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8e23910908fb07661ccd22d76d34856024cb710a4ca1cfc9a86caf0bab7e9543 | Text | 86 | 2 | # All-Code-Tooth-Sentinels-Cell-Reports-2025
All custom code used within publication.
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5f188bca29ae3338c340554269a416ca18df61f1a5cdd026a05dab2a1c7ea8d7 | Text | 104 | 1 | This contains code to replicate analyses and figures from Bizzotto, Stronge, and Talukdar et al., 2025.
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d42587e7bed5e0d4543570ab73dcd3bf428c53d29d04f6b44f1c0f401edd2dab | Text | 121 | 2 | # subpallium_development_Tead_and_YT_KO
scRNASeq analysis of neural progenitors in MGE at multiple stages of development
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f43fb43c0e915c250cc71c00b5e3455622d27fc4a1335c33cb8c64927653d8ed | Text | 129 | 2 | # SAGEFusionNet
SAGEFusionNet is a Graph Neural Network based architecture specifically designed to enhance brain age prediction
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d9c14d43e00d9ebbfe38f8c3cada073511f481a4a774a91de7860c1242f490be | Text | 130 | 1 | Data and code for *Personalized MRI-based Characterization of Subcortical Anomalies in Ataxia-Telangiectasia Using Deep-Learning*
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f4bce4ada94a14e87fbe9a6f5338bf9a1fd4c1f8770097155ee5067620478e51 | Text | 133 | 2 | # ISAR_images_of_aircrafts
The database contains 1267 synthesized inverse synthetic aperture radar images of seven aircraft models.
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3f4d597786b51699d11bc221d4ae225c39c20a7a8c61a37e6fa4e9041c218b3c | Text | 135 | 3 | # AQEA-QAS
This is AQEA-QAS code for the paper "AQEA-QAS: An Adaptive Quantum Evolutionary Algorithm for Quantum Architecture Search"
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17b17e6e0623e7376f93465ba196f5fbdb07f765c95d73b6114962951b2d0bc6 | Text | 158 | 5 | # Zoo BIDS
Behavioral and MRI data of the study by Wittkuhn et al. 2025.
For details on the dataset structure, please see the Brain Imaging Data Structure.
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75107e9d6bc7c3713ac965199da6c46f098117364d5fe3b36022a359a43fb7bf | Text | 170 | 2 | # AdaptiveDBSAnalysis
Analysis code for Wilkins et al. "Beta burst-driven adaptive deep brain stimulation for gait impairment and freezing of gait in Parkinson's disease
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9a69b750ef6a956adda0d2f9325da606ce71445e6c5dc9b77b4eca5d824216f6 | Text | 170 | 2 | # enhancing-pedagogical-practices-with-ai-in-math-bio
Enhancing pedagogical practices through data in the age of AI to engage the next generation in Mathematical Biology
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87185d04ffe0d6f5fd56d078f05ebc64d8d82b47df783a91d377ad28a5edefab | Text | 177 | 2 | Contextual memory engrams and semilunar granule cell recruitment (eLife: 10.7554/eLife.101428)
For more information, please see full text: https://doi.org/10.7554/eLife.101428
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a95d8c9530cbbc26ebc83564af411cc7b2d35d4b8bf85fdcffdc2240a74698bb | Text | 183 | 2 | # scRNA-clustering
The data and pre-trained models used in papers can be downloaded at https://pan.baidu.com/s/12F5J-TGdPtCbEYrqVs1L7A?pwd=w5jj. You can use them to repo our results.
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2e4984b9c7a395dde00f0e1b6a17f4db335a4517b1c022362a1aea91ddef9fc6 | Text | 185 | 2 | # kreitzerPhotometry
A set of Matlab files to analyze photometry data. These files were developed in the lab of Anatol Kreitzer at UCSF and shared with the community by Didi Mamaligas.
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efa3a69c5b4214c71f733b8105dd2357fbf0109a76193b82145632644a89c82a | Text | 188 | 2 | # Representational-drift-in-piriform-cortex
Python code to run the model described in Morales et al. 2025: Representational drift and learning-induced stabilization in the piriform cortex
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d14d9e2efee0aca94a337361a195dea8f665b77673e22655976cded51f9c4550 | Text | 201 | 2 | # claustrum-integration
Code and data for generating most figures in Shelton et al., "Single neurons and networks in the mouse claustrum integrate input from widespread cortical sources" eLife (2024)
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52554914869b0e7244d84a2f202025bb2841a793f8c8dc3bfdf4939c61c1a70e | Text | 208 | 2 | # Enhancing pedagogical practices through data in the age of AI to engage the next generation in Mathematical Biology
[](https://doi.org/10.5281/zenodo.14866879)
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0d55510a9a0a9aa7ea35bd34515d60cad9809602c518ecbbb30a000e51682545 | Text | 211 | 2 | # TDANet
The scripts used to train neural network models on stem cell images and persistent homology in the paper "Topological data analysis of pattern formation of human induced pluripotent stem cell colonies"
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56b1edd9cdc4c5f2445c27fb643852beaf52914ab2326298fde5a9b49be8d760 | Text | 216 | 13 | Command line instructions
Git global setup
git config --global user.name "user"
git config --global user.email "user@bsse.ethz.ch"
Create a new repository
git clone git@git.bsse.ethz.ch:hima_public/HDsort.git
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579219760e4d4281b48cf56db35e398dc10a47b9ce5e752c5b286bec4f25d62b | Text | 221 | 7 | # Biohybrid-Swimmer-Digital-Assets
Supplementary digitial assets related to "A fast, muscle-actuated biohybrid swimming robot", Preprint
Contents:
(1) MATLAB Codes
(2) PNG CAD renderings
(3) AutoCAD preliminary drawings
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14e43802f1ea86dde58de0fbfdb69b2556853e2e3c34d4cbbdac7a9564cabcd0 | Text | 230 | 2 | # Progressive-Decomposition-Network-with-Joint-Transformer-and-Resnet
The primary goal is to address the issue of infrared and visible image fusion, employing a Progressive Decomposition Network with Joint Transformer and ResNet.
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0f86c85b435416860604b67e4f79641ffe11b60c6ea9fa6c2c0a57ee9d3c92ed | Text | 232 | 6 | # Welcome to the Ramirez Lab Wiki!
Here you could find Tutorials, script library, gallery, FAQ, and a little bit more for pharmacoinformatics and drug design.
The contributions have been done by the Ramirez Lab team.
Have fun!!!
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1bdaaa207f316cdd0f167b8ffb00960c6c578fd28b22070c5454efb2c5da90e9 | Text | 232 | 10 | # stroke_outcome
Outcome modelling
[](https://zenodo.org/badge/latestdoi/494435010)
Key content published as a Jupyter Book:
https://samuel-book.github.io/stroke_outcome/intro.html
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c2d445644eaf8525feb6c991c27781d9a2f491096984e0740b9ee30d1da60e26 | Text | 250 | 3 | - fit: code for the fits in the main text;
- fit_margin: code for additional fits including the margin (discussed in the Supplemental Note);
- fit_compressible: code for additional fits of the compressible model (discussed in the Supplemental Note).
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2136fd059e6417ae2e1d1a2351f3377d19678cf3107d9c5e3b80540b23e9d6dc | Text | 270 | 8 | # Nematode Classification-
Deep learning for cell stage classification of first embryonic division across embryos of diverse Caenorhabditis sp.
## How to use:
1. Run the IPYNB file in GoogleCollab
### Python deep learning module:
1. torch 1.10.2 & torchvision 0.11.2
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a9e5a4ee397f3f6aa22fcbd161c0f8c6a5c5844ec54c55aa69f50c5c42517f25 | Text | 285 | 3 | COS cells were fixed and immunostained for microtubules using anti alpha-tubulin antibodies and a donkey anti-mouse Alexa Fluor 647 secondary antibody.
They were imaged on a Nikon N-STORM microscope as described in [Jimenez et al., 2020](https://doi.org/10.1016/j.ymeth.2019.05.008). |
7447521282ee371bffab8da2c03495dd7d5ba4f3b810bea054616c0c7020fdc6 | Text | 286 | 6 | # Pub_CA_INR
Code base for our paper published in Scientific Report (2025)
DOI: https://doi.org/10.1038/s41598-025-11092-w
The temperature data is available at https://esgf-node.llnl.gov/projects/e3sm/ and topography data is available at https://www.temis.nl/data/gmted2010/index.php
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e61572eb6e2d2cc407ccaa67382dd7c2e6d9957b292937a44637415b87b0b95d | Text | 300 | 3 | Code used to generate the figures in Shabanzadeh et al., Nature Cardiovascular Research (2025), https://doi.org/10.1038/s44161-025-00691-5
To use, change the location of Director.MAIN to the main director of the manuscript data, which is available from Zenodo (https://zenodo.org/records/15843737).
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ab78ede40f3bbc4b0571a2d4569b4021ec9026528173a03f790e4b25982b3671 | Text | 304 | 8 | # 2025_FeigeEtAl_2P4M
The included directories contain only R-based analysis scripts.
Complete directories that contain all analysis scripts, processed data and plots can be found in this Google Drive directory:
https://drive.google.com/drive/folders/1Qnv4TorlnZ5z_Z6yzBMwJAIleGyXP9x9?usp=drive_link
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9be51514d59a2d35d6116592d0d78ebade01431604dcac14994352a5f9e0db5f | Text | 311 | 1 | This repo aimed to develop a ML-based screening tool for a two-step prediction of the need for and type of nutritional therapy (enteral, parenteral, or combined) using Nutrition Risk Screening-2002 (NRS-2002) and other demographic parameters from the Optimal Nutrition Care for All (ONCA) national cohort data.
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d35311ea611831bf16f0a45f00d2f7febb59fe9af6a9b08a86550193aa791705 | Text | 329 | 4 | # NanobeamNN
NanobeamNN predicts local crystal lattice strain and orientation from scanning x-ray nanodiffraction microscopy data.
This software release corresponds to the article "Deep Learning of Structural Morphology Imaged by Scanning X-ray Diffraction Microscopy", which may be found here: https://arxiv.org/abs/24... |
569325636eb9120760a4f841c278db1dc2cdb045e3185c38159f15d27195b721 | Text | 338 | 2 | # Photinus_pyralis_ORs
This repository hosts [code associated with analysis and figure generation](https://selower.github.io/Photinus_pyralis_ORs) for a study investigating the evolution of odorant receptor gene structure, genomic location, and expression in the common eastern firefly, Photinus pyralis. Click to view t... |
43c9d132fc68f3f8812660f7ca5207679bbe240aee75ca67f073454863cd7429 | Text | 357 | 8 | # Supplementary material for the manuscript titled "Human brain cell-type-specific aging clocks based on single-nuclei transcriptomics"
### Requirements to use the python scripts and notebooks
* Python 3.8.17
* requirements.txt
### Data
The developped aging clocks and other relevant data can be found as part of the S... |
78be16086f19c54ddd73d21bbd3230933d4af652a81b03948e9522ab7b1b02fd | Text | 359 | 6 | # Stephan_2025_CurrBiol
This repository contains scripts associated to all main figures of the publication: **"Cortical activity upon awakening from sleep reveals consistent spatio-temporal gradients across sleep stages in human EEG"**.
Data have to be downloaded from the following Zenodo database and saved in a folder... |
892b16707a04bb7f2561ccb565af9a7b4615674ff66fff7b34ca3f07854de1a5 | Text | 366 | 13 | RDP
===
The Ramer–Douglas–Peucker algorithm roughly ported from the pseudo-code provided
by http://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm
Example Usage::
>>> from RDP import rdp
>>> line = [(0,0),(1,0),(2,0),(2,1),(2,2),(1,2),(0,2),(0,1),(0,0)]
>>> print rdp(line, 1.0)
... |
9dd90475b87c8b6aa9bf6806755823cd748d21c8314fa4869f06c3821ddd0730 | Text | 378 | 14 | # altricial_brain_vocal_learning
To reproduce all the figures, run the codes in the following order:
1. Fig2A.ipynb
2. SensitivityAnalysis_and_SuppFig.ipynb
3. Fig2B_and_2C.ipynb
4. Fig2D.ipynb
5. Fig3.ipynb
In each one of the notebooks, change the variable **PYTHON_PATH** to the location you placed the repository:... |
7a4b6b92fb376755cbb55118b43d55e367477e6639db20b63f17ee1252efac3d | Text | 381 | 6 | # HVU_Code
A Novel Hybrid Vision UNet Architecture for Brain Tumor Segmentation and Classification
The segmentation and classification tasks in the study are conducted using the BraTS2020 and Figshare dataset. It can be downloaded from
https://www.kaggle.com/datasets/awsaf49/brats20-dataset-training-validation
https... |
456c91dc4a2d0be1173201228261f442a88106c7bac943c5590cfdc86bae14b6 | Text | 391 | 5 | # Khatri_Lassers_EC_CA3_Pathway_Effect
Read Me
This repository contains MATLAB code for analyzing and visualizing resutls from no stimulation simulation of the trisynaptic circuit.
Each No_Stim_Spike_Dynamics file contains code the main code for main analyses.
The Shannon_entropy_test file contains the code... |
e6e1fbf759019c12b9936e1587217f7d5fa794ad2a6333d9c9e220523aae1dcb | Text | 394 | 6 | # ML-UED
Machine Learning scripts for analyzing and planning Ultrafast Electron Diffraction experiments
The repo contains two folders `CNN`, which contains code to classify UED patterns from MoTe2 crystals into different crystal structures and `VAE` which uses latent space agebra to identify if a given UED pattern re... |
a0c2f480b8e7234b205d170f2c7a09f95ec803e32bb41a0ce877d9975a918b38 | Text | 413 | 24 | # Probabilistic Graphical Model
Code for the paper: Advancing Healthcare Analytics with Probabilistic Graphical Model: A Deep Dive into Patient Visits
## Requirements:
``` bash
pip install -r requirements.txt
```
## 1.Run masked diagnosis prediction task
``` python
sh run_diagnosis
```
## 2.Run mortality predicti... |
7d4ca0de363dc4b1f5b3148288a64f69838de7cd7b43f8308d1a8addb4dfa750 | Text | 462 | 10 | # Pattern recognition in living cells through the lens of machine learning
The presente repository contains a Jupyter notebooks to replicate the figures included in the article.
## Dependencies
* Google Drive account
* Python packages: numpy, scipy, biocircuits, bokeh
## Licence
This repository was build under the MI... |
887479d2cae8bb6b81728b00566b9f49aadef3c7f0b1096d9f0b425221db846e | Text | 473 | 6 | # uSMAART_public
To process and analyze fiber photometry uSMAART data.
This repository aggregates essential pre-processing and analysis code for uSMAART, a fiber-optic-based technology used to measure aggregate fluorescence signal from brain tissues.
uSMAART can measure up to 2 fluorescent sensor signals from up to t... |
8ec322da11bf811f674b1566397deda7fde30bee0880c7a51096cce291f292f2 | Text | 478 | 7 | # cogmodels
A repository for cognitive modeling with common modeling frameworks for biological decision making, associated with [this paper](https://doi.org/10.1101/2023.11.10.566306). Includes:
* classical temporal difference RL model (Sutton and Barto 2018)
* PearceHall model (Pearce et al. 1980)
* Bayesian inference... |
1dac53780cde59436ba62e48e7502167c56c24879bc82c2e3c5409ff7be5953e | Text | 488 | 1 | A Python implementation of an algorithm for computing the statistical significance of comparing two sets of predictions by ROC AUC. Also can compute variance of a single ROC AUC estimate. [X. Sun and W. Xu, "Fast Implementation of DeLong’s Algorithm for Comparing the Areas Under Correlated Receiver Operating Characteri... |
64cbd6a1191b45f33d6450d84b6f08d8a4d7c9425eb1f6b4ddc642a3a19c8c03 | Text | 518 | 12 | # calibrate
calibration for trackball, audio, and videos
## Audio calibrate
* RUN_audio_calibrate loads 4 videos
* each video is followed by a rating for 10 seconds
* Afterwards, the screen pauses and experimenters are prompted to adjust the volume for the participant
## Trackball/semicircle scale practice
* This p... |
b92baca97fe8632969da5a5d303bb5c1c64b15713f734a5d022b829485a6c372 | Text | 519 | 12 | # PISA
* [PISA User's Guide](https://shiquan.github.io/PISA.html)
* [FASTQ+ Specification](https://shiquan.github.io/fastq.html)
# Workflows
* [From raw reads to gene counts](https://shiquan.github.io/workflow1.html)
* [Annotate various features for alignment](https://shiquan.github.io/anno.html)
# News
* PISA (from... |
fdb8c25055058a8147d29ae456378fc9ab977c6e0fd02c84379beaf902525290 | Text | 523 | 1 | RNA pseudouridine, as an important modification on RNA molecules, has a profound impact on the structure and function of RNA. Its wide distribution in organisms and multiple biological functions, such as enhancing transcript stability and participating in codon recognition, make the accurate identification of pseudouri... |
7097756a0aaeb73308b7690736a4fca71dbcf3a148722a0a8c62f56cf3c30b5f | Text | 536 | 16 | # eNCApsulate
Bringing Neural Cellular Automata to Capsule Endoscopes.
Currently, this project is part of NCALab.
The project will be migrated to this repo soon, using NCALab as an external dependency.
Please find the code for segmentation (eNCApsulateS) here:
https://github.com/MECLabTUDA/NCAlab/tree/main/tasks/segm... |
49b5cf1cba2c53b6b17f8ff75175d094b691503d4330db8ad9625ef1baafb2be | Text | 552 | 11 | Welcome to the analysis of the Visium data for melanoma brain metastases.
To reproduce this pipeline, SpaCET script should be run first, in order to avoid missing files as this scripts is used to estimate the celular proportions within a feature and to classify it as a tumor/stoma/interface. Following this, scripts f... |
fe793b1ad279d5e410ea2034ce4c48443d8677ba4e9ace86e8493fadf359bae7 | Text | 571 | 12 | # Filopodia-Membrane-recruitment
Analyses filopodia density and plasma membrane vs. ER abundance in Maximum intensity projections of cells
Updates with version 03:
- fixed fragmentation ... |
847f12b89fd9afbec85ecabc65c8e4f5e61a800535f7ec6e4b3fb1e97818c649 | Text | 578 | 6 | # ripNet_CNN
Custom convolutional neural network for detecting hippocampal sharp wave ripples. Described in Cooper _et al._ 2025 [link](https://elifesciences.org/reviewed-preprints/101105)
The model takes as input 8 LFP channels, 4 from the cortex and 4 from the hippocampus. As the model is trained on silicon probe da... |
163a4e2fbbb1d9b0714d59a3369d11e2ee3bdd5752d5b130e5303eab99179492 | Text | 582 | 10 | # ShanghaiTech Dataset
Dataset proposed in CVPR 2016 paper [Single Image Crowd Counting via Multi Column Convolutional Neural Network](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_Single-Image_Crowd_Counting_CVPR_2016_paper.pdf)
# Download link
Baidu Cloud Disk(updated in 2024): https://pan... |
5d38e71f4906d3ba32edc52437e42dd94373bb9629b79aac387ede7d71f7ded7 | Text | 588 | 15 | # MDD PRS
This repository was made to summarise scripts for preparing genetic data and calculating polygenic risk scores.
## Prepare GWAS summary statistics:
Change SNP names from CHR:BP to RS format: https://github.com/xshen796/ENIGMA_mdd_prs/blob/main/script/PREP_genetic/SNP_rsID.md
## Polygenic risk scores
Crea... |
009935203eea789dd8b69d7ee982cab01848d681f39d1521f351b6f5f1a6de7b | Text | 606 | 18 | # plaids-model
Before running a simulation for the first time, run in terminal:
> pip install -e .
To run a simulation, adjust parameters in scripts/simulate.py and run in terminal:
> make
Results of simulation, including .csv files and plots, will appear in results directory.
Data directory contains .csv files o... |
a4dffb54707e9e978d04ea36233eb4ed0822caac1dcf45a5705348a2fa5422b4 | Text | 617 | 6 | # JohnPark-ASD
In data directory, IMB015.RDS is made by gapseq (Zimmermann, J., Kaleta, C. & Waschina, S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol 22, 81 (2021). https://doi.org/10.1186/s13059-021-02295-1) with "gut" gap-filling.
in "code... |
14e4c7fd7f4956da8dd7cc23d41bd14f80f498adf3bf0ec5cdffec441a0643da | Text | 643 | 27 | # Multi-view Graph Imputation Network
the source code of paper "Multi-view Graph Imputation Network"
## Requirement
[pytorch](https://pytorch.org/)
[pytorch_geometric](https://github.com/pyg-team/pytorch_geometric)
## Train
```bash
python main.py --dataset [name]
```
## Citation
If you think this implementation ... |
e0060ecfa8981cd34a37fb38519b794177248713ecfe9d7bf39b797a0d684d0a | Text | 677 | 14 | # Sato2025_Fruitfly_collectives
IN this repository you find codes used in Sato & Takahashi (2025) Neurogenomic and behavioral principles shape freezing dynamics and synergistic performance in _Drosophila melanogaster_.
## Codes
- All the scripts used to analyze data and make figures are stored in `/codes/` directo... |
8dcbb940314c8e99a78b3614a6e219518fb673446cce381bce893498a0985474 | Text | 681 | 17 | # EVAdb
Clinical Decision Support System and LIMS for NGS Applications
<a href="doc/DatabaseStructure1.png">Database Structure 1</a><br>
<a href="doc/DatabaseStructure2.png">Database Structure 2</a><br>
<a href="doc/readme_database_installation.txt">Database Installation</a><br>
<a href="doc/readme_web_application.tx... |
3281cd7c3207d89a7069be673ddfd210410982ebb3896b3fa92144d6d749fac7 | Text | 690 | 19 | # dMRIqcpy
[](https://github.com/scilus/dmriqcpy/releases)
Diffusion MRI Quality Check in python
To install dmriqcpy, run the following command:
```
pip install git+https://github.com/scilus/dmriqcpy.git
```
dmriqcpy is also ... |
3c3c0415ae6c7216a45824714c9b1000a03a14aa0261304a259cdb684f9940d5 | Text | 697 | 13 | # CoopCodingRate
Source code for simulations, supplementing the part of the article "Cooperative coding of continuous variables in networks with sparsity constraint" that deals with rate networks
## Content
- cooperative_coding_simulations.ipynb: Jupyter notebook for running the simulations of 1D and 2D cooperatively... |
fc676fa2f1fbd50d9ef36ea58d6234d44c39cfeab5bb2437f0acc31768158eb8 | Text | 718 | 12 | # greenin-whitehead-et-al
Code for Greenin-Whitehead et al.
Greenin-Whitehead et al. also uses the code in [`https://github.com/aclinlab/calcium-imaging/`](https://github.com/aclinlab/calcium-imaging/) and [`https://github.com/AusbornLab/KC_NaChBac_Model`](https://github.com/AusbornLab/KC_NaChBac_Model)
`analyzeNaCh... |
011d3cd4f211923eff37ee19372720ea4256cd628c1e001c8d68de7386d597e1 | Text | 723 | 16 | # PROMINENT
## Installation
`conda create -n prominent python=3.8`\
`conda activate prominent`\
`conda install pytorch torchvision`\
`pip install PROMINENT-methylation`
## Running PROMINENT
For helps, run `functions -h`.\
Data preparation for model input.\
`PROMINENT-data_prepare --input_csv <filename> --input_gmt <fi... |
51cc92adc2c36c065758d6c82dc32bfacaa51c8c66b896d6c409c3579cb10c2f | Text | 723 | 3 | # **Overview of repository**
This repositiory includes a collection of analysis code used in our study: *Stable cortical body maps before and aftrer arm amputation*. I've uploaded a jupyter notebook: NatureNeuroscience_analyses.ipynb, which includes cells for how we plotted each panel in the 3 main-text figures. The s... |
9b7cfaada69d3728f08a56012ec492d44e6839927d53fa61b823027e7f6a5b19 | Text | 730 | 28 | # stGRL
stGRL: spatial domain identification, denoising, and imputation algorithm for spatial transcriptome data based on multi-task graph contrastive representation learning
# Overview
<img width="586" height="806" alt="image" src="https://github.com/user-attachments/assets/4c9909c8-f303-4a0c-97c6-97c7036e16de" />
#... |
d3292d175ae948a1ad41d1e3ef0f8de10a6b6218348086e1bbe22856138530e2 | Text | 730 | 24 | ## A theory of temporal self-supervised learning in neocortical layers
This repository contains the code to run the experiments for the paper: Self-supervised predictive learning accounts for cortical layer-specificity.
## Dependencies
To install the requrired packages, create a new conda environment using:
```
con... |
68d7764252572eebefa5702b452a507ffa318259fb1fafe3fc12de9ca2fd8db8 | Text | 756 | 26 | # FUS network
This repository contains a simplistic network model to run simulations of FUS cortical neuromodulation, as reported in the Nature BME paper by Estrada et al. (2025).
## Installation
- Downaload and install an [Anaconda](https://www.anaconda.com/download/) distribution
- Create a new Anaconda environme... |
55224c20036190dfb9a7d8b7ed0f2aafdf643f5afb9b7e1711f6be9c21122554 | Text | 783 | 13 | # svrLSMpy
PYthon code for Lesion Symptom Mapping using Support Vector Regression
## Getting started
in the symptoms folder,
make a folder with the symptom name, (for example: example_symptom)
in example_symptom, place your behavioral scores csv file (2 columns, 'filename': containing the full filenames of the bin... |
c5e51739843739e260f757a51bb1cd77934d4ae19144e749286f90299de61734 | Text | 801 | 11 | # CoReU
Convolutional unmixing of reference channel for optical imaging
## Introduction
<img src="https://github.com/user-attachments/assets/d973468d-b127-4c0b-8dce-6c65bc39e89d" width="40%" alt="convolutional unmixing - data model">
<img src="https://github.com/user-attachments/assets/d8f68623-2a69-43bc-af32-5ddb5b... |
e3434f8b861a95228f9c75e543a15038248c2a28e9bdec2dc36759dafe08038b | Text | 810 | 26 | ## Self-supervised predictive learning accounts for cortical layer-specificity
https://www.nature.com/articles/s41467-025-61399-5 (Nature Comms 2025)
This repository contains the code to run the experiments for the paper: Self-supervised predictive learning accounts for cortical layer-specificity.
## Dependencies
T... |
511e0df1f5184e2f20432ae38efbf57f88463a0a9e595fa8ac26de36cbb177d7 | Text | 824 | 36 | [][docs]
neuprint-python
===============
Python client utilties for interacting with the [neuPrint][neuprint] connectome analysis service.
[neuprint]: https://neuprint.janelia.org
## Install
If you're using pixi, use this:
```shell
pixi init -c flyem-forge -c conda-... |
ff2b90ec335088f0fa7401ad9816a54ee577db2bc962863c724c1cb5ad9d0a1e | Text | 843 | 30 | # Meningioma snRNA-seq Analysis Code
This repository contains code used for the analysis in the study:
Single-cell analysis reveals a longitudinal trajectory of meningioma evolution and heterogeneity
## 1. Pseudo-bulk DEG Analysis (Fig 2)
01_DEG_analysis/fig2_pseudobulk_deseq2.R
- Performs DESeq2-based DEG analys... |
62c7f12eda6474204b26f47e80ae4098a370a69fb5407db0be2171445ccccb72 | Text | 854 | 13 | # iDeepS2
iDeepS2 can handle sequence with varaible lengths, it encodes sequence and strcture in to one-hot encode vector.
The encoding code for sequence and structure is from pysster
# Dependency <br>
python 2.7 <br>
<a href=https://github.com/fchollet/keras/>keras v2.0 library</a> and its backend is TensorFlow 1.1... |
92abd28afe866606057837c65e65ff09a722b612316a5a2ce0e6ab2fdb251830 | Text | 865 | 15 | # Rhabdomyosarcoma fusion oncoprotein initially pioneers a neural signature in vivo
## Overview
This is the code for computational analysis around PAX3::FOXO1 in vivo activity in zebrafish embryos. This code is mean to document the steps involved in running this analysis
The commands used in the code can be found in t... |
c44919d87821b3a00e902c71929a6bd63214affbd1646f3d160da795b377e738 | Text | 868 | 21 | # PFM_urgetoblink
First run segment_MRI.sh to segment the structural scan.
Next run run_CSF_compcor.sh to compute the prinicipal components of CSF voxels.
Then run the scripts in 3DMESPFM_scripts.
Next run 4D_clustering.sh or 4D_clustering_shuffled.sh to perform spatiotemporal clustering.
Then run PFM_preproc_AFNI... |
ab8991a7c12f012809cfdad60edae3465b85f1c5a190912bf8efb136d25bbfb3 | Text | 885 | 20 | # Happythelium
Happythelium is a Matlab computer vision application designed for segmenting objects on the retinal pigment epithelium (RPE).
## Installation
- Download Windows installer [here](https://github.com/raffaelemazziotti/hAPPythelium_code/raw/main/hAPPythelium_installer.exe).
- Start the application from th... |
1555c32798e045b340f658ba2f5f075bcbc6ded427f1eccd9dd421b28a980735 | Text | 891 | 31 | ## Frechet Inception Distance for Keras GANs
This module contains an implementation of the Frechet Inception Distance (FID)
metric for Keras-based generative adversarial network (GAN) generators.
The FID is defined here: https://arxiv.org/abs/1706.08500
### Usage
A basic example:
```python
import fid
generator = .... |
493568805e38b7cd6264b531a97b221992582c858f726b0e3cbfc6e3cf771708 | Text | 899 | 14 | The code SV2A_propagation_simulation.Rmd with simulated data can be run to replicate the major analyses of the article:
Luan et al., Synaptic loss pattern is constrained by brain connectome and modulated by phosphorylated tau in Alzheimer’s disease
System requirements:
MacOS or windows workstation running RStudio
All ... |
be7264fc8b5569303efd989c037315ba7229c4b69831d40925482600602d6a99 | Text | 901 | 29 | ====================
DFT DATASETS
====================
For each molecular conformation in the .hdf5 datasets, you will find the following properties:
-'atNUM': Atomic numbers (N)
-'atXYZ': Atoms coordinates [Ang] (Nx3)
-'ePBE0+MBD': Total PBE0+MBD energy [eV] (1)
-'ePBE0': PBE0 energy [eV] (1)
-'DI... |
f624e44c7c33d1a110c1e8bf6c102d509b7812c16d14391e6f0308fd276d114d | Text | 923 | 14 | # Disordered-guiding-photonic-chip-enabled-high-dimensional-light-field-detection
This is a repository for neural network training for the intelligence detector, all the datasets, the history of model training and verification are included. The work URL is linked to this repository.
# README.md
Please read the README.m... |
354ee783d82a382a6d60798a776011a5b6ee3139d29a3a57386ad9b82d3feec9 | Text | 943 | 10 | Code accompanying the preprint “Model mimicry limits conclusions about neural tuning and can mistakenly imply unlikely priors”.
Data and pre-computed results is available at https://osf.io/bdf74/
Python code was tested and run using python version 3.10.8 on Linux and Windows. It requires numpy, scipy, seaborn, pandas,... |
11f1ffb2067fbf1bbfee3892f3af03cd8477742fded3d404eb09594d5e8bd207 | Text | 952 | 7 | # dmft_wide_networks
Code to solve DMFT equations for infinite width feature learning networks and reproduce the figures in our [Neurips 2022 paper](https://arxiv.org/abs/2205.09653).
1. Nonlinear DMFT equations with Monte-Carlo solver [](https... |
09fba104bd99604abd2ebdb5a1581b30227d7fde3bf5fe0961f786fdae299bc2 | Text | 963 | 11 | # Photometry pre-processing
This repository contains a Python notebook [Photometry data preprocessing.ipynb](https://github.com/ThomasAkam/photometry_preprocessing/blob/master/Photometry%20data%20preprocessing.ipynb) which demonstrates methods for pre-processing neuroscience fiber photometry d... |
f4febfaaeecc2b2af55e7668e3e769d7ad9437c5694499ead7943cac26e45b2a | Text | 998 | 4 | We constructed a spectrum formation classifier (SFC) that optimizes a modified multiclass log-loss function incorporating the overlap weights between smple groups and a general classifier (GC) that optimizes a general multiclass log-loss function. This is based on the assumption that information prioritized in the SFC,... |
bacac80dd218b532afa0da09a329164d5453c60688c68d923ae36e5c6b25c2f7 | Text | 1,001 | 10 | # tone_discrim_2025
Contains code for associated spiking data processing used in Gauthier et al. 2025
## Rate Level Function Fitting
This code can be ran as is. All functions are self contained in the script. Spiking data is included in both an example spiking dataset for trouble shooting as well as the full Auditory... |
f27a708b50c685ef1bffd26ecd6b2562df61cdf7575b5a1a1e8b5e116190b634 | Text | 1,001 | 45 | # Connectome-based Predictive Modeling of Handwriting and Reading
## Introduction
This repository contains data and code for the Connectome-based Predictive Modeling (CPM) analysis of individual behavior prediction based on brain functional networks. The study aims to predict handwriting speed and reading using ge... |
3b6495e13b0c8b91a74ccb8db235b5201adfc09b60d463acfcd69165da4494e7 | Text | 1,020 | 17 | PSF-Engineered Meta-Optics in TensorFlow
This repository contains a Google Colab notebook for designing RGB meta-optics that produce customized point spread functions (PSFs) using TensorFlow.
Summary
- Simulates meta-optics that generate 7×7 RGB kernels, inspired by convolutional filters from CIFAR-10.
- Each kernel is... |
952d553c965b0777ccbbc729b51ae98ab796b7fdb6b972d60bf25ce9cdde701b | Text | 1,049 | 17 | [](https://doi.org/10.5281/zenodo.3936045)
# longCombat: Longitudinal ComBat R Package
Longitudinal ComBat uses an empirical Bayes method to harmonize means and variances of the residuals across batches in a linear mixed effects model framework. Detailed ... |
a3411eb9d53eea9b85ead28855ac8122c8011f640bc1e614c85b8288290563cd | Text | 1,055 | 22 | # Protocol design of PGSE
The code implements protocol design of PGSE developed in [Gabriel Ramos-Llorden, Hong-Hsi Lee, ..., Susie Y Huang, Nature Biomedical Engineering, 2025](), helping to decide the shortest TE for the given b-value.
* **Demo 1, b-value for trapzoidal pulse-gradient sequence**
* **Demo 2, calcula... |
b848d6727c0b039ecafa13cf504a194a31e2bb4d50c54ecbe3c14741b7aa24e6 | Text | 1,076 | 45 |
GASTON-Mix - A unified model of spatial gradients and domains with spatial mixture-of-experts
===========================================================================================
Overview
--------
GASTON-Mix is a spatial mixture-of-experts (MoE) model for learning domain-specific topographic maps of a tissue... |
9799c1c59724e8127eca8e0bce2d3d7eb1e81e6c38a1a0e0dd6a8ca2ead6ca5c | Text | 1,078 | 33 | # IoMT-IDS-DL
IoMT-IDS: Deep Learning-Based Intrusion Detection System
📌 Overview
This project presents an Intrusion Detection System (IDS) for Internet of Medical Things (IoMT) to enhance security and detection efficiency.
The model integrates advanced feature selection techniques and deep learning for improved an... |
7d650b7dab2e1ea2e79bff34cdf9bdc6839ff29c8f2b1aca2dfa55282f81412e | Text | 1,087 | 18 | # GlaucomadMRIdenoising
[](https://doi.org/10.5281/zenodo.15015932)
Repository for data and code for replicating results and figures of the following paper:
Taguma, D., Ogawa, S. & Takemura, H. (2025) Evaluating the impact of denoising diffusion MRI data on tractometry me... |
a96289939fe7c042fa63b991bb0e9097675f5b54199f4ad1d4e0c5fcd474d0d0 | Text | 1,089 | 21 | # LLM-Metadata-Extraction-Paper-2025
Materials for the paper "Large Language Models Can Extract Metadata for Annotation of Human Neuroimaging Publications" (submitted to Frontiers in Neuroinformatics).
## Setup
The notebooks provided expect to have a `.env` file in the GitHub root folder that contains the following ... |
bf1a77d1e61aa31f128c3c20ac2136b1226cb303fe0839378e98497147f011db | Text | 1,120 | 18 | # time-averaged-da
This repository includes data and code used to generate a figure in the following paper:
Roitman MF & McCutcheon JE (2025). What’s the occasion? Phasic dopamine signaling and interoception. Current Opinion in Neurobiology.
The figure is based on data presented in full in the following paper:
Konan... |
105c04ce2ca636b023a7453d118bedce8b474d30d6473464f1963062b5848e1a | Text | 1,144 | 24 | # MAARS
Official repository for Multimodal AI for ventricular Arrhythmia Risk Stratification (MAARS): accurate prediction of arrhythmic death events using multimodal medical data. This project is primarily developed with Hypertrophic Cardiomyopathy patients' data.
## Get Started
### Environment
Conda enviroment file... |
1bcd8247fc8c5c3fdfb00b0f1ec10597758695c030f22d04348efc3077a8bfab | Text | 1,159 | 25 | # Biophysical-modeling
[](https://doi.org/10.5281/zenodo.15319985)
This repository contains MATLAB scripts for axon diameter mapping and SANDI (Soma and Neurite Density Imaging) analyses based on high-gradient diffusion MRI data acquired using the 3T Con... |
36171bcdd91acd8b3adc844f30864aa34955c8d4626aeecb4c4e27e60acfa4ba | Text | 1,168 | 25 | # Biophysical-modeling
[](https://doi.org/10.5281/zenodo.15319948)
This repository contains MATLAB scripts for axon diameter mapping and SANDI (Soma and Neurite Density Imaging) analyses based on high-gradient diffusion MRI data acquired using the 3T Con... |
22d706ede917b59ada996f2188810fc498c0db9b473ab04ccb4a945d68ba44b8 | Text | 1,169 | 25 | # PyALFE
Python implementation of Automated Lesion and Feature Extraction (pyALFE) pipeline.
We developed this pipeline for analysis of brain MRIs of patients suffering from conditions that cause brain lesions. It utilizes image processing tools, image registration tools, and deep learning segmentation models to produ... |
e40c69f691e69f989feaba7e59b70585bc566ac08f7c70464f3fb62e4dc883fa | Text | 1,178 | 8 | # Nodal_Modularity
Nodal modularity (nQ) is a mathematical extension of classical modularity (Q) to individual nodes. That is, a node's contribution to overall segregation within a network. It has been implemented for both single and multi-layer networks.
This code was created to be used with pre-calculated measures ... |
56aa1d14b3655aba411a24abe873de7c1327e8d221fad2c8fdff0b415c4881a8 | Text | 1,187 | 36 | # SNAP
Scalable Nucleotide Alignment Program - <https://www.microsoft.com/en-us/research/project/snap/>
## Overview
SNAP is a fast and accurate aligner for short DNA reads. It is optimized for
modern read lengths of 100 bases or higher, and takes advantage of these reads
to align data quickly through a hash-based in... |
c055c82ce08df604a5870b79f22bf7ec35c5a96a7c1221b0c1f51c8465264689 | Text | 1,216 | 10 | # Tsimring-Enhanced-synaptic-dynamics-drive-the-reorganization-of-binocular-circuit
Repository for the code that was used to process and analyze data collected for "Enhanced synaptic dynamics drive the reorganization of binocular circuits in mouse visual cortex". This also includes code for the computational model.
#... |
cb8abe74048f94faf68971643c462c83baba0d628c568c76e86003a7e7689216 | Text | 1,216 | 35 | # qeegfeats
Pipeline to generate QEEG features for various EEG classification/prediction/forecasting tasks.
The algorithms to generate QEEG features are located in utils/preproc.py
## Requirements
- Python (3.8 or later)
- CUDA 12.1
- CUDNN 8.5
- Multiple Python packages in requirements.txt
- To install these Python... |
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