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M S K, Rajaguru H, Nair AR. Enhancement of Classifier Performance with Adam and RanAdam Hyper-Parameter Tuning for Lung Cancer Detection from Microarray Data-In Pursuit of Precision. Bioengineering (Basel) 2024; 11:314. [PMID: 38671736 PMCID: PMC11047746 DOI: 10.3390/bioengineering11040314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 03/18/2024] [Accepted: 03/20/2024] [Indexed: 04/28/2024] Open
Abstract
Microarray gene expression analysis is a powerful technique used in cancer classification and research to identify and understand gene expression patterns that can differentiate between different cancer types, subtypes, and stages. However, microarray databases are highly redundant, inherently nonlinear, and noisy. Therefore, extracting meaningful information from such a huge database is a challenging one. The paper adopts the Fast Fourier Transform (FFT) and Mixture Model (MM) for dimensionality reduction and utilises the Dragonfly optimisation algorithm as the feature selection technique. The classifiers employed in this research are Nonlinear Regression, Naïve Bayes, Decision Tree, Random Forest and SVM (RBF). The classifiers' performances are analysed with and without feature selection methods. Finally, Adaptive Moment Estimation (Adam) and Random Adaptive Moment Estimation (RanAdam) hyper-parameter tuning techniques are used as improvisation techniques for classifiers. The SVM (RBF) classifier with the Fast Fourier Transform Dimensionality Reduction method and Dragonfly feature selection achieved the highest accuracy of 98.343% with RanAdam hyper-parameter tuning compared to other classifiers.
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Affiliation(s)
- Karthika M S
- Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam 638401, India;
| | - Harikumar Rajaguru
- Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam 638401, India;
| | - Ajin R. Nair
- Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam 638401, India;
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M T, Koti MS, B A N, V G, K P S, Mathivanan SK, Dalu GT. Lung cancer diagnosis based on weighted convolutional neural network using gene data expression. Sci Rep 2024; 14:3656. [PMID: 38351141 PMCID: PMC10864291 DOI: 10.1038/s41598-024-54124-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 02/08/2024] [Indexed: 02/16/2024] Open
Abstract
Lung cancer is thought to be a genetic disease with a variety of unknown origins. Globocan2020 report tells in 2020 new cancer cases identified was 19.3 million and nearly 10.0 million died owed to cancer. GLOBOCAN envisages that the cancer cases will raised to 28.4 million in 2040. This charge is superior to the combined rates of the former generally prevalent malignancies, like breast, colorectal, and prostate cancers. For attribute selection in previous work, the information gain model was applied. Then, for lung cancer prediction, multilayer perceptron, random subspace, and sequential minimal optimization (SMO) are used. However, the total number of parameters in a multilayer perceptron can become extremely large. This is inefficient because of the duplication in such high dimensions, and SMO can become ineffective due to its calculating method and maintaining a single threshold value for prediction. To avoid these difficulties, our research presented a novel technique including Z-score normalization, levy flight cuckoo search optimization, and a weighted convolutional neural network for predicting lung cancer. This result findings show that the proposed technique is effective in precision, recall, and accuracy for the Kent Ridge Bio-Medical Dataset Repository.
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Affiliation(s)
- Thangamani M
- Department of Computer Science and Engineering, Hindusthan Institute of Technology, Valley Campus, Pollachi Highway, Othakkalmandapam (Post), Coimbatore, Tamil Nadu, 641032, India
| | - Manjula Sanjay Koti
- Department of Master of Computer Applications, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, 560082, India
| | - Nagashree B A
- Department of Computer Science, School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, 570026, India
| | - Geetha V
- Department of Computer Science, School of Applied Sciences, REVA University, Bangalore, 560064, India
| | - Shreyas K P
- Department of Computer Science and Applications, School of Computer Science and Applications, REVA University, Bangalore, 560064, India
| | | | - Gemmachis Teshite Dalu
- Department of Software Engineering, College of Computing and Informatics, Haramaya University, POB 138, Dire Dawa, Ethiopia.
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M S K, Rajaguru H, Nair AR. Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene-A Paradigm Shift. Bioengineering (Basel) 2023; 10:933. [PMID: 37627818 PMCID: PMC10451477 DOI: 10.3390/bioengineering10080933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 08/03/2023] [Accepted: 08/04/2023] [Indexed: 08/27/2023] Open
Abstract
Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. The dataset used for the research is the Lung Harvard 2 Dataset (LH2) which consists of 150 Adenocarcinoma subjects and 31 Mesothelioma subjects. The paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Nonlinear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%.
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Affiliation(s)
- Karthika M S
- Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam 638401, India;
| | - Harikumar Rajaguru
- Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam 638401, India;
| | - Ajin R. Nair
- Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam 638401, India;
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A Holistic Performance Comparison for Lung Cancer Classification Using Swarm Intelligence Techniques. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6680424. [PMID: 34373776 PMCID: PMC8349254 DOI: 10.1155/2021/6680424] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 07/17/2021] [Indexed: 12/22/2022]
Abstract
In the field of bioinformatics, feature selection in classification of cancer is a primary area of research and utilized to select the most informative genes from thousands of genes in the microarray. Microarray data is generally noisy, is highly redundant, and has an extremely asymmetric dimensionality, as the majority of the genes present here are believed to be uninformative. The paper adopts a methodology of classification of high dimensional lung cancer microarray data utilizing feature selection and optimization techniques. The methodology is divided into two stages; firstly, the ranking of each gene is done based on the standard gene selection techniques like Information Gain, Relief–F test, Chi-square statistic, and T-statistic test. As a result, the gathering of top scored genes is assimilated, and a new feature subset is obtained. In the second stage, the new feature subset is further optimized by using swarm intelligence techniques like Grasshopper Optimization (GO), Moth Flame Optimization (MFO), Bacterial Foraging Optimization (BFO), Krill Herd Optimization (KHO), and Artificial Fish Swarm Optimization (AFSO), and finally, an optimized subset is utilized. The selected genes are used for classification, and the classifiers used here are Naïve Bayesian Classifier (NBC), Decision Trees (DT), Support Vector Machines (SVM), and K-Nearest Neighbour (KNN). The best results are shown when Relief-F test is computed with AFSO and classified with Decision Trees classifier for hundred genes, and the highest classification accuracy of 99.10% is obtained.
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Jayanthi S, Rene Robin CR. Analysis of Microarray Data by Empirical Wavelet Transform for Cancer Classification Using Block by Block Method. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
In this study, DNA microarray data is analyzed from a signal processing perspective for cancer classification. An adaptive wavelet transform named Empirical Wavelet Transform (EWT) is analyzed using block-by-block procedure to characterize microarray data. The EWT wavelet basis depends
on the input data rather predetermined like in conventional wavelets. Thus, EWT gives more sparse representations than wavelets. The characterization of microarray data is made by block-by-block procedure with predefined block sizes in powers of 2 that starts from 128 to 2048. After characterization,
a statistical hypothesis test is employed to select the informative EWT coefficients. Only the selected coefficients are used for Microarray Data Classification (MDC) by the Support Vector Machine (SVM). Computational experiments are employed on five microarray datasets; colon, breast, leukemia,
CNS and ovarian to test the developed cancer classification system. The obtained results demonstrate that EWT coefficients with SVM emerged as an effective approach with no misclassification for MDC system.
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Affiliation(s)
- S. Jayanthi
- Research Scholar, Anna University, 600025, Tamilnadu, India; Department of Computer Science and Engineering, Agni College of Technology, 600130, Tamilnadu, India
| | - C. R. Rene Robin
- Department of Computer Science and Engineering, Jerusalem College of Engineering, 600100, Tamilnadu, India
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Baliarsingh SK, Vipsita S. Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification. IET Syst Biol 2020; 14:85-95. [PMID: 32196467 DOI: 10.1049/iet-syb.2019.0028] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Microarray technology plays a significant role in cancer classification, where a large number of genes and samples are simultaneously analysed. For the efficient analysis of the microarray data, there is a great demand for the development of intelligent techniques. In this article, the authors propose a novel hybrid technique employing Fisher criterion, ReliefF, and extreme learning machine (ELM) based on the principle of chaotic emperor penguin optimisation algorithm (CEPO). EPO is a recently developed metaheuristic method. In the proposed method, initially, Fisher score and ReliefF are independently used as filters for relevant gene selection. Further, a novel population-based metaheuristic, namely, CEPO was proposed to pre-train the ELM by selecting the optimal input weights and hidden biases. The authors have successfully conducted experiments on seven well-known data sets. To evaluate the effectiveness, the proposed method is compared with original EPO, genetic algorithm, and particle swarm optimisation-based ELM along with other state-of-the-art techniques. The experimental results show that the proposed framework achieves better accuracy as compared to the state-of-the-art schemes. The efficacy of the proposed method is demonstrated in terms of accuracy, sensitivity, specificity, and F-measure.
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Affiliation(s)
- Santos Kumar Baliarsingh
- DST-FIST Bioinformatics Lab, Department of Computer Science and Engineering, International Institute of Information Technology, Bhubaneswar, India.
| | - Swati Vipsita
- DST-FIST Bioinformatics Lab, Department of Computer Science and Engineering, International Institute of Information Technology, Bhubaneswar, India
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Jain D, Singh V. A Novel Hybrid Approach for Chronic Disease Classification. INTERNATIONAL JOURNAL OF HEALTHCARE INFORMATION SYSTEMS AND INFORMATICS 2020. [DOI: 10.4018/ijhisi.2020010101] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A two-phase diagnostic framework based on hybrid classification for the diagnosis of chronic disease is proposed. In the first phase, feature selection via ReliefF method and feature extraction via PCA method are incorporated. In the second phase, efficient optimization of SVM parameters via grid search method is performed. The proposed hybrid classification approach is then tested with seven popular chronic disease datasets using a cross-validation method. Experiments are then conducted to evaluate the presented classification method vis-à-vis four other existing classifiers that are applied on the same chronic disease datasets. Results show that the presented approach reduces approximately 40% of the extraneous and surplus features with substantial reduction in the execution time for mining all datasets, achieving the highest classification accuracy of 98.5%. It is concluded that with the presented approach, excellent classification accuracy is achieved for each chronic disease dataset while irrelevant and redundant features may be eliminated, thereby substantially reducing the diagnostic complexity and resulting computational time.
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Affiliation(s)
- Divya Jain
- Computer Science and Engineering, The NorthCap University, Gurugram, India
| | - Vijendra Singh
- Computer Science and Engineering, The NorthCap University, Gurugram, India
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Alanni R, Hou J, Azzawi H, Xiang Y. Deep gene selection method to select genes from microarray datasets for cancer classification. BMC Bioinformatics 2019; 20:608. [PMID: 31775613 PMCID: PMC6880643 DOI: 10.1186/s12859-019-3161-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 10/15/2019] [Indexed: 12/15/2022] Open
Abstract
Background Microarray datasets consist of complex and high-dimensional samples and genes, and generally the number of samples is much smaller than the number of genes. Due to this data imbalance, gene selection is a demanding task for microarray expression data analysis. Results The gene set selected by DGS has shown its superior performances in cancer classification. DGS has a high capability of reducing the number of genes in the original microarray datasets. The experimental comparisons with other representative and state-of-the-art gene selection methods also showed that DGS achieved the best performance in terms of the number of selected genes, classification accuracy, and computational cost. Conclusions We provide an efficient gene selection algorithm can select relevant genes which are significantly sensitive to the samples’ classes. With the few discriminative genes and less cost time by the proposed algorithm achieved much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.
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Affiliation(s)
- Russul Alanni
- School of Information Technology, Deakin University, Geelong, Victoria, Australia.
| | - Jingyu Hou
- School of Information Technology, Deakin University, Geelong, Victoria, Australia
| | - Hasseeb Azzawi
- School of Information Technology, Deakin University, Geelong, Victoria, Australia
| | - Yong Xiang
- School of Information Technology, Deakin University, Geelong, Victoria, Australia
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Dorosti S, Jafarzadeh Ghoushchi S, Sobhrakhshankhah E, Ahmadi M, Sharifi A. Application of gene expression programming and sensitivity analyses in analyzing effective parameters in gastric cancer tumor size and location. Soft comput 2019. [DOI: 10.1007/s00500-019-04507-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Bayrak T, Oğul H. A New Approach for Predicting the Value of Gene Expression: Two-way Collaborative Filtering. Curr Bioinform 2019. [DOI: 10.2174/1574893614666190126144139] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Background:
Predicting the value of gene expression in a given condition is a challenging
topic in computational systems biology. Only a limited number of studies in this area have
provided solutions to predict the expression in a particular pattern, whether or not it can be done
effectively. However, the value of expression for the measurement is usually needed for further
meta-data analysis.
Methods:
Because the problem is considered as a regression task where a feature representation of
the gene under consideration is fed into a trained model to predict a continuous variable that refers
to its exact expression level, we introduced a novel feature representation scheme to support work
on such a task based on two-way collaborative filtering. At this point, our main argument is that
the expressions of other genes in the current condition are as important as the expression of the
current gene in other conditions. For regression analysis, linear regression and a recently popularized
method, called Relevance Vector Machine (RVM), are used. Pearson and Spearman correlation
coefficients and Root Mean Squared Error are used for evaluation. The effects of regression
model type, RVM kernel functions, and parameters have been analysed in our study in a gene expression
profiling data comprising a set of prostate cancer samples.
Results:
According to the findings of this study, in addition to promising results from the experimental
studies, integrating data from another disease type, such as colon cancer in our case, can
significantly improve the prediction performance of the regression model.
Conclusion:
The results also showed that the performed new feature representation approach and
RVM regression model are promising for many machine learning problems in microarray and high
throughput sequencing analysis.
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Affiliation(s)
- Tuncay Bayrak
- Computer Engineering Department, Baskent University, Eskisehir Road 20. Km Baglica Campus, 06560, Ankara, Turkey
| | - Hasan Oğul
- Computer Engineering Department, Baskent University, Eskisehir Road 20. Km Baglica Campus, 06560, Ankara, Turkey
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Huang H, Huang Q, Tang T, Zhou X, Gu L, Lu X, Liu F. Differentially Expressed Gene Screening, Biological Function Enrichment, and Correlation with Prognosis in Non-Small Cell Lung Cancer. Med Sci Monit 2019; 25:4333-4341. [PMID: 31181055 PMCID: PMC6582684 DOI: 10.12659/msm.916962] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background The aim of this study was to explore the differently expressed genes and pathways in non-small cell lung cancer (NSCLC) and their correlation with the prognosis. Material/Methods Gene expression data series of GSE19804, GSE101929, and GSE33532 were downloaded from the Gene Expression Ominibus (GEO) database. The overlaping differently expressed genes (DEGs) were identified form the above 3 data series. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEEG) were used to analyze the biological functions and signal pathways of DEGs. The protein–protein interaction (PPI) was analyzed thorough Search Tool for the Retrieval of Interacting Gens (STRING). The relationship between the expression of hub genes and the prognosis of patients was analyzed by Kaplan-Meier Plotter online software. Results Twenty-nine DEGs were identified, with 22 upregulated genes and 7 downregulated genes. The enriched biological processes were mainly related to diet-induced thermogenesis and actin filament binding. The KEGG pathways were enriched in calcium signaling, regulation of lipolysis in adipocytes, and PPAR signaling. Two downregulated genes (MMP1 and SPP1) were identified as hub genes by Cytohubba. Twenty-two dysregulated genes were correlated with patient prognosis. Conclusions Differentially expressed genes are common in NSCLC patients and can be used as biomarkers for patient prognosis.
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Affiliation(s)
- He Huang
- Department of Respiratory, Zhejiang Hospital, Hangzhou, Zhejiang, China (mainland)
| | - Qingdong Huang
- Department of Respiratory, Zhejiang Hospital, Hangzhou, Zhejiang, China (mainland)
| | - Tingyu Tang
- Department of Respiratory, Zhejiang Hospital, Hangzhou, Zhejiang, China (mainland)
| | - Xiaoxi Zhou
- Department of Respiratory, Zhejiang Hospital, Hangzhou, Zhejiang, China (mainland)
| | - Liang Gu
- Department of Respiratory, Zhejiang Hospital, Hangzhou, Zhejiang, China (mainland)
| | - Xiaoling Lu
- Department of Respiratory, Zhejiang Hospital, Hangzhou, Zhejiang, China (mainland)
| | - Fang Liu
- Department of Respiratory, Zhejiang Hospital, Hangzhou, Zhejiang, China (mainland)
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Alanni R, Hou J, Azzawi H, Xiang Y. Cancer adjuvant chemotherapy prediction model for non‐small cell lung cancer. IET Syst Biol 2019; 13:129-135. [DOI: 10.1049/iet-syb.2018.5060] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Affiliation(s)
- Russul Alanni
- School of Information Technology, Deakin UniversityBurwoodAustralia
| | - Jingyu Hou
- School of Information Technology, Deakin UniversityBurwoodAustralia
| | - Hasseeb Azzawi
- School of Information Technology, Deakin UniversityBurwoodAustralia
| | - Yong Xiang
- School of Information Technology, Deakin UniversityBurwoodAustralia
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Alanni R, Hou J, Azzawi H, Xiang Y. A novel gene selection algorithm for cancer classification using microarray datasets. BMC Med Genomics 2019; 12:10. [PMID: 30646919 PMCID: PMC6334429 DOI: 10.1186/s12920-018-0447-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 12/07/2018] [Indexed: 12/18/2022] Open
Abstract
Background Microarray datasets are an important medical diagnostic tool as they represent the states of a cell at the molecular level. Available microarray datasets for classifying cancer types generally have a fairly small sample size compared to the large number of genes involved. This fact is known as a curse of dimensionality, which is a challenging problem. Gene selection is a promising approach that addresses this problem and plays an important role in the development of efficient cancer classification due to the fact that only a small number of genes are related to the classification problem. Gene selection addresses many problems in microarray datasets such as reducing the number of irrelevant and noisy genes, and selecting the most related genes to improve the classification results. Methods An innovative Gene Selection Programming (GSP) method is proposed to select relevant genes for effective and efficient cancer classification. GSP is based on Gene Expression Programming (GEP) method with a new defined population initialization algorithm, a new fitness function definition, and improved mutation and recombination operators. . Support Vector Machine (SVM) with a linear kernel serves as a classifier of the GSP. Results Experimental results on ten microarray cancer datasets demonstrate that Gene Selection Programming (GSP) is effective and efficient in eliminating irrelevant and redundant genes/features from microarray datasets. The comprehensive evaluations and comparisons with other methods show that GSP gives a better compromise in terms of all three evaluation criteria, i.e., classification accuracy, number of selected genes, and computational cost. The gene set selected by GSP has shown its superior performances in cancer classification compared to those selected by the up-to-date representative gene selection methods. Conclusion Gene subset selected by GSP can achieve a higher classification accuracy with less processing time.
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Affiliation(s)
- Russul Alanni
- School of Information Technology, Deakin University, Burwood, 3125, VIC, Australia.
| | - Jingyu Hou
- School of Information Technology, Deakin University, Burwood, 3125, VIC, Australia
| | - Hasseeb Azzawi
- School of Information Technology, Deakin University, Burwood, 3125, VIC, Australia
| | - Yong Xiang
- School of Information Technology, Deakin University, Burwood, 3125, VIC, Australia
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Zhang L, Chen J, Gao C, Liu C, Xu K. An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming. Med Biol Eng Comput 2018; 56:1771-1779. [DOI: 10.1007/s11517-018-1811-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 02/27/2018] [Indexed: 02/06/2023]
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Al-Anni R, Hou J, Abdu-Aljabar RD, Xiang Y. Prediction of NSCLC recurrence from microarray data with GEP. IET Syst Biol 2017; 11:77-85. [PMID: 28518058 DOI: 10.1049/iet-syb.2016.0033] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Lung cancer is one of the deadliest diseases in the world. Non-small cell lung cancer (NSCLC) is the most common and dangerous type of lung cancer. Despite the fact that NSCLC is preventable and curable for some cases if diagnosed at early stages, the vast majority of patients are diagnosed very late. Furthermore, NSCLC usually recurs sometime after treatment. Therefore, it is of paramount importance to predict NSCLC recurrence, so that specific and suitable treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology data and predictions are not reliable in many cases. The microarray gene expression (GE) technology provides a promising and reliable way to predict NSCLC recurrence by analysing the GE of sample cells. This study proposes a new model from GE programming to use microarray datasets for NSCLC recurrence prediction. To this end, the authors also propose a hybrid method to rank and select relevant prognostic genes that are related to NSCLC recurrence prediction. The proposed model was evaluated on real NSCLC microarray datasets and compared with other representational models. The results demonstrated the effectiveness of the proposed model.
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Affiliation(s)
- Russul Al-Anni
- School of Information Technology, Deakin University, Victoria, Australia.
| | - Jingyu Hou
- School of Information Technology, Deakin University, Victoria, Australia
| | | | - Yong Xiang
- School of Information Technology, Deakin University, Victoria, Australia
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