Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning.

ABSTRACT: Disease classification based on machine learning has become a crucial research topic in the fields of genetics and molecular biology. Generally, disease classification involves a supervised learning style; i.e., it requires a large number of labelled samples to achieve good classification performance. However, in the majority of the cases, labelled samples are hard to obtain, so the amount of training data are limited. However, many unclassified (unlabelled) sequences have been deposited in public databases, which may help the training procedure. This method is called semi-supervised learning and is very useful in many applications. Self-training can be implemented using high- to low-confidence samples to prevent noisy samples from affecting the robustness of semi-supervised learning in th

SUBMITTER: Yin C 

PROVIDER: S-EPMC7551840 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature