Namjun Kim, Chanmo Yang, Dae-Il Cho, Seung Hyeon Geum, Ki-Woong Park
The current COVID-19 pandemic has resulted in many changes in the IT systems and services of institutions, which also heightened the concerns regarding the potential increase in intrusion incidents, especially when most works in institutions are performed at home. The need for pre-training against intrusion incidents has then become extremely necessary. Unfortunately, current learning methods in existing studies are insufficient for application in the present demand because these methods were originally designed for environments that are tailored-fit for learners and not in actual environments. This paper proposes a training system, namely, computer emergency response team (CERT), that can be specifically designed for learners in an institution to provide intrusion-incident cases using a Web-based training system. CERT can easily replicate the service or system in an institution to a honeypot environment to automatically collect and classify intrusion incidents using diverse evaluation criteria so that learning can be achieved from different perspectives. Hence, the institution operating service and system can easily be replicated. Artifacts of intrusion incidents are collected using the Docker container technology and event-recordable container, which are analyzed using a Web browser without installing a separate program. Thus, optimal learning results from the analysis of actual attacks are expected.
{"title":"CERT Training Platform over the Event-Recordable Container","authors":"Namjun Kim, Chanmo Yang, Dae-Il Cho, Seung Hyeon Geum, Ki-Woong Park","doi":"10.1145/3440943.3444738","DOIUrl":"https://doi.org/10.1145/3440943.3444738","url":null,"abstract":"The current COVID-19 pandemic has resulted in many changes in the IT systems and services of institutions, which also heightened the concerns regarding the potential increase in intrusion incidents, especially when most works in institutions are performed at home. The need for pre-training against intrusion incidents has then become extremely necessary. Unfortunately, current learning methods in existing studies are insufficient for application in the present demand because these methods were originally designed for environments that are tailored-fit for learners and not in actual environments. This paper proposes a training system, namely, computer emergency response team (CERT), that can be specifically designed for learners in an institution to provide intrusion-incident cases using a Web-based training system. CERT can easily replicate the service or system in an institution to a honeypot environment to automatically collect and classify intrusion incidents using diverse evaluation criteria so that learning can be achieved from different perspectives. Hence, the institution operating service and system can easily be replicated. Artifacts of intrusion incidents are collected using the Docker container technology and event-recordable container, which are analyzed using a Web browser without installing a separate program. Thus, optimal learning results from the analysis of actual attacks are expected.","PeriodicalId":310247,"journal":{"name":"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132973636","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}
Tzer-Long Chen, Nan-Kai Hsieh, Jhih-Chung Chang, Ming Chen Ho, Y. Chang, Po-Ya Chuang
Fish-farming is an entertainment for many people, but changing the water always takes a lot of time. The temperature of water will be changed during changing the water, and the quality of the water usually affects the fish life. This research was accompanied with smart sensor and fish tanks to achieve the smart fish tank. The smart fish tank owns Wi-Fi module which could collect the information of environment and control switch to maintain temperature, quality, inflow and outflow of water. IFTTT is used to send message in the smart fish tank. Besides, the light opening time was controlled to be suitable for nature light time that can bring more convenience for owner.
{"title":"The Implementation of Smart Aquarium System with Intelligent Sensors","authors":"Tzer-Long Chen, Nan-Kai Hsieh, Jhih-Chung Chang, Ming Chen Ho, Y. Chang, Po-Ya Chuang","doi":"10.1145/3440943.3444715","DOIUrl":"https://doi.org/10.1145/3440943.3444715","url":null,"abstract":"Fish-farming is an entertainment for many people, but changing the water always takes a lot of time. The temperature of water will be changed during changing the water, and the quality of the water usually affects the fish life. This research was accompanied with smart sensor and fish tanks to achieve the smart fish tank. The smart fish tank owns Wi-Fi module which could collect the information of environment and control switch to maintain temperature, quality, inflow and outflow of water. IFTTT is used to send message in the smart fish tank. Besides, the light opening time was controlled to be suitable for nature light time that can bring more convenience for owner.","PeriodicalId":310247,"journal":{"name":"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129297946","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}
Wesley Huang, K. Hsu, Chia-Sui Wang, Yih-Feng Chang, Chia-Mao Yei
This paper was mainly applied to image identification of metallographic structure of carbon steel. Though metallographic image identification is now needed by industry, it is rarely discussed in literature due to its industrial characteristics, let alone the theory of identifying complex structures. The identification of metallographic structure of common carbon steel is mostly carried out manually, which is mainly plagued by empiricism and subjective identification. This paper intended to calculate the percentage of spheroidized carbide in metallography. However, the distribution of carbides is affected by the insufficient heating process. For example, low heating temperature or short holding time will result in carbide connection, which leads to the reduction of the accuracy rate in calculating the spheroidization rate of carbide. However, the algorithm proposed in this paper mainly strengthens the accuracy rate of carbide cutting, and the connected carbide is morphologically cut to improve the identification accuracy rate. For carbide cutting, it is carried out in two stages. First, all disconnected components are cut by using the connected components, and then morphological erosion and expansion calculus are carried out for all carbides to cut connected carbides.
{"title":"An Image Processing Approach for Improving the Recognition of Cluster-like Spheroidized Carbides","authors":"Wesley Huang, K. Hsu, Chia-Sui Wang, Yih-Feng Chang, Chia-Mao Yei","doi":"10.1145/3440943.3444746","DOIUrl":"https://doi.org/10.1145/3440943.3444746","url":null,"abstract":"This paper was mainly applied to image identification of metallographic structure of carbon steel. Though metallographic image identification is now needed by industry, it is rarely discussed in literature due to its industrial characteristics, let alone the theory of identifying complex structures. The identification of metallographic structure of common carbon steel is mostly carried out manually, which is mainly plagued by empiricism and subjective identification. This paper intended to calculate the percentage of spheroidized carbide in metallography. However, the distribution of carbides is affected by the insufficient heating process. For example, low heating temperature or short holding time will result in carbide connection, which leads to the reduction of the accuracy rate in calculating the spheroidization rate of carbide. However, the algorithm proposed in this paper mainly strengthens the accuracy rate of carbide cutting, and the connected carbide is morphologically cut to improve the identification accuracy rate. For carbide cutting, it is carried out in two stages. First, all disconnected components are cut by using the connected components, and then morphological erosion and expansion calculus are carried out for all carbides to cut connected carbides.","PeriodicalId":310247,"journal":{"name":"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114947178","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}
Soonhong Kwon, HeeDong Yang, Manhee Lee, Jong‐Hyouk Lee
With the advent of the 4th industrial era, ICT technologies such as artificial intelligence and autonomous driving are rapidly developing. However, unlike these positive aspects, malicious hackers target IoT devices around us using malwares such as viruses, worms, and Trojan horses to steal confidential information or prevent IoT devices from operating normally. In addition, malicious hackers are developing and using intelligent and advanced malwares so that malware cannot be easily detected. In recent years, studied/development of malware detection technology using machine learning and deep learning technologies has been conducted to detect intelligent and advanced variants of malwares. In this paper, based on the KISA Data Challenge Dataset, basic machine learning based malware detection is performed and the limitations that have occurred are analyzed.
{"title":"Machine Learning based Malware Detection with the 2019 KISA Data Challenge Dataset","authors":"Soonhong Kwon, HeeDong Yang, Manhee Lee, Jong‐Hyouk Lee","doi":"10.1145/3440943.3444745","DOIUrl":"https://doi.org/10.1145/3440943.3444745","url":null,"abstract":"With the advent of the 4th industrial era, ICT technologies such as artificial intelligence and autonomous driving are rapidly developing. However, unlike these positive aspects, malicious hackers target IoT devices around us using malwares such as viruses, worms, and Trojan horses to steal confidential information or prevent IoT devices from operating normally. In addition, malicious hackers are developing and using intelligent and advanced malwares so that malware cannot be easily detected. In recent years, studied/development of malware detection technology using machine learning and deep learning technologies has been conducted to detect intelligent and advanced variants of malwares. In this paper, based on the KISA Data Challenge Dataset, basic machine learning based malware detection is performed and the limitations that have occurred are analyzed.","PeriodicalId":310247,"journal":{"name":"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125739037","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}
Janghoon Kim, Hyunpyo Choi, Jiho Shin, Jung-Taek Seo
This study proposed an anomaly detection technique in an industrial control system using supervised and unsupervised machine learning algorithms. For the dataset for learning, the HIL-based Augmented ICS (HAI) dataset provided for the study on security in industrial control systems was used. For the learning model, Light Gradient Boosted Machine -- a supervised learning algorithm and One-Class Support Vector Machine and Isolation Forest as unsupervised learning algorithms were employed. The proposed technique is presented in this paper, which is organized as follows: Feature selection, Data preprocessing, Hyperparameter optimization and verification, and Experiment and analysis of results. The performance difference according to the algorithm and model configuration was exhibited through the experimental results. In addition, issues to be considered in model configuration and future study directions for anomaly detection techniques in industrial control systems were presented based on the experimental results.
本研究提出了一种基于监督和无监督机器学习算法的工业控制系统异常检测技术。用于学习的数据集，使用了基于hil的增强ICS (HAI)数据集，该数据集是为工业控制系统安全研究提供的。学习模型采用有监督学习算法Light Gradient boosting Machine和无监督学习算法One-Class Support Vector Machine和Isolation Forest。本文主要从特征选择、数据预处理、超参数优化与验证、实验与结果分析四个方面进行了介绍。实验结果显示了不同算法和模型配置的性能差异。此外，根据实验结果，提出了模型配置中需要考虑的问题和工业控制系统异常检测技术的未来研究方向。
{"title":"Study on Anomaly Detection Technique in an Industrial Control System Based on Machine Learning","authors":"Janghoon Kim, Hyunpyo Choi, Jiho Shin, Jung-Taek Seo","doi":"10.1145/3440943.3444743","DOIUrl":"https://doi.org/10.1145/3440943.3444743","url":null,"abstract":"This study proposed an anomaly detection technique in an industrial control system using supervised and unsupervised machine learning algorithms. For the dataset for learning, the HIL-based Augmented ICS (HAI) dataset provided for the study on security in industrial control systems was used. For the learning model, Light Gradient Boosted Machine -- a supervised learning algorithm and One-Class Support Vector Machine and Isolation Forest as unsupervised learning algorithms were employed. The proposed technique is presented in this paper, which is organized as follows: Feature selection, Data preprocessing, Hyperparameter optimization and verification, and Experiment and analysis of results. The performance difference according to the algorithm and model configuration was exhibited through the experimental results. In addition, issues to be considered in model configuration and future study directions for anomaly detection techniques in industrial control systems were presented based on the experimental results.","PeriodicalId":310247,"journal":{"name":"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128794053","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}
Yueh-Shiu Lee, Yen-Chiao Chuang, M. Chang, Mei-Wen Huang
1E-commerce nowadays has made its official way into the clicks-and-mortar era. The advantage of a clicks-and-mortar model is that the "mortar" counterpart can enjoy logistics support, which complements any operational inadequacy on the side of the virtual website - the "clicks" counterpart. In a clicks-and-mortar smart contract system, the clicks-and-mortar application enables any transaction to automatically execute each operational step. When a consumer selects a product and completes the payment for it, the smart contract can proceed to execute any terms established in its contents, ensuring the proper practice of every item in the contract.
{"title":"A Clicks-and-Mortar Smart System","authors":"Yueh-Shiu Lee, Yen-Chiao Chuang, M. Chang, Mei-Wen Huang","doi":"10.1145/3440943.3444342","DOIUrl":"https://doi.org/10.1145/3440943.3444342","url":null,"abstract":"1E-commerce nowadays has made its official way into the clicks-and-mortar era. The advantage of a clicks-and-mortar model is that the \"mortar\" counterpart can enjoy logistics support, which complements any operational inadequacy on the side of the virtual website - the \"clicks\" counterpart. In a clicks-and-mortar smart contract system, the clicks-and-mortar application enables any transaction to automatically execute each operational step. When a consumer selects a product and completes the payment for it, the smart contract can proceed to execute any terms established in its contents, ensuring the proper practice of every item in the contract.","PeriodicalId":310247,"journal":{"name":"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications","volume":"2020 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128069768","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}
I. Chang, Ya-Hsueh Chuang, Tzer-Long Chen, Yiming Yin, Yen Ni Liu, T. Chen
The education reform has been thirty years since 1990, and in recent years the learning enthusiasm and class participation of student was decrease by the internet popularity. With the borderless online communication becoming more widespread, there are many information and method to learn, however, sometimes this information is inaccurate or wrong. No one can deny that the world is different than it was when our current teaching method was changed. According to education scholar's experience, the main reason for a lack of learning motivation. Several educators have already indicated that innovative teaching and curriculum such as PaGamo and MPAS that was applied by National Taiwan University prof. Ping-Cheng Yeh and Taichung Municipal Shuang Wen Junior High School Teacher Alex Wang. The PaGamo and MPAS used for get back for learning enthusiasm and motivation for student. This research will discussion the Blockchain technology and mobile app was used for campus learning coin to increase teaching effectiveness. The campus learning coin will be distributed in class and combined with consumption discount of campus to improve learning motivation and learning objectives.
{"title":"A Study on the Mechanism of Blockchain Cryptocurrency Implementation: Learning Coin of Campus","authors":"I. Chang, Ya-Hsueh Chuang, Tzer-Long Chen, Yiming Yin, Yen Ni Liu, T. Chen","doi":"10.1145/3440943.3444337","DOIUrl":"https://doi.org/10.1145/3440943.3444337","url":null,"abstract":"The education reform has been thirty years since 1990, and in recent years the learning enthusiasm and class participation of student was decrease by the internet popularity. With the borderless online communication becoming more widespread, there are many information and method to learn, however, sometimes this information is inaccurate or wrong. No one can deny that the world is different than it was when our current teaching method was changed. According to education scholar's experience, the main reason for a lack of learning motivation. Several educators have already indicated that innovative teaching and curriculum such as PaGamo and MPAS that was applied by National Taiwan University prof. Ping-Cheng Yeh and Taichung Municipal Shuang Wen Junior High School Teacher Alex Wang. The PaGamo and MPAS used for get back for learning enthusiasm and motivation for student. This research will discussion the Blockchain technology and mobile app was used for campus learning coin to increase teaching effectiveness. The campus learning coin will be distributed in class and combined with consumption discount of campus to improve learning motivation and learning objectives.","PeriodicalId":310247,"journal":{"name":"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129754915","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}
Edge computing is a computing technology that collects, analyses and processes data near the devices that generate the data. This enables services providers to curtail unnecessary data transmissions and deliver low latency services. Cloud computing technology has evolved from centralized environment to edge computing environment due to the need of handling drastically increasing amount of data in cloud computing environments. As the cloud computing environment went through changes, there has been emerging security issues caused by the high mobility of containers. The increased mobility of containers in edge computing environment increased the possibility of data leakage because containers migrate between the edge nodes and we cannot be assured that the data was completely deleted from the edge node the container previously resided. To enhance security of the data in the edge cloud computing environment, we suggest a blueprint of the secure container to protect data from potential threats.
{"title":"Blueprint for a Secure Container to Protect Data in Edge Cloud Computing Environment","authors":"Seong-Jin Kim, K. Park","doi":"10.1145/3440943.3444355","DOIUrl":"https://doi.org/10.1145/3440943.3444355","url":null,"abstract":"Edge computing is a computing technology that collects, analyses and processes data near the devices that generate the data. This enables services providers to curtail unnecessary data transmissions and deliver low latency services. Cloud computing technology has evolved from centralized environment to edge computing environment due to the need of handling drastically increasing amount of data in cloud computing environments. As the cloud computing environment went through changes, there has been emerging security issues caused by the high mobility of containers. The increased mobility of containers in edge computing environment increased the possibility of data leakage because containers migrate between the edge nodes and we cannot be assured that the data was completely deleted from the edge node the container previously resided. To enhance security of the data in the edge cloud computing environment, we suggest a blueprint of the secure container to protect data from potential threats.","PeriodicalId":310247,"journal":{"name":"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129165761","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}
With1 the advent of the next generation of computing, news is available in various environments anytime, anywhere. This is a positive aspect of rapid information sharing, but information with unclear sources was produced in a news format and quickly spread to the public through social network services. The concept of fake news, which began to draw attention as of the 2016 U.S. presidential election, is now causing many economic and social damage around the world. As a result, IT and the industry are paying attention to classifying fake news and active research is ongoing. Therefore, identifying fake news and obtaining accurate information is a very important area in the information age. In this paper, after analyzing the Fake News Dataset of the ISOT, an Information Security and Object Technology, two methods of weighting were used. Based on this, Soft Voting Classifier, an ensemble method that showed the highest performance when using TF-IDF values as weight, is proposed as a fake news classification model.
{"title":"A Classification method of Fake News based on Ensemble Learning","authors":"Sae-Bom Lee, Joon Shik Lim, Jin-Soo Cho, Sang-Yeob Oh, T. Whangbo, Chang-Hyun Choi","doi":"10.1145/3440943.3444362","DOIUrl":"https://doi.org/10.1145/3440943.3444362","url":null,"abstract":"With1 the advent of the next generation of computing, news is available in various environments anytime, anywhere. This is a positive aspect of rapid information sharing, but information with unclear sources was produced in a news format and quickly spread to the public through social network services. The concept of fake news, which began to draw attention as of the 2016 U.S. presidential election, is now causing many economic and social damage around the world. As a result, IT and the industry are paying attention to classifying fake news and active research is ongoing. Therefore, identifying fake news and obtaining accurate information is a very important area in the information age. In this paper, after analyzing the Fake News Dataset of the ISOT, an Information Security and Object Technology, two methods of weighting were used. Based on this, Soft Voting Classifier, an ensemble method that showed the highest performance when using TF-IDF values as weight, is proposed as a fake news classification model.","PeriodicalId":310247,"journal":{"name":"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications","volume":"90 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116693789","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}
In 2016, 12,676 new cases of breast cancer were diagnosed among Taiwan women. In 2018 the standardized death rate of breast cancer was 12.5 per 100,000 persons. Previous studies have integrated data and text mining to yield fusion genes, identify genetic factors for breast cancer and select single-gene feature sets for colon cancer discrimination. However, our study is the first to select significantly different expression between breast normal tissue and cancer using TCGA data and biostatistics, excluding know genes using abstracts from PubMed and natural language processing. The top twenty genes for research potential from the selection of Mining-BreastCancer are EML3, ABCB9, GRASP, KANK3, GPR146, ZNF623, CCDC9, ADCY4, DLL1, ADAM33, GRRP1, LRRN4CL, C14orf180, ABCD4, ABCC6P1, PEAR1, FAM43A, C20orf160, KIF21A and PP-FIA3. Few studies for these genes exist, but they hold significantly different expressions between breast cancer and normal tissue, each pathologic tumor and lymph node, or between each pathologic metastasis. These results show that MiningBreastCancer can help scientists select genes for research potential. MiningBreastCancer is available through http://bio.yungyun.com.tw/MiningBreastCancer.aspx.
{"title":"MiningBreastCancer: Selection of Candidate Gene Associated with Breast Cancer via Comparison between Data Mining of TCGA and Text Mining of PubMed","authors":"Chou-Cheng Chen, Y. Kuo, Chi-Hui Chiang","doi":"10.1145/3440943.3444718","DOIUrl":"https://doi.org/10.1145/3440943.3444718","url":null,"abstract":"In 2016, 12,676 new cases of breast cancer were diagnosed among Taiwan women. In 2018 the standardized death rate of breast cancer was 12.5 per 100,000 persons. Previous studies have integrated data and text mining to yield fusion genes, identify genetic factors for breast cancer and select single-gene feature sets for colon cancer discrimination. However, our study is the first to select significantly different expression between breast normal tissue and cancer using TCGA data and biostatistics, excluding know genes using abstracts from PubMed and natural language processing. The top twenty genes for research potential from the selection of Mining-BreastCancer are EML3, ABCB9, GRASP, KANK3, GPR146, ZNF623, CCDC9, ADCY4, DLL1, ADAM33, GRRP1, LRRN4CL, C14orf180, ABCD4, ABCC6P1, PEAR1, FAM43A, C20orf160, KIF21A and PP-FIA3. Few studies for these genes exist, but they hold significantly different expressions between breast cancer and normal tissue, each pathologic tumor and lymph node, or between each pathologic metastasis. These results show that MiningBreastCancer can help scientists select genes for research potential. MiningBreastCancer is available through http://bio.yungyun.com.tw/MiningBreastCancer.aspx.","PeriodicalId":310247,"journal":{"name":"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128026358","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：