Cancer-dedicated gene set interpretation
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Updated
Aug 30, 2023 - R
Cancer-dedicated gene set interpretation
Easy-to-use python module for training multi-layer perceptrons (neural networks) from molecular SMILES and known associated properties
Chemios Framework 👨🏾🔬: Accelerating Science through Automation
Python package for removal of duplicates in (solid state) structural databases
BIOMERO - A python library for easy connecting between OMERO (jobs) and a Slurm cluster
Metric Learning for Combinatorial Datasets
An R package that enables the user to perform High Throughput Biological Screening.
High-level module for ultra-fast structural optimisation and property calculations of organic co-polymers.
Tox21 quantitative high throughput screening (qHTS) 10K library data
Code and analysis scripts for analyzing newly transcribed RNA in large-scale compound screen experiments
An SGC Open Chemistry Networks Project (number 21) dedicated to finding hits vs CHIKV nsp2 helicase based on a HTS of a Asinex library.
High-throughput detection and enumeration of tumor cells in blood using Digital Holographic Microscopy (DHM) and Deep Learning.
This paper develops new methods to handle false positives in High-Throughput Screening experiments.
We present a detailed study of the asymptotic behavior of the distribution of the tails of these, perhaps, most commonly used statistical tests under non-standard conditions, that is, releasing the underlying assumptions of normality, independence and identical distribution and considering a more general case where one only assumes that the vect…
Plan and analyse large-scale liquid transfers
This project aims to develop a chemical probe of CHIKV-nsP3-macrodomain starting from a fragment screen
Compound dilutions entirely within an Echo acoustic liquid handler.
An SGC Chemical Networks Project devoted to the DENV Rdrp, focusing specifically on exploring the LifeChemicals library. Use the link below to learn about our other READDI-AViDD projects.
A tool for automatic neurite outgrowth and cell viability estimation using deep learning and graph theory.
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