Particle physicists have a long history of developing and applying machine learning (ML) techniques. From early applications of neural networks to charged particle tracking in the 1980s [1] to the Higgs boson discovery in 2012, in which boosted decision trees improved the sensitivity to the decay mode [2], ML has changed the way particle physicists conduct searches and measurements. It is an essential and versatile tool that we use to improve existing approaches, and it enables fundamentally new approaches. In recent years, the subfield of ML in particle physics has grown exponentially in the number of publications and expanded to cover a wide variety of topics and use cases, as indexed by the HEP ML Living Review [3]. In these proceedings, we present selected recent results that highlight how LHC experiments are applying novel ML techniques. In particular, we briefly describe the ML techniques and results for improved classification, faster simulation, unfolding, and anomaly detection.

The number of experimentally determined, high-resolution structures deposited in the Protein Data Banki (PDB) [1] has grown immensely since its beginning in 1976, enabling research into biological mechanisms, and in turn the development of novel therapeutics and industrial applications. This growth is, however, outpaced exponentially by that of known protein sequences increasingly impacted by high-throughput metagenomic experiments which yield billions of entries per experiment. Closing the ever-increasing gap between protein sequence and annotations of structure and function is thus a desideratum in molecular and medical biology research.

Accurate hepatocellular carcinoma (HCC) risk prediction is helpful in reducing mortality. Existing HCC risk scores usually include a few known risk factors and preselected parameters. Machine learning allows for direct selection of predictive parameters without subjective preselection. HCC ridge score (HCC-RS) built from machine learning modelling has higher accuracy than existing HCC risk scores. HCC-RS may be incorporated into electronic medical health systems to facilitate real-time update of HCC risk.

Nine technologies developed at MIT Lincoln Laboratory have been selected as R&D 100 Award winners for 2021. Since 1963, this awards program has recognized the 100 most significant technologies transitioned to use or introduced into the marketplace over the past year. The winners are selected by an independent panel of expert judges. R&D World, an online publication that serves research scientists and engineers worldwide, announces the awards. The winning technologies are diverse in their applications.

Reviewed by Emily Henderson, B.Sc.Oct 27 2020 Scientists from the iMolecule group at Skoltech Center for Computational and Data-Intensive Science and Engineering (CDISE) developed BiteNet, a machine learning (ML) algorithm that helps find drug binding sites, i.e. potential drug targets, in proteins. BiteNet can analyze 1,000 protein structures in 1.5 minutes and find optimal spots for drug molecules to attach. The research was published in the Communications Biology journal. Proteins, the molecules that control most biological processes, are typically the common targets for drugs. To produce a therapeutic effect, drugs should attach to proteins at specific sites called binding sites.

Computers used to take up entire rooms. Today, a two-pound laptop can slide effortlessly into a backpack. But that wouldn't have been possible without the creation of new, smaller processors -- which are only possible with the innovation of new materials. But how do materials scientists actually invent new materials? Through experimentation, explains Sanket Deshmukh, an assistant professor in the chemical engineering department whose team's recently published computational research might vastly improve the efficiency and costs savings of the material design process.

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