Biomarker identification method based on genetic algorithm
The invention provides a biomarker identification method based on a genetic algorithm, and relates to the technical field of machine learning. The method comprises the following steps: firstly, filtering high-dimensional gene microarray data by using an mRMR algorithm; and then generating an initial...
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| creator | YANG JINZHU PAN ZHICHAO XIE WEIDONG QIN WENJUN CHO DAEUL LI WEI ZHAO MINQING CAO PENG FENG CHAOLU |
| description | The invention provides a biomarker identification method based on a genetic algorithm, and relates to the technical field of machine learning. The method comprises the following steps: firstly, filtering high-dimensional gene microarray data by using an mRMR algorithm; and then generating an initialized population by combining feature selection results of various machine learning methods with an OBL algorithm, and finally selecting an optimal feature subset by using an improved genetic algorithm to realize identification of the biomarker. According to the method, the advantages of different feature selection algorithms are fused, global search and local search are combined for feature selection, the obtained optimal individual vector can reserve a small number of features and has high classification accuracy, and a good classification effect is achieved.
本发明提供一种基于遗传算法的生物标志物识别方法，涉及机器学习技术领域。该方法首先利用mRMR算法对高维的基因微阵列数据进行过滤；然后通过多种机器学习方法的特征选择结果与OBL算法相结合生成初始化种群，最后使用改进的遗传算法进行最优特征子集的选择，实现生物标志物的识别。该方法融合了不同特征选择算法的优势，还结合了全 |
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本发明提供一种基于遗传算法的生物标志物识别方法，涉及机器学习技术领域。该方法首先利用mRMR算法对高维的基因微阵列数据进行过滤；然后通过多种机器学习方法的特征选择结果与OBL算法相结合生成初始化种群，最后使用改进的遗传算法进行最优特征子集的选择，实现生物标志物的识别。该方法融合了不同特征选择算法的优势，还结合了全</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS ; PHYSICS</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240517&DB=EPODOC&CC=CN&NR=118053501A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,309,781,886,25566,76549</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240517&DB=EPODOC&CC=CN&NR=118053501A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>YANG JINZHU</creatorcontrib><creatorcontrib>PAN ZHICHAO</creatorcontrib><creatorcontrib>XIE WEIDONG</creatorcontrib><creatorcontrib>QIN WENJUN</creatorcontrib><creatorcontrib>CHO DAEUL</creatorcontrib><creatorcontrib>LI WEI</creatorcontrib><creatorcontrib>ZHAO MINQING</creatorcontrib><creatorcontrib>CAO PENG</creatorcontrib><creatorcontrib>FENG CHAOLU</creatorcontrib><title>Biomarker identification method based on genetic algorithm</title><description>The invention provides a biomarker identification method based on a genetic algorithm, and relates to the technical field of machine learning. The method comprises the following steps: firstly, filtering high-dimensional gene microarray data by using an mRMR algorithm; and then generating an initialized population by combining feature selection results of various machine learning methods with an OBL algorithm, and finally selecting an optimal feature subset by using an improved genetic algorithm to realize identification of the biomarker. According to the method, the advantages of different feature selection algorithms are fused, global search and local search are combined for feature selection, the obtained optimal individual vector can reserve a small number of features and has high classification accuracy, and a good classification effect is achieved.
本发明提供一种基于遗传算法的生物标志物识别方法，涉及机器学习技术领域。该方法首先利用mRMR算法对高维的基因微阵列数据进行过滤；然后通过多种机器学习方法的特征选择结果与OBL算法相结合生成初始化种群，最后使用改进的遗传算法进行最优特征子集的选择，实现生物标志物的识别。该方法融合了不同特征选择算法的优势，还结合了全</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLByyszPTSzKTi1SyExJzSvJTMtMTizJzM9TyE0tychPUUhKLE5NUQDy01PzUksykxUSc9LzizJLMnJ5GFjTEnOKU3mhNDeDoptriLOHbmpBfnxqcUFiMkhHvLOfoaGFgamxqYGhozExagCVgi86</recordid><startdate>20240517</startdate><enddate>20240517</enddate><creator>YANG JINZHU</creator><creator>PAN ZHICHAO</creator><creator>XIE WEIDONG</creator><creator>QIN WENJUN</creator><creator>CHO DAEUL</creator><creator>LI WEI</creator><creator>ZHAO MINQING</creator><creator>CAO PENG</creator><creator>FENG CHAOLU</creator><scope>EVB</scope></search><sort><creationdate>20240517</creationdate><title>Biomarker identification method based on genetic algorithm</title><author>YANG JINZHU ; PAN ZHICHAO ; XIE WEIDONG ; QIN WENJUN ; CHO DAEUL ; LI WEI ; ZHAO MINQING ; CAO PENG ; FENG CHAOLU</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118053501A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>YANG JINZHU</creatorcontrib><creatorcontrib>PAN ZHICHAO</creatorcontrib><creatorcontrib>XIE WEIDONG</creatorcontrib><creatorcontrib>QIN WENJUN</creatorcontrib><creatorcontrib>CHO DAEUL</creatorcontrib><creatorcontrib>LI WEI</creatorcontrib><creatorcontrib>ZHAO MINQING</creatorcontrib><creatorcontrib>CAO PENG</creatorcontrib><creatorcontrib>FENG CHAOLU</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>YANG JINZHU</au><au>PAN ZHICHAO</au><au>XIE WEIDONG</au><au>QIN WENJUN</au><au>CHO DAEUL</au><au>LI WEI</au><au>ZHAO MINQING</au><au>CAO PENG</au><au>FENG CHAOLU</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Biomarker identification method based on genetic algorithm</title><date>2024-05-17</date><risdate>2024</risdate><abstract>The invention provides a biomarker identification method based on a genetic algorithm, and relates to the technical field of machine learning. The method comprises the following steps: firstly, filtering high-dimensional gene microarray data by using an mRMR algorithm; and then generating an initialized population by combining feature selection results of various machine learning methods with an OBL algorithm, and finally selecting an optimal feature subset by using an improved genetic algorithm to realize identification of the biomarker. According to the method, the advantages of different feature selection algorithms are fused, global search and local search are combined for feature selection, the obtained optimal individual vector can reserve a small number of features and has high classification accuracy, and a good classification effect is achieved.
本发明提供一种基于遗传算法的生物标志物识别方法，涉及机器学习技术领域。该方法首先利用mRMR算法对高维的基因微阵列数据进行过滤；然后通过多种机器学习方法的特征选择结果与OBL算法相结合生成初始化种群，最后使用改进的遗传算法进行最优特征子集的选择，实现生物标志物的识别。该方法融合了不同特征选择算法的优势，还结合了全</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS PHYSICS |
| title | Biomarker identification method based on genetic algorithm |
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