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Song X, Liang K, Li J. WGRLR: A Weighted Group Regularized Logistic Regression for Cancer Diagnosis and Gene Selection. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1563-1573. [PMID: 36044492 DOI: 10.1109/tcbb.2022.3203167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Sparse regressions applied to cancer diagnosis suffer from noise reduction, gene grouping, and group significance evaluation. This paper presented the weighted group regularized logistic regression (WGRLR) for dealing with the above problems. Clean data was separated from noisy gene expression profile data, based on which gene grouping and model building were performed. An interpretable gene group significance evaluation criterion was proposed based on symmetrical uncertainty and module eigengene. A group-wise individual gene significance evaluation criterion was also presented. The performances of the proposed method were compared with WGGL, ASGL-CMI, SGL, GL, Elastic Net, and lasso on acute leukemia and brain cancer data. Experimental results demonstrate that the proposed method is superior to the other six methods in cancer diagnosis accuracy and gene selection.
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Li J, Cao F, Gao Q, Liang K, Tang Y. Improving diagnosis accuracy of non-small cell lung carcinoma on noisy data by adaptive group lasso regularized multinomial regression. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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3
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Li J, Zhang H, Gao F. Identification of miRNA biomarkers for breast cancer by combining ensemble regularized multinomial logistic regression and Cox regression. BMC Bioinformatics 2022; 23:434. [PMID: 36258162 PMCID: PMC9580207 DOI: 10.1186/s12859-022-04982-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 10/05/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Breast cancer is one of the most common cancers in women. It is necessary to classify breast cancer subtypes because different subtypes need specific treatment. Identifying biomarkers and classifying breast cancer subtypes is essential for developing appropriate treatment methods for patients. MiRNAs can be easily detected in tumor biopsy and play an inhibitory or promoting role in breast cancer, which are considered promising biomarkers for distinguishing subtypes. RESULTS A new method combing ensemble regularized multinomial logistic regression and Cox regression was proposed for identifying miRNA biomarkers in breast cancer. After adopting stratified sampling and bootstrap sampling, the most suitable sample subset for miRNA feature screening was determined via ensemble 100 regularized multinomial logistic regression models. 124 miRNAs that participated in the classification of at least 3 subtypes and appeared at least 50 times in 100 integrations were screened as features. 22 miRNAs from the proposed feature set were further identified as the biomarkers for breast cancer by using Cox regression based on survival analysis. The accuracy of 5 methods on the proposed feature set was significantly higher than on the other two feature sets. The results of 7 biological analyses illustrated the rationality of the identified biomarkers. CONCLUSIONS The screened features can better distinguish breast cancer subtypes. Notably, the genes and proteins related to the proposed 22 miRNAs were considered oncogenes or inhibitors of breast cancer. 9 of the 22 miRNAs have been proved to be markers of breast cancer. Therefore, our results can be considered in future related research.
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Affiliation(s)
- Juntao Li
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, China
| | - Hongmei Zhang
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, China.
| | - Fugen Gao
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, China
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4
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Nageswaran S, Arunkumar G, Bisht AK, Mewada S, Kumar JNVRS, Jawarneh M, Asenso E. Lung Cancer Classification and Prediction Using Machine Learning and Image Processing. BIOMED RESEARCH INTERNATIONAL 2022; 2022:1755460. [PMID: 36046454 PMCID: PMC9424001 DOI: 10.1155/2022/1755460] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/21/2022] [Accepted: 07/30/2022] [Indexed: 11/18/2022]
Abstract
Lung cancer is a potentially lethal illness. Cancer detection continues to be a challenge for medical professionals. The true cause of cancer and its complete treatment have still not been discovered. Cancer that is caught early enough can be treated. Image processing methods such as noise reduction, feature extraction, identification of damaged regions, and maybe a comparison with data on the medical history of lung cancer are used to locate portions of the lung that have been impacted by cancer. This research shows an accurate classification and prediction of lung cancer using technology that is enabled by machine learning and image processing. To begin, photos need to be gathered. In the experimental investigation, 83 CT scans from 70 distinct patients were utilized as the dataset. The geometric mean filter is used during picture preprocessing. As a consequence, image quality is enhanced. The K-means technique is then used to segment the images. The part of the image may be found using this segmentation. Then, classification methods using machine learning are used. For the classification, ANN, KNN, and RF are some of the machine learning techniques that were used. It is found that the ANN model is producing more accurate results for predicting lung cancer.
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Affiliation(s)
- Sharmila Nageswaran
- Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Tamil Nadu, India
| | - G. Arunkumar
- Department of Computer Science and Engineering, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India
| | - Anil Kumar Bisht
- Department of CS&IT, MJP Rohilkhand University, Bareilly, U. P., India
| | - Shivlal Mewada
- Department of Computer Science, Govt. College, Makdone (Vikram University), Ujjain, India
| | | | | | - Evans Asenso
- Department of Agricultural Engineering, University of Ghana, Ghana
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Li X, Wang Y, Ruiz R. A Survey on Sparse Learning Models for Feature Selection. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:1642-1660. [PMID: 32386172 DOI: 10.1109/tcyb.2020.2982445] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Feature selection is important in both machine learning and pattern recognition. Successfully selecting informative features can significantly increase learning accuracy and improve result comprehensibility. Various methods have been proposed to identify informative features from high-dimensional data by removing redundant and irrelevant features to improve classification accuracy. In this article, we systematically survey existing sparse learning models for feature selection from the perspectives of individual sparse feature selection and group sparse feature selection, and analyze the differences and connections among various sparse learning models. Promising research directions and topics on sparse learning models are analyzed.
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Lung cancer prediction using multi-gene genetic programming by selecting automatic features from amino acid sequences. Comput Biol Chem 2022; 98:107638. [DOI: 10.1016/j.compbiolchem.2022.107638] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 12/22/2021] [Accepted: 02/01/2022] [Indexed: 02/07/2023]
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Li J, Liang K, Song X. Logistic regression with adaptive sparse group lasso penalty and its application in acute leukemia diagnosis. Comput Biol Med 2021; 141:105154. [PMID: 34952336 DOI: 10.1016/j.compbiomed.2021.105154] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 12/14/2021] [Accepted: 12/15/2021] [Indexed: 01/15/2023]
Abstract
Cancer diagnosis based on gene expression profile data has attracted extensive attention in computational biology and medicine. It suffers from three challenges in practical applications: noise, gene grouping, and adaptive gene selection. This paper aims to solve the above problems by developing the logistic regression with adaptive sparse group lasso penalty (LR-ASGL). A noise information processing method for cancer gene expression profile data is first presented via robust principal component analysis. Genes are then divided into groups by performing weighted gene co-expression network analysis on the clean matrix. By approximating the relative value of the noise size, gene reliability criterion and robust evaluation criterion are proposed. Finally, LR-ASGL is presented for simultaneous cancer diagnosis and adaptive gene selection. The performance of the proposed method is compared with the other four methods in three simulation settings: Gaussian noise, uniformly distributed noise, and mixed noise. The acute leukemia data are adopted as an experimental example to demonstrate the advantages of LR-ASGL in prediction and gene selection.
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Affiliation(s)
- Juntao Li
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007, China.
| | - Ke Liang
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007, China.
| | - Xuekun Song
- College of Information Technology, Henan University of Chinese Medicine, Zhengzhou, 450046, China.
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Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using Wilcoxon Gain and Generator. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:5912051. [PMID: 34691378 PMCID: PMC8528612 DOI: 10.1155/2021/5912051] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/27/2021] [Accepted: 09/27/2021] [Indexed: 01/15/2023]
Abstract
Cancer is a complicated worldwide health issue with an increasing death rate in recent years. With the swift blooming of the high throughput technology and several machine learning methods that have unfolded in recent years, progress in cancer disease diagnosis has been made based on subset features, providing awareness of the efficient and precise disease diagnosis. Hence, progressive machine learning techniques that can, fortunately, differentiate lung cancer patients from healthy persons are of great concern. This paper proposes a novel Wilcoxon Signed-Rank Gain Preprocessing combined with Generative Deep Learning called Wilcoxon Signed Generative Deep Learning (WS-GDL) method for lung cancer disease diagnosis. Firstly, test significance analysis and information gain eliminate redundant and irrelevant attributes and extract many informative and significant attributes. Then, using a generator function, the Generative Deep Learning method is used to learn the deep features. Finally, a minimax game (i.e., minimizing error with maximum accuracy) is proposed to diagnose the disease. Numerical experiments on the Thoracic Surgery Data Set are used to test the WS-GDL method's disease diagnosis performance. The WS-GDL approach may create relevant and significant attributes and adaptively diagnose the disease by selecting optimal learning model parameters. Quantitative experimental results show that the WS-GDL method achieves better diagnosis performance and higher computing efficiency in computational time, computational complexity, and false-positive rate compared to state-of-the-art approaches.
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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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Şeker M, Özbek Y, Yener G, Özerdem MS. Complexity of EEG Dynamics for Early Diagnosis of Alzheimer's Disease Using Permutation Entropy Neuromarker. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 206:106116. [PMID: 33957376 DOI: 10.1016/j.cmpb.2021.106116] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 04/11/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Electroencephalogram (EEG) is one of the most demanded screening tools that investigates the effects of Alzheimer's Disease (AD) on human brain. Identification of AD in early stage gives rise to efficient treatment in dementia. Mild Cognitive Impairment (MCI) is considered as a conversion stage. Reducing EEG complexity can be used as a marker to detect AD. The aim of this study is to develop a 3-way diagnostic classification using EEG complexity in the detection of MCI/AD in clinical practice. This study also investigates the effects of different eyes states, i.e. eyes-open, eyes-closed on classification performance. METHODS EEG recordings from 85 AD, 85 MCI subjects, and 85 Healthy Controls with eyes-open and eyes- closed are analyzed. Permutation Entropy (PE) values are computed from frontal, central, parietal, temporal, and occipital regions for each EEG epoch. Distribution of PE values are visualized to observe discrimination of MCI/AD with HC. Visual investigations are combined with statistical analysis using ANOVA to determine whether groups are significant or not. Multinomial Logistic Regression model is applied to feature sets in order to classify participants individually. RESULTS Distribution of measured PE shows that EEG complexity is lower in AD and higher in HC group. MCI group is observed as an intermediate form due to heterogeneous values. Results from 3-way classification indicate that F1-scores and rates of sensitivity and specificity achieve the highest overall discrimination rates reaching up to 100% for at TP8 for eyes-closed condition; and C3, C4, T8, O2 electrodes for eyes-open condition. Classification of HC from both patient groups is achieved best. Eyes-open state increases discrimination of MCI and AD. CONCLUSIONS This nonlinear EEG methodology study contributes to literature with high discrimination rates for identification of AD. PE is recommended as a practical diagnostic neuro-marker for AD studies. Resting state EEG at eyes-open condition can be more advantageous over eyes-closed EEG recordings for diagnosis of AD.
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Affiliation(s)
- Mesut Şeker
- Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey.
| | - Yağmur Özbek
- Department of Neurosciences, Health Science Institute, Dokuz Eylül University, Izmir
| | - Görsev Yener
- Department of Neurosciences, Health Science Institute, Dokuz Eylül University, Izmir; Izmir Biomedicine and Genome Center, Izmir, Turkey; Department of Neurology, Faculty of Medicine, Izmir Ekonomi University, Izmir, Turkey; Department of Neurology, Faculty of Medicine, Dokuz Eylül University, Izmir, Turkey
| | - Mehmet Siraç Özerdem
- Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey
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11
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Wang L, Li J, Liu J, Chang M. RAMRSGL: A Robust Adaptive Multinomial Regression Model for Multicancer Classification. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5584684. [PMID: 34122617 PMCID: PMC8172296 DOI: 10.1155/2021/5584684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 05/12/2021] [Indexed: 11/17/2022]
Abstract
In view of the challenges of the group Lasso penalty methods for multicancer microarray data analysis, e.g., dividing genes into groups in advance and biological interpretability, we propose a robust adaptive multinomial regression with sparse group Lasso penalty (RAMRSGL) model. By adopting the overlapping clustering strategy, affinity propagation clustering is employed to obtain each cancer gene subtype, which explores the group structure of each cancer subtype and merges the groups of all subtypes. In addition, the data-driven weights based on noise are added to the sparse group Lasso penalty, combining with the multinomial log-likelihood function to perform multiclassification and adaptive group gene selection simultaneously. The experimental results on acute leukemia data verify the effectiveness of the proposed method.
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Affiliation(s)
- Lei Wang
- Department of Basic Science Teaching, Henan Polytechnic Institute, Nanyang, 473000 Henan, China
| | - Juntao Li
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007 Henan, China
| | - Juanfang Liu
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007 Henan, China
| | - Mingming Chang
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007 Henan, China
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12
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Chen L, Li J, Chang M. Cancer Diagnosis and Disease Gene Identification via Statistical Machine Learning. Curr Bioinform 2021. [DOI: 10.2174/1574893615666200207094947] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Diagnosing cancer and identifying the disease gene by using DNA microarray gene
expression data are the hot topics in current bioinformatics. This paper is devoted to the latest
development in cancer diagnosis and gene selection via statistical machine learning. A support
vector machine is firstly introduced for the binary cancer diagnosis. Then, 1-norm support vector
machine, doubly regularized support vector machine, adaptive huberized support vector machine
and other extensions are presented to improve the performance of gene selection. Lasso, elastic
net, partly adaptive elastic net, group lasso, sparse group lasso, adaptive sparse group lasso and
other sparse regression methods are also introduced for performing simultaneous binary cancer
classification and gene selection. In addition to introducing three strategies for reducing multiclass
to binary, methods of directly considering all classes of data in a learning model (multi_class
support vector, sparse multinomial regression, adaptive multinomial regression and so on) are
presented for performing multiple cancer diagnosis. Limitations and promising directions are also
discussed.
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Affiliation(s)
- Liuyuan Chen
- Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control, College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007, China
| | - Juntao Li
- Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control, College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007, China
| | - Mingming Chang
- Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control, College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007, China
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13
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Abstract
Background:
The evolutionary history of organisms can be described by phylogenetic
trees. We need to compare the topologies of rooted phylogenetic trees when researching the
evolution of a given set of species.
Objective:
Up to now, there are several metrics measuring the dissimilarity between rooted
phylogenetic trees, and those metrics are defined by different ways.
Methods:
This paper analyzes those metrics from their definitions and the distance values
computed by those metrics by terms of experiments.
Results:
The results of experiments show that the distances calculated by the cluster metric, the
partition metric, and the equivalent metric have a good Gaussian fitting, and the equivalent metric
can describe the difference between trees better than the others.
Conclusion:
Moreover, it presents a tool called as CDRPT (Computing Distance for Rooted
Phylogenetic Trees). CDRPT is a web server to calculate the distance for trees by an on-line way.
CDRPT can also be off-line used by means of installing application packages for the Windows
system. It greatly facilitates the use of researchers. The home page of CDRPT is
http://bioinformatics.imu.edu.cn/tree/.
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Affiliation(s)
- Juan Wang
- School of Computer Science, Inner Mongolia University, Hohhot, China
| | - Xinyue Qi
- School of Computer Science, Inner Mongolia University, Hohhot, China
| | - Bo Cui
- School of Computer Science, Inner Mongolia University, Hohhot, China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
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Li J, Chang M, Gao Q, Song X, Gao Z. Lung Cancer Classification and Gene Selection by Combining Affinity Propagation Clustering and Sparse Group Lasso. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191017103557] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Background:
Cancer threatens human health seriously. Diagnosing cancer via gene expression
analysis is a hot topic in cancer research.
Objective:
The study aimed to diagnose the accurate type of lung cancer and discover the pathogenic
genes.
Methods:
In this study, Affinity Propagation (AP) clustering with similarity score was employed
to each type of lung cancer and normal lung. After grouping genes, sparse group lasso was adopted
to construct four binary classifiers and the voting strategy was used to integrate them.
Results:
This study screened six gene groups that may associate with different lung cancer subtypes
among 73 genes groups, and identified three possible key pathogenic genes, KRAS, BRAF
and VDR. Furthermore, this study achieved improved classification accuracies at minority classes
SQ and COID in comparison with other four methods.
Conclusion:
We propose the AP clustering based sparse group lasso (AP-SGL), which provides
an alternative for simultaneous diagnosis and gene selection for lung cancer.
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Affiliation(s)
- Juntao Li
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007, China
| | - Mingming Chang
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007, China
| | - Qinghui Gao
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007, China
| | - Xuekun Song
- School of Information Technology, Henan University of Chinese Medicine, Zhengzhou, 450046, China
| | - Zhiyu Gao
- School of Information Technology, Henan University of Chinese Medicine, Zhengzhou, 450046, China
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Basavegowda HS, Dagnew G. Deep learning approach for microarray cancer data classification. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2020. [DOI: 10.1049/trit.2019.0028] [Citation(s) in RCA: 110] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Affiliation(s)
- Hema Shekar Basavegowda
- Department of Studies and Research in Computer ScienceMangalore UniversityMangaloreKarnatakaIndia
| | - Guesh Dagnew
- Department of Studies and Research in Computer ScienceMangalore UniversityMangaloreKarnatakaIndia
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New Approaches in Metaheuristic to Classify Medical Data Using Artificial Neural Network. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2019. [DOI: 10.1007/s13369-019-04026-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Cornelis B, Blinder D, Jansen B, Lagae L, Schelkens P. Fast and robust Fourier domain-based classification for on-chip lens-free flow cytometry. OPTICS EXPRESS 2018; 26:14329-14339. [PMID: 29877473 DOI: 10.1364/oe.26.014329] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 04/09/2018] [Indexed: 06/08/2023]
Abstract
The development of portable haematology analysers receives increased attention due to their deployability in resource-limited or emergency settings. Lens-free in-line holographic microscopy is one of the technologies that is being pushed forward in this regard as it eliminates complex and expensive optics, making miniaturisation and integration with microfluidics possible. On-chip flow cytometry enables high-speed capturing of individual cells in suspension, giving rise to high-throughput cell counting and classification. To perform a real-time analysis on this high-throughput content, we propose a fast and robust framework for the classification of leukocytes. The raw data consists of holographic acquisitions of leukocytes, captured with a high-speed camera as they are flowing through a microfluidic chip. Three different types of leukocytes are considered: granulocytes, monocytes and T-lymphocytes. The proposed method bypasses the reconstruction of the holographic data altogether by extracting Zernike moments directly from the frequency domain. By doing so, we introduce robustness to translations and rotations of cells, as well as to changes in distance of a cell with respect to the image sensor, achieving classification accuracies up to 96.8%. Furthermore, the reduced computational complexity of this approach, compared to traditional frameworks that involve the reconstruction of the holographic data, allows for very fast processing and classification, making it applicable in high-throughput flow cytometry setups.
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