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Nies HW, Mohamad MS, Zakaria Z, Chan WH, Remli MA, Nies YH. Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1232. [PMID: 34573857 PMCID: PMC8472068 DOI: 10.3390/e23091232] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/14/2021] [Accepted: 09/16/2021] [Indexed: 12/12/2022]
Abstract
Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal-like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer subtypes and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. Therefore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes.
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Affiliation(s)
- Hui Wen Nies
- School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia; (Z.Z.); (W.H.C.)
| | - Mohd Saberi Mohamad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain 17666, United Arab Emirates;
| | - Zalmiyah Zakaria
- School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia; (Z.Z.); (W.H.C.)
| | - Weng Howe Chan
- School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia; (Z.Z.); (W.H.C.)
| | - Muhammad Akmal Remli
- Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, Kota Bharu 16100, Malaysia;
| | - Yong Hui Nies
- Department of Anatomy, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur 56000, Malaysia;
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Kim EH, Kim S, Kim HJ, Jeong HO, Lee J, Jang J, Joo JY, Shin Y, Kang J, Park AK, Lee JY, Lee S. Prediction of Chronic Periodontitis Severity Using Machine Learning Models Based On Salivary Bacterial Copy Number. Front Cell Infect Microbiol 2020; 10:571515. [PMID: 33304856 PMCID: PMC7701273 DOI: 10.3389/fcimb.2020.571515] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 10/20/2020] [Indexed: 12/12/2022] Open
Abstract
Periodontitis is a widespread chronic inflammatory disease caused by interactions between periodontal bacteria and homeostasis in the host. We aimed to investigate the performance and reliability of machine learning models in predicting the severity of chronic periodontitis. Mouthwash samples from 692 subjects (144 healthy controls and 548 generalized chronic periodontitis patients) were collected, the genomic DNA was isolated, and the copy numbers of nine pathogens were measured using multiplex qPCR. The nine pathogens are as follows: Porphyromonas gingivalis (Pg), Tannerella forsythia (Tf), Treponema denticola (Td), Prevotella intermedia (Pi), Fusobacterium nucleatum (Fn), Campylobacter rectus (Cr), Aggregatibacter actinomycetemcomitans (Aa), Peptostreptococcus anaerobius (Pa), and Eikenella corrodens (Ec). By adding the species one by one in order of high accuracy to find the optimal combination of input features, we developed an algorithm that predicts the severity of periodontitis using four machine learning techniques. The accuracy was the highest when the models classified “healthy” and “moderate or severe” periodontitis (H vs. M-S, average accuracy of four models: 0.93, AUC = 0.96, sensitivity of 0.96, specificity of 0.81, and diagnostic odds ratio = 112.75). One or two red complex pathogens were used in three models to distinguish slight chronic periodontitis patients from healthy controls (average accuracy of 0.78, AUC = 0.82, sensitivity of 0.71, and specificity of 0.84, diagnostic odds ratio = 12.85). Although the overall accuracy was slightly reduced, the models showed reliability in predicting the severity of chronic periodontitis from 45 newly obtained samples. Our results suggest that a well-designed combination of salivary bacteria can be used as a biomarker for classifying between a periodontally healthy group and a chronic periodontitis group.
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Affiliation(s)
- Eun-Hye Kim
- Department of R&D, Helixco Inc., Ulsan, South Korea.,College of Pharmacy and Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Suncheon, South Korea
| | - Seunghoon Kim
- Department of Biomedical Engineering, College of Information-Bio Convergence Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.,Korean Genomics Center, UNIST, Ulsan, South Korea
| | - Hyun-Joo Kim
- Department of Periodontology, Dental and Life Science Institute, Pusan National University, School of Dentistry, Yangsan, South Korea.,Department of Periodontology and Dental Research Institute, Pusan National University Dental Hospital, Yangsan, South Korea
| | - Hyoung-Oh Jeong
- Department of Biomedical Engineering, College of Information-Bio Convergence Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.,Korean Genomics Center, UNIST, Ulsan, South Korea
| | - Jaewoong Lee
- Department of Biomedical Engineering, College of Information-Bio Convergence Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.,Korean Genomics Center, UNIST, Ulsan, South Korea
| | - Jinho Jang
- Department of Biomedical Engineering, College of Information-Bio Convergence Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.,Korean Genomics Center, UNIST, Ulsan, South Korea
| | - Ji-Young Joo
- Department of Periodontology, Dental and Life Science Institute, Pusan National University, School of Dentistry, Yangsan, South Korea.,Department of Periodontology and Dental Research Institute, Pusan National University Dental Hospital, Yangsan, South Korea
| | - Yerang Shin
- Department of R&D, Helixco Inc., Ulsan, South Korea
| | - Jihoon Kang
- Department of R&D, Helixco Inc., Ulsan, South Korea
| | - Ae Kyung Park
- College of Pharmacy and Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Suncheon, South Korea
| | - Ju-Youn Lee
- Department of Periodontology, Dental and Life Science Institute, Pusan National University, School of Dentistry, Yangsan, South Korea.,Department of Periodontology and Dental Research Institute, Pusan National University Dental Hospital, Yangsan, South Korea
| | - Semin Lee
- Department of Biomedical Engineering, College of Information-Bio Convergence Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.,Korean Genomics Center, UNIST, Ulsan, South Korea
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3
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Mahendran N, Durai Raj Vincent PM, Srinivasan K, Chang CY. Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions. Front Genet 2020; 11:603808. [PMID: 33362861 PMCID: PMC7758324 DOI: 10.3389/fgene.2020.603808] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 10/29/2020] [Indexed: 12/20/2022] Open
Abstract
Gene Expression is the process of determining the physical characteristics of living beings by generating the necessary proteins. Gene Expression takes place in two steps, translation and transcription. It is the flow of information from DNA to RNA with enzymes' help, and the end product is proteins and other biochemical molecules. Many technologies can capture Gene Expression from the DNA or RNA. One such technique is Microarray DNA. Other than being expensive, the main issue with Microarray DNA is that it generates high-dimensional data with minimal sample size. The issue in handling such a heavyweight dataset is that the learning model will be over-fitted. This problem should be addressed by reducing the dimension of the data source to a considerable amount. In recent years, Machine Learning has gained popularity in the field of genomic studies. In the literature, many Machine Learning-based Gene Selection approaches have been discussed, which were proposed to improve dimensionality reduction precision. This paper does an extensive review of the various works done on Machine Learning-based gene selection in recent years, along with its performance analysis. The study categorizes various feature selection algorithms under Supervised, Unsupervised, and Semi-supervised learning. The works done in recent years to reduce the features for diagnosing tumors are discussed in detail. Furthermore, the performance of several discussed methods in the literature is analyzed. This study also lists out and briefly discusses the open issues in handling the high-dimension and less sample size data.
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Affiliation(s)
- Nivedhitha Mahendran
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - P. M. Durai Raj Vincent
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Kathiravan Srinivasan
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Chuan-Yu Chang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Douliu, Taiwan
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Morris JS, Luthra R, Liu Y, Duose DY, Lee W, Reddy NG, Windham J, Chen H, Tong Z, Zhang B, Wei W, Ganiraju M, Broom BM, Alvarez HA, Mejia A, Veeranki O, Routbort MJ, Morris VK, Overman MJ, Menter D, Katkhuda R, Wistuba II, Davis JS, Kopetz S, Maru DM. Development and Validation of a Gene Signature Classifier for Consensus Molecular Subtyping of Colorectal Carcinoma in a CLIA-Certified Setting. Clin Cancer Res 2020; 27:120-130. [PMID: 33109741 DOI: 10.1158/1078-0432.ccr-20-2403] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 09/28/2020] [Accepted: 10/23/2020] [Indexed: 11/16/2022]
Abstract
PURPOSE Consensus molecular subtyping (CMS) of colorectal cancer has potential to reshape the colorectal cancer landscape. We developed and validated an assay that is applicable on formalin-fixed, paraffin-embedded (FFPE) samples of colorectal cancer and implemented the assay in a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory. EXPERIMENTAL DESIGN We performed an in silico experiment to build an optimal CMS classifier using a training set of 1,329 samples from 12 studies and validation set of 1,329 samples from 14 studies. We constructed an assay on the basis of NanoString CodeSets for the top 472 genes, and performed analyses on paired flash-frozen (FF)/FFPE samples from 175 colorectal cancers to adapt the classifier to FFPE samples using a subset of genes found to be concordant between FF and FFPE, tested the classifier's reproducibility and repeatability, and validated in a CLIA-certified laboratory. We assessed prognostic significance of CMS in 345 patients pooled across three clinical trials. RESULTS The best classifier was weighted support vector machine with high accuracy across platforms and gene lists (>0.95), and the 472-gene model outperforming existing classifiers. We constructed subsets of 99 and 200 genes with high FF/FFPE concordance, and adapted FFPE-based classifier that had strong classification accuracy (>80%) relative to "gold standard" CMS. The classifier was reproducible to sample type and RNA quality, and demonstrated poor prognosis for CMS1-3 and good prognosis for CMS2 in metastatic colorectal cancer (P < 0.001). CONCLUSIONS We developed and validated a colorectal cancer CMS assay that is ready for use in clinical trials, to assess prognosis in standard-of-care settings and explore as predictor of therapy response.
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Affiliation(s)
- Jeffrey S Morris
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
| | - Rajyalakshmi Luthra
- Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Yusha Liu
- Department of Biostatistics, University of Chicago School of Medicine, Chicago, Illinois
| | - Dzifa Y Duose
- Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Wonyul Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Neelima G Reddy
- Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | - Huiqin Chen
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Zhimin Tong
- Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Baili Zhang
- Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Wei Wei
- Cleveland Clinic Foundation, Cleveland, Ohio
| | - Manyam Ganiraju
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Bradley M Broom
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Hector A Alvarez
- Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alicia Mejia
- Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Omkara Veeranki
- Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Mark J Routbort
- Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Van K Morris
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Michael J Overman
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - David Menter
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Riham Katkhuda
- Department of Pathology, University of Chicago Medical Center, Chicago, Illinois
| | - Ignacio I Wistuba
- Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jennifer S Davis
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Scott Kopetz
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Dipen M Maru
- Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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Chunhe Y. Evaluation of maker space index system based on machine learning and intelligent interactive system. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-189068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Machine learning technology is the core of artificial intelligence and the basis of computer intelligence. In recent years, machine learning technology has integrated and developed different learning methods, and the research of integrated learning system with more flexible and efficient form is also emerging. In this paper, the authors analyze the maker space index system based on machine learning and intelligent interactive system. As a comprehensive innovation and entrepreneurship platform, mass innovation space has the characteristics of both existing entrepreneurship service system and knowledge innovation driven. Through the index score calculation, the related evaluation system is constructed, the final score of social support system is 61.4.Multi-factor performance evaluation system based on machine learning and artificial intelligence,this paper reveals the development and change law of maker space, and provides theoretical basis for the future operation and decision-making of maker space.
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Affiliation(s)
- Yang Chunhe
- School of Economics and Management, Harbin Engineering University, Harbin, China
- Shenyang University, Shenyang, China
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6
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Kong F, Wang Y. Design of computer interactive system for sports training based on artificial intelligence and improved support vector. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019. [DOI: 10.3233/jifs-179200] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Fei Kong
- Department of Industrial Design, School of Electromechanic Engineering, Qingdao University, Qingdao, China
| | - Yumin Wang
- School of Sciences, Qingdao University of Technology, Qingdao, China
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7
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Tian S, Wang C, Wang B. Incorporating Pathway Information into Feature Selection towards Better Performed Gene Signatures. BIOMED RESEARCH INTERNATIONAL 2019; 2019:2497509. [PMID: 31073522 PMCID: PMC6470448 DOI: 10.1155/2019/2497509] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 03/07/2019] [Indexed: 12/29/2022]
Abstract
To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pathways. If the biological knowledge contained within these pathways is taken into account, the resulting method is a pathway-based algorithm. Studies have demonstrated that a pathway-based method usually outperforms its gene-based counterpart in which no biological knowledge is considered. In this article, a pathway-based feature selection is firstly divided into three major categories, namely, pathway-level selection, bilevel selection, and pathway-guided gene selection. With bilevel selection methods being regarded as a special case of pathway-guided gene selection process, we discuss pathway-guided gene selection methods in detail and the importance of penalization in such methods. Last, we point out the potential utilizations of pathway-guided gene selection in one active research avenue, namely, to analyze longitudinal gene expression data. We believe this article provides valuable insights for computational biologists and biostatisticians so that they can make biology more computable.
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Affiliation(s)
- Suyan Tian
- Division of Clinical Research, The First Hospital of Jilin University, 71 Xinmin Street, Changchun, Jilin 130021, China
| | - Chi Wang
- Department of Biostatistics, Markey Cancer Center, The University of Kentucky, 800 Rose St., Lexington, KY 40536, USA
| | - Bing Wang
- School of Life Science, Jilin University, 2699 Qianjin Street, Changchun, Jilin 130012, China
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Ventura-Molina E, Alarcón-Paredes A, Aldape-Pérez M, Yáñez-Márquez C, Adolfo Alonso G. Gene selection for enhanced classification on microarray data using a weighted k-NN based algorithm. INTELL DATA ANAL 2019. [DOI: 10.3233/ida-173720] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Elías Ventura-Molina
- Centro de Investigación en Computación, Instituto Politécnico Nacional. Av. Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal. Col. Nueva Industrial Vallejo, Gustavo A. Madero, 07738, Ciudad de México, México
| | - Antonio Alarcón-Paredes
- Facultad de Ingeniería, Universidad Autónoma de Guerrero. Av. Lázaro Cárdenas s/n, Ciudad Universitaria Zona Sur, 39087. Chilpancingo Guerrero, México
| | - Mario Aldape-Pérez
- Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, México. Av. Juan de Dios Bátiz, Col. Nueva Industrial Vallejo, 07700, Ciudad de México, México
| | - Cornelio Yáñez-Márquez
- Centro de Investigación en Computación, Instituto Politécnico Nacional. Av. Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal. Col. Nueva Industrial Vallejo, Gustavo A. Madero, 07738, Ciudad de México, México
| | - Gustavo Adolfo Alonso
- Facultad de Ingeniería, Universidad Autónoma de Guerrero. Av. Lázaro Cárdenas s/n, Ciudad Universitaria Zona Sur, 39087. Chilpancingo Guerrero, México
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Feres M, Louzoun Y, Haber S, Faveri M, Figueiredo LC, Levin L. Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles. Int Dent J 2018; 68:39-46. [DOI: 10.1111/idj.12326] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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10
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Ramos-González J, López-Sánchez D, Castellanos-Garzón JA, de Paz JF, Corchado JM. A CBR framework with gradient boosting based feature selection for lung cancer subtype classification. Comput Biol Med 2017; 86:98-106. [PMID: 28527352 DOI: 10.1016/j.compbiomed.2017.05.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Revised: 05/10/2017] [Accepted: 05/10/2017] [Indexed: 11/19/2022]
Abstract
Molecular subtype classification represents a challenging field in lung cancer diagnosis. Although different methods have been proposed for biomarker selection, efficient discrimination between adenocarcinoma and squamous cell carcinoma in clinical practice presents several difficulties, especially when the latter is poorly differentiated. This is an area of growing importance, since certain treatments and other medical decisions are based on molecular and histological features. An urgent need exists for a system and a set of biomarkers that provide an accurate diagnosis. In this paper, a novel Case Based Reasoning framework with gradient boosting based feature selection is proposed and applied to the task of squamous cell carcinoma and adenocarcinoma discrimination, aiming to provide accurate diagnosis with a reduced set of genes. The proposed method was trained and evaluated on two independent datasets to validate its generalization capability. Furthermore, it achieved accuracy rates greater than those of traditional microarray analysis techniques, incorporating the advantages inherent to the Case Based Reasoning methodology (e.g. learning over time, adaptability, interpretability of solutions, etc.).
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Affiliation(s)
- Juan Ramos-González
- Department of Computer Science and Automation, Faculty of Science, University of Salamanca, Plaza de los Caídos, s/n, 37008 Salamanca, Spain.
| | - Daniel López-Sánchez
- Department of Computer Science and Automation, Faculty of Science, University of Salamanca, Plaza de los Caídos, s/n, 37008 Salamanca, Spain.
| | - Jose A Castellanos-Garzón
- Department of Computer Science and Automation, Faculty of Science, University of Salamanca, Plaza de los Caídos, s/n, 37008 Salamanca, Spain
| | - Juan F de Paz
- Department of Computer Science and Automation, Faculty of Science, University of Salamanca, Plaza de los Caídos, s/n, 37008 Salamanca, Spain
| | - Juan M Corchado
- Department of Computer Science and Automation, Faculty of Science, University of Salamanca, Plaza de los Caídos, s/n, 37008 Salamanca, Spain
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