Unsupervised Machine Learning Clustering for Stratification of Cardiac Risk in Patients With Exercise Echocardiography Negative for Ischemia

Omar, Alaa Mabrouk Salem; Ramírez, Roberto; Haddadin, Faris; Sabharwal, Basera; Khandaker, Mariam; Patel, Yash; Argulian, Edgar

doi:10.1016/s0735-1097(19)30718-1

Cited by 1 publication

(2 citation statements)

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“…The applications of GAN in accelerated molecular discoveries are overviewed in this Perspective. As opposed to the supervised ML algorithms, GAN is generally considered as unsupervised, which are algorithms that can be used to solve problems like clustering and visualization . The GAN needs to simultaneously train two models, a generator and a discriminator.…”

Section: Approaches In Computational Materials Sciencementioning

confidence: 99%

“…As opposed to the supervised ML algorithms, GAN is generally considered as unsupervised, 45 which are algorithms that can be used to solve problems like clustering and visualization. [46][47][48] The GAN needs to simultaneously train two models, a generator and a discriminator. In supervised learning, a prediction is made by learning from labeled input and output.…”

Section: Generative Modeling Using Dnnsmentioning

confidence: 99%

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Machine learning and artificial neural network accelerated computational discoveries in materials science

Yang

Hou

Jiang

et al. 2019

WIREs Comput Mol Sci

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Artificial intelligence (AI) has been referred to as the "fourth paradigm of science," and as part of a coherent toolbox of data-driven approaches, machine learning (ML) dramatically accelerates the computational discoveries. As the machinery for ML algorithms matures, significant advances have been made not only by the mainstream AI researchers, but also those work in computational materials science. The number of ML and artificial neural network (ANN) applications in the computational materials science is growing at an astounding rate. This perspective briefly reviews the state-of-the-art progress in some supervised and unsupervised methods with their respective applications. The characteristics of primary ML and ANN algorithms are first described. Then, the most critical applications of AI in computational materials science such as empirical interatomic potential development, ML-based potential, property predictions, and molecular discoveries using generative adversarial networks (GAN) are comprehensively reviewed. The central ideas underlying these ML applications are discussed, and future directions for integrating ML with computational materials science are given. Finally, a discussion on the applicability and limitations of current ML techniques and the remaining challenges are summarized.

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Section: Approaches In Computational Materials Sciencementioning

confidence: 99%