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pro vyhledávání: '"Orhobor, Oghenejokpeme I."'
The key to success in machine learning (ML) is the use of effective data representations. Traditionally, data representations were hand-crafted. Recently it has been demonstrated that, given sufficient data, deep neural networks can learn effective i
Externí odkaz:
http://arxiv.org/abs/1811.03392
Peer reviewed: True
The representation of the protein-ligand complexes used in building machine learning models play an important role in the accuracy of binding affinity prediction. The Extended Connectivity Interaction Features (ECIF) is one s
The representation of the protein-ligand complexes used in building machine learning models play an important role in the accuracy of binding affinity prediction. The Extended Connectivity Interaction Features (ECIF) is one s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65d19a906389e8ad67c84e1f3d990665
The early stages of the drug design process involve identifying compounds with suitable bioactivities via noisy assays. As databases of possible drugs are often very large, assays can only be performed on a subset of the candidates. Selecting which a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9252cbd463bc460c4076145a1e7525c0
https://www.repository.cam.ac.uk/handle/1810/341569
https://www.repository.cam.ac.uk/handle/1810/341569
Publikováno v:
Machine Learning; Apr2023, Vol. 112 Issue 4, p1365-1387, 23p
Akademický článek
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Akademický článek
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Publikováno v:
Discovery Science
The key to success in machine learning is the use of effective data representations. The success of deep neural networks (DNNs) is based on their ability to utilize multiple neural network layers, and big data, to learn how to convert simple input re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmc_________::a4d4d5dd23c63c13cf510cf4517b98e7
http://europepmc.org/articles/PMC7556385
http://europepmc.org/articles/PMC7556385
Publikováno v:
Discovery Science
Ensemble learning has been shown to significantly improve predictive accuracy in a variety of machine learning problems. For a given predictive task, the goal of ensemble learning is to improve predictive accuracy by combining the predictive power of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmc_________::586e5ff1a4a45ccffad9ab3f2c4797d8
http://europepmc.org/articles/PMC7556384
http://europepmc.org/articles/PMC7556384
The features in some machine learning datasets can naturally be divided into groups. This is the case with genomic data, where features can be grouped by chromosome. In many applications it is common for these groupings to be ignored, as interactions
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbe6adb67f80d840e6c7fe82593eca43
https://www.repository.cam.ac.uk/handle/1810/312723
https://www.repository.cam.ac.uk/handle/1810/312723
Akademický článek
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