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26b178245dc81895153139b58759192b336c6e7abbffb96ec9635464c3adea3f | Text | 1,983 | 35 | # Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothing
Project repository for the article by Georgios Rizos, Jenna Lawson, Simon Mitchell, Pranay Shah, Xin Wen, Cristina Banks-Leite, Robert Ewers, Bjoern W. Schuller
https://arxiv.org/abs/2210.10526
## General description
The code is for bio... |
1fc319feec17c2ef096dcc0449882e14dfb989921ed4cf7ad5e94583bbde0316 | Text | 1,993 | 51 | # A Novel Approach to Identify the Brain Regions that Best Classify ADHD by means of EEG and Deep Learning
## Name
Source code for the paper "A Novel Approach to Identify the Brain Regions that Best Classify ADHD by means of EEG and Deep Learning".
## Description
In this project we provide source code to perform the... |
bc123c6a288d2a3596672feacff56dacf8dda68123751f1010f43bb679e99fc1 | Text | 1,995 | 40 | [](https://pypi.org/project/nltools/)
[](https://github.com/cosanlab/nltools/actions/workflows/auto_... |
8c0259bb82e272c1a523fe9469566b841b9fcde7a270c6f46ccca169ba2170bd | Text | 2,027 | 40 | To reproduce the main figures of the study, follow these steps:
1. Python Version and Package Requirements:
Make sure you have Python 2.7 installed on your system.
Ensure you have the following packages installed: numpy, scipy, matplotlib, pickle, sklearn, and joblib.
2. Data:
The data/ folder con... |
2d3730952930768b6e1c885d87c81c9bf33f58ff20f643d5ceccef063ed2ed5c | Text | 2,033 | 61 | # DGCCA-pytorch:
A Pytorch Implementation of Deep Generalized Canonical Correlation Analysis as described in:
Adrian Benton, Huda Khayrallah, Biman Gujral, Dee Ann Reisinger, Sheng Zhang, and Raman Arora. Deep Generalized Canonical Correlation Analysis. The 4th Workshop on Representation Learning for NLP. 2019
[(Pape... |
7282d037f28bf0dca669dbccf925b3b0920cd963cdda5bd7871b117395e99659 | Text | 2,099 | 65 | -------------------------------------------------------------------------
REFERENCE
Repository containing the Matlab code for the scientific article :
Cardio-audio synchronization elicits prediction in auditory sequences during human wakefulness and sleep.
Andria Pelentritou, Christian Pfeiffer, Sophie Schwartz, Ma... |
cf63588ef6089c73e6553690f2d8491d87ebf08f5a176edd438d77160b04ce09 | Text | 2,124 | 53 | # Rhddmjags - Repository for example Hierarchical Drift Diffusion Model (HDDM) code using JAGS in R <img src="figures/hex_RHDDMJAGS.png" style="float:right; width:200px;" />
#### (Repository version 0.0.0.9002)
## Prerequisites
- install R and RStudio on a laptop
- install the packages {`R2jags`}, {`coda`} from CRAN... |
ce100396adf1104e18ff9857a81127f60ee8c9029541264ff74cc109e7537f13 | Text | 2,141 | 54 | # MobiDL
MobidicWDL workflows
## Goals
Providing wdl workflows to treat NGS data
## Repos architecture and requirements
- Each step can be found in dedicated wdls in modules/
- A workflow imports individual wdls and run them, see panelCapture.wdl for details
- json files are provided as examples
- needs [crowmw... |
7d6c7d366924ae1d9e2e86ca90160c53efc3751e8dfd6fd19713dadf8e35dc21 | Text | 2,150 | 31 | # NeMo-TMS Toolbox
Neuron Modeling for TMS (NeMo-TMS) toolbox is for multi-scale modeling of the effects of Transcranial Magnetic Stimulation on single-neuron activity. The instructions and open-source codes provided here enable users with different levels of expertise to investigate the neuronal behavior under TMS.
R... |
91508cf31082b94c25c345cbc5f15644b67c8be62f8d916da9e18409a7218b06 | Text | 2,153 | 51 | [](https://pypi.org/project/nltools/)
[](https://github.com/cosanlab/nltools/actions/workflows/ci.yml)
[](https://doi.org/10.5281/zenodo.10359818)
## Executing code in this repository
To reproduce the figures, data needs to be downloaded from https://doi.org/10.6084/m9.figshare.24777990. Once this data is downloaded, line 16 of EEG_modelling/model/network... |
38e5969ab0f77e9167f8eaddcad0d0f4c696669016d53f6339eb8a21fa7b2e20 | Text | 2,201 | 21 | # Light-field-fiber-bundle-imaging
# General information
1. The algorithm was developed in MATLAB environment.
2. The algorithm was developed based on reference: Optical fiber bundles: Ultra-slim light field imaging probes by A. Orth, etc. Please also cite this paper if you use this code for your project.
3. exampl... |
61c1640218310119ac9636989492ce88e53ad55a7eae8a2cbeb50362853540b8 | Text | 2,202 | 32 | # Common Mitochondrial Deletions in RNA-Seq: Evaluation of Bulk, Single-Cell and Spatial Transcriptomic Datasets
This repository contains code accompanying the following paper:
Omidsalar *et al.* "Common Mitochondrial Deletions in RNA-Seq: Evaluation of Bulk, Single-Cell, and Spatial Transcriptomic Datasets" [Link TB... |
c2770d8a966678cee4a5ca173d7334f772e958ca1a864905b62c934ff5795c7d | Text | 2,237 | 28 | # TransLearning
This repository includes MATLAB scripts for constructing the adaptive Riemannian decoders utilized in (https://doi.org/10.1093/pnasnexus/pgae076).
This repository utilizes functions introduced in the following toolboxes:
* Covariance toolbox (https://github.com/alexandrebarachant/covariancetoolbox)
*... |
9b6c2d7d075d2f7dea06a0a15f0c9117222fc81b9786a452f3b4dd43630045fd | Text | 2,252 | 37 | # ISTART Data
This repository contains the final code for managing and processing all of the data in our ISTART project. The data live on OpenNeuro (https://openneuro.org/datasets/ds004920), and a preprint of a data paper has been posted to PsyArxiv (https://osf.io/preprints/psyarxiv/vqpnx) and is under consideration a... |
6b4defd1815aab5f007d92e4e0dce27b95c077f92c090ecac23d83a451462cca | Text | 2,295 | 50 | # DL-RP-MDS
[](https://zenodo.org/badge/latestdoi/577130441)
Source code and data used in the article "Integration of Deep Learning with Ramachandran Plot Molecular Dynamics Simulation for Genetic Variant Classification" (Tam et al. _iScience_, 2023).
This repository aim... |
fd2a28303a85e2f4af07e7938fa22a6221cc9ba8bc3030c231e9093ece5749e5 | Text | 2,305 | 44 |
snakebids
=========
[](https://github.com/akhanf/snakebids/actions/workflows/test.yml?query=branch%3Amain)
[](https://snakebids.readthed... |
dbf3e5a1545c6056dfa99061c8d5c5be4e38eafd741f9fd6e472f5f5e399b8ae | Text | 2,323 | 26 | # Evaluation of beamformer algorithms for estimating orientations of neural sources
This repository contains the scripts and data to reproduce the results presented in our forthcoming paper on the performance of beamformer algorithms in estimating orientations of neural sources based on EEG and MEG data (preprint avai... |
e60ae6aac3a88d05fb4cd1bb8c67d61457ec89219ebf1041de23f4af9a72b342 | Text | 2,337 | 59 | # Silicon microprobes
***Open source dissemination***
For information on obtaining pre-fabricated probes, visit https://masmanidislab.neurobio.ucla.edu/technology.html
*******************************
***Silicon microprobe design files***
1. Silicon microprobe designs.dwg - AutoCAD DWG file of an assortment of... |
298f5121937b68f7e366dd0ac96166b006f4b3996edbae068fdb7c38c2f75927 | Text | 2,348 | 32 | Code description: The MATLAB code is used for data analysis and generating the temporary data
The versions for MATLAB is MATLAB R2015b
Functions: trca_S.m %% computation of correlation matrices for task-related component analysis
corr2_new.m %% computa... |
9da9daac27c13384f4c396d3f25545a0fb1e803bbc9bb3737ce1a85dce2560d6 | Text | 2,368 | 52 | # Automated Inversion Time selection for Late Gadolinium Enhanced Cardiac MRI
This repository provides tools and a pre-trained model to determine the optimal time inversion (TI) in LGE cardiac MRI imaging. The methodology uses CNN-LSTM architectures to infer the optimal TI based on the input data (Scout Imaging Series... |
bb81ab9a38dff29f1aa34a3e425d23faae3e790c2017ac06251d2718d82f5f0e | Text | 2,377 | 27 | ## Executing code in this repository
To reproduce the figures, data needs to be downloaded from https://doi.org/10.6084/m9.figshare.24777990.v2. Once this data is downloaded, line 16 of EEG_modelling/model/network_simulation_beluga.m needs to be updated, such that the variable resourceFolder points to the data folder.... |
aa4238b235f7f0086bebd59028e8f6fd8dd1b399c0f5ff11d473e1a960b70607 | Text | 2,387 | 62 | # mia - Microbiome analysis <img src="man/figures/mia_logo.png" align="right" width="120" />
<!-- badges: start -->
[](https://bioconductor.org/packages/release/bioc/html/mia.html)
[
Implements Conditional Random Fields as Recurrent Neural Networks as in [the repository from the original authors.](https://github.com/sadeepj/crfasrnn_keras)
The main differences are:
1. There is CPU and a GPU kerne... |
450ec6584d019778692a4d1dbcbed65ace34f9784682406688571c556bf677b0 | Text | 2,399 | 22 | # ExSeqProcessing
This is the software library for processing Expansion Sequencing, ExSeq, experiments (Alon et al 2020). This pipeline takes multi-round in situ sequencing data (including morphological information for spatial context) and convert it into RNA reads in 3D. This software has succesfully processed over 3... |
ae38f6063f36ef29cd90dcb28df9eafe6a8c1f71595deb81ae88b3fdc6da71e6 | Text | 2,399 | 16 | # ML_DefectDetection
Machine learning models to detect topological defects in biological tissues. A full description of the rationale and methodology can be found online at https://arxiv.org/abs/2303.08166.
This repository contains a pre-trained convolutional neural network (CNN) model, created using TensorFlow, for d... |
5995eae0eb8a4a087648c6c06fe4115f7e160336fd255982b08c1fdbf982cf75 | Text | 2,429 | 54 | # Codes for implementation of DH-SNNs
This code implements the DH-SNNs for various tasks. We select some typical training codes for tasks in the paper to present.
1) files and folders description:
* The pre-processing and training codes can be found in the folder that corresponds to the task. (The folder na... |
9bb9bcd7efb7bf1bf5df7a410e40dd6fa08188a9d4f7700e5a8cbc79bde4a0e3 | Text | 2,442 | 32 | [](https://doi.org/10.5281/zenodo.10359817)
## Paper
This code repository is associated with the following publication:
Brake, N., Duc, F., Rokos, A. *et al.* A neurophysiological basis for aperiodic EEG and the background spectral trend. *Nat Commun* *... |
d79b6d95a6a8cb302c1c8ee2d0497d3205306701b8abf6e91af0fcda5a7549c8 | Text | 2,443 | 53 | # NnU-Net versus mesh-growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast enhanced T1 MRI scans
This repository contains the custom code written to support our article.
Mainly, this includes:
- The code used to convert datasets to the format expected by nnU-Net
- ... |
d4ed4a52deccae7987e699af201874bd8a941f2e7b801f4f0468ad1621cc6ba1 | Text | 2,458 | 15 | # network_modularity_mammalian_connectome
## Details
Here you'll find MATLAB scripts and functions to analyze the mammalian connectomes from the MAMI database. Results are reported in the paper "Relation of connectome topology to brain volume across 103 mammalian species".
## Paper abstract
The brain connectome is ... |
2811956bbd4003f35ca64dd5746f9f5adb5fe20edfe014d926c9637d98cf0aef | Text | 2,487 | 56 | ## *Converting an allocentric goal into an egocentric steering signal*
This repository contains code for producing the analyses and figures in: [Mussells Pires et al, *Converting an
allocentric goal into an egocentric steering signal*](https://www.biorxiv.org/content/10.1101/2022.11.10.516026v1)
The pipeline for anal... |
e73e538e737f3646e79b0027336201756b91fe2845284245413f64c5aa718f8c | Text | 2,494 | 61 | corgie: COnnectomics Registration Generalizable Inference Engine
================================================================
|Docs Badge| |Python Badge| |Zenodo Badge|
Welcome to corgie! corgie is a toolkit for registration of large 3D volumes.
The recommended installation method is `pip <https://pip.pyp... |
a98d5cbc4d2b5109db6bd42eda1299c476d8901a21fea2f4d442d57304e70cb1 | Text | 2,498 | 39 | # equivariant_electron_density
Generate and predict molecular electron densities with Euclidean Neural Networks
Below is a workflow for how to use the scripts in this repository.
## Step 1: Generate molecular electron densities from a set of input coordinates.
The only neccesary inputs are a atomic coordinates file an... |
1e65c3472ae4de0ac5fc4045e908db99a3c68ae7da1a614b4b884ce747c92b9e | Text | 2,580 | 58 | [](https://doi.org/10.5281/zenodo.10232698)
## *Converting an allocentric goal into an egocentric steering signal*
This repository contains code for producing the analyses and figures in: [Mussells Pires et al, *Converting an
allocentric goal into an ego... |
231ea4ebf65563d327f39c5408f0063bd15e2997498b40c3d620c62628241b36 | Text | 2,613 | 81 | ## 1. Setting Up the Conda Environment
To set up the `dmb` conda environment, follow the steps below:
### 1.1 Clone the repository:
```bash
git clone https://github.com/pvtodorov/dmb.git
cd dmb
```
### 1.2 Check for Conda:
Make sure you have [Anaconda](https://www.anaconda.com/products/distribution) or [Minicon... |
236ff450b988186072ea9544b4d564f950c5e4ca0d2122dfe7ff7b8099965835 | Text | 2,660 | 58 | # TinySleepNet
An efficient deep learning model for sleep stage scoring based on raw, single-channel EEG. This work is an extension of our previous work, DeepSleepNet [[paper](http://ieeexplore.ieee.org/document/7961240/)][[github](https://github.com/akaraspt/deepsleepnet)].
Code for the model in the paper *TinySleep... |
4d92c53e2e7ea10289fec1e45f87c818f97048bc046ae56f5845e4d7dd6ccca7 | Text | 2,662 | 31 | # Pattern_separation_toolbox
A set of functions allowing for easy analysis of pattern separation in spike train ensembles. In particular it includes the new information-theoretic measures described in 'Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus... |
4683571fa6fe002d5d3a4e21c66594a0624b3415361b521e6942998aaa31d677 | Text | 2,663 | 44 | # BWASD
A Bio-inspired Neural Network for Credit and Loan Approval Classification
Implementation of a 3-layer feed-forward neural network model that is trained using a bio-inspired weights-and-structure-determination (BWASD) algorithm for credit and loan approval classification.\
The purpose of this package is to deter... |
395c2aa9aacbf92e9f80483a297de8eb550fd5e6f99de411cfeb4a82bbf6f816 | Text | 2,678 | 78 | # Classification of Underlying Paroxysmal Supraventricular Tachycardia Types Using Deep Learning of Sinus Rhythm Electrocardiograms
This repository contains the code for the experiments conducted in our research paper titled [*Classification of Underlying Paroxysmal Supraventricular Tachycardia Types Using Deep Learni... |
918bc3e98ef308fdb6e2e9f6a5e98ad5bf1c0684051a8016cd2013d9e124ed43 | Text | 2,685 | 67 | # NeuroEditor
Neuromorphological tracings editor
NeuroEditor is a software tool for the visualization and edition of morphological tracings. It offers manual edition capabilities together with a set of algorithms that can automatically identify potential errors in the tracings and, in some cases, propose a set of act... |
c2eca4e62b1b6e88924d3242f9f83402a0f5f89e6833b7917d6baeab2ab2e51a | Text | 2,687 | 53 | # This repository contains data and code for the manuscript "Midbrain signaling of identity prediction errors depends on orbitofrontal cortex networks"
The code has been tested using R (4.1.2)/RStudio (2022.07.1), Matlab (version R2020b, Mathworks Inc).
## SourceData
Source data are published with the manuscript. To... |
00af8a5bdc34aea01d72eed6db3bddf2f1c1504205d120e1fc5ccaeec1acd5e3 | Text | 2,695 | 18 | # ADT Network: A Novel Nonlinear Method for Decoding Speech Envelopes from EEG Signals
We introduce here ADT Network, a new nonlinear method for decoding speech envelopes from electroencephalogram (EEG) signals. ADT Network is a deep learning-based method that can decode speech envelopes by learning a nonlinear mappin... |
566934bb4077ea8de7f1778b1077da83886003f037de380530ff29282fcf2ab6 | Text | 2,724 | 30 | # SHIP
_Spiking (neural network) Hardware In PyTorch_ is an emulation platform intended for hardware-based SNNs, based on compact models to mimic the behavior and functionalitites of the SNN components, and reliant on a PyTorch backend, so to exploit its available conventional machine-learning techniques.
The develop... |
9779a74df541235feb7172ffe1c019ac3511cc58d1df8ac96476acb6a18c70b0 | Text | 2,771 | 47 | # CorticalFoldingAnalysisTools
Matlab scripts to extract features of cortical folding from Freesurfer folders. Current version 1.0
## What can this do?
Given a list of Freesurfer subjects, the provided code will automatically extract among other measures: the average cortical thickness (slightly different to FS's own... |
e3e09db3e17cd26f5c95a90927bd713cec3d9eb35e6566a789dec1878d200345 | Text | 2,774 | 19 | <p align="center">
<img src="https://github.com/divelab/MoleculeX/blob/molx/imgs/MX-logo.jpg" width="800" class="center" alt="logo"/>
<br/>
</p>
------
## Project Links:
- [Molecule3D](https://github.com/divelab/MoleculeX/tree/molx/Molecule3D)
- [BasicProp](https://github.com/divelab/MoleculeX/tree/molx/BasicProp... |
74ea6afada4a2f239e55f243567b00c1c41edc19df1dc8abd17d540bd145bff3 | Text | 2,785 | 39 | # Virtual histological staining of unlabeled autopsy tissue
This repository contains codes and images necessary for reproducing the deep learning-based virtual staining of unstained autopsy tissue slides.
## Network Training Testing Codes
You can find the codes for network testing in this folder, due to the si... |
245e8c9c85074b59cd7ba6d7c7b298381156ea96b73b4d226d1be6f1ca53775a | Text | 2,789 | 39 | # Virtual histological staining of unlabeled autopsy tissue
This repository contains codes and images necessary for reproducing the deep learning-based virtual staining of unstained autopsy tissue slides.
## Network Training Testing Codes
You can find the codes for network testing in this folder, due to the si... |
7a8024d477de42e14a59ffbb8680dd003242c9ecbf6767e46fb78fd52ea20b64 | Text | 2,823 | 50 | # Simulation-based optimization of prosthetic vision through end-to-end neural nets.
## About
The optimization of the neural stimulation protocol remains an important challenge to be addressed in the development of visual prosthetics. Our research explores a simulation-based deep learning pipeline for the automated o... |
315ee6a118dd69bdf54081c7e805a98531635b598393b96fc8843ccd5c4b093b | Text | 2,826 | 28 | # overleaf-manuscript
Latex source and data for Zapit's [eLife manuscript](https://elifesciences.org/reviewed-preprints/111872).
## Licence
- Manuscript text and figures (`*.tex`, `references.bib`, images under `Figures/`): [CC BY 4.0](LICENSE-CC-BY.txt)
- Figure data and code (`Figures/Code/`, `Figures/Supplement... |
203beeb69ce6c301cb61483d04495dc82ce0d6e0442bb5d6a3cbd89e9a40e00c | Text | 2,837 | 64 | # Change detection datasets
This dataset can be used to perform change detection techniques in multitemporal hyperspectral images.
It includes two different hyperspectral scenes from the AVIRIS sensor:
- The **Santa Barbara** scene, taken on the years 2013 and 2014 with the AVIRIS sensor over the Santa Barbara regio... |
b9e3213194924abd03f824fedf95d65035fff87f0e7761ab6696166139423f5d | Text | 2,841 | 50 | # Supporting Information: *"Simulation and data-driven modeling of the transport properties of the Mie fluid"*
This repository is part of the Supporting Information of the article *Simulation and data-driven modeling of the transport properties of the Mie fluid* by Gustavo Chaparro and Erich A. Müller. (Published in J... |
4761289066ba90c02bbddf9304d0bb7454f35d815267ef2354e15e4db18de0e0 | Text | 2,853 | 28 | # Multiscale Atlas of Gene Expression for Integrative Cortical Cartography
Welcome to the MAGICC! This repository contains the data and code used to visualise and analyse vertex-level maps of cortical gene expression.
## Overview
The atlas was generated using [the Allen Human Brain Atlas](https://human.brain-map.org/... |
15814c47281e16954d0da1043a1ebacb82d9c5ebbc3ff3e642e70cce1b8a5ec2 | Text | 2,873 | 47 | # Zapit
[](https://uk.mathworks.com/matlabcentral/fileexchange/122142-zapit)
Zapit is software that runs a scanning opto-stim system for head-fixed mouse behavior.
## How it works
A pair of scan mirrors deflect an i... |
d74cb9a6638e69506b77adf17339a00584f6b8c12b0793cf92116ee05c605c6c | Text | 2,886 | 83 | [](https://travis-ci.com/murraylab/gemmr)
[](https://codecov.io/gh/murraylab/gemmr)
[
The Human Behaviour-Change Project is a collaboration to synthesise what we know about behaviour change. It is a collaboration between behavioural scientists and systems architects at University College London and computer scientists at ... |
69656517d69db263b10026d1abc14f31bf67a799d18bcca25302d5417f1060d2 | Text | 2,968 | 54 | # TVB-NEST-proof-of-concept
Repository for the proof of concept for generalisation of TVB-NEST
This repository contains the proof of concept for Neuron and Neurolib
WARNING: not updated README
## Topic
This repository is a demonstration for the co-simulation between TVB, NEST and Elephant.
It should be flexible and ... |
45cd47419651aa06bb22a7ddaf90042ec1b8c97844569d45d4b2376941dd346e | Text | 2,983 | 84 | [](https://travis-ci.com/murraylab/gemmr)
[](https://codecov.io/gh/murraylab/gemmr)
[
## Dependencies
Scripts were developed using MATLAB 2020b (Mac OS/Linux).
Toolboxes: [SpatiotemporalpRFs](https://github.com/VPNL/spatiotemporalPRFs), [PRFmodels](https://github.com/vistalab/PRFmodel), [Vistasoft](https://github.com/KimInsub/vistasoft.... |
102aff8bf28fb3724540381ed22cc5dd51ef3e24aa17e9c2494aee50b403702f | Text | 3,028 | 42 |
# CoVES
Structure-based scoring and sampling of 'Combinatorial Variant Effects from Structure' (CoVES)
For the accompanying paper see: https://www.biorxiv.org/content/10.1101/2022.10.31.514613v2
## Folder Structure & Usage
The respective folders contain example notebooks of how to perform a given analysis.
### Sup... |
2f518fdc04dbac01fc0053cca4a9e4b987f8a7e18d9e9e97bd331e19632d330c | Text | 3,042 | 37 | 20190916T104520, 20190916T105526... and other 78 folders are all screenshots extracted from ultrasound videos.
It contains multiple subfolders, and the name of each folder corresponds to the name of the original video.
File structure, for example:
--20190909T155747 (including all screenshots and corresponding anno... |
3efe2a3205b322178721bb9adacf90ea9edb62b0cff464c5b5fa98feee52f799 | Text | 3,075 | 87 | # FastEval Parkinsonism
FastEval Parkinsonism
(https://fastevalp.cmdm.tw/) is a deep learning-driven video-based system, providing
users to capture keypoints, estimate the severity, and summarize in a report.
## Cite this work
```
@article{yang2024fasteval,
title={FastEval Parkinsoni... |
722f3c373c30968ff704611b35ca5bbf34bebe6a02f43f3fc3b18f17482e249d | Text | 3,098 | 100 | # A Lightweight Hybrid Model with Location-Preserving ViT for Efficient Food Recognition
The repository contains the source code for training the model proposed in the paper for food image recognition tasks.
## Installation
The source code can be obtained in the local python environment using the below command... |
15bad9427ea804fc7d8969a0b1b712276577a9c5dc5ac17055bb6541df1a553b | Text | 3,117 | 102 | References
----------
Prem K. Gopalan, David M. Blei. Efficient discovery of overlapping communities
in massive networks. To appear in the Proceedings of the National Academy of
Sciences, 2013 (published ahead of print August 15, 2013, doi:10.1073/pnas.1221839110).
Article: http://www.pnas.org/content/early/2013/08... |
708b0a9b0eebd338aee7a75b9fad0671db287dbbdabfdde846adcf65d02eefb9 | Text | 3,130 | 41 | # Update 2025-03-21:
A bug in the lesion generation process resulted in rectangular bounding box artifacts. This issue has been fixed, and due to the consistent nature of the artifacts, no changes in experimental results are expected.
# Benchmarking the influence of pre-training on explanation performance in MR image ... |
cd1c5eb3a5746f4da4a0dbac55d276fee56fce2f827200deaeea938a27bb9f76 | Text | 3,144 | 65 | <h1 align="center">
<img src="https://gitlab.com/polavieja_lab/idtrackerai/-/raw/master/docs/source/_static/logo_neutral.svg" width="400">
</h1><br>
[](https://pypi.python.org/pypi/idtrackerai/)
](https://opensource.org/licenses/MIT)
![Last
Commit](https://img.shields.io/github/last-commit/RubD/Giotto/m... |
b12a25c4153bd462f1535dc9a760041fb325540469fb7e770797b089bc60ece3 | Text | 3,211 | 114 | # MetroEM
Train all aligners
# Get started
MetroEM is based on modelhouse, artificery, and scalenet packages.
It's useful to understand these packages before proceeding.
## Create a model
Create a directory, with subdirectories for each module.
It's easiest to adapt an existing model.
For example, use `modelhouse` to... |
4b3619f98d6966255c8c12b53f9ad3ba9ab9081121cbff09eef950e8df7db500 | Text | 3,246 | 101 | # Pediatric automated single- or multi-parametric skull stripping with nnUNet
This pipeline can be used to generate AI-predicted brain masks and skull-stripped images for pediatric patients with single- or multi-parametric MRIs. It was trained using the nnU-Net framework on a multi-institutional, heterogeneous dataset... |
9ba5d104eaf11f5746a83de927bc425fb684c13388c06e357c603d5e47c546bf | Text | 3,268 | 69 |
<div align="center">
# Embracing firefly flash pattern variability with data-driven species classification
</div>
## Description
Training the model uses representative sequences for each of the North American firefly species in the dataset to teach a recurrent neural network about the temporal relatio... |
a71f91e5fadfb03772be1f76b0bab7116eea1bfc6cc9bc4fb190ce95dd275264 | Text | 3,273 | 78 | 
<p align="center">
<b>Chroma - the open-source data infrastructure for AI</b>. <br />
</p>
<p align="center">
<a href="https://discord.gg/MMeYNTmh3x" target="_blank">
... |
a2aeb23a9a5e304c25716abe0445ef91bd4409fdb8f2a584fed41d23c6c01c82 | Text | 3,311 | 75 | # OptimizeNeurostim
GP-BO optimization of neurostimulation.
Companion code for the following paper : [Link to paper available upon publication]
Please cite the present code as:
Bonizzato M., Guay-Hottin R., Laferrière S., Caron-Grenier O., Lajoie G. and Dancause N. 2022. “OptimizeNeurostim”, GitHub. https://github.... |
131d3bc56a72b0463a098b5b55a4791d29b61210eae35876a0f56dfbdce2fdff | Text | 3,343 | 60 | # Cnapse: Automated synapse detection in *C*. *elegans* EM images
*Cnapse* is a tool that enables automated synapse detection in *C*. *elegans* electron microscopy (EM) images. *Cnapse* is designed to detect only chemical synapses as these synapses have clear visual features in EM images such as synaptic vesicles and ... |
1b05d84e81ab953a4c61f7407102ab94b5d4315e2aab9c46c9db9b8897e28a63 | Text | 3,379 | 87 | Knowing Neurons Users
================
Peris-Yague et al. 2023
17/6/2023
Shield: [![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa]
This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[c... |
abfeee48ed99c303a000c552813e9f7843bc6fe57fae26034b44e6a287713e9a | Text | 3,401 | 24 | This directory contains all of the code necessary to run FILET (Finding Introgressed Loci using Extra-Trees). Briefly, this tool uses an Extra-Trees classifier (Geurts et al. 2005: http://www.montefiore.ulg.ac.be/~ernst/uploads/news/id63/extremely-randomized-trees.pdf) to classify genomic windows from a as one of three... |
69f5c9caef3556b546ddf212247a94ac673837d23dcacbf4a56ce54f2f7c3077 | Text | 3,412 | 38 | # FastQC
**A Quality Control application for FastQ files**
FastQC is a program designed to spot potential problems in high througput sequencing datasets. It runs a set of analyses on one or more raw sequence files in fastq or bam format and produces a report which summarises the results.
, you can use:
`sniffles -i... |
84e328c9da9f198df372bd69baa15154781dac96fbe7c76bb027ef3464f67bd9 | Text | 3,438 | 50 | # scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data
# Introduction
Based on the variational graph autoencoder (VGAE) framework, scGraph2Vec extends the framework's ability to perform link prediction and module detection simultaneously, and enhances the... |
27895140d69347bc42a9aaccf50732a384806c20ae688799cceaa08a1b15c02a | Text | 3,463 | 53 | # M2OR : Database of Olfactory Receptors-Molecule Measurments [1]
The sense of smell, is initiated by the activation specific receptors expressed at the
surface of olfactory neuron, induced by the binding of an odorant. This first step activates signaling pathways leading to the perception
of distinct odors. To gain a... |
a4c566f64f6d4cf37fcc4242e7105fe76e7858017025f14257e4e0a0a2d8de17 | Text | 3,474 | 75 | # Dynamics of activation in the voltage-sensing domain of Ci-VSP
This repository contains custom code for analyzing simulations of Ci-VSD for [ref. 1][1].
## Data
Example data to run the create [Figure 5](./notebooks/figures/FIG1_S4.ipynb) of the publication can
be obtained as a file `data.tar.gz` as Source Data with... |
1b258c42ac6da852d32b0248186b92eb38fed9439e28b696348a5788d13b6fd9 | Text | 3,487 | 67 | # Acronimity
Global acronyms are used in written text without their formal definitions. This makes it difficult to
automatically interpret their sense as acronyms tend to be ambiguous. Supervised machine learning approaches to
sense disambiguation require large training datasets. In clinical applications, large datase... |
976b33a923dfba2a226741ccc47e66f6603a5f0c58162c3424e093a64bf06d70 | Text | 3,543 | 92 | # BioMSA
> Multiple Sequence Alignment in JavaScript
BioMSA is a JavaScript library for computing alignments of biological
sequences (DNA or Protein) locally in the browser.
It performs progressive alignments using a weighting scheme similarly to
what programs like ClustalW or MUSCLE do.
To the best of my knowledge, ... |
1bffb8dca36365ea8f51ee88c88d80921c055e26727596819c8382abb4050a70 | Text | 3,552 | 62 | # Human L2/3 Cortical Circuit Model for Testing New Pharmacology in Depression --- Guet-McCreight-et-al.-2023
===================================================================================================================================================================
Author: Alexandre Guet-McCreight
This is the ... |
7def5f37342d7fa1c5604a4b7bfcafd638ff845a67e7e970d76cd6f77f4c2eff | Text | 3,587 | 73 | # Sniffles2
A fast structural variant caller for long-read sequencing, Sniffles2 accurately detect SVs on germline, somatic and population-level for PacBio and Oxford Nanopore read data.
## Quick Start: Germline SV calling using Sniffles2
To call SVs from long read alignments (PacBio / ONT), you can use:
`sniffles -i... |
1c90d686599b597b0b79749c69f783fb3e3b989abbe928872aeb70287dfc537a | Text | 3,593 | 84 | # Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules using Convolutional Neural Networks
All source code and images are associated with the paper:
"Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules using Convolutional Neural Networks"
[J. Chem. Inf. Model (2024)](https:/... |
930776048f65c3122d8aca1c2629605cf9820f84b9f39de369a0599760ed42bf | Text | 3,612 | 40 | # COVID-CXNet
[](https://github.com/armiro/COVID-CXNet/blob/master/LICENSE)
[, Heersink School of
Medic... |
9384f1180fd6bf941a4719a23033a8f5ec61828c36a0bbd750335a99922ff1cd | Text | 3,666 | 80 | # fMRI Phase Toolbox
I've been a bit lazy in updating this package, but this is a v0.2 release of the fMRI phase toolbox for processing spatiotemporal image phase fluctuations during fMRI scans. It contains an implementation of the method described in:
Detection of respiration-induced field modulations in fMRI: A co... |
b9d5cb987f3925dd13f24ef8958653ec3e0e779d1dc3087cbfc6b84dfc0d78d1 | Text | 3,711 | 116 | ===========
PymoNNtorch
===========
.. image:: https://raw.githubusercontent.com/cnrl/PymoNNtorch/main/docs/_images/pymoNNtorch-logo-t-256.png
:width: 256
:alt: pymonntorch logo
|
.. image:: https://img.shields.io/pypi/v/pymonntorch.svg
:target: https://pypi.python.org/pypi/pymonntorch
.. .. image:... |
1656ca71bfd7ea33f96811148b186b347014ae32a2775e96ab5b75282762b511 | Text | 3,756 | 93 | # Rank-biased Overlap (RBO)
[](https://circleci.com/gh/changyaochen/rbo/tree/master)
[](https://badge.fury.io/py/rbo)
This project contains a Python implementation of Rank-Biased Overlap (RBO... |
45dfc0269a41fbf766276e1fc7709f1acb7ba31ed743bb444dd85bac429e5f99 | Text | 3,771 | 79 | # Functionally-informed Z-score Imputation (FIZI)
FIZI leverages functional information together with reference linkage-disequilibrium (LD) to
impute GWAS summary statistics (Z-score).
This README is a working draft and will be expanded soon.
[//]: # (This repository serves as the home for the python implementation o... |
ea5bb162aae68bebf5b17f65878222de94136629eec7be57883231017c4a488a | Text | 3,848 | 76 | FlexiTerm: a software tool to automatically recognise multi-word terms in text documents.
FlexiTerm takes as input a corpus of ASCII documents and outputs a ranked list of automatically recognised multi-word terms.
If you use FlexiTerm in your work/research, please cite the following papers:
[1] Spasic I, Gree... |
9fc2dc748c36656fe26da8797e1f0f98adefbe7e6334d9d4355143fd5981b2fe | Text | 3,858 | 118 | SVINET implements sampling based algorithms that derive from
stochastic variational inference under the (assortative)
mixed-membership stochastic blockmodel. For details see the following
references:
Prem K. Gopalan, David M. Blei. Efficient discovery of overlapping communities
in massive networks. To appear in the P... |
adf494ce251f9b2adcfc28e385e8509fe1eda6978fdbb54902106b65080f7e91 | Text | 3,871 | 51 | # Bio-FlatScopeNHP
This is an open source repository of the Bio-FlatScopeNHP (Link to be updated).
"Mesoscopic calcium imaging in head-unrestrained non-human primates using a lensless microscope", Jimin Wu<sup>&</sup>, Yuzhi Chen<sup>&</sup>, Ashok Veeraraghavan, Eyal Seidemann* and Jacob T. Robinson*
<sub><sup>&</su... |
dee5a0dbb21f89a96159af6640a9781057a3c2b308eabdcc1dce127b8e912e90 | Text | 3,889 | 71 | # URI-CADS
## Description
This repository contains the code and documentation to implement URI-CADS (Ultrasound Renal Imaging Computer Aided Diagnosis System), a benchmark for US kidney pathology detection.
```
URI-CADS: a fully automated computer-aided diagnosis system for ultrasound renal imaging,
Miguel Molina-Mo... |
9ff6abaec50a9e7fc00f20b5be608b5d75514d6c31c387706ef6bd2c17cf44b3 | Text | 3,954 | 55 | # GBMatch_CNN - predicting transcriptional subtypes and risk from glioblastoma whole slide images
# Description
The pipeline implemented here predicts transcriptional subtypes and survival of glioblastoma patients based on H&E stained whole slide scans. Sample data is provided in this repository. To test the basic fun... |
c1c85c51d85c1bd66de3a5aabb5196fe4829d5adaa7ee8ddde01e7e1d5333b19 | Text | 3,955 | 65 | # LocoTool_MotorSpikes
Import EMG data from Spike2 for analysis in MATLAB.
These scripts were written for MATLAB by Maxime Lemieux, research assistant in the laboratory of Prof. Frédéric Bretzner at Université Laval, Québec, Canada.
They process data sampled and processed by Spike2 (CED). Data were acquired with a Po... |
a80433fb96957a7527724ed3aa1f500314322981a49fc3d0e3e73c0f5ef5a72a | Text | 3,961 | 123 | # spatial datasets
Repository of spatial datasets analyzed by [Giotto](https://rubd.github.io/Giotto/). Each dataset has its own folder with subfolders for:
- count matrix
- locations
- giotto R-script with secondary analyis (when available)
- raw data (optional)
These datasets can be downloaded directly f... |
9a4721d0f41ea04e5af116cb8451270e3fea27be8f5e192f24f63614b14062e5 | Text | 4,004 | 73 | # Human L2/3 Cortical Circuit Model for Testing New Pharmacology in Depression --- Guet-McCreight-et-al.-2023
===================================================================================================================================================================
Author: Alexandre Guet-McCreight
This is the ... |
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