Zobrazeno 1 - 10
of 211
pro vyhledávání: '"Alfonso Rodríguez Patón"'
Autor:
Alfonso Patón
Publikováno v:
Proceedings of MOL2NET 2019, International Conference on Multidisciplinary Sciences, 5th edition.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c6ab9aed3ebb4e4b80f8c5a2091a6bf
https://doi.org/10.3390/mol2net-05-06735
https://doi.org/10.3390/mol2net-05-06735
Autor:
Tongmao Ma, David Méndez-Merino, Graciela Uría-Regojo, Cristina Sánchez-Fernández, Lucía Giner-Sánchez, Sara Guerrero-Aspizua, Cristina Quílez-López, Alfonso Rodríguez-Patón
Publikováno v:
Methods. 210:36-43
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5f8aeac6a1c44d4a98a36e750dd7555a
https://doi.org/10.1016/j.ymeth.2023.01.001
https://doi.org/10.1016/j.ymeth.2023.01.001
Autor:
Gan Wang, Xudong Zhang, Zheng Pan, Alfonso Rodríguez Patón, Shuang Wang, Tao Song, Yuanqiang Gu
Publikováno v:
Biomolecules, Vol 12, Iss 5, p 644 (2022)
Prediction on drug–target interaction has always been a crucial link for drug discovery and repositioning, which have witnessed tremendous progress in recent years. Despite many efforts made, the existing representation learning or feature generati
Externí odkaz:
https://doaj.org/article/0f681a298a574adfa0720056ce778df5
Publikováno v:
BMC Medical Genomics, Vol 10, Iss S5, Pp 45-53 (2017)
Abstract Background Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene–disease data verified by biological experiments, we can apply computational methods to perform accurate predictions
Externí odkaz:
https://doaj.org/article/fb1f682326084db0902f43c5c6d6ca8d
Publikováno v:
PLoS ONE, Vol 14, Iss 9, p e0221720 (2019)
Artificial intelligence (AI) tools have been applied to diagnose or predict disease risk from medical images with recent data disclosure actions, but few of them are designed for mobile terminals due to the limited computational power and storage cap
Externí odkaz:
https://doaj.org/article/93bbe4b37653471d90e1cce2781d4ed5
Autor:
Xun Wang, Changnan Gao, Peifu Han, Xue Li, Wenqi Chen, Alfonso Rodríguez Patón, Shuang Wang, Pan Zheng
Publikováno v:
International Journal of Molecular Sciences; Volume 24; Issue 2; Pages: 1146
Recent years have seen tremendous success in the design of novel drug molecules through deep generative models. Nevertheless, existing methods only generate drug-like molecules, which require additional structural optimization to be developed into ac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef1871ce9ee6c90f031efe5a0d9527c3
Publikováno v:
Applied Intelligence. 52:846-857
Drug repositioning, which recommends approved drugs to potential targets by predicting drug-target interactions (DTIs), can save the cost and shorten the period of drug development. In this work, we propose a novel knowledge graph based deep learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3aab24b7ee0318728a07975453e7f4d7
https://doi.org/10.1007/s10489-021-02454-8
https://doi.org/10.1007/s10489-021-02454-8
Publikováno v:
Information Sciences. 546:206-219
Tissue P systems with channel states are non-deterministic bio-inspired computing devices that evolve by the interchange of objects among regions, determined by the existence of some special objects on channels called states. However, in cellular bio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24580b6f8027ba62046c8a0819aef3cb
https://doi.org/10.1016/j.ins.2020.08.030
https://doi.org/10.1016/j.ins.2020.08.030
Publikováno v:
Life, Vol 9, Iss 1, p 14 (2019)
We present a scheme for implementing a version of task switching in engineered bacteria, based on the manipulation of plasmid copy numbers. Our method allows for the embedding of multiple computations in a cellular population, whilst minimising resou
Externí odkaz:
https://doaj.org/article/cea44e24acc54287aa544ca3317b7da6
Publikováno v:
IEEE Transactions on NanoBioscience. 18:176-190
Spiking neural P systems (SN P systems) are a class of distributed and parallel neural-like computing models, inspired from the way neurons communicate by means of spikes. In this paper, a new variant of the systems, called SN P systems with learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f444e917adbf3f0efc305610736a46b3
https://doi.org/10.1109/tnb.2019.2896981
https://doi.org/10.1109/tnb.2019.2896981