Mathematical Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications
Mathematical modeling is routinely used in physical and engineering sciences to help understand complex systems and optimize industrial processes. Mathematical modeling differs from Artificial Intelligence because it does not exclusively use the collected data to describe an industrial phenomenon or...
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| Μορφή: | Online |
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| Γλώσσα: | Αγγλικά |
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MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Θέματα: | |
| Διαθέσιμο Online: | ONIX_20220224_9783036528410_32 |
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| _version_ | 1869525024843497472 |
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| collection | Directory of Open Access Books |
| description | Mathematical modeling is routinely used in physical and engineering sciences to help understand complex systems and optimize industrial processes. Mathematical modeling differs from Artificial Intelligence because it does not exclusively use the collected data to describe an industrial phenomenon or process, but it is based on fundamental laws of physics or engineering that lead to systems of equations able to represent all the variables that characterize the process. Conversely, Machine Learning methods require a large amount of data to find solutions, remaining detached from the problem that generated them and trying to infer the behavior of the object, material or process to be examined from observed samples. Mathematics allows us to formulate complex models with effectiveness and creativity, describing nature and physics. Together with the potential of Artificial Intelligence and data collection techniques, a new way of dealing with practical problems is possible. The insertion of the equations deriving from the physical world in the data-driven models can in fact greatly enrich the information content of the sampled data, allowing to simulate very complex phenomena, with drastically reduced calculation times. Combined approaches will constitute a breakthrough in cutting-edge applications, providing precise and reliable tools for the prediction of phenomena in biological macro/microsystems, for biotechnological applications and for medical diagnostics, particularly in the field of precision medicine. |
| format | Online |
| id | doab-20.500.12854ir-78734 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-787342024-03-28T03:32:24Z Mathematical Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications Bianchini, Monica Sampoli, Maria Lucia algorithm identification Alzheimer predator–prey model herd behaviour herd shape linear functional response Holling type II functional response bifurcation analysis deep learning convolutional neural networks semantic segmentation generative adversarial networks chest X-ray image augmentation tropospheric ozone machine learning El Paso-Juarez semi-arid climate visual sequential search test episode matching trail making test sequence alignment alignment score eye tracking Til Making Test neurological diseases thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science Mathematical modeling is routinely used in physical and engineering sciences to help understand complex systems and optimize industrial processes. Mathematical modeling differs from Artificial Intelligence because it does not exclusively use the collected data to describe an industrial phenomenon or process, but it is based on fundamental laws of physics or engineering that lead to systems of equations able to represent all the variables that characterize the process. Conversely, Machine Learning methods require a large amount of data to find solutions, remaining detached from the problem that generated them and trying to infer the behavior of the object, material or process to be examined from observed samples. Mathematics allows us to formulate complex models with effectiveness and creativity, describing nature and physics. Together with the potential of Artificial Intelligence and data collection techniques, a new way of dealing with practical problems is possible. The insertion of the equations deriving from the physical world in the data-driven models can in fact greatly enrich the information content of the sampled data, allowing to simulate very complex phenomena, with drastically reduced calculation times. Combined approaches will constitute a breakthrough in cutting-edge applications, providing precise and reliable tools for the prediction of phenomena in biological macro/microsystems, for biotechnological applications and for medical diagnostics, particularly in the field of precision medicine. 2022-02-24T10:34:38Z 2022-02-24T10:34:38Z 2022 book ONIX_20220224_9783036528410_32 9783036528410 9783036528403 https://directory.doabooks.org/handle/20.500.12854/78734 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/4825 https://mdpi.com/books/pdfview/book/4825 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-2841-0 10.3390/books978-3-0365-2841-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036528410 9783036528403 102 Basel open access |
| spellingShingle | algorithm identification Alzheimer predator–prey model herd behaviour herd shape linear functional response Holling type II functional response bifurcation analysis deep learning convolutional neural networks semantic segmentation generative adversarial networks chest X-ray image augmentation tropospheric ozone machine learning El Paso-Juarez semi-arid climate visual sequential search test episode matching trail making test sequence alignment alignment score eye tracking Til Making Test neurological diseases thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science Mathematical Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications |
| title | Mathematical Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications |
| title_full | Mathematical Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications |
| title_fullStr | Mathematical Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications |
| title_full_unstemmed | Mathematical Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications |
| title_short | Mathematical Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications |
| title_sort | mathematical modelling and machine learning methods for bioinformatics and data science applications |
| topic | algorithm identification Alzheimer predator–prey model herd behaviour herd shape linear functional response Holling type II functional response bifurcation analysis deep learning convolutional neural networks semantic segmentation generative adversarial networks chest X-ray image augmentation tropospheric ozone machine learning El Paso-Juarez semi-arid climate visual sequential search test episode matching trail making test sequence alignment alignment score eye tracking Til Making Test neurological diseases thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science |
| topic_facet | algorithm identification Alzheimer predator–prey model herd behaviour herd shape linear functional response Holling type II functional response bifurcation analysis deep learning convolutional neural networks semantic segmentation generative adversarial networks chest X-ray image augmentation tropospheric ozone machine learning El Paso-Juarez semi-arid climate visual sequential search test episode matching trail making test sequence alignment alignment score eye tracking Til Making Test neurological diseases thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science |
| url | ONIX_20220224_9783036528410_32 |