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The Antibody Reproducibility Crisis

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Ontario Institute for Cancer Research

661 University Avenue

West Tower, Suite 510

Toronto, ON M5G0A3

Canada

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BioLab at the Ontario Institute for Cancer Research (OICR) and BenchSci will co-host a seminar on The Antibody Reproducibility Crisis

About this Event

Antibodies are the most commonly used tools in the life sciences. They are invaluable in many experiments to identify and isolate other molecules. But it is now clear that they are among the most common causes of problems, too. From the batch-to-batch variability, cross-reactivity, specificity, and wrong application, antibodies can also be a source for dramatically differing results, which can cause projects to be abandoned, and waste time, money and samples.

Join us to learn about BenchSci, a Canadian life science machine learning company that has developed open-access “out-of-crisis” solution by offering AI-assisted antibody selection platform that helps scientists to navigate through peer-reviewed research data and experimental contexts including technique, tissues and cell lines to find antibodies that suit their experimental conditions.

Dr. Maurice Shen, PhD, Head of Academic Relations, BenchSci

Maurice obtained his PhD in neuropharmacology at the University of Toronto. Upon the completion of his degree, he joined the BenchSci co-founding team to develop the AI-assisted antibody selection platform. Maurice works closely with various stakeholders in academic research to ensure the adoption of the BenchSci platform, and to guide platform development based on.

Dr. Casandra Mangroo, PhD, Head of Science, BenchSci

Casandra applies the research experience from her Ph.D. in Virology from the University of Toronto as the Science Team lead and product manager of the knowledge graph and data pipeline at BenchSci. Casandra is closely involved with developing the machine learning training sets and is responsible for the integrity and comprehensiveness of the scientific data onthe BenchSci platform. She also works closely with the BenchSci co-founders, R&D and engineering teams to implement new data.

3 - 3:30 p.m.

The Antibody Reproducibility Crisis: Leveraging machine learning for AI-assisted antibody selection

Dr. Maurice Shen, Head of Academic Relations, BenchSci

The “reproducibility crisis” has generated much attention in the research community over the past years. While the issue is multifaceted at its core, rogue antibodies have been identified as one of the major culprits. This talk we will review what is known about the "Antibody Crisis" in the literature and introduce BenchSci’s approach to solving this problem.

The BenchSci will present their innovative open-access resource that uses a machine-learning algorithm to screen the literature and identify which and how antibodies have been cited. The resulting peer-reviewed data are searchable by protein targets or product identifier and are filterable by experimental contexts as cited in papers, including technique, tissues, cell lines.

3:30 - 4 p.m.

AI-Assisted Reagent Selection: The application of machine learning to accelerate the experimental design

Dr. Casandra Mangroo, Head of Science, BenchSci

Advances in the fields of AI and machine learning paved the way for the development of BenchSci’s proprietary image and text-based machine learning algorithms along with bioinformatics ontologies, to extract relevant experimental data from original research resources. This information is contextualized within a knowledge graph that powers an AI-assisted antibody selection platform. This technology will soon expand into other scientific reagents and experiments and ultimately facilitate the entire experimental design process.

Artificial intelligence is truly revolutionizing the life science industry. While there are many AI companies focused on the target and lead compound identification, it is only the first step. There is a gap in the application of AI in the many years of bench work required to validate these candidates effectively. Our goal is to shorten the R&D and pre-clinical phases by giving research scientists the ability to leverage the power of AI technology to streamline the experimental design process and ultimately bring treatments to market faster.

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Ontario Institute for Cancer Research

661 University Avenue

West Tower, Suite 510

Toronto, ON M5G0A3

Canada

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