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Source Code: Deep learning recognises antibiotic modes of action from brightfield images

This repository contains the source code to reproduce the analysis from the article 'Deep learning recognises antibiotic modes of action from brightfield images'.

Krentzel, Daniel, Kho, Kelvin, Petit, Julienne, Mahtal, Nassim, Delerue, Thomas, Huber, Max Erhard, Zettor, Agnès, Chiaravalli, Jeanne, Shorte, Spencer, Brönstrup, Mark, Wehenkel, Anne Marie, Gomperts Boneca, Ivo, Zimmer, Christophe
mit-licenseView resource
Tool

Source Code: Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria (v0.1.0)

Source code for 'Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria '.

Krentzel, Daniel, Petit, Julienne, Boudehen, Yves-Marie, Mahtal, Nassim, Sadowski, Elodie, Zettor, Agnes, Aubry, Alexandra, Chiaravalli, Jeanne, Aulner, Nathalie, Petrella, Stephanie, Alzari, Pedro, Zimmer, Christophe, Wehenkel, Anne Marie
mit-licenseView resource
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SHARK enables homology assessment in unalignable and disordered sequences

Intrinsically disordered regions (IDRs) are structurally flexible protein segments with regulatory functions in multiple contexts, such as in the assembly of biomolecular condensates. Since IDRs undergo more rapid evolution than ordered regions, identifying homology of such poorly conserved regions remains challenging for state-of-the-art alignment-based methods that rely on position-specific conservation of residues. Thus, systematic functional annotation and evolutionary analysis of IDRs have been limited, despite comprising ∼21% of proteins. To accurately assess homology between unalignable sequences, we developed an alignment-free sequenc

Chow, Chi Fung Willis, Ghosh, Soumyadeep, Hadarovich, Anna, Toth-Petroczy, Agnes
Not specifiedView resource
Tool

BrainCog-X/Brain-Cog: v0.2.7.18

Brain-inspired Cognitive Intelligence Engine (BrainCog) is a brain-inspired spiking neural network based platform for Brain-inspired Artificial Intelligence and simulating brains at multiple scales. The long term goal of BrainCog is to provide a comprehensive theory and system to decode the mechanisms and principles of human intelligence and its evolution, and develop artificial brains for brain-inspired conscious living AI in future Human-AI symbiotic Society.

sunbaby, Guobin-Shen, Dongcheng Zhao, Yang Li, wangjihang3, Yi Zeng, chengcheng du, bing88b, Feifei Zhao, Xiang He, Cola, Sunyinqian, Agnes, Tiger0044, Yuxuan Zhao, lczd, Liu Xin, lqnankai, wyw-cas, Jindong Li, fenghuihuihui
other-openView resource

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