A flexible Julia toolkit for high-dimensional cellular profiles
- julia
- morphological-analysis
- high-content-screening
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A flexible Julia toolkit for high-dimensional cellular profiles
:game_die: Neutrino research
Generative models for transcriptomic-driven or protein-driven molecular design (PaccMann^RL).
Utility library to pretty print physical values in proper metric units
DetecDiv provides a comprehensive set of tools to analyze time microscopy images using deep learning methods. The software structure is such that data can be processed either at the command line or using a graphical user-interface. Detecdiv classification models include : image classification and regression, semantic segmentation, LSTM networks to analyze data and image timeseries.
A tiny easy math include addition multiplication subtraction division.
A collection of matrix handling scripts
DeepAEG: A model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies
Python library for statistical analysis of mRNA and protein distributions in fluorescent microscopy images
Distributed Simulation of Transport Networks
Functions for the input/output and visualization of medical imaging data that follow either the ANALYZE, NIfTI or AFNI formats.
Interpretable drug response prediction with graph neural networks over drugs, cell lines, and genes.
High performance, zero-allocation, low memory usage (8 bytes), low precision (17 digits), partially compatible with int64 and shopspring Decimal
GNN-based gene-selection deep learning model for drug response prediction
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