Cancer MiRNA biomarker classification based on Improved Generative Adversarial Network optimized with Mayfly Optimization Algorithm

人工智能 计算机科学 深度学习 卷积神经网络 机器学习 模式识别(心理学)
作者
G. Tamilmani,V. Brindha Devi,T. Sujithra,Francis H. Shajin,P. Rajesh
出处
期刊:Biomedical Signal Processing and Control [Elsevier BV]
卷期号:75: 103545-103545 被引量:19
标识
DOI:10.1016/j.bspc.2022.103545
摘要

Nowadays, cancer diagnosis becomes a paradigm shift by incorporating molecular biomarkers as part of a routine diagnostic panel. Ranges of molecular changes include DNA, RNA, micro RNA (miRNAs) and proteins. In recent years, deep learning based methods have been more inspired to health researcher’s regarding the performance of cancer diagnosis. The application of deep learning-based approach gradually becomes clearer in classification accuracy for a problem that separates data related to cancer survival. In this manuscript, an Improved Generative Adversarial Network optimized with Mayfly Optimization Algorithm is proposed to overcome the super class issues. Improved Generative Adversarial Network is the combination of deep convolutional generative adversarial network (DCG) and modified convolutional neural network (MCNN); hence it is called DCG-MCNN. Initially, the DCG is used to balance the dataset by creating more samples in the training dataset. Based on the training dataset, cancer miRNA biomarker classification is improved with the help of modified CNN diagnosis model. The proposed method is activated in python, moreover, its efficiency is analyzed with Cancer Genome Atlas dataset. Here, performance metrics, viz accuracy, sensitivity, specificity, precision, F-measure balanced error rate are calculated. The experimental results of the proposed method shows higher accuracy 99.26%, higher sensitivity 95.23%, higher specificity 92.56% compared with the existing methods, like Validation of miRNAs as breast cancer biomarkers with a machine learning approach (CMiRNA-BC-RF-SVM), Cancer miRNA biomarkers classification using a new representation algorithm and evolutionary deep learning (CMiRNA-BC-CNN) and multi-omics data using graph convolutional networks allowing patient classification and biomarker identification (CMiRNA-BC-GCNN).
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
1秒前
奥特超曼应助十七采纳,获得10
2秒前
2秒前
3秒前
活力鸡完成签到,获得积分10
3秒前
NexusExplorer应助yang采纳,获得10
4秒前
4秒前
英姑应助泥嚎采纳,获得10
4秒前
夜猫酱酱子完成签到,获得积分10
4秒前
imkhun1021发布了新的文献求助10
6秒前
mingming发布了新的文献求助10
7秒前
8秒前
黎雪芳完成签到,获得积分10
8秒前
9秒前
imkhun1021完成签到,获得积分10
11秒前
11秒前
12秒前
12秒前
张雯思发布了新的文献求助10
12秒前
爆米花应助小明采纳,获得10
12秒前
我很厉害的完成签到,获得积分10
14秒前
深情安青应助mingming采纳,获得10
14秒前
典雅长颈鹿完成签到,获得积分10
14秒前
Akim应助断棍豪斯采纳,获得10
16秒前
bxbxbx发布了新的文献求助10
17秒前
章宇发布了新的文献求助10
17秒前
少爷完成签到,获得积分10
18秒前
温暖烨霖完成签到,获得积分10
19秒前
20秒前
22秒前
黎明发布了新的文献求助10
24秒前
24秒前
25秒前
jzs完成签到 ,获得积分10
26秒前
29秒前
sxx发布了新的文献求助10
29秒前
断棍豪斯发布了新的文献求助10
29秒前
30秒前
微笑芷蕾发布了新的文献求助10
31秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989660
求助须知:如何正确求助?哪些是违规求助? 3531826
关于积分的说明 11255082
捐赠科研通 3270447
什么是DOI,文献DOI怎么找? 1804981
邀请新用户注册赠送积分活动 882136
科研通“疑难数据库(出版商)”最低求助积分说明 809176