Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

被引:11
|
作者
Bostanci, Erkan [1 ]
Kocak, Engin [2 ]
Unal, Metehan [1 ]
Guzel, Mehmet Serdar [1 ]
Acici, Koray [3 ]
Asuroglu, Tunc [4 ]
机构
[1] Ankara Univ, Fac Engn, Dept Comp Engn, TR-06830 Ankara, Turkiye
[2] Univ Hlth Sci, Fac Gulhane Pharm, Dept Analyt Chem, TR-06018 Ankara, Turkiye
[3] Ankara Univ, Fac Engn, Dept Artificial Intelligence & Data Engn, TR-06830 Ankara, Turkiye
[4] Tampere Univ, Fac Med & Hlth Technol, Tampere 33720, Finland
关键词
transcriptomics; RNA-seq; machine learning; deep learning; classification; cancer prediction; exRNA; CLASSIFICATION; AGREEMENT; HEALTH;
D O I
10.3390/s23063080
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.
引用
收藏
页数:28
相关论文
共 50 条
  • [21] Computational analysis of bacterial RNA-Seq data
    McClure, Ryan
    Balasubramanian, Divya
    Sun, Yan
    Bobrovskyy, Maksym
    Sumby, Paul
    Genco, Caroline A.
    Vanderpool, Carin K.
    Tjaden, Brian
    NUCLEIC ACIDS RESEARCH, 2013, 41 (14)
  • [22] Dynamic Model for RNA-seq Data Analysis
    Li, Lerong
    Xiong, Momiao
    BIOMED RESEARCH INTERNATIONAL, 2015, 2015
  • [23] A comprehensive review on RNA-seq data analysis
    Zhang, Li
    Liu, Xuejun
    Transactions of Nanjing University of Aeronautics and Astronautics, 2016, 33 (03) : 339 - 361
  • [24] RseqFlow: workflows for RNA-Seq data analysis
    Wang, Ying
    Mehta, Gaurang
    Mayani, Rajiv
    Lu, Jingxi
    Souaiaia, Tade
    Chen, Yangho
    Clark, Andrew
    Yoon, Hee Jae
    Wan, Lin
    Evgrafov, Oleg V.
    Knowles, James A.
    Deelman, Ewa
    Chen, Ting
    BIOINFORMATICS, 2011, 27 (18) : 2598 - 2600
  • [25] RNA-Seq data analysis and the prediction of host-pathogen interaction networks
    Linde, J.
    Tierney, L.
    Mueller, S.
    Brunke, S.
    Freitag, J.
    Molina, J. C.
    Hube, B.
    Schoeck, U.
    Guthke, R.
    Kuchler, K.
    MYCOSES, 2012, 55 : 69 - 69
  • [26] A Comprehensive Review on RNA-seq Data Analysis
    Zhang Li
    Liu Xuejun
    TransactionsofNanjingUniversityofAeronauticsandAstronautics, 2016, 33 (03) : 339 - 361
  • [27] Parametric analysis of RNA-seq expression data
    Konishi, Tomokazu
    GENES TO CELLS, 2016, 21 (06) : 639 - 647
  • [28] Combining Multiple RNA-Seq Data Analysis Algorithms Using Machine Learning Improves Differential Isoform Expression Analysis
    Dimopoulos, Alexandros C.
    Koukoutegos, Konstantinos
    Psomopoulos, Fotis E.
    Moulos, Panagiotis
    METHODS AND PROTOCOLS, 2021, 4 (04)
  • [29] voomDDA: discovery of diagnostic biomarkers and classification of RNA-seq data
    Zararsiz, Gokmen
    Goksuluk, Dincer
    Klaus, Bernd
    Korkmaz, Selcuk
    Eldem, Vahap
    Karabulut, Erdem
    Ozturk, Ahmet
    PEERJ, 2017, 5
  • [30] RNA-Seq UD: A bioinformatics plattform for RNA-Seq analysis
    Ramirez, Miguel
    Alejandro Rojas-Quintero, Cristian
    Enrique Vera-Parra, Nelson
    2015 10TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI), 2015,