Predicting Mechanism of Action Using AI - Webinar
In this webinar, Mikalai Malinouski Ph.D.demonstrated how BioSymetrics uses AI in the prediction of mechanism of action (MOA), and provided a an example of how machine learning workflows can benefit both novel drug discovery and drug repositioning.
Mitigating Batch Effects in Cell Painting Data
With the advent of high content screening methodologies (e.g. cellular imaging, transcriptomics, etc.), it becomes more challenging to tease apart and visualize batch effects. This is further compounded when building machine learning models which can easily use these confounding variables instead of real biological signal to generate predictions leading to poor real world relevance.
De-noising CMap L1000 Data
As with any assay, L1000 data is noisy. Experimental replicates (the same compound tested on the same cell line under the same conditions) often result in different levels of expression being measured. The process of de-noising the L1000 data makes it easier to see true assay response, and pick a representative concentration for each compound.
When are Two Compounds the Same?
When are two compounds the same? The effect of Simplified Molecular Input Line Entry System (SMILES) format on chemical database overlap including best practice for canonicalization and harmonization to understand the impact of these compound effects on a particular dataset and specific application.
Dealing with Missing Values in Healthcare Data
In this post, we highlight the challenges of missing values when modelling with time-series data of EMRs and discuss some techniques to address it.
Phenotype/Mechanism Prediction
Use Case: BioSymetrics ML framework generates structure-based activity predictions using phenotypic assays and HCS data to identify protein targets and affected pathways
Bias Reduction in HCS Data
Bias Reduction in High Content Screening can be solved using BioSymetrics Augusta™ machine learning platform.
Feature Engineering of Electronic Medical Records
A comprehensive overview of data cleaning and feature engineering techniques for clinical data