Tandem mass spectrometry is a powerful analytical tool used to characterize complex mixtures in drug discovery and other fields.
Now, Purdue University innovators have created a new method of applying machine learning concepts to the tandem mass spectrometry process to improve the flow of information in the development of new drugs. Their work is published in Chemical Science.
“Mass spectrometry plays an integral role in drug discovery and development,” said Gaurav Chopra, an assistant professor of analytical and physical chemistry in Purdue’s College of Science. “The specific implementation of bootstrapped machine learning with a small amount of positive and negative training data presented here will pave the way for becoming mainstream in day-to-day activities of automating characterization of compounds by chemists.”
Chopra said there are two major problems in the field of machine learning used for chemical sciences. Methods used do not provide chemical understanding
The roar of a lion is one of the most thrilling and captivating sounds of the wild. This characteristic call is typically delivered in a bout consisting of one or two soft moans followed by several loud, full-throated roars and a terminating sequence of grunts.
A team of scientists based in WildCRU at the University of Oxford, well-known for their research involving Cecil the Lion, has teamed up with colleagues in the Department of Computer Science to discover the precise ways in which each lion’s roar is distinct, identifiable and trackable.
Harnessing new machine learning techniques, the group designed a device, known as a biologger, which can be attached to an existing lion GPS collar to record audio and movement data. The biologgers allow the scientists to confidently associate each roar with the correct lion by cross-referencing movement and audio data through the large datasets of
Niharika Sharma is a Senior Software Engineer for Nasdaq’s Machine Intelligence Lab. She designs systems that gather, process and apply machine learning/natural language processing technologies on natural language data, generating valuable insights to support business decisions. Over the past years, she worked on Natural Language Generation (NLG) and Surveillance Automation for Nasdaq Advisory Services. We sat down with Niharika to learn more about how she got her start in computer science and how she approaches challenges in her career.
Can you describe your day-to-day as a senior software engineer at Nasdaq?
My day-to-day work involves collaborating with Data Scientists to solve problems, ideating business possibilities with product teams and working with Data/Software Engineers to transform ideas into solutions.
How did you become involved in the technology industry, and how has technology influenced your role?
My first exposure to Computer Science was a Logo programming class that I took as a
A human research team and a machine learning algorithm have found that we need to rethink much of what we know about iridium oxide.
Iridium oxide is an excellent catalyst for electrochemical reactions, and is typically used for the production of energy carriers such as hydrogen from water. Now it turns out that research on iridium oxide carried out so far has been based on a wrong basic assumption: The arrangement of the atoms on its surface is completely different to that previously assumed.
The way in which this surprising result was determined gives a tantalizing first glimpse of how research might be performed in the future: a collaborative effort between a human research team and artificial intelligence analyzed the same problem, and came to the same conclusion. Since the researchers at the TU Wien and the TU Munich reached the same result at the same
These would affect all aspects of HR functions such as the way HR professionals on-board and hire people, and the way they train them
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Artificial intelligence (AI) is changing all aspects of our lives and that too at a rapid pace. This includes our professional lives, too. Experts expect that in the days ahead, AI would become a greater part of our careers as all companies are moving ahead with adopting such technology. They are using more machines that use AI technology that would affect our daily professional activities. Soon enough, we would see machine learning and deep learning in HR too. It would affect all aspects of HR (human resources) such
The global machine safeguarding solutions market size is poised to grow by USD 774.41 million during 2020-2024, progressing at a CAGR of almost 4% throughout the forecast period, according to the latest report by Technavio. The report offers an up-to-date analysis regarding the current market scenario, latest trends and drivers, and the overall market environment. The report also provides the market impact and new opportunities created due to the COVID-19 pandemic. Download a Free Sample of REPORT with COVID-19 Crisis and Recovery Analysis.
This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20201007005678/en/
Technavio has announced its latest market research report titled Global Machine Safeguarding Solutions Market 2020-2024 (Graphic: Business Wire)
The machine safeguarding solutions market is driven by the growth of end-users. Several machining operations that are carried out in the automotive and industrial machine manufacturing industry involve bending, boring, grinding, and milling. Manufacturers use transmission systems such
Apple’s Vice President of Platform Architecture offers insight on the new A14 Bionic processor, the importance of machine learning, and how Apple continues to separate itself from its competitors in a new interview.
According to Apple, the A14 Bionic offers a 30% boost for CPU performance, while using a new four-core graphics architecture for a 30% faster graphics boost, compared against the A12 Bionic used in the iPad Air 3. Against the A13, the benchmarks suggest the A14 offers a 19% improvement in CPU performance and 27% for graphics.
In an interview with German magazine Stern, Apple’s Vice President of Platform Architecture, Tim Millet, offered some insight into what makes the A14 Bionic processor tick.
Millet explains that while Apple did not invent machine learning and neural engines — “the foundations for this go back many decades” — they did help to find ways to accelerate the process.
“Earlier this year, I attended a conference and was shocked to find that you could actually buy voting machines on eBay. So I bought one, two months ago, and have been able to open it up and look at the chips.”
Beatrice Atobatele is trying to hack one of the most commonly used voting machines in the US, to look for security vulnerabilities, but not with any criminal intentions.
Beatrice is actually one of more than 200 people who have signed up to a volunteer group of security experts and hackers called the Election Cyber Surge.
And by understanding how this machine works, she hopes she can ensure any vulnerabilities are fixed.
“I’ve bypassed the authentication itself,” she says.
“I’m still learning and trying to find any new vulnerabilities that might not be known about yet.”
A machine learning technique rapidly rediscovered rules governing catalysts that took humans years of difficult calculations to reveal — and even explained a deviation. The University of Michigan team that developed the technique believes other researchers will be able to use it to make faster progress in designing materials for a variety of purposes.
“This opens a new door, not just in understanding catalysis, but also potentially for extracting knowledge about superconductors, enzymes, thermoelectrics, and photovoltaics,” said Bryan Goldsmith, an assistant professor of chemical engineering, who co-led the work with Suljo Linic, a professor of chemical engineering.
The key to all of these materials is how their electrons behave. Researchers would like to use machine learning techniques to develop recipes for the material properties that they want. For superconductors, the electrons must move without resistance through the material. Enzymes and catalysts need to broker exchanges of electrons, enabling new medicines
Daniel Ratner, head of SLAC’s machine learning initiative, explains the lab’s unique opportunities to advance scientific discovery through machine learning.
DOE/SLAC National Accelerator Laboratory
Machine learning is ubiquitous in science and technology these days. It outperforms traditional computational methods in many areas, for instance by vastly speeding up tedious processes and handling huge batches of data. At the Department of Energy’s SLAC National Accelerator Laboratory, machine learning is already opening new avenues to advance the lab’s unique scientific facilities and research.
For example, SLAC scientists have already used machine learning techniques to operate accelerators more efficiently, to speed up the discovery of new materials, and to uncover distortions in space-time caused by astronomical objects up to 10 million times faster than traditional methods.
The term “machine learning” broadly refers to techniques that let computers “learn by example” by inferring their own conclusions from large sets of