Đăng nhập
 
Tìm kiếm nâng cao
 
Tên bài báo
Tác giả
Năm xuất bản
Tóm tắt
Lĩnh vực
Phân loại
Số tạp chí
 

Bản tin định kỳ
Báo cáo thường niên
Tạp chí khoa học ĐHCT
Tạp chí tiếng anh ĐHCT
Tạp chí trong nước
Tạp chí quốc tế
Kỷ yếu HN trong nước
Kỷ yếu HN quốc tế
Book chapter
Bài báo - Tạp chí
712 (2020) Trang: 19-28
Tạp chí: IT Convergence and Security, Lecture Notes in Electrical Engineering

Metagenomic is now a novel source for supporting diagnosis and prognosis human diseases. Numerous studies have pointed to crucial roles of metagenomics in personalized medicine approaches. Recent years, machine learning has been widely deploying in a vast amount of metagenomic research. Usually, gene family data are characterized by very high dimension which can be up to millions of features. However, the number of obtained samples is rather small compared to the number of attributes. Therefore, the results in validation sets often exhibit poor performance while we can get high accuracy during training phrases. Moreover, a very large number of features on each gene family dataset consumes a considerable time in processing and learning. In this study, we propose feature selection methods using Ridge Regression on datasets including gene families, then the new obtained set of features is binned by an equal width binning approach and fetched into either a Linear Regression and a One-Dimensional Convolutional Neural Network (CNN1D) to do prediction tasks. The experiments are examined on more than 1000 samples of gene family abundance datasets related to Liver Cirrhosis, Colorectal Cancer, Inflammatory Bowel Disease, Obesity and Type 2 Diabetes. The results from the proposed method combining between feature selection algorithms and binning show significant improvements in both prediction performance and execution time compared to the state-of-the-art methods.

Các bài báo khác
Marcin Hernes, Krystian Wojtkiewicz, Edward Szczerbicki (2020) Trang: 74-86
Tạp chí: Communications in Computer and Information Science book series
Tran Khanh Dang, Josef Küng, Makoto Takizawa, Tai M. Chung (2020) Trang: 130-148
Tạp chí: Communications in Computer and Information Science
11 (2020) Trang: 667-675
Tạp chí: International Journal of Advanced Computer Science and Applications,
Tran Khanh Dang, Josef Küng, Makoto Takizawa, Tai M. Chung (2020) Trang: 340-357
Tạp chí: Communications in Computer and Information Science
11 (2020) Trang: 651-657
Tạp chí: International Journal of Advanced Computer Science and Applications
11814 (2019) Trang: 307-319
Tạp chí: Lecture Notes in Computer Science book series (LNCS)
11 (2020) Trang: 630 - 638
Tạp chí: International Journal of Advanced Computer Science and Applications
(2019) Trang: 381-385
Tạp chí: The 11th International Conference on Knowledge and Systems Engineering (KSE 2019) --- October 24-26, 2019 | Da Nang, Vietnam
(2019) Trang: 231-236
Tạp chí: RIVF (Research, Innovation and Vision for the Future), Đà Nẵng, 20-22/03/2019
(2017) Trang:
Tạp chí: NIPS 2017 Workshop on Machine Learning for Health, in Long Beach, CA, USA 08/12/2017
(2016) Trang: 106-115
Tạp chí: 8th Asian Conference on Intelligent Information and Database Systems
11 (2015) Trang: 53-58
Tạp chí: RIVF-2015: The 11th IEEE-RIVF International Conference on Computing and Communication Technologies
 


Vietnamese | English






 
 
Vui lòng chờ...