Lingbo Liu, Jiaqi Chen, Hefeng Wu, Guanbin Li, Chenglong Li, Liang Lin. "Cross-Modal Collaborative Representation Learning and a L

37 Dec 08, 2022
torchbearer: A model fitting library for PyTorch

Note: We're moving to PyTorch Lightning! Read about the move here. From the end of February, torchbearer will no longer be actively maintained. We'll

631 Jan 04, 2023
CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer

CSAW-M This repository contains code for CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer. Source code for tr

Yue Liu 7 Oct 11, 2022
🥈78th place in Riiid Solution🥈

Riiid Answer Correctness Prediction Introduction This repository is the code that placed 78th in Riiid Answer Correctness Prediction competition. Requ

ds wook 14 Apr 26, 2022
Code for paper "Document-Level Argument Extraction by Conditional Generation". NAACL 21'

Argument Extraction by Generation Code for paper "Document-Level Argument Extraction by Conditional Generation". NAACL 21' Dependencies pytorch=1.6 tr

Zoey Li 87 Dec 26, 2022
MultiTaskLearning - Multi Task Learning for 3D segmentation

Multi Task Learning for 3D segmentation Perception stack of an Autonomous Drivin

2 Sep 22, 2022
Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors

PSML paper: Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors PSML_IONE,PSML_ABNE,PSML_DEEPLINK,PSML_SNNA: numpy

13 Nov 27, 2022
A treasure chest for visual recognition powered by PaddlePaddle

简体中文 | English PaddleClas 简介 飞桨图像识别套件PaddleClas是飞桨为工业界和学术界所准备的一个图像识别任务的工具集,助力使用者训练出更好的视觉模型和应用落地。 近期更新 2021.11.1 发布PP-ShiTu技术报告,新增饮料识别demo 2021.10.23 发

4.6k Dec 31, 2022
A Domain-Agnostic Benchmark for Self-Supervised Learning

DABS: A Domain Agnostic Benchmark for Self-Supervised Learning This repository contains the code for DABS, a benchmark for domain-agnostic self-superv

Alex Tamkin 81 Dec 09, 2022
Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning

SkFlow has been moved to Tensorflow. SkFlow has been moved to http://github.com/tensorflow/tensorflow into contrib folder specifically located here. T

3.2k Dec 29, 2022
Self-supervised Augmentation Consistency for Adapting Semantic Segmentation (CVPR 2021)

Self-supervised Augmentation Consistency for Adapting Semantic Segmentation This repository contains the official implementation of our paper: Self-su

Visual Inference Lab @TU Darmstadt 132 Dec 21, 2022
Repository for self-supervised landmark discovery

self-supervised-landmarks Repository for self-supervised landmark discovery Requirements pytorch pynrrd (for 3d images) Usage The use of this models i

Riddhish Bhalodia 2 Apr 18, 2022
TensorFlow implementation of "Attention is all you need (Transformer)"

[TensorFlow 2] Attention is all you need (Transformer) TensorFlow implementation of "Attention is all you need (Transformer)" Dataset The MNIST datase

YeongHyeon Park 4 Jan 05, 2022
Memory-efficient optimum einsum using opt_einsum planning and PyTorch kernels.

opt-einsum-torch There have been many implementations of Einstein's summation. numpy's numpy.einsum is the least efficient one as it only runs in sing

Haoyan Huo 9 Nov 18, 2022
Semantic Edge Detection with Diverse Deep Supervision

Semantic Edge Detection with Diverse Deep Supervision This repository contains the code for our IJCV paper: "Semantic Edge Detection with Diverse Deep

Yun Liu 12 Dec 31, 2022
Python scripts for performing object detection with the 1000 labels of the ImageNet dataset in ONNX.

Python scripts for performing object detection with the 1000 labels of the ImageNet dataset in ONNX. The repository combines a class agnostic object localizer to first detect the objects in the image

Ibai Gorordo 24 Nov 14, 2022
Official implementation of the PICASO: Permutation-Invariant Cascaded Attentional Set Operator

PICASO Official PyTorch implemetation for the paper PICASO:Permutation-Invariant Cascaded Attentive Set Operator. Requirements Python 3 torch = 1.0 n

Samira Zare 0 Dec 23, 2021
This reporistory contains the test-dev data of the paper "xGQA: Cross-lingual Visual Question Answering".

This reporistory contains the test-dev data of the paper "xGQA: Cross-lingual Visual Question Answering".

AdapterHub 18 Dec 09, 2022
Neuron Merging: Compensating for Pruned Neurons (NeurIPS 2020)

Neuron Merging: Compensating for Pruned Neurons Pytorch implementation of Neuron Merging: Compensating for Pruned Neurons, accepted at 34th Conference

Woojeong Kim 33 Dec 30, 2022
Improving adversarial robustness by a coupling rejection strategy

Adversarial Training with Rectified Rejection The code for the paper Adversarial Training with Rectified Rejection. Environment settings and libraries

Tianyu Pang 29 Jan 06, 2023