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Machine Learning


Virtual Immunostaining for Digital Pathology

   by Victor Dillard    209
Virtual Immunostaining for Digital Pathology

Owkin is a French-American startup, backed by Google Ventures among others, that deploys Artificial Intelligence (AI) and Federated Learning for medical research. The company was co-founded in 2016 by Thomas Clozel, a hematologist oncologist and researcher, and Gilles Wainrib, a computer science teacher-researcher at the École Normale Supérieure, and a Stanford University PostDoc.

Over the years, the team at Owkin has developed AI systems to analyze and interpret multimodal medical data, including pathology images, radiology images, genetic data, lab analysis, and clinical outcomes. The company recently announced the launch of a new product -- Virtual Staining, integrated as a feature in Owkin Studio platform.

[Interview] Using Generative AI to Rapidly Identify Novel Therapies for COVID-19

   by Andrii Buvailo    437
[Interview] Using Generative AI to Rapidly Identify Novel Therapies for COVID-19

Generative models have become one of the hottest areas in de-novo molecular design over just several years, basically revolutionizing our perception of what can be done with artificial intelligence in this area. One important aspect of generative models is that they can produce new quality hit molecules using combined data from various experimental and theoretical sources -- and output results rapidly. 

One notable drug discovery startup betting on deep learning and generative models for innovative drug design is Vancouver-based Variational AI.

Tzager - A Smart AI Agent For Biomedical Research

   by Nikos Tzagarakis    752
Tzager - A Smart AI Agent For Biomedical Research

Tzager is an A.I. agent built for biomedicine research, drug discovery and personalized medicine, with the main features being Biochemical Analysis, Predictor Research/Models and Literature Review/Management. The difference with Tzager is that it is not just another deep learning algorithm trained to solve very specific problems, but the intelligence system with its own framework based on Causal Equations and Bayesian Networks.

AI For Commercial Life Sciences: 3 Trends You Can’t Ignore In 2020

   by Rasim Shah    350
AI For Commercial Life Sciences: 3 Trends You Can’t Ignore In 2020

As we enter a new decade, our belief in the the impact of Artificial intelligence (AI) is only getting stronger. Supporting the industry to drive the right drug to the right patient at speed is a huge responsibility that we take very seriously. Towards the end of the last decade we have seen great progress made within life sciences and the use of AI, but moving into 2020 the spotlight on commercial teams and gaining competitive advantage with AI will intensify.

ConstruQt – The Beginnings of the Chemical Data Revolution

   by Peter Jarowski    314
ConstruQt – The Beginnings of the Chemical Data Revolution

Chemical Data Has Problems

The state of data access, quality and dissemination in Chemistry is extremely poor - so poor that it is blocking advances in machine learning (ML) and artificial intelligence (AI), and also impeding research and development in traditional methods. The recent surge in AI skepticism is a direct consequence of years of over-hype and promises based on precarious data. Over-the-top expectation were offered without enough consideration for the data quality and volume required to train fancy algorithms. The old adage “^&$% in, ^&$% out” holds true (we can say ‘crap’ right?). This opinion is in line with recent statements by the CEO of Novartis, for example, who runs the second largest pharmaceutical company in the world, lamenting the difficulty in accessing quality datasets to make AI effective.