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AI in Biotechnology

#artificialintelligence

The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. A subset of artificial intelligence is machine learning, which refers to the concept that computer programs can automatically learn from and adapt to new data without being assisted by humans. Deep learning techniques enable this automatic learning through the absorption of huge amounts of unstructured data such as text, images, or video. At its simplest, biotechnology is technology based on biology -- biotechnology harnesses cellular and biomolecular processes to develop technologies and products that help improve our lives and the health of our planet. We have used the biological processes of microorganisms for more than 6,000 years to make useful food products, such as bread and cheese, and to preserve dairy products. Biotechnology can be categorized into a few types agricultural biotechnology, medical biotechnology, animal biotechnology, industrial biotechnology, and bioinformatics. Let us see how Artificial Intelligence is impacting these branches of biotechnology.


Artem Cherkasov and Olexandr Isayev on Democratizing Drug Discovery With NVIDIA GPUs

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It may seem intuitive that AI and deep learning can speed up workflows -- including novel drug discovery, a typically years-long and several-billion-dollar endeavor. But professors Artem Cherkasov and Olexandr Isayev were surprised to find that no recent academic papers provided a comprehensive, global research review of how deep learning and GPU-accelerated computing impact drug discovery. In March, they published a paper in Nature to fill this gap, presenting an up-to-date review of the state of the art for GPU-accelerated drug discovery techniques. Cherkasov, a professor in the department of urologic sciences at the University of British Columbia, and Isayev, an assistant professor of chemistry at Carnegie Mellon University, join NVIDIA AI Podcast host Noah Kravitz this week to discuss how GPUs can help democratize drug discovery. In addition, the guests cover their inspiration and process for writing the paper, talk about NVIDIA technologies that are transforming the role of AI in drug discovery, and give tips for adopting new approaches to research.


A celebrated AI has learned a new trick: How to do chemistry

#artificialintelligence

Artificial intelligence has changed the way science is done by allowing researchers to analyze the massive amounts of data modern scientific instruments generate. It can find a needle in a million haystacks of information and, using deep learning, it can learn from the data itself. AI is accelerating advances in gene hunting, medicine, drug design and the creation of organic compounds. Deep learning uses algorithms, often neural networks that are trained on large amounts of data, to extract information from new data. It is very different from traditional computing with its step-by-step instructions.


Synthetic Data Is About To Transform Artificial Intelligence

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These people do not exist. These faces were artificially generated using a form of deep learning ... [ ] known as generative adversarial networks (GANs). Synthetic data like this is becoming increasingly indistinguishable from real-world data. Imagine if it were possible to produce infinite amounts of the world's most valuable resource, cheaply and quickly. What dramatic economic transformations and opportunities would result? This is a reality today. It is called synthetic data. Synthetic data is not a new idea, but it is now approaching a critical inflection point in terms of real-world impact.


Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations - PubMed

#artificialintelligence

The promise of highly personalized oncology care using artificial intelligence (AI) technologies has been forecasted since the emergence of the field. Cumulative advances across the science are bringing this promise to realization, including refinement of machine learning- and deep learning algorithms; expansion in the depth and variety of databases, including multiomics; and the decreased cost of massively parallelized computational power. Examples of successful clinical applications of AI can be found throughout the cancer continuum and in multidisciplinary practice, with computer vision-assisted image analysis in particular having several U.S. Food and Drug Administration-approved uses. Techniques with emerging clinical utility include whole blood multicancer detection from deep sequencing, virtual biopsies, natural language processing to infer health trajectories from medical notes, and advanced clinical decision support systems that combine genomics and clinomics. Substantial issues have delayed broad adoption, with data transparency and interpretability suffering from AI's "black box" mechanism, and intrinsic bias against underrepresented persons limiting the reproducibility of AI models and perpetuating health care disparities.


Biotech giant Benchling launch Alphafold AI from DeepMind

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An artificial intelligence program developed by DeepMind that can predict the 3D structure of a protein from an amino acid sequence with unprecedented accuracy, AlphaFold is emblematic of a new era of modern biotech -- "data-driven, open-sourced, collaborative and ultimately, faster than ever", according to Benchling. Born out of a Benchling hackathon, the vast majority of labs are unable to access AlphaFold today, despite being open source to use. With Benchling's AlphaFold beta feature, scientists can not only predict 3D structures of novel proteins directly within Benchling, but also centralise experimental context, collaborate with teammates, and connect with downstream scientific workflows on a single, secure platform. President and co-founder of Benchling, Ashu Singhal, explained: "Our team gets excited about two things: science and bringing software to science. By making AlphaFold available to the biotech industry at the click of a button, scientists will be able to seamlessly experiment with this exciting advancement and find new ways to leverage AlphaFold output in their research. While the use cases for AlphaFold are still being explored and proven, Benchling's goal with its beta feature is to support its community."


Contrastive Representation Learning for 3D Protein Structures

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In recent years, learning on 3D protein structures has gained a lot of attention in the fields of protein modeling and structural bioinformatics. K different PDB entries, and the SCOPe database contains 226 K annotated structures. These numbers are orders of magnitude lower than the data set sizes which led to the major breakthroughs in the field of deep learning. In order, to take advantage of unlabeled data, researchers have, over the years, designed different algorithms, that are able to learn meaningful representations from such data without labels (Hadsell et al., 2006; Ye et al., 2019; Chen et al., 2020a) However, these algorithms were designed for arbitrary graphs and did not take into account the underlying structure of proteins. In this work, we introduce a contrastive learning framework for representation learning of 3D protein structures.


Recent Technological Advances in Drug Discovery - CBIRT

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The goal of drug discovery and development is to bring new medicines to patients suffering from critical illnesses. Earlier, drug discovery was a tedious process. Bringing a drug to market still takes 10 to 15 years. As a result, there is a lot of interest in finding new approaches to developing drugs using novel technological approaches. Machine learning tools and techniques are proving their importance at every stage of drug discovery and reducing the risk, and lowering the cost and expenditure used in clinical trials. It proves crucial in QSAR analysis, de novo drug design, hit discoveries, target validation, prognostic biomarkers, digital pathology, etc. The discovery of a drug has a lengthy procedure to go through before reaching the market.


Pharmaceutical Sales prediction Using LSTM Recurrent Neural Network

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LSTM methodology, while introduced in the late 90's, has only recently become a viable and powerful forecasting technique.In this article, we are going to use LSTM RNN on a Rossman Pharmaceutical time series dataset to predict sales on a real-world business problem taken from Kaggle. Problem Statement Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied.


Meet the Seattle-area teen geeks that just won awards at an international science fair

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The bleak and all-too-common spectacle of roadkill was upsetting to Vedant Srinivas -- particularly when his uncle and cousin's beloved German Shepherd-Rottweiler mix was fatally hit by a car. More importantly, the losses made the high school student wonder if he could do something about it. What if Srinivas could stop the pet owners' broken hearts, save wildlife and deflect the economic impacts caused by the collisions? This month his efforts were rewarded. The sophomore from Eastlake High School in Sammamish, Wash., brought home a $5,000, first place grand award for the category of Environmental Engineering from the Regeneron International Science and Engineering Fair (ISEF).