Nguyen Hai Thanh Primary Author mixed material bibliography Bandung ITB Journal Publisher 2021 e English

Artikel Jurnal

hlm : 134-150 Abstract Advancements in machine learning in general and in deep learning in particular have achieved great success in numerous fields. For personalized medicine approaches, frameworks derived from learning algorithms play an important role in supporting scientists to investigate and explore novel data sources such as metagenomic data to develop and examine methodologies to improve human healthcare. Some challenges when processing this data type include its very high dimensionality and the complexity of diseases. Metagenomic data that include gene families often have millions of features. This leads to a further increase of complexity in processing and requires a huge amount of time for computation. In this study, we propose a method combining feature selection using perceptron weight-based filters and synthetic image generation to leverage deep-learning advancements in order to predict various diseases based on gene family abundance data. An experiment was conducted using gene family datasets of five diseases, i.e. liver cirrhosis, obesity, inflammatory bowel diseases, type 2 diabetes, and colorectal cancer. The proposed method provides not only visualization for gene family abundance data but also achieved a promising performance level. Ilmu Teknik JETS 23375779 Perpustakaan Teknik UPI YAI JETS V53N1 Januari 2021 JETS14-009 Perpustakaan FT UPI YAI JETS V53N1 Januari 2021 JETS_ITB.png.png 58866 2023-01-30 13:12:12 2023-01-30 13:12:12 machine generated