Deep learning in bioinformatics : techniques and applications in practice /

Introduces the topic in an easy-to-understand way, exploring how it can be utilized for addressing important problems in bioinformatics, including drug discovery, de novo molecular design, sequence analysis, protein structure prediction, gene expression regulation, protein classification, biomedical...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Izadkhah, Habib (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, 2022.
Temas:
Bioinformatics.
Machine learning.
Computational Biology
Machine Learning
Bio-informatique.
Apprentissage automatique.
Bioinformatics
Machine learning
Acceso en línea:Texto completo

MARC

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100 1 |a Izadkhah, Habib,  |e author. 
245 1 0 |a Deep learning in bioinformatics :  |b techniques and applications in practice /  |c Habib Izadkhah. 
260 |a London :  |b Academic Press,  |c 2022. 
300 |a 1 online resource (xv, 363 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
505 0 0 |t Why Life Science? --  |t A Review of Machine Learning --  |t An Introduction of Python Ecosystem for Deep Learning --  |t Basic Structure of Neural Networks --  |t Training Multilayer Neural Networks --  |t Classification in Bioinformation --  |t Introduction to Deep Learning --  |t Medical Image Processing: an Insight to Convolutional Neural Networks --  |t Popular Deep Learning Image Classifiers --  |t Electrocardiogram (ECG) Arrhythmia Classification --  |t Autoencoders and Deep Generative Models in Bioinformatics --  |t Recurrent Neural Networks: Generating New Molecules and Proteins Sequence Classification --  |t Application, Challenge, and Suggestion. 
520 |a Introduces the topic in an easy-to-understand way, exploring how it can be utilized for addressing important problems in bioinformatics, including drug discovery, de novo molecular design, sequence analysis, protein structure prediction, gene expression regulation, protein classification, biomedical image processing and diagnosis, biomolecule interaction prediction, and in systems biology. The book also presents theoretical and practical successes of deep learning in bioinformatics, pointing out problems and suggesting future research directions. Dr. Izadkhah provides valuable insights and will help researchers use deep learning techniques in their biological and bioinformatics studies. 
650 0 |a Bioinformatics. 
650 0 |a Machine learning. 
650 2 |a Computational Biology  |0 (DNLM)D019295 
650 2 |a Machine Learning  |0 (DNLM)D000069550 
650 6 |a Bio-informatique.  |0 (CaQQLa)201-0313075 
650 6 |a Apprentissage automatique.  |0 (CaQQLa)201-0131435 
650 7 |a Bioinformatics  |2 fast  |0 (OCoLC)fst00832181 
650 7 |a Machine learning  |2 fast  |0 (OCoLC)fst01004795 
776 0 8 |i Print version:  |z 9780128238363 
776 0 8 |i Print version:  |z 0128238224  |z 9780128238226  |w (OCoLC)1268111169 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780128238226  |z Texto completo 

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