About
High-throughput phenotypic screening, based on high content imaging, is increasingly often used as a tool in the context of drug discovery. Compound screens are used to find hits that produce the desired phenotypes in relevant cellular assays. Genomic screens are used to elucidate the underlying molecular pathways and identify suitable drug targets. Since a wealth of data is produced in the process of high- content screening, data science approaches such as statistics, machine learning and neural networks can play an important role in making the most of the collected data. Much like virtual screening can be performed in more classical chemoinformatic settings by, e.g., learning predictive models for QSAR (quantitative structure-activity relations) from data obtained through compound screens, similar approaches can be taken in the context of high-throughput phenotypic screening.
The ICGEB-TRAIN event will bring together a diverse group of experts covering the different topics of high-content screening, image analysis, chemoinformatics and machine learning. This will allow graduate students, as well as researchers from academia and industry, to familiarize themselves with these highly modern and important topics.
This will be the first event of its kind on this set of hot topics in the region of Slovenia and Friuli-Venezia- Giulia. There is ample potential audience in the region, both in terms of graduate students and researchers from academia and industry. The event will have an impact both on the academic and industrial sector in the region, as there are many biotech companies, both large and small, in the region. The INTERREG V-A Italy-Slovenia 2014-2020 project TRAIN (Big Data and Disease Models: A Cross- border Platform for Validated Biotech Industry Kits) brings together some of the academic and industrial players from the region and demonstrates interest in the topic.
Videos

Kernel-based predictive modelling of drug-protein binding affinities
Jun 28, 2019
·
76 views

Multi-Target Prediction with Trees and Tree Ensembles
Jun 28, 2019
·
166 views

Meta-QSAR and Multi-Task QSAR Learning
Jun 28, 2019
·
205 views

Multi-task learning in the analysis of phenotypic data
Jun 28, 2019
·
87 views

Exploring high‐content screening as a functional genomics tool in biomedicine
Jun 28, 2019
·
100 views

Searching for innovative biological drugs
Jun 28, 2019
·
76 views

Improving the reproducibility of experiments and reusability of research outputs...
Jun 28, 2019
·
66 views

The Automation of Science
Jun 28, 2019
·
135 views

Graph neural networks for computational drug repurposing
Jun 28, 2019
·
162 views

Virtual Screening and Library Design
Jun 28, 2019
·
63 views

Workshop opening
Jun 28, 2019
·
89 views

Transformative Machine Learning: Explicit is Better than Implicit
Jun 28, 2019
·
146 views

Life beyond the pixels: machine learning and image analysis methods for HCS
Jun 28, 2019
·
107 views

Deep learning for computational chemistry: compound representation, ADMET profil...
Jun 28, 2019
·
109 views

Building Chemogenomics Models from a Large-Scale Public Dataset and Applying the...
Jun 28, 2019
·
83 views

Network embeddings for modeling polypharmacy and drug-drug interactions
Jun 28, 2019
·
121 views

Bio- and Cheminformatics Methods for Mode of Action Analysis
Jun 28, 2019
·
89 views

High content screening: from large libraries to functional hits
Jun 28, 2019
·
157 views

Semi-supervised multi-target prediction for analysis of screening data
Jun 28, 2019
·
79 views