A Comprehensive Overview of Autoencoder Algorithms to Leverage the Diagnosis of Complex Diseases | Chapter 6 | Research Aspects in Biological Science Vol. 9

 Complex diseases are now better understood thanks to recent developments in high-throughput sequencing technologies like whole genome sequencing, single-cell sequencing, and others. However, it is not simple to extract biological meaning from the data produced by these technologies. Recently, a number of analysis methods, including machine learning algorithms, have been proposed. Recently, it has become clear that these methods are useful in the medical industry. Autoencoders (AEs) and variational autoencoders (VAEs), two neural network-based unsupervised learning techniques, have demonstrated promising outcomes. Applications ranging from cancer to healthy patient tissues have been shown on various forms of data and in varied scenarios. In this book chapter, we discuss how AEs and VAEs have been used in biomedical settings. Specifically, here we discuss their current applications and the improvements achieved in the diagnostic and survival of patients.

Author(s) Details:

Justine Labory,
Université Côte d’Azur, Center of Modeling, Simulation and Interactions, 06200, Nice, France.

David Pratella,
Université Côte d’Azur, Center of Modeling, Simulation and Interactions, 06200, Nice, France.

Jasmine Singh,
Université Côte d’Azur, Center of Modeling, Simulation and Interactions, 06200, Nice, France.

Jean-Elisée Yao,
Université Côte d’Azur, Center of Modeling, Simulation and Interactions, 06200, Nice, France.

Samira Ait-El-Mkadem Saadi,
Université Côte d’Azur, Inserm U1081, CNRS UMR 7284, Institute for Research on Cancer and Aging, Nice (IRCAN), Centre Hospitalier Universitaire (CHU) de Nice-06200, Nice, France.

Sylvie Bannwarth,
Université Côte d’Azur, Inserm U1081, CNRS UMR 7284, Institute for Research on Cancer and Aging, Nice (IRCAN), Centre Hospitalier Universitaire (CHU) de Nice-06200, Nice, France.

Véronique Paquis-Fluckinger,
Université Côte d’Azur, Inserm U1081, CNRS UMR 7284, Institute for Research on Cancer and Aging, Nice (IRCAN), Centre Hospitalier Universitaire (CHU) de Nice-06200, Nice, France.

Silvia Bottini,
Université Côte d’Azur, Center of Modeling, Simulation and Interactions, 06200, Nice, France.

Please see the link here: https://stm.bookpi.org/RABS-V9/article/view/8347