Biological Sequence Classification Using Deep Learning Architectures

被引：0

作者：

Sivasubramanian, Arrun ^{[1
]}

Prashanth, V. R. ^{[1
]}

Kumar, S. Sachin ^{[1
]}

Soman, K. P. ^{[1
]}

机构：

[1] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Ctr Computat Engn & Networking, Coimbatore, Tamil Nadu, India

来源：

INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING AND COMMUNICATIONS, ICICC 2022, VOL 1 | 2023年 / 473卷

关键词：

Bio sequence; Perceptron; CNN; BiLSTM; GRU; SARS; SARS-CoV-2; MERS;

D O I：

10.1007/978-981-19-2821-5_44

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Finding similar biological sequences to categorize into respective families is an important task. The present works attempt to use machine learning-based approaches to find the family of a given sequence. The first task in this direction is to convert the sequences to vector representations and then train a model using a suitable machine learning architecture. The second task is to find which family the sequence belongs to. In this work, deep learning-based architectures are proposed to do the task. A comparative study on how effective various deep learning architectures for this problem is also discussed in this work.

引用

页码：529 / 537

页数：9

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