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1-12 of 37

aiproteomics

Generate and compare deep learning models for generating synthetic mass spectral libraries

Machine learning
mass spectrometry
phosphoproteomics
+ 1

Python
Shell

AstronomicAL

An interactive dashboard for visualisation, integration and classification of data using Active Learning.

Active Learning
Classification
Data Analysis
+ 11

Python
TeX

bird-cloud-gnn

bird-cloud-gnn is a Python package that helps generate a graph representation from point cloud data and fits graph neural networks for classification problem

Classification
Graph Neural Network
Machine learning
+ 2

Python

Clustering Geo-data Cubes

Tool to perform cluster analysis of multi-dimensional geospatial data, running on local or distributed systems.

Big data
geospatial
Machine learning
+ 1

Python
Jupyter Notebook
TeX

chimp-classifier

The Python package `junglesounds` is a machine learning pipeline for classifying bioacoustic data using machine learning. The pipeline is reusable for other settings and species or vocalization types as long as a certain amount of labeled data has been collected.

audio
Audio processing
bioacoustics
+ 2

Jupyter Notebook
Python
Shell

cleanX

CleanX is an open-source python library for exploring, cleaning and augmenting large datasets of X-rays, or certain other types of radiological images.

Image processing
Machine learning

Jupyter Notebook
Python
TeX
+ 3

DeepRank

Deep learning framework for data mining protein-protein interactions using CNN

Big data
Machine learning
Optimized data handling

Python
R
C
+ 2

DIANNA

Deep Insight And Neural Network Analysis, DIANNA is the only Explainable AI, XAI library for scientists supporting Open Neural Network Exchange, ONNX - the de facto standard models format.

Explainable AI
Image processing
Machine learning
+ 2

Jupyter Notebook
Python
TeX

Dive

Interactively explore millions of 2D and 3D data points in your browser, without the need to install anything.

Big data
Machine learning
Visualization

HTML
JavaScript
CSS
+ 1

DoE2Vec

DoE2Vec is a self-supervised approach to learn exploratory landscape analysis features from design of experiments. The model can be used for downstream meta-learning tasks such as learninig which optimizer works best on a given optimization landscape.

Machine learning
optimization
python

Python
R
Makefile

e2e-Dutch

Coreference resolution for Dutch: automatically links references to the same entity in Dutch written texts.

Machine learning
Text analysis & natural language processing

Python
C++
Shell

eEecology Classification

Automatic classification of accelerometer data using a supervised learning approach.

Machine learning

Java
JavaScript
HTML
+ 3