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Climate-smart agriculture (CSA) aims to transform and reorient farming systems to decrease greenhouse gas emissions, boost adaptive capacity, and improve productivity while supporting incomes and, ostensibly, food security. In Ghana—the world's sec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e0fa9224b2583dc10a2a31525444781
https://www.repository.cam.ac.uk/handle/1810/306691
https://www.repository.cam.ac.uk/handle/1810/306691
© 2018 Imbalanced datasets are commonly encountered in real-world classification problems. Many machine learning algorithms are originally designed for well-balanced datasets, therefore re-sampling has become an important step to pre-process imbalan
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https://explore.openaire.eu/search/publication?articleId=od_______363::b7e61e20e451c612ad7ba54df11faddb
https://hdl.handle.net/10453/128105
https://hdl.handle.net/10453/128105
© 2016 IEEE. Multi-clustering has been widely used. It acts as a pre-training process for identifying protein-ligand binding in structure-based drug design. Then, the Support Vector Machine (SVM) is employed to classify the sites most likely for bin
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https://explore.openaire.eu/search/publication?articleId=od_______363::853d0a717bd724274bb17cb2c572af39
https://hdl.handle.net/10453/80681
https://hdl.handle.net/10453/80681
© 2014 IEEE. Large imbalanced datasets have introduced difficulties to classification problems. They cause a high error rate of the minority class samples and a long training time of the classification model. Therefore, re-sampling and data size red
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https://explore.openaire.eu/search/publication?articleId=od_______363::d795d9c3aadb5ea9f4c4113807bf6958
https://hdl.handle.net/10453/3751610453/33123
https://hdl.handle.net/10453/3751610453/33123
Imbalanced datasets are commonly encountered in real-world classification problems. However, many machine learning algorithms are originally designed for well-balanced datasets. Re-sampling has become an important step to preprocess imbalanced datase
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::e24f361449b3039a1063cb44ec740e17
https://hdl.handle.net/10453/119873
https://hdl.handle.net/10453/119873
Identification of protein-ligand binding site is an important task in structure-based drug design and docking algorithms. In these two decades, many different approaches have been developed to predict the binding site, such as geometric, energetic an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::403da072662c68eefd827d35bf20cc40
https://hdl.handle.net/10453/31510
https://hdl.handle.net/10453/31510
Autor:
David A. Scheinberg, Herbert F. Oettgen, Dantes L, Mary Agnes Templeton, Carlos Cordon-Cardo, Beverly V. Cody, Paul B. Chapman, Alan N. Houghton, Wong Gy, Dean F. Bajorin
Publikováno v:
Melanoma research. 2(5-6)
R24 is a mouse IgG3 monoclonal antibody that reacts with the ganglioside GD3 expressed by melanoma cells and other cells of neuroectodermal origin (e.g. adrenal medulla). Antitumour activity of R24 was demonstrated in initial phase I and pilot trials
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2775b29ad30eacf4f8a440a1e90e4ce3
https://pubmed.ncbi.nlm.nih.gov/1292783
https://pubmed.ncbi.nlm.nih.gov/1292783
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