sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
7eb75445d228ea759d52c4471c3351d2e2a45bd6be7564c60047be30a362775d | Text | 6,297 | 107 | # EAT Assessment
This project encompasses two main components:
A system for detecting specific anatomical landmarks in medical images using a Reinforcement Learning (RL) approach,
and another system for assessing epicardial adipose tissue (EAT) through segmentation.
## Installation
Befor... |
0e5ebdd0b0c92a2d4ccd9ef47dc5d18f7fc0bca80682a381993f483762dbc132 | Text | 6,313 | 154 | # CoNFILD
[](https://doi.org/10.5281/zenodo.14058397) [](https://doi.org/10.5281/zenodo.14037782)
This is the codebase for the paper [P. Du, MH. Parikh, X. Fan, X.-Y. Liu, J.-X. Wang. Conditi... |
64dfe438294039b60dfdaad9d0e2f7df631c02a8ac7da652f0de0ea1171e58ce | Text | 6,315 | 156 | [](https://travis-ci.org/urmi-21/orfipy)
[](https://anaconda.org/bioconda/orfipy)
[
[](https://github.com/NanoComp/meep/actions/workflows/build-ci.yml)
[](https://github.com/NanoComp/mee... |
ab4eafc114eca3e6ce2a5e5a357ccf31d0cc453401e09242b73352562c64526d | Text | 6,437 | 139 |
# pathology-whole-slide-learning
Framework for weak whole slide learning with attention: compress and classify whole slide images.
Requires ASAP and the wholeslidedata package and optionally pathology-whole-slide-packer.
Preprocessing: For slides bigger then ~50k pixels in height/width packing is recommended,
as ... |
7ba6e9afd9d5869156fe2f1ec840f51487235ef229bfef2365777881772263fe | Text | 6,439 | 131 | # Learning to quantify uncertainty in off-target activity for CRISPR guide RNAs
> crispAI is a deep learning-based tool that predicts the off-target cleavage activity for a given single guide RNA (sgRNA) and target DNA sequence pair and quantifies the associated uncertainty.
> crispAI-aggregate is an uncertainty aware... |
58f953bb2f44537e3bd940c4f219e1c76e7433f9430be37884a417a64f871fa4 | Text | 6,445 | 39 |
Behavioral data, fMRI metadata, and experiment code accompanying:
Coraline Rinn Iordan, Victoria J.H. Ritvo, Kenneth A. Norman, Nicholas B. Turk-Browne, Jonathan D. Cohen. Sculpting new visual categories into the human brain. Proceedings of the National Academy of Sciences (in press).
Abstract:
Learning requires ch... |
4806bfbdabc8b5308c1d2f889bb902c32a07da688ebc5543d865811794660cd5 | Text | 6,450 | 159 | # plant-seg-tools
Simple python tools for plant-seg https://github.com/hci-unihd/plant-seg
## Index
* [Install PlantSeg Tools](#install-plantseg-tools)
* [Segmentation to Meshes](#segmentation-to-meshes)
* [Proofreading tool](#proofreading-tool)
* [Automated Segmentation Proofread from seeds](#automated-segmentation-p... |
176819360dba96210feb592a8e716349e9f4a147f0c997d28713f5a1f0219db5 | Text | 6,475 | 181 | # SpliceTransformer
The SpliceTransformer (SpTransformer) is a deep learning tool designed to predict tissue-specific splicing sites from pre-mRNA sequences.
# Update log
2024.10.24 Our paper has been published in Nature Communications! [Link](https://www.nature.com/articles/s41467-024-53088-6)
**We are continuously... |
642a3713391b8cf7e6a21800f0ac386f0f886765ab07ed5ef5b0c2a0801b6695 | Text | 6,475 | 99 | ## EpiBERT
<img src="docs/graphical_abstract.png" width="400">
EpiBERT learns representations of accessible sequence that generalize across cell types via "masked accessibility modeling" which can be used for downstream fine-tuning tasks, such as caQTL predictions and RAMPAGE-seq prediction in new cell types. [Link to... |
d3421b829cdb88e5d0162713c3a26b41d372ebc42c13b9ea0174d13d68a00a89 | Text | 6,500 | 101 | [](https://codeocean.com/capsule/5579071/tree/v1)
# HierbaNetV1
HierbaNetV1 is a novel convolutional neural network (CNN) architecture with a cutting-edge feature extraction technique. This architecture extracts and integrate... |
37c8c40ef780d233eaf47015892c63f2960bbb0116561b1d6280ab9115aa13ed | Text | 6,542 | 142 | # SPaiNN - Equivariant Message Passing for Excited-State Nonadiabatic Molecular Dynamics
<p align="center">
<img src="docs/_static/spainn.svg">
</p>
## An Interface between SchNetPack and SHARC for performing Machine Learning-accelerated Photodynamics Simulations
[ plays a crucial role in evaluating the performance of drug-target affinity (DTA) predictions. Despite its importance, the current implementations within DeepDTA and GraphDTA mode... |
f6506e838236cd7c477c6027481c379b92b212800d7834abdf16841d417e3446 | Text | 6,603 | 88 | # AMIRE
Improving MRI quality without overly restrictive standardization - the AMIRE (Animal MrI standaRdization initiativE)
***NEWS ALERT***
<details>
<summary>
View
</summary>
May 2025: We are inviting researchers to participate in the phantom project.
The publication describing our collaborative effor... |
cbe83716de0dfa219b11d2592ae519a3193df441f873d4c1800e53afcab595f3 | Text | 6,627 | 127 | # ChromA: Chromatin Landscape Annotation Tool
ChromA is a probabilistic model to annotate chromatin regions into accessible or inaccessible, open or closed, based on their ATACseq profile. ChromA can process bulk datasets, single-cell or integrate information from a combination of both. Even more, ChromA can integrate... |
d9c635e946e9be58a838c0cc1fc5f75e237f562be7a38966229de8d214b28dc1 | Text | 6,656 | 59 | # PyReliMRI: Python-based Reliability in MRI
[](https://github.com/demidenm/PyReliMRI/actions/workflows/python-package-conda.yml)
[![Documentation Status](https://readthedocs.org/proje... |
3e232277c04dede888d52ae5393fc711989172b616ae80cbe1bb0b7e39e072a2 | Text | 6,701 | 185 |
<!-- README.md is generated from README.Rmd. Please edit that file -->
# STADIA <img src="man/figures/logo.png" align="right" width="160" />
<!--[](https://github.com/yanfang-li/stadia/actions/workflows/R-CMD-check.yaml)... |
8e493040f029b871c7d7c4c02560dc8ddcaefd9519d87d2f0f42eaa6d07841f7 | Text | 6,718 | 141 | # MR-CLOVER: MRI CLinical resOlution brain VolumEtRics
## Description
MR-CLOVER (Tool for Assessing MR-based Clinical resOlution brain VolumEtRics) is a Python-based pipeline that combines publicly available tools from ANTs and Freesurfer to extract the brain from clinical MRI scans and return segmented grey and whit... |
204cf4aa50ad7d2c796540c025f038d47316e711d11eeb3cd95a6ca924199604 | Text | 6,781 | 130 | [](https://deepwiki.com/GiszterLab/SdoAnalysisToolkit)
# SDO Analysis Toolkit (SAT) [V 1.1]
MATLAB Package for implementing Stochastic Dynamic Operator (SDO) methods for stochastic control and prediction, using time series and point process data.
When applied to neurophys... |
ab0ed2b619af6e146b68e8f48c8a43b231553727d7caf58730cb35fa8605dd8f | Text | 6,831 | 71 | # CompartmentalAstrocyteModel
This repository contains the code for the simulation of the astrocyte compartmental model by Bezerra & Roque, Dopamine facilitates the response to glutamatergic inputs in astrocyte cell models. PLOS Computational Biology. 2023 (DOI:).
The main code is divided into four files: astrocyte_m... |
7a82e19042913c4a029e9530d23b271d53f955ecf9e957c849f94c6b23af414e | Text | 6,863 | 135 | > **Overview**
> -
> _Reference_: https://pubs.acs.org/doi/10.1021/acsomega.4c03873.
>
> ---
> <div style="text-align:justify;">
> Remaining Useful Life (RUL) prediction is crucial for simplifying maintenance procedures and extending the life span of
> aero engines. Therefore, research on RUL prediction metho... |
497c02ea434e4f740d9d52efc5c755285a649c136733a09d9e3115017d0cc981 | Text | 6,873 | 63 | # PyReliMRI: Python-based Reliability in MRI
[](https://github.com/demidenm/PyReliMRI/actions/workflows/python-package-conda.yml)
[](https://matlab.mathworks.com/open/github/v1?repo=dnafinder/cuzick&file=cuzick.m)
# cuzick
## 📘 Overview
cuzick is a MATLAB function that implements **Cuzick’s test for trend**, a Wilcoxon-type nonparametric test ... |
535f8b8d0ec51751dbb8c944ba19665936b2b79e95af1b16d0708f01a681a2b9 | Text | 7,016 | 139 | [![CC BY-NC 4.0][cc-by-nc-shield]][cc-by-nc]
[cc-by-nc]: https://creativecommons.org/licenses/by-nc/4.0/
[cc-by-nc-shield]: https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg
# You Only Watch Once (YOWO)
PyTorch implementation of the article "[You Only Watch Once: A Unified CNN Architecture for Rea... |
1eb27299a28dcd9ad383d4de5e21a59c954bbe80c1e00b67bec0643e7decbecd | Text | 7,025 | 166 | # 🔄 maxEntropy
Julia implementation of **recurrence-matrix entropy** for quantifying the complexity of physiological time series.
[](https://doi.org/10.1063/1.5022154)
[ framework designed to address the complexity of miRNA targeting and to provide the whole spectrum of the miRNA interactome. By leveraging a multi-layered CNN design and incorporating data from over 60,000 miRNA binding si... |
32e54ed39f99bc9308dfe29a425893cc788b9f821da7564c2130c90dc421b781 | Text | 7,161 | 113 | # scanphyslog2bids
[](https://travis-ci.org/lukassnoek/scanphyslog2bids)
Code to convert Philips physiology files ("SCANPHYSLOG") to the BIDS-format, including the estimation of volume triggers using the logged gradient, volume markers... |
d8d9036ce3dcc758b358e96b1141759930d7b190d1cf20caaa35572d540c5716 | Text | 7,172 | 134 | 
[](https://pypi.org/project/irescue/)
[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-bri... |
c0ba1930d4857aa0e9dbba6bd3eaeb4a21678dcdec1db3c807da2397e71fc17d | Text | 7,188 | 123 | # **CARL: Computer-Assisted Response Labeler**
## **Overview**
--------
<img src="util/logo.png" style="float: right" alt="logo" width="30%"/>
Computer-Assisted Response Labeler (CARL) is a MATLAB GUI tool that aims to speed-up manual labeling of short verbal responses. It has been particularly developed with the goa... |
0a448b3eb4184c368795419941ea2c59a9e0cc067c44ed773e592ff93aae5ad1 | Text | 7,255 | 83 | # excitation_inhibition_topology
Analyzing the topology of inhibitory to excitatory connectivity
This is the custom analysis code for our preprint:
_Specific inhibition and disinhibition in the higher-order structure of a cortical connectome_.
## Requirements
Analyses are using python (3.8 or above) in jupyter noteboo... |
4d474ca1d4721d248b96cb41fd28b52a8df92e862185a622798f5c412d6ff89e | Text | 7,261 | 165 | # Classification models 3D Zoo for Keras 3
This repository contains 3D variants of popular classification CNN models like ResNets, DenseNets, VGG, etc for keras module. It also contains weights
obtained by converting ImageNet weights from the same 2D models.
This repository is based on great [classification_models]... |
81afb852c8b8c95303120b2aa690595423fcdd56348f430eae1ad1a8a1161ce1 | Text | 7,287 | 124 | # **CARL: Computer-Assisted Response Labeler**
[](https://zenodo.org/badge/latestdoi/444434425)
## **Overview**
--------
<img src="util/logo.png" style="float: right" alt="logo" width="30%"/>
The Computer-Assisted Response Labeler (CARL) is a MATLAB GUI tool that aims to ... |
03375f72af75163c2a50f3e646c6e61af05e23ee545f6c257e7fc41502118986 | Text | 7,324 | 118 | # Smaller transcriptomic models do not exhibit superior generalization performance
<!-- usage note: edit the H1 title above to personalize the manuscript -->
[](https://greenelab.github.io/generalization-manuscript/)
[](https://github.com/AspirinCode/GENiPPI)
[-red)](https://doi.org/10.1186/s13321-024-00930-0)
[](https://do... |
326362c82784498c6e1b686701384be48fdc7b03427b5a7ba7ae0d2736c09be3 | Text | 7,405 | 155 | [](https://codecov.io/gh/miykael/atlasreader)
[](https://coveralls.io/github/miykael/atlasreader?branch=master)
[ in OpenVINO2021.3.
Based on https://github.com/mystic123/tensorflow-yolo-v3
**Supported model**
- YOLOv4
- YOLOv4-relu
- YOLOv4-tiny
- [YOLOv4-tiny-3l](https://github.com/TNTWEN/OpenVINO-YOLOV4/... |
15aab8f6799dce42624ee20cce3c51b5da52e07e607115a4b72711f55bccc262 | Text | 7,415 | 132 | # DLLabelsCT
DLLabelsCT (Deep Learning Labels Computed Tomography) is an annotation tool for abdomen CT scans. It can use Pytorch segmentation models to assist in annotating.
## Dependencies
- Python 3.10+
- PyQt6
- Pytorch
- Pydicom
- OpenCV-Python
- Scikit-image
## Using DLLabelsCT
The GUI is divided into three ... |
172679206be0c46746605fe1c159227d9161bb6aa6ef333811bf746d721c444c | Text | 7,440 | 169 | 
# cocoa
`cocoa` is a Python library for **co**mparative **co**nnectomics **a**nalyses.
It implements various dataset-agnostic as well as dataset-specific methods
for matching, connectivity, co-clustering and cell typing.
Currently implemented are:
1. [FlyWire](https://flywire.ai)
2... |
b2db5ad47044d2e9f3e5a0540418c6deecbc6fc46fb59cc1d25f1e6a8d02da16 | Text | 7,456 | 153 | # scGraphformer: A Graph Transformer Network for Single-Cell RNA Sequencing Analysis
## Table of Contents
1. [Introduction](#introduction)
2. [Installation](#installation)
3. [Data Preparation](#data-preparation)
4. [Usage](#usage)
- 4.1 [Intra-experiment Analysis](#intra-ex... |
aeb413ed714ed5c4a442daea7b4bff6a545c6657272a044439b1219dd3417c0a | Text | 7,478 | 107 | # gnn4dm
GNN4DM: A Graph Neural Network-based method to identify overlapping functional disease modules
This is the original implementation of GNN4DM, a graph neural network-based structured model that automates the discovery of overlapping functional disease modules. GNN4DM effectively integrates network topology wit... |
e6a1d5b024bcca23e799378cf0654b8fd98fc424703da421a100ae1c4fc7478e | Text | 7,491 | 51 | # image-to-surface #############################################################
A small toolbox built to convert a 2-D representation of an object from image space to a label on the cortical surface. A common problem for visual neuroscience using MRI is cortical placement of regions of interest. If you are stimulating... |
341d25b5da051b7d9207810b5048071d1469aca177f96a7465b25ea646c3456c | Text | 7,498 | 172 | louvain-igraph
==============
This package implements the louvain algorithm in ``C++`` and exposes it to
``python``. It relies on ``(python-)igraph`` for it to function. Besides the
relative flexibility of the implementation, it also scales well, and can be run
on graphs of millions of nodes (as long as they can fit ... |
f8dd005b6f1462b46c7f9103bbe4a5020b5aba10c94f26ae22441b95b22690fc | Text | 7,506 | 172 | <h4 align="center">
CalDenoise: Automated Denoising Software for Calcium Imaging Signals Using Deep Learning
</h4>
<div align="center">
<a href="#installation-guide"><b>Installation Guide</b></a> |
<a href="#usage"><b>Usage Guide</b></a> |
<a href="https://github.com/SharifAmit/CalDenoise/tree/master/test/"><... |
b36ba1e941b7333f01c02ece1523c09c274239c8081f2d83a31ae2f508a9f933 | Text | 7,527 | 90 | # Mouse Brain Hierarchy
* Hierarchy searching algorithm based on layer-projection patterns of cortico-cortical, thalamo-cortical, and cortico-thalamic connections constructed from viral tracing experiments using Cre-driver lines
* Code used in Harris et al (2019) “Hierarchical organization of cortical and thalamic con... |
05f614feed3f331ecfa517c31b737bd3e7aae812fba07267b2f729578e2778db | Text | 7,540 | 165 |
SITK-IBEX: Aligning images acquired with the IBEX microscopy imaging technique
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
This Python package implementation is part of the development of the
Iterative Bleaching Extends Multiplexity (IBEX) imaging technique. It enables
the alignmen... |
4bcfeea329d17fc55f8e49265738df42f785260053c223a619a3c847e979eeb1 | Text | 7,551 | 130 | # The Right Atrium Affects In Silico Arrhythmia Vulnerability in Both Atria
Author: Patricia Martínez Díaz
patricia.martinez@kit.edu / publications@ibt.kit.edu
[doi:10.1016/j.hrthm.2024.01.047](https://doi.org/10.1016/j.hrthm.2024.01.047)
This dataset contains 8 biatrial meshes and 8 monoatrial (left-only) meshes, de... |
a78418c85f7cefed57f66f48a82d4081fb06dbfdb44f2c49413e07bfa89cb02f | Text | 7,585 | 146 | # LAVASET
[](https://badge.fury.io/py/lavaset)
[](https://pepy.tech/project/lavaset)
[](https://zenodo.org/doi/10.5281/zenodo.10816838)
[
The current release of the WiGiTs pipeline is v3.0. Release notes are [here](./pipeline/docs/v3_0/WiGiTs_v3.0_ReleaseNotes.pdf).
We recommend running... |
8e53cb93ccbd2a7cd5e29e0f36ab9c3e63f2c268ca8a7cbc0ddafecf19d0dccc | Text | 7,782 | 57 | # psi-sectored-classification
This is a pipeline for doing colony segmetation and classification of *Saccharomyces cerevisiae* colonies exhbiting $[PSI^+]$ and $[psi^-]$ phenotypes.
This code base is designed for implementation on Google Colaboratory. You will first need to clone this repository, then upload the ent... |
bef8c2100e3e2940fb1f551352f526004ef82176e635168d2b7cd8900351037e | Text | 7,813 | 51 | # HD-GLIO-XNAT docker container
This container allows to integrate our automated processing tool for MRI in neuro-oncology (brain tumor segmentation, volumetric tumor response assessment) as described within our previous publication (https://doi.org/10.1093/neuonc/noac189) into existing radiological infrastructures thr... |
1eea616497a729192222b075ec011bda3c96e2f2024bc9e375c81f414291f210 | Text | 7,815 | 213 |
.. image:: https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white
:alt: PyTorch
:target: https://pytorch.org/get-started/locally/
.. image:: https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white
:alt: Lightning
:target: https://pytorchlightning.ai/
.. ... |
604e5f1842229352a51a5860a026bdac62eb5c10d606755bfc7898b050c459d0 | Text | 7,822 | 230 | # Cortexa SplicePCA Example
In this tutorial, we will walk you through the process of analyzing your own files for the SplicePCA tool.
## Use SplicePCA
As a first step, to get familiar with the SplicePCA tool, you can use the analyzed files deposited in `data/`. These are control and *Nova2*-KD samples from the deve... |
72ac4680575fc0fd9f39e49a35f486abe3cf7afc1e2987dbc1281ebc0c25c970 | Text | 7,840 | 74 | ANT-R task
=============
christophe@pallier.org
The repository <https://github.com/chrplr/Posner_attention_networks_taks> contains several Python scripts implementing variants of the ANT task.
`ant-r-classic.py` is a script implementing the ANT-R task described in Fan, J. et al (2009).
](https://badge.fury.io/py/image-classifiers) [](https://travis-ci.com/qubvel/classification_models)
# Classification models Zoo - Keras (and TensorFlow Keras)
Trained on... |
6640bbff4865e9222847d272fbcf439802094d90005a98e41a39494c0c4e670e | Text | 7,934 | 181 | Optuna Examples
================
This page contains a list of example codes written with Optuna.
<details open>
<summary>Simplest Codeblock</summary>
```python
import optuna
def objective(trial):
x = trial.suggest_float("x", -100, 100)
return x ** 2
if __name__ == "__main__":
study = optuna.create_st... |
39e6dc1a09aa4ee949e1499cf9d5f7aee9be97c19e7b28daf2d2dfff7eb18ae4 | Text | 7,938 | 171 | ## README
For the Nature Methods submission "[Automated analysis of whole brain vasculature using machine learning](https://www.biorxiv.org/content/10.1101/613257v1)"
Authors: Johannes C. Paetzold and Giles Tetteh
This code can be executed in a public code ocean capsule at https://codeocean.com/capsule/05115... |
738e91156fcf1dc67d49821ee34d6b635b3da5fc51917df266988ed7e578aed8 | Text | 7,949 | 186 | [](https://pypi.org/project/MOFF/)
[](https://github.com/MDhewei/MOFF/blob/master/LICENSE)
## Introduction of MOFF
<div align="left">... |
d91bd9b93b5bbed19d8fb898824ab2f3b53da84ba2a0ff405329a971781936f3 | Text | 8,020 | 227 | # ScribbleDom
A method to find spatial domain from Spatial Transcriptomics data using scribble annotated histology image, or using
output of other possibly non-spatial spatial domain detection method (e.g. mclust).
# Prerequisites
Recommended OS: Linux</br>
Recommended Python version: 3.10.6</br>
Recommended R... |
9b4c1678e1d94d0d9fdaffe58c0d5f17484b2e454660d168400fcef0360dd9b5 | Text | 8,022 | 173 | ---
Title: Bimodal Neural Network for Cancer Prognosis Prediction
Authors:
- Bo-Run Wu
- Tsung-Wei Lin
- Zow Ormazabal
---
# Exploiting Common Patterns in Diverse Cancer Types via Multi-Task Learning
We developed a Python code base to interact with the REST API provided by the GDC Portal for data filtering a... |
8bfd4af27717b04705479b0ccc81579bf8e9178ec94141441645e966ed29bf18 | Text | 8,041 | 78 | # multi-EXR
Code base accompanying Kang*, Schroeder* et al., 2023.
## Broad overview of multi-ExR analysis pipeline
1. Image pre-processing (Fiji)
2. Image registration (MATLAB or Python)
3. (Optional): Z-stack shortening
4. Registration quality check (MATLAB)
5. (Optional) region of interest (ROI) identification (Fi... |
3cda246f5105255440fd14c6be02496690045a9655a1734d96104b49537f61fd | Text | 8,081 | 100 | # Banana Quest & the Digital Fruit Fly Knowledge Base
[English](#english) · [中文](#中文)
---
<a id="english"></a>
## English
A zero-dependency **browser game driven by real whole-brain connectomes** (a leaky integrate-and-fire network over 139k–176k neurons), a peer-reviewed-style **technical note on data-packaging pi... |
127d4830dc255fe1bbda830cc362ac1dcc6b76f0a71381036abfe9d0f831d3fb | Text | 8,115 | 234 | treem – Neuron Morphology Processing Tool
===============================================
The `treem` module (pronounced "trim") provides data structures and
command-line tools for accessing and manipulating digital reconstructions
of neuronal morphology in the Stockley-Wheal-Cannon (SWC) format.
<table>
<t... |
f4653cdb2c02cf7c940d604b3eb446423182a3d32ca531ff7036e0c51a6d661a | Text | 8,189 | 96 | # singleDeep
A deep learning workflow for samples phenotype prediction with single-cell RNA-Seq data.
## Dependencies
SingleDeep has been tested with R 4.2.0 and Python 3.8.3, although it is likely to work with other versions. To install the Python dependencies with pip, run the following code.
```{bash}
pip instal... |
9a6d6e3419647a36a26c099545fde0ec12bcae7aa3b69f5be9c8005cea634507 | Text | 8,206 | 106 | # BrainSimulation
Simulation of the human brain.
## How to build
**Linux**
1. Clone the repository.
2. From the working directory root run
`$ make`
3. The makefile supports the `install` target (optional).
**Windows**
1. Clone the repository.
2. Open *win32\win32.sln* in Visual Studio.
3. Build using Visual S... |
57b095b2af80580051e576a807848754212c5f7b50c659409dd92cbda0a85343 | Text | 8,229 | 75 | ========================
root-tissue-segmentation
========================
.. image:: https://github.com/waseju/root-tissue-segmentation/workflows/Train%20root-tissue-segmentation%20using%20CPU/badge.svg
:target: https://github.com/waseju/root-tissue-segmentation/actions?query=workflow%3A%22Train+root-tissue-s... |
16725079bad6e285898882687eb369f72a2407a5fe98b72304a189ef42a5060c | Text | 8,240 | 104 | # ISMRM-OSIPI DCE-MRI Challenge Repository
This repository accompanies the ISMRM-OSIPI DCE-MRI Challenge and provides supplementary data from the manuscript. OSIPI TF6.2 designed this OSIPI-DCE challenge for evaluating DCE-MRI methods that estimate the volume transfer constant ($K^{trans}$) in brain tumors.
A set of ... |
1ef855721aa6007974fc3435bdbc2a6c4d813b884c4c18ae4b3efe7e1a2bf474 | Text | 8,245 | 104 | # gene2probe
Probe-based spatial transcriptomics technologies offer great flexibility in terms of the transcripts that can be profiled.
While technologies such as VisiumHD and VisiumFFPE enable the capture of most protein-coding genes, there are many applications that might require the design of custom probes.
A few ... |
1c9df371234d8df6b0b50b3bbf10cfc254d24486a1db3311a1f65c3c7a1c4f31 | Text | 8,261 | 189 | # <i>ref</i>orm
[*ref*orm](https://gencore.bio.nyu.edu//) is a Python-based command-line tool for fast, robust, and flexible editing of reference genome sequence and annotation files.
To perform an edit, *ref*orm requires a reference genome (FASTA), its annotation file (GFF or GTF), a novel sequence to be inserted (F... |
063a30a506954339e058b5dccb0ca9d4bb71876254b0525d3ae7377ebc224f1e | Text | 8,541 | 104 | # Banana Quest & the Digital Fruit Fly Knowledge Base
[English](#english) · [中文](#中文)
---
<a id="english"></a>
## English
A zero-dependency **browser game driven by real whole-brain connectomes** (a leaky integrate-and-fire network over 139k–176k neurons), a peer-reviewed-style **technical note on data-packaging pi... |
6d7ed140f3e5598969eed74f0b4b3edefdb75feb411d9dd80881e6757107a174 | Text | 8,545 | 180 | 
[](https://pypi.org/project/irescue/)
[![container](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fquay.io%... |
ea2369accf0105f18dfa714a54cc677da215b1d01d9bb097607d752fb672c5a3 | Text | 8,555 | 193 | <div align="center">
<img src="https://raw.githubusercontent.com/bayesian-optimization/BayesianOptimization/master/docsrc/static/func.png"><br><br>
</div>
# Bayesian Optimization
[ to count the number of colocalized synaptic puncta in fluorescence microscopy images. The macro and related experimental methods are thoroughly described in [Savage et al., 2024 Cell R... |
44550b2c0602acbc2486962a8068bb9996d615edc0a1291d6ad44837df22282d | Text | 8,679 | 168 | # AutoFocus
The repository contains scripts to replicate findings in the paper **Schweickart et al., "_AutoFocus: A hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction"_ (2023)**
<br>
## Requirements and Installation
### Hardware Requirements
... |
4051164d5f2fa14ae8d4149c88b295d952a9c735326f6a59f03ddac0bf2ff2bf | Text | 8,720 | 102 | # SytSim_Matlab
**Author:** Chris Norman
**Contact:** chris.alex.norman@outlook.com
Toolbox for simulating neurotransmitter release from synaptic vesicles under 'release of inhibition' models in response to arbitrary calcium stimuli.
The modelling framework and constraining of parameters are described in the [manus... |
a667e8926409bd821aaf456ab8ac7b0038e0c777746078e790690e86decddfcf | Text | 8,733 | 128 | # Detection of breast cancer in MRI
This repository contains a pre-trained convolutional neural network for breast cancer detection in DCE-MRI. The provided pipeline takes as an input three volumetric MR images: A T1w post-contrast image, a DCE-in image and a DCE-out image (pre-processing for generating the difference... |
2a021fe2876ec0b761305f9458ebcec6f25b9ab2fd00df122f786b67759b7098 | Text | 8,736 | 191 | # <div align="center"> SynergyNet</div>
3DV 2021: Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry
Cho-Ying Wu, Qiangeng Xu, Ulrich Neumann, CGIT Lab at University of Souther California
[<a href="https://arxiv.org/abs/2110.09772">paper</a>] [<a href="https://youtu.be/i1Y8U2Z20ko">video</a>] [<a... |
db26e3bfe937497337741f6f0d2679583962745ea614bdd6f547ada865ca0cc0 | Text | 8,792 | 166 | # Autism-3D-CNN-brain-sMRI
This project aimed at training 3D-CNN models to predict Autism on structural brain MRI data.
We also used the Guided Grad-CAM and HighRes3DNet algorithms in order to build a method of interpretation of the trained models.
When using our code or pipeline, please include the following citat... |
6fa6e97a9a9e71feaeb1da8bde9290fbd931767a64305a192e0aa84183d3d3f6 | Text | 8,793 | 137 | # Image to BIDS to SQLite ETL Tool
## Introduction
This repository is a collection of workflows, pipelines and python-tools to extract data from IMAGES to transform them into BIDS format and load them into a SQLite database. The tool is designed to be flexible and can be adapted to different data sources and target d... |
b650a7de53c4615bad8eabaa5234b0fb60d9e3c66b5d25c15c42d941053c8dc3 | Text | 8,957 | 76 |
[](https://pypi.org/project/fba/)
[](https://github.com/RangamaniLabUCSD/smart/actions/workflows/test_fenics_smart.yml)
[](https://pypi.org/project/fenics-smart/)
[ by Mark R. Saddler and Josh H. McDermott (2024, Nature Communications). Models in the paper and in this repository were all d... |
93a0e1bf078c44be497bc56da28593d6afa54ae85fc113b5ac62ce39bdff888e | Text | 9,087 | 193 | 
    
```
Xiaojia Tang*, Naresh Prodduturi*, Kevin J Thompson, Richard Weinshilboum, Ciara C O’Sullivan,
Judy C Boughey, Hamid R Tizhoosh, Eric W Klee, Liewei Wang, Matthew P Goetz, Vera S... |
227983b72984bede0d76e2ccaee4c318df92ab37f7f9e92b92d35570e802a192 | Text | 9,142 | 106 | # meanMLP
This repo contains the meanMLP model implementation and the experimental setup from the NeuroImage paper ["A simple but tough-to-beat baseline for fMRI time-series classification"](https://doi.org/10.1016/j.neuroimage.2024.120909).
**Also, check out the [ml4fmri](https://github.com/neuroneural/meanMLP/tree/m... |
e2e8eafccf5befb8907088e7f9afb217c726b022874d47ff3537ce65a08f63f1 | Text | 9,174 | 186 | # GluckLab
<div align="center">
<a href="https://brainhealth.rutgers.edu/">
<img src=./imgs/Rutgers-AgingAndBrainHealthAllianceLogo.png width=500/>
</a>
</div>
<p align="center">
【 <a href="./documentation/fish15_documentation.md">Fish 15</a> | <a href="./documentation/choose34_documentation.md">Choose 34</a... |
a6f8621563097d3cef384d8312c7a12f36345b10eea59ead112e10ab746c0c28 | Text | 9,175 | 196 | # extended-event-modeling
This repository manages code to:
- Generate object locations for all frames from semi-labels (1 frame is labeled every 10s)
- Compute features (for each frame) useful for modeling (e.g. hand-object distance, velocity of joints, etc.)
- Training and validating SEM (skip the previous two steps... |
3d9da4b8e96d7bd391dc236f8266a09a083f622c07070c8036b6f33b4a210741 | Text | 9,205 | 269 | [](https://img.shields.io/github/v/release/murphycj/agfusion)
[](https://img.shields.io/github/last-commit/murphycj/agfusion)
[.
Spherical Textures can be used in ilastik, or as a conda instal... |
e1722caeb544cb2e7bef68c93f280315f7efdad361bf6a17c333b4b99bd9e901 | Text | 9,249 | 204 | # BrainMetaSeg: Transfer Learning Approaches for Brain Metastases Screenings
[](https://doi.org/10.3390/biomedicines12112561)
[](LICENSE)
Official code for our... |
96c0f381bb5b601fd5fbf3ef0fcc4085d62459554630069b4734d0bb360e4c37 | Text | 9,265 | 204 | # BAAIWorm
Welcome to BAAIWorm, an **integrative data-driven model of *C. elegans***, linking brain, body and environment to faithfully replicate *C. elegans* locomotion behavior.
For a comprehensive introduction to BAAIWorm, please refer to [our paper](https://www.nature.com/articles/s43588-024-00738-w).
<div ... |
1972d8c00d6a8d06adf77fab5f308b89169a8748a7894e239e4143b19d65264b | Text | 9,271 | 104 | BigArrays.jl
============
[](https://travis-ci.org/seung-lab/BigArrays.jl)
[](https://coveralls.io/github/seung-lab/BigArrays.jl?branch=mast... |
29ae5bd111cc7c28d4927ff5fedfd90e52dd5caf43233402ca2af398d7492aeb | Text | 9,289 | 182 | #Authors: Pyenson,Huisken,Gupta,Rehan
#Ceratina calcarata cell type atlas
#Fastq files generated and analyzed in this study are available under BioProject PRJNA1039807 at NCBI ( https://www.ncbi.nlm.nih.gov/)
###Files, directories, and their descriptions and notes detailed below
#File: ccal_ct_atlas.R
#Description: R*... |
789ff9999596a3dd73ed40fdd4691237faa17a85a9a51f9b9b9481418e1807cc | Text | 9,306 | 193 | # Bayesian_DPA_TISR:Time-lapse Image Super-resolution Neural Network with Reliable Confidence Evaluation for Optical Microscopy
Codes, representative pre-trained models, test data for Bayesian DPA-TISR
<div align="center">
✨ [**Method**](#-Method-overview) **|** 🚩 [**Paper**](#-Paper) **|** 🔧 [**Install**](#Install... |
eec25c8a5d9bd6e1674e0089a82af384095f41106e71f2006bbc78e5d34eea6a | Text | 9,425 | 140 | # PatchCamelyon (PCam)
_That which is measured, improves._ - Karl Pearson
The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating ... |
42ad99563a925bba521b004562add8268a6fa3e02a2adbd95fb2109b4faa3725 | Text | 9,439 | 226 | # Parallel Development of Social Behavior in Biological and Artificial Fish
## Joshua D McGraw, Donsuk Lee, Justin N Wood. Nature Communications 2024.
# Contents:
- All Data (Extracted from Experi... |
6cb386002af4ebd7992cf5e5aaff9b2417c4a334eb47da085d17e2095e5b8385 | Text | 9,440 | 142 | <!--
# 
-->
#  qbic-pipelines/root-tissue-analysis
**A reproducible analysis pipeline for pH measurements derived from fluorescence microscopy of _A. thaliana_ root tissue**.
[![Nextflow]... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.