Results 31 to 40 of about 977,116 (306)
JMIR Medical Informatics, 2021 BackgroundThe rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy.
Wael Abdelkader +7 more
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Prediction of GPCR activity using machine learning
Computational and Structural Biotechnology Journal, 2022 GPCRs are the target for one-third of the FDA-approved drugs, however; the development of new drug molecules targeting GPCRs is limited by the lack of mechanistic understanding of the GPCR structure-activity-function relationship. To modulate the GPCR activity with highly specific drugs and minimal side-effects, it is necessary to quantitatively ...
Prakarsh Yadav +4 more
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Risks, 2018 Advanced machine learning has achieved extraordinary success in recent years. “Active” operational risk beyond ex post analysis of measured-data machine learning could provide help beyond the regime of traditional statistical analysis when it
Udo Milkau, Jürgen Bott
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Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction. [PDF]
NPJ Comput MaterBhatia N, Rinke P, Krejčí O.
europepmc +3 more sources
Cooperative Learning and its Application to Emotion Recognition from Speech [PDF]
, 2014 In this paper, we propose a novel method for highly efficient exploitation of unlabeled data-Cooperative Learning. Our approach consists of combining Active Learning and Semi-Supervised Learning techniques, with the aim of reducing the costly effects of ...
Coutinho, E +3 more
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Active Learning of Nondeterministic Finite State Machines [PDF]
Mathematical Problems in Engineering, 2013 We consider the problem of learning nondeterministic finite state machines (NFSMs) from systems where their internal structures are implicit and nondeterministic. Recently, an algorithm for inferring observable NFSMs (ONFSMs), which are the potentially learnable subclass of NFSMs, has been proposed based on the hypothesis that the complete testing ...
Warawoot Pacharoen +3 more
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Active-Learning Approaches for Landslide Mapping Using Support Vector Machines
Remote Sensing, 2021 Ex post landslide mapping for emergency response and ex ante landslide susceptibility modelling for hazard mitigation are two important application scenarios that require the development of accurate, yet cost-effective spatial landslide models.
Zhihao Wang, Alexander Brenning
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Active machine learning for transmembrane helix prediction [PDF]
BMC Bioinformatics, 2010 Abstract Background About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited in the Protein Data Bank due to the difficulty in ...
Osmanbeyoglu, Hatice U +3 more
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Physically regularized machine learning emulators of aerosol activation [PDF]
Geoscientific Model Development, 2020 Abstract. The activation of aerosol into cloud droplets is an important step in the formation of clouds and strongly influences the radiative budget of the Earth. Explicitly simulating aerosol activation in Earth system models is challenging due to the computational complexity required to resolve the necessary chemical and physical processes and their ...
Sam J. Silva +3 more
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Knowledge discovery in Chinese herbal medicine: a machine learning perspective [PDF]
MATEC Web of Conferences, 2021 Traditional Chinese Medicine (TCM) has attracted more and more attention due to its remarkable effects on treating diseases, and Chinese herbal medicine (CHM) is an important partition of TCM, rich in natural active ingredients.
Liang Nan, Liang Qing, Ji Fenglei
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