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Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis最新文献

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Design and Procedures for the Investigation Conducted 所进行调查的设计和程序
Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-7316-7.ch004
{"title":"Design and Procedures for the Investigation Conducted","authors":"","doi":"10.4018/978-1-7998-7316-7.ch004","DOIUrl":"https://doi.org/10.4018/978-1-7998-7316-7.ch004","url":null,"abstract":"In this chapter, the design of each proposed case study model mentioned in Chapter 3 is presented with their different experimental procedures. The chapter includes the data preparation, suitable parameters and data pre-processing, and detailed design of two case studies. Case 1: examining the accuracy and efficiency (time complexity) of high-performance gene selection and cancer classification algorithms; Case 2: A two-stage hybrid multi-filter feature selection method for high colon-cancer classification. It shows the experimental setup and environment and the description of the hardware and software components used.","PeriodicalId":134297,"journal":{"name":"Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122931858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Hybrid-AutoML System Development 混合动力汽车系统开发
Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-7316-7.ch011
{"title":"Hybrid-AutoML System Development","authors":"","doi":"10.4018/978-1-7998-7316-7.ch011","DOIUrl":"https://doi.org/10.4018/978-1-7998-7316-7.ch011","url":null,"abstract":"This chapter presents the Hybrid-AutoML system requirements, design materials, model algorithms, and model design, which encompasses the design goals, architecture (a three-layered architecture), components, and characteristics of the Hybrid-AutoML toolkit developed in this research for automatic mode and model selection on single or multi-varying datasets. The mode components, decision learning and AutoProbClass unsupervised algorithms, and application API are described. The testing and evaluation of the model is conducted by two case studies.","PeriodicalId":134297,"journal":{"name":"Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis","volume":"733 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122940470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Final Remarks for the Research With Advanced Machine Learning Methods in Colon Cancer Analysis 先进机器学习方法在结肠癌分析中的研究述评
Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-7316-7.ch007
{"title":"Final Remarks for the Research With Advanced Machine Learning Methods in Colon Cancer Analysis","authors":"","doi":"10.4018/978-1-7998-7316-7.ch007","DOIUrl":"https://doi.org/10.4018/978-1-7998-7316-7.ch007","url":null,"abstract":"Generally, classification accuracy is very important to gene processing and selection and cancer classification. It is needed to achieve better cancer treatments and improve medical drug assignments. However, the time complexity analysis will enhance the application's significance. To answer the research questions in Chapter 1, several case studies have been implemented (see Chapters 4 and 5), each was essential to sustain the methodologies discussed in Chapter 3. The study used a colon-cancer dataset comprising 2000 genes. The best search algorithm, GA, showed high performance with a good efficient time complexity. However, both DTs and SVMs showed the best classification contribution with reference to performance accuracy and time efficiency. However, it is difficult to apply a completely fair comparative study because existing algorithms and methods were tested by different authors to reflect the effectiveness and powerful of their own methods.","PeriodicalId":134297,"journal":{"name":"Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123928879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research Approach With Machine Learning Underpinned 基于机器学习的研究方法
Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-7316-7.ch003
{"title":"Research Approach With Machine Learning Underpinned","authors":"","doi":"10.4018/978-1-7998-7316-7.ch003","DOIUrl":"https://doi.org/10.4018/978-1-7998-7316-7.ch003","url":null,"abstract":"This chapter describes several methodologies and proposed models used to examine the accuracy and efficiency of high-performance colon-cancer feature selection and classification algorithms to solve the problems identified in Chapter 2. An elaboration of the diverse methods of gene/feature selection algorithms and the related classification algorithms implemented throughout this study are presented. A prototypical methodology blueprint for each experiment is developed to answer the research questions in Chapter 1. Each system model is also presented, and the measures used to validate the performance of the model's outcome are discussed.","PeriodicalId":134297,"journal":{"name":"Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126253774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Overview on Bioinformatics 生物信息学综述
Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-7316-7.ch002
{"title":"An Overview on Bioinformatics","authors":"","doi":"10.4018/978-1-7998-7316-7.ch002","DOIUrl":"https://doi.org/10.4018/978-1-7998-7316-7.ch002","url":null,"abstract":"This chapter presents a thorough background and deep literature review of the current topic of study. It also presents and defines the key concepts utilised throughout this investigation. It consists of ten sections: (1) a background on bioinformatics, (2) a discussion of colon cancer, (3) an overview of the microarray technology that is used to extract the dataset, (4) an overview of the colon cancer dataset, (5) a review of the most prevalent algorithms employed for gene selection and cancer classification, (6) a presentation of related works from the literature, (7) identification of feature selection approaches and procedures, (8) an investigation of the ML concept, (9) a review of algorithm efficiency and time complexity analysis, and (10) identification of current problems in the research area.","PeriodicalId":134297,"journal":{"name":"Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130394302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Importance of Information Working With Colon Cancer Research 信息在结肠癌研究中的重要性
Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-7316-7.ch001
{"title":"Importance of Information Working With Colon Cancer Research","authors":"","doi":"10.4018/978-1-7998-7316-7.ch001","DOIUrl":"https://doi.org/10.4018/978-1-7998-7316-7.ch001","url":null,"abstract":"Modern science helps us to understand the changing world around us, across fields such as biology, computer science, mathematics, statistics, chemistry, computational biology, biotechnology, biochemistry, and many others. An important branch of science that has had a large impact on the medical field is bioinformatics. This chapter introduces the importance of information science into colon cancer research. According to the American Cancer Association, in the United States in 2018, 97,220 new cases of colon cancer (CC) were identified. The research into this topic area is an immediate need to save many lives and improve people's living standards.","PeriodicalId":134297,"journal":{"name":"Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132092519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research Output for the Hybrid-AutoML System 混合-自动驾驶系统的研究成果
Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-7316-7.ch012
{"title":"Research Output for the Hybrid-AutoML System","authors":"","doi":"10.4018/978-1-7998-7316-7.ch012","DOIUrl":"https://doi.org/10.4018/978-1-7998-7316-7.ch012","url":null,"abstract":"In this chapter, the authors use a set of use cases to evaluate how the hybrid autoML system is used to achieve the goals set out in the aims and objectives of this research. The authors map each use case to their aims and contributions as outlined in Section 1.3 of this research. A performance comparison is also made between autoWeka and the hybrid autoML system on 33 datasets. The comparison is carried out based on three main evaluation metrics such as the percentage accuracy (or correlation coefficient where applicable), the mean absolute error (MAE), and the time (in seconds) spent building the model on training data. It is observed that the hybrid autoML system fully outperforms autoWeka with regards to the time spent on building models or finding the best algorithms in the first instance.","PeriodicalId":134297,"journal":{"name":"Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis","volume":"380 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129120892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Analysis, Discussion, and Evaluations for the Case Studies 案例研究的分析、讨论和评估
Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-7316-7.ch006
{"title":"Analysis, Discussion, and Evaluations for the Case Studies","authors":"","doi":"10.4018/978-1-7998-7316-7.ch006","DOIUrl":"https://doi.org/10.4018/978-1-7998-7316-7.ch006","url":null,"abstract":"The purpose of this chapter is to discuss and analyse the results produced in Chapter 5. To evaluate the proposed models, this chapter compares the models with others existing in the literature. Additionally, the chapter discusses the evaluation measures used to validate the experimental results of Chapter 5. For example, from experiments, GA/DT demonstrated the highest average accuracy (92%) for classifying colon cancer, compared with other algorithms. PSO/DT presented 89%, PSO/SVM presented 89%, and IG/DT presented 89%, demonstrating very good classification accuracy. PSO/NB presented 57% and GA/NB presented 58%: less classification accuracy. Table ‎6.1 lists all accuracies resulting from experiments of case study one, as applied to the full data set. There are 45 algorithmic incorporation methods that have accuracy above 80% when applied to the full dataset. One algorithm presents an accuracy of 92%. Nine others scored below 60%.","PeriodicalId":134297,"journal":{"name":"Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133861698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Findings for the Conducted Investigations 所进行调查的结果
Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-7316-7.ch005
{"title":"Findings for the Conducted Investigations","authors":"","doi":"10.4018/978-1-7998-7316-7.ch005","DOIUrl":"https://doi.org/10.4018/978-1-7998-7316-7.ch005","url":null,"abstract":"This chapter focuses on the results produced from each case study experiment. For case one, the experiments were conducted in three phases. Phase one implemented GA, PSO, and IG as the gene/feature selection algorithms over the entire dataset. Phase =two2 utilised the original dataset to implement only the cancer classification algorithms without involving any gene/feature selection algorithms. Four recognised classification algorithms are employed: SVM, NB, GP, and DT. The third phase implemented the combined approach of gene selection and cancer classification algorithms. The results of these phases are presented in the next subsections. For case two, these experiments were implemented in two phases. Phase one implemented the classification algorithms over the features selected by the hybridised selection algorithms (GA+IG), whereas Phase two classified the features using the proposed two-stage multifilter selection system. In this section, the results are presented as follows","PeriodicalId":134297,"journal":{"name":"Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114100675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Overview of Big Data With Machine Learning Approach 基于机器学习方法的大数据概述
Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-7316-7.ch009
{"title":"Overview of Big Data With Machine Learning Approach","authors":"","doi":"10.4018/978-1-7998-7316-7.ch009","DOIUrl":"https://doi.org/10.4018/978-1-7998-7316-7.ch009","url":null,"abstract":"This chapter provides discussions on what is already known in the area of this research, touching particularly on the key concepts, theories, and factors and how they are relevant to this research. Some inconsistencies, limitations, and problem in existing literature are discussed. Discussions on why some of these limitations and inconsistencies occur, how the knowledge relates to this research, as well as issues still yet to study effectively are carried out. Finally, it sets the basis for what contributions this research makes and who will benefit from such a study.","PeriodicalId":134297,"journal":{"name":"Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131765717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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