Consortium will expand development of a software platform for genomics and health data and apply it to COVID-19. The $5.1M project, called COVID Cloud, aims to increase Canada’s capacity to harness exponentially growing volumes of genomics and biomedical data to advance precision health.
As part of the collaboration, the parties will use BioSymetrics’ Contingent-AI™ engine across several projects to characterize high-risk populations, measure and predict disease progression based on biological risk factors and treatment course, and identify markers for clinical phenotype and severity of disease.
We were recently approached by a clinical-stage biotech client to investigate one of their lead compounds used to treat COPD. Specifically, we wanted to know whether this compound would have any activity against SARS-CoV-2.
"BioSymetrics’ participation in the ecosystem and integration with Accenture's INTIENT network will give scientists another important capability to help interpret the growing volumes of rich and diverse data that is being generated in drug discovery.”
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.
Both target and phenotype driven drug discovery strategies rely on identifying relationships between specific drugs and targets of interest. In this poster we present our automated way to integrate diverse data sets and develop machine learning models against specific protein targets.
Already working with clinicians, leveraging international collaborations to put prediction platforms in place, and re-investigating our drug lead pipeline, BioSymetrics will pursue any collaboration to have a positive impact against COVID and support the heroes working tirelessly in the hospitals and clinics around the world.
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.
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? 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.
Anthony Iacovone, Chairman of BioSymetrics, joins host Dr. Bob Kaiser for a discussion about what biomedical artificial intelligence and machine learning are and how they can be used to improve outcomes in the areas of drug discovery, clinical diagnostics and value-based care as well as reduce healthcare costs.
Gabe Musso, Chief Science Offer will be speaking at the AI in Pharma summit in Boston on October 9th. His unique perspective on data management in drug discovery and clinical trials will try to wrestle with the timeless question on how to manage and process fragmented data.
A daughter's desire to please her parents demonstrates how a data scientist with good intentions can cause far more harm and expense in the long run, through the selection and creation of the wrong features during data pre-processing.