{"id":1377,"date":"2024-01-01T20:31:58","date_gmt":"2024-01-01T19:31:58","guid":{"rendered":"http:\/\/pages.di.unipi.it\/bacciu\/?page_id=1377"},"modified":"2024-01-02T11:18:31","modified_gmt":"2024-01-02T10:18:31","slug":"books","status":"publish","type":"page","link":"https:\/\/pages.di.unipi.it\/bacciu\/publications\/books\/","title":{"rendered":"Books"},"content":{"rendered":"\n

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2022<\/h3>
1.<\/div>

Bacciu, Davide; Lisboa, Paulo J. G.; Vellido, Alfredo<\/p>

Deep Learning in Biology and Medicine<\/a> Book<\/span> <\/p>

World Scientific Publisher, <\/span>2022<\/span>, ISBN: 978-1-80061-093-4<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@book{BacciuBook2022,
\r\ntitle = {Deep Learning in Biology and Medicine},
\r\nauthor = {Davide Bacciu and Paulo J. G. Lisboa and Alfredo Vellido},
\r\ndoi = {doi.org\/10.1142\/q0322 },
\r\nisbn = {978-1-80061-093-4},
\r\nyear  = {2022},
\r\ndate = {2022-02-01},
\r\nurldate = {2022-02-01},
\r\npublisher = {World Scientific Publisher},
\r\nabstract = {Biology, medicine and biochemistry have become data-centric fields for which Deep Learning methods are delivering groundbreaking results. Addressing high impact challenges, Deep Learning in Biology and Medicine provides an accessible and organic collection of Deep Learning essays on bioinformatics and medicine. It caters for a wide readership, ranging from machine learning practitioners and data scientists seeking methodological knowledge to address biomedical applications, to life science specialists in search of a gentle reference for advanced data analytics.
\r\nWith contributions from internationally renowned experts, the book covers foundational methodologies in a wide spectrum of life sciences applications, including electronic health record processing, diagnostic imaging, text processing, as well as omics-data processing. This survey of consolidated problems is complemented by a selection of advanced applications, including cheminformatics and biomedical interaction network analysis. A modern and mindful approach to the use of data-driven methodologies in the life sciences also requires careful consideration of the associated societal, ethical, legal and transparency challenges, which are covered in the concluding chapters of this book.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {book}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Biology, medicine and biochemistry have become data-centric fields for which Deep Learning methods are delivering groundbreaking results. Addressing high impact challenges, Deep Learning in Biology and Medicine provides an accessible and organic collection of Deep Learning essays on bioinformatics and medicine. It caters for a wide readership, ranging from machine learning practitioners and data scientists seeking methodological knowledge to address biomedical applications, to life science specialists in search of a gentle reference for advanced data analytics.
\r\nWith contributions from internationally renowned experts, the book covers foundational methodologies in a wide spectrum of life sciences applications, including electronic health record processing, diagnostic imaging, text processing, as well as omics-data processing. This survey of consolidated problems is complemented by a selection of advanced applications, including cheminformatics and biomedical interaction network analysis. A modern and mindful approach to the use of data-driven methodologies in the life sciences also requires careful consideration of the associated societal, ethical, legal and transparency challenges, which are covered in the concluding chapters of this book.<\/div>
Close<\/a><\/p><\/div>