life sciences


WekaIO raises $31.7 million to develop file systems optimized for AI and technical workloads

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No matter the domain, data-intensive apps share one requirement in common: a reliable file system that ensures data is available to them on demand. Pure Storage, NetApp, VAST Data, IBM Spectrum Scale, and Dell EMC provide this, as does San Jose, California-based company WekaIO. WekaIO's high-velocity Matrix platform takes advantage of flash storage, off-the-shelf components, and sophisticated software techniques to deliver enormous speedups at exabyte scale. In fact, the company claims Matrix is the fastest parallel file system on the market for AI and technical compute workloads, as measured by independent SPEC SFS 2014 benchmark tests. To lay the groundwork for future growth in AI and analytics, life sciences, manufacturing, media and entertainment, and financial services, WekaIO has closed a $31.7 million series C financing round led by Hewlett Packard Enterprise (HPE), with participation from a host of storage and computing industry giants including Mellanox, Nvidia, Seagate, Western Digital Capital, and Qualcomm.


Robots are coming to a hospital near you

Fast Company

Hospitals and medical practices are already using a fair amount of automation. Some hospitals are set up for delivery robots to open remote-control doors and even use elevators to get around the building. Robots can also assist with more complex tasks, like surgery. Their participation can range from simply helping stabilize a surgeon's tools all the way to autonomously performing the entire procedure. Perhaps the most famous robotic surgery system lets a surgeon operate full-size, ergonomically friendly equipment as a remote control to direct extremely tiny instruments what to do inside a patient's body, often through extremely small incisions.


2019 Global life sciences sector outlook Deloitte

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Forward-thinking pharma companies are moving beyond pilots and focusing on how new technologies can add value. These are some of the technologies driving digital transformation in life sciences. Artificial Intelligence (AI): AI is just beginning to be applied in life sciences to help with intelligent use of data. It has the potential to revolutionize diagnoses, treatment planning, patient monitoring, and drug discovery. Internet-of-Medical-Things (IoMT): The rising number of connected medical devices--together with advances in the systems and software that support medical grade data and connectivity--have created the IoMT.


6 impactful applications of AI to the life sciences [new essay]

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In 2013, the machine learning (ML) research community demonstrated the uncanny ability for deep neural networks trained with backpropagation on graphics processing units to solve complex computer vision tasks. The same year, I wrapped up my PhD in cancer research that investigated the genetic regulatory circuitry of cancer metastasis. Over the 6 years that followed, I've noticed more and more computer scientists (we call them bioinformaticians:) and software engineers move into the life sciences. This influx is both natural and extremely welcome. The life sciences have become increasingly quantitative disciplines thanks to high-throughput omics assays such as sequencing and high-content screening assays such as multi-spectral, time-series microscopy. If we are to achieve a step-change in experimental productivity and discovery in life sciences, I think it's uncontroversial to posit that we desperately need software-augmented workflows. This is the era of empirical computation (more on that here). But what life science problems should we tackle and what software approaches should we develop?


6 impactful applications of AI to the life sciences [new essay]

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Drug discovery can be viewed as a multi-parameter optimisation problem that stretches over vast length scales. Successful drugs are those that exhibit desirable molecular, pharmacokinetic and target binding properties. These pharmacokinetic and pharmacology properties are expressed as absorption, distribution, metabolism, and excretion (ADME), as well as toxicity in humans and protein-ligand (i.e. Traditionally, these features are examined empirically in vitro using chemical assays and in vivo using animal models. To do so, most academic labs will rely on lab scientists endlessly pipetting and transferring small amounts of liquids between plastic vials, tissue culture and various pieces of analytical equipment.


The New Techno-Fusion: The Merging Of Technologies Impacting Our Future

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The process of systems integration (SI) functionally links together infrastructure, computing systems, and applications. SI can allow for economies of scale, streamlined manufacturing, and better efficiency and innovation through combined research and development. New to the systems integration toolbox are the emergence of transformative technologies and, especially, the growing capability to integrate functions due to exponential advances in computing, data analytics, and material science. These new capabilities are already having a significant impact on creating our future destinies. The systems integration process has served us well and will continue to do so.


EL Embeddings: Geometric construction of models for the Description Logic EL ++

arXiv.org Artificial Intelligence

An embedding is a function that maps entities from one algebraic structure into another while preserving certain characteristics. Embeddings are being used successfully for mapping relational data or text into vector spaces where they can be used for machine learning, similarity search, or similar tasks. We address the problem of finding vector space embeddings for theories in the Description Logic $\mathcal{EL}^{++}$ that are also models of the TBox. To find such embeddings, we define an optimization problem that characterizes the model-theoretic semantics of the operators in $\mathcal{EL}^{++}$ within $\Re^n$, thereby solving the problem of finding an interpretation function for an $\mathcal{EL}^{++}$ theory given a particular domain $\Delta$. Our approach is mainly relevant to large $\mathcal{EL}^{++}$ theories and knowledge bases such as the ontologies and knowledge graphs used in the life sciences. We demonstrate that our method can be used for improved prediction of protein--protein interactions when compared to semantic similarity measures or knowledge graph embedding


Bionic Hands Let Amputees Feel and Grip

IEEE Spectrum

If you're sitting near a coffee mug, pick it up, and note how easy it is to do without really looking. You feel the curvature of the handle, the width of the cup, the slipperiness of the ceramic. Your hand glides into place and you squeeze, getting a sense of the weight, and bring the cup to your mouth. Now, imagine trying to do that with a robotic hand that gives you no sensory feedback. You get no information about the tiny adjustments that your fingers must make in order to grasp it properly.


AI in Biopharmaceutical R&D Accenture

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No matter where we turn, we're never far from the reality of how artificial intelligence (AI) is impacting our daily lives. Whether it's asking Alexa to give today's news headlines, or our thermostats knowing more about our whereabouts and travel plans than we do, it's clear that AI will fundamentally change our world in ways that we may not yet fully anticipate. Which is why it's not surprising that AI is one of the most common topics we've been discussing with our life sciences clients. They're looking for insights into how to apply AI in their businesses--when and where they should invest, what they need to do to prepare, and how to tell what is real and what is just marketing hype. The reality is that there are no simple answers, as AI is a constellation of capabilities with a broad range of maturity.


AI in life sciences: The tow man's dilemma - Technology - MM&M - Medical Marketing and Media

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An acquaintance of mine recently found himself in a fairly common plight: His car broke down miles from home, and so he called for a tow. Tow truck drivers not only ply a noble trade; they also rank among life's most sagacious observers. During the ride to the garage, this driver told my friend that most of his calls actually involve newer cars whose electrical systems have gone awry, rendering the vehicles inoperable. Given the complexity of today's autos, the tow man shared, he spends his days bailing out one stranded new car owner after another. We've come to rely on technology in many facets of our lives, from the computers in our cars to the AI-driven home assistants sitting atop our dressers.