We can read and decode our geomes like poetry and provide zen level insights
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Battle tested, state-of-the-art pipelines from pioneers of Bioinformatics around the world.
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The basic objective of biomarker search is to identify factors that indicate the presence of a particular disease or an abnormal condition. The identification of molecular biomarkers from a multitude of test compounds and components is equivalent of the feature subset selection problem (FSS) known in the field of machine learning.
Power of deep learning in Cancer discovery
Two kinds of problem should be solved in order to make the models built using deep neural networks, especially generative models, a valuable option in ligand-based drug discovery
Baskin II. Epub 2020 Mar 31
De novo drug design with AI
De novo design, the generation of novel molecular entities with desired pharmacological properties from scratch, can be considered as one of the most challenging computer-assisted tasks in drug discovery, due to the cardinality of the chemical space of drug-like molecules.
Automated synthesis planning
The majority of all known organic compounds can be synthesized with a limited number of robust reactions. However, reliable and fully automated synthesis planning in chemistry is a challenge that is yet to be met.
No black box computing
Explainable AI, multitask, and meta-learning will pave the way for a new generation of predictive models with increased interpretability and robust performance in low-data regimes.
Data based lead discovery
From Conventional to Machine Learning Methods
Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures.