Deep learning model for metagenome fragment classification using spaced k-mers feature extraction

Abstract

An open challenge in bioinformatics is the analysis of the sequenced metagenomes from the various environments. Several studies demonstrated bacteria classification at the genus level using k-mers as feature extraction where the highest value of k gives better accuracy but it is costly in terms of computational resources and computational time. Spaced k-mers method was used to extract the feature of the sequence using 111 1111 10001 where 1 was a match and 0 was the condition that could be a match or did not match. Currently, deep learning provides the best solutions to many problems in image recognition, speech recognition, and natural language processing. In this research, two different deep learning architectures, namely Deep Neural Network (DNN) and Convolutional Neural Network (CNN), trained to approach the taxonomic classification of metagenome data and spaced k-mers method for feature extraction. The result showed the DNN classifier reached 90.89 % and the CNN classifier reached 88.89 % accuracy at the genus level taxonomy.

Analisa Dini Gangguan Disleksia Anak Sekolah dengan Metode Backpropagation

“…Menggunakan fungsi pelatihan dan fungsi transfer untuk memberikan hasil prediksi dengan akurasi bervariasi, didasari oleh nilai parameter atau teknik yang digunakan dan data yang diuji [34], [35], [36], [37], [38]. Oleh sebab itu, penelitian ini menggunakan algoritma backpropagation dalam melakukan fungsi pelatihan.…”

Analisis Optimasi Fungsi Pelatihan Machine Learning Neural Network dalam Peramalan Kemiskinan

“…[31] Pemanfaatan fungsi transfer maupun fungsi pelatihan menghasilkan peramalan yang tingkat keakuratan berbeda-beda, yang dipengaruhi oleh parameter atau metode yang diberikan serta data yang akan di uji. [32]- [36] Sesuai dengan hal tesebut, penelitian ini fokus membahas pada penggunaan fungsi pelatihan dalam mengoptimalkan kemampuan algoritma backpropogation standard.…”