The success of personalized genomic medicine depends on our ability to assess the pathogenicity of rare human variants, including the important class of missense variation. There are many challenges in training accurate computational systems, e.g., in finding the balance between quantity, quality, and bias in the variant sets used as training examples and avoiding predictive features that can accentuate the effects of bias. Here, we describe VARITY, which judiciously exploits a larger reservoir of training examples with uncertain accuracy and representativity. To limit circularity and bias, VARITY excludes features informed by variant annotation and protein identity. To provide a rationale for each prediction, we quantified the contribution of features and feature combinations to the pathogenicity inference of each variant.
Kristóf is Founder and CTO at Turbine.AI, and holds a PhD in molecular biology and bioinformatics. To inquire about contributed articles from outside experts, contact firstname.lastname@example.org. Could you predict how an airplane flies only based on an inventory of its parts? This – with proteins – is the essence of the protein folding challenge. Two weeks ago, the organizers of the CASP protein folding challenge just announced that DeepMind's AlphaFold essentially solved the challenge – its prediction score was just below experimental error.
AI in healthcare is something that is revolutionizing the industry and medical treatment that we as the patients receive. But AI, in general, is making inroads into virtually every field and aspect of society. Healthcare AI companies like NVIDIA healthcare and Google DeepMind Health are breaking new ground, with innovations that are helping to save lives. Let's dive into the world of AI so that you can have a better understanding of what it is all about and where it is going. AI stands for artificial intelligence.
As a leading mind in the field of computational biology and a pioneer of CMU's program on the topic, Murphy himself has played a strong role in this. In 2011, he penned a commentary noting that machine learning would play a role of growing importance in the drug discovery process. But his argument went a step further, advocating for the use of active machine learning, or a subset of ML in which the user offers the machine feedback on desired outcomes, improving its efficiency and accuracy over time. In the drug discovery process, the number of experiments required to screen a specific compound on a specific target while monitoring impact on other targets can quickly become unwieldy. Active ML offers researchers the opportunity to direct the experiment, supervising the computer as it iteratively chooses experiments that are most likely to improve the model.
IBM research contributed two platforms to this project. The RXN for chemistry uses natural language processing to automate synthetic chemistry and AI to make predictions about the success rate of the compounds used in the medicines. The company also uses an automated platform RoboRXN for molecule synthesis. The other company Arctoris used its automated platform Ulysses for the project which uses robots and digital data to conduct lab experiments in cell biology, molecular biology, and biophysics. And the experiments conducted by Ulysses generated 100 times more data in comparison to the industry-standard manual methods.
Proteins perform critical processes in all living systems: converting solar energy into chemical energy, replicating DNA, as the basis of highly performant materials, sensing and much more. While an incredible range of functionality has been sampled in nature, it accounts for a tiny fraction of the possible protein universe. If we could tap into this pool of unexplored protein structures, we could search for novel proteins with useful properties that we could apply to tackle the environmental and medical challenges facing humanity. This is the purpose of protein design. Sequence design is an important aspect of protein design, and many successful methods to do this have been developed.
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Verdict lists five of the most popular tweets on disruptive tech in August 2021 based on data from GlobalData's Influencer Platform. The top tweets were chosen from influencers as tracked by GlobalData's Influencer Platform, which is based on a scientific process that works on pre-defined parameters. Influencers are selected after a deep analysis of the influencer's relevance, network strength, engagement, and leading discussions on new and emerging trends. Ronald van Loon, principal and CEO of the Intelligent Network, an influencer network that connects businesses and experts with new audiences, shared a video on robotic floor tiles that can provide an immersive virtual reality (VR) and augmented reality (VR) experience. The tiles can automatically position themselves beneath a person's feet by anticipating their steps and creating the illusion of walking.
In this post, we discuss how to use the AWS Cloud and its services to accelerate observational studies for life sciences customers. We provide a reference architecture for architects, business owners, and technology decision-makers in the life sciences industry to automate the processes in clinical studies. Observational studies lead the way in research, allowing you to formulate hypotheses and test those hypotheses in controlled experiments. These studies are a powerful tool to help researchers learn what happens in real-life studies. You can use this research as a precursor to drug discovery and new drug indications.