If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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.
When the novel coronavirus led to a global pandemic last year, doctors and researchers rushed to learn as much as possible about the virus and how our bodies respond to it. They needed a lot of information, and they needed it fast. Doctors studied whether available medicines could effectively treat the symptoms of COVID-19. Virologists, biologists, and chemists scrambled to understand how the virus affects the molecular workings of cells, information key to designing medicine to treat infection and resulting disease. Medical and biological data flowed fast and furiously.
Scientists have developed a method using machine learning to better analyze data from a powerful scientific tool: Nuclear magnetic resonance (NMR). One way NMR data can be used is to understand proteins and chemical reactions in the human body. NMR is closely related to magnetic resonance imaging (MRI) for medical diagnosis. NMR spectrometers allow scientists to characterize the structure of molecules, such as proteins, but it can take highly skilled human experts a significant amount of time to analyze that data. This new machine learning method can analyze the data much more quickly and just as accurately.
Dementias are characterised by the build-up of different types of protein in the brain, which damages brain tissue and leads to cognitive decline. In the case of Alzheimer's disease, these proteins include beta-amyloid, which forms'plaques', clumping together between neurons and affecting their function, and tau, which accumulates inside neurons. Molecular and cellular changes to the brain usually begin many years before any symptoms occur. Diagnosing dementia can take many months or even years. It typically requires two or three hospital visits and can involve a range of CT, PET and MRI scans as well as invasive lumber punctures.
Predicting RNA (ribonucleic acid) structures may help accelerate the discovery and development of new drugs to treat diseases and disorders. A new Stanford study published in Science uses artificial intelligence (AI) machine learning to predict RNA structures with state-of-the-art performance results. "Few RNA structures are known, however, and predicting them computationally has proven challenging," wrote the Stanford scientists. "We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures." In molecular biology, RNA (ribonucleic acid) is involved in many important cellular functions.
Convert String data to Numeric format so we can process the data in Apache Spark ML Library. Welcome to this project on predicting the Cellular Localization Sites of Proteins in Yest in Apache Spark Machine Learning using Databricks platform community edition server which allows you to execute your spark code, free of cost on their server just by registering through email id. In this project, we explore Apache Spark and Machine Learning on the Databricks platform. I am a firm believer that the best way to learn is by doing. That's why I haven't included any purely theoretical lectures in this tutorial: you will learn everything on the way and be able to put it into practice straight away.
Prof. Yanay Ofran's amazing story about the pursuit of an antibody that will save the world from disease Shlomit Lan and Gali Weinreb Professor Yanay Ofran, founder and CEO of Biolojic Design, a company that develops smart antibodies designed to treat a variety of diseases, is frustrated. "Humanity invests $300 billion each year in drug development, and what do we get? At most, we get a few dozen medications a year, most of which don't solve the problems, and give an additional three weeks of life on average to patients with pancreatic cancer, or manage to inject a medication that to date was given via infusion. Those are the breakthroughs," he says despairingly. But Ofran does not think the pharmaceutical companies are the only culprit. "The drug companies are portrayed as a devil who says, 'I won't cure this because it's not worth my while.' But these companies do have a legal obligation towards their shareholders, not to develop drugs unless there's an economic incentive. The problem, as analyzed by Ofran, is much more complicated and therefore far more difficult to treat. "There are three players sitting around the drug development table: science, regulation and the business world.
Deep learning is solving biology's deepest secrets at breathtaking speed. Just a month ago, DeepMind cracked a 50-year-old grand challenge: protein folding. A week later, they produced a totally transformative database of more than 350,000 protein structures, including over 98 percent of known human proteins. Structure is at the heart of biological functions. The data dump, set to explode to 130 million structures by the end of the year, allows scientists to foray into previous "dark matter"--proteins unseen and untested--of the human body's makeup.
In its on-going campaign to reveal the inner workings of the Sar-CoV-2 virus, the U.S. Department of Energy's (DOE) Argonne National Laboratory is leading efforts to couple artificial intelligence (AI) and cutting-edge simulation workflows to better understand biological observations and accelerate drug discovery. Argonne collaborated with academic and commercial research partners to achieve near real-time feedback between simulation and AI approaches to understand how two proteins in the SARS-CoV-2 viral genome, nsp10 and nsp16, interact to help the virus replicate and elude the host's immune system. The team achieved this milestone by coupling two distinct hardware platforms: Cerebras CS-1, a processor-packed silicon wafer deep learning accelerator; and ThetaGPU, an AI- and simulation-enabled extension of the Theta supercomputer, housed at the Argonne Leadership Computing Facility, a DOE Office of Science User Facility. To enable this capability, the team developed Stream-AI-MD, a novel application of the AI method called deep learning to drive adaptive molecular dynamics (MD) simulations in a streaming manner. Data from simulations is streamed from ThetaGPU onto the Cerebras CS-1 platform to simultaneously analyze how the two proteins interact.
Many people believe that the process for achieving breakthrough innovations is chaotic, random, and unmanageable. Breakthroughs can be systematically generated using a process modeled on the principles that drive evolution in nature: variance generation, which creates a variety of life-forms; and selection pressure to select those that can best survive in a given environment. Flagship Pioneering, the venture-creation firm behind Moderna Therapeutics, uses such an approach, which it calls emergent discovery. It involves prospecting for ideas in novel spaces; developing speculative conjectures; and relentlessly questioning hypotheses. On November 30, 2020, Moderna Therapeutics announced that Phase III clinical trials for its messenger RNA vaccine demonstrated 95% protective efficacy against the SARS-CoV-2 virus that had killed almost 1.5 million people worldwide in the previous 10 months. A relative upstart in the Covid-19 vaccine race and a company that few people had heard of before the pandemic, Moderna looked to be an overnight success. But as its CEO, Stéphane Bancel, has noted, that success was 10 years in the making. Far from a one-and-done stroke of luck, the vaccine was the product of a repeatable process that has been used countless times by the company from which Moderna emerged: Flagship Pioneering, a venture-creation firm based in Cambridge, Massachusetts, whose mission is to conceive, make, and commercialize breakthrough innovations in previously unexplored domains of the life sciences. The misconception about the Moderna case, as with many other breakthrough innovations, is understandable. Breakthrough innovations are typically seen as the result of chaotic, random, and unmanageable efforts--the product of pure serendipity or the inspiration of a rare visionary. That view, we believe, is deeply flawed. From our different vantage points (Afeyan has spent the past three decades starting ventures based on breakthrough science and technology, and Pisano has studied innovation processes during the same period), we have come to realize that breakthroughs tend to emerge from a relatively well-defined process modeled on the basic principles that drive evolution in nature: variance generation, which creates a variety of life-forms, and selection pressure to select those that can best survive and reproduce in a given environment. The approach, called emergent discovery, is a structured and disciplined process of intellectual leaps, iterative search and experimentation, and selection.