Sharing of contents on mitochondrial encounter networks

Overview

mito-network-sharing

Sharing of contents on mitochondrial encounter networks

Required: R with igraph, brainGraph, ggplot2, and XML libraries; igraph libraries for C

igraph can be a pain to install for C. See bingo.c below for how to optionally run the code without this dependency.

Wrapper scripts

runcode.sh calls R code to extract encounter networks from XML trajectory information in Data/, then C code to generate other networks for comparison and simulate the "bingo" game on these.

plots.R calls R code to produce summary plots of the results.

Code

trajectory-analysis.R extracts encounter networks from XML files and imposes any required restrictions (for example, truncating trajectory lengths)

bingo.c is the workhorse C code for network generation and "bingo" simulation. For full functionality, this needs the igraph library. However, a preprocessor directive within the code can be removed, removing the igraph dependency. In this case, zeroes are output for all network statistics that igraph would provide. The followup R code can be used to compute these statistics for a subset of the networks generated in the simulations.

The various bingo-...-script.R scripts produce visualisations of the different aspects of the simulations.

Owner
Stochastic Biology Group
We're a research group of scientists interested in biological and medical questions where random effects play a role. PI: Iain Johnston at University of Bergen.
Stochastic Biology Group
TorchGeo is a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.

TorchGeo is a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.

Microsoft 1.3k Dec 30, 2022
Python implementation of Wu et al (2018)'s registration fusion

reg-fusion Projection of a central sulcus probability map using the RF-ANTs approach (right hemisphere shown). This is a Python implementation of Wu e

Dan Gale 26 Nov 12, 2021
Unsupervised Feature Loss (UFLoss) for High Fidelity Deep learning (DL)-based reconstruction

Unsupervised Feature Loss (UFLoss) for High Fidelity Deep learning (DL)-based reconstruction Official github repository for the paper High Fidelity De

28 Dec 16, 2022
Code to accompany our paper "Continual Learning Through Synaptic Intelligence" ICML 2017

Continual Learning Through Synaptic Intelligence This repository contains code to reproduce the key findings of our path integral approach to prevent

Ganguli Lab 82 Nov 03, 2022
Config files for my GitHub profile.

Canalyst Candas Data Science Library Name Canalyst Candas Description Built by a former PM / analyst to give anyone with a little bit of Python knowle

Canalyst Candas 13 Jun 24, 2022
This is a clean and robust Pytorch implementation of DQN and Double DQN.

DQN/DDQN-Pytorch This is a clean and robust Pytorch implementation of DQN and Double DQN. Here is the training curve: All the experiments are trained

XinJingHao 15 Dec 27, 2022
Code for "Unsupervised Layered Image Decomposition into Object Prototypes" paper

DTI-Sprites Pytorch implementation of "Unsupervised Layered Image Decomposition into Object Prototypes" paper Check out our paper and webpage for deta

40 Dec 22, 2022
Constrained Language Models Yield Few-Shot Semantic Parsers

Constrained Language Models Yield Few-Shot Semantic Parsers This repository contains tools and instructions for reproducing the experiments in the pap

Microsoft 43 Nov 23, 2022
A project which aims to protect your privacy using inexpensive hardware and easily modifiable software

Protecting your privacy using an ESP32, an IR sensor and a python script This project, which I personally call the "never-gonna-catch-me-in-the-act-ev

8 Oct 10, 2022
NanoDet-Plusāš”Super fast and lightweight anchor-free object detection model. šŸ”„Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphonešŸ”„

NanoDet-Plusāš”Super fast and lightweight anchor-free object detection model. šŸ”„Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphonešŸ”„

4.8k Jan 07, 2023
Genetic Programming in Python, with a scikit-learn inspired API

Welcome to gplearn! gplearn implements Genetic Programming in Python, with a scikit-learn inspired and compatible API. While Genetic Programming (GP)

Trevor Stephens 1.3k Jan 03, 2023
Deeper DCGAN with AE stabilization

AEGeAN Deeper DCGAN with AE stabilization Parallel training of generative adversarial network as an autoencoder with dedicated losses for each stage.

Tyler Kvochick 36 Feb 17, 2022
A big endian Gentoo port developed on a Pine64.org RockPro64

Gentoo-aarch64_be A big endian Gentoo port developed on a Pine64.org RockPro64 The endian wars are over... little endian won. As a result, it is incre

Rory Bolt 6 Dec 07, 2022
Unofficial PyTorch implementation of Attention Free Transformer (AFT) layers by Apple Inc.

aft-pytorch Unofficial PyTorch implementation of Attention Free Transformer's layers by Zhai, et al. [abs, pdf] from Apple Inc. Installation You can i

Rishabh Anand 184 Dec 12, 2022
This project is used for the paper Differentiable Programming of Isometric Tensor Network

This project is used for the paper "Differentiable Programming of Isometric Tensor Network". (arXiv:2110.03898)

Chenhua Geng 15 Dec 13, 2022
Normalizing Flows with a resampled base distribution

Resampling Base Distributions of Normalizing Flows Normalizing flows are a popular class of models for approximating probability distributions. Howeve

Vincent Stimper 24 Nov 03, 2022
Spiking Neural Network for Computer Vision using SpikingJelly framework and Pytorch-Lightning

Spiking Neural Network for Computer Vision using SpikingJelly framework and Pytorch-Lightning

Sami BARCHID 2 Oct 20, 2022
GitHub repository for "Improving Video Generation for Multi-functional Applications"

Improving Video Generation for Multi-functional Applications GitHub repository for "Improving Video Generation for Multi-functional Applications" Pape

Bernhard Kratzwald 328 Dec 07, 2022
Pytorch implementation code for [Neural Architecture Search for Spiking Neural Networks]

Neural Architecture Search for Spiking Neural Networks Pytorch implementation code for [Neural Architecture Search for Spiking Neural Networks] (https

Intelligent Computing Lab at Yale University 28 Nov 18, 2022
An implementation of Deep Forest 2021.2.1.

Deep Forest (DF) 21 DF21 is an implementation of Deep Forest 2021.2.1. It is designed to have the following advantages: Powerful: Better accuracy than

LAMDA Group, Nanjing University 795 Jan 03, 2023