Zongjin Li, Changxin Song, Zeyu Jia, Dong Chen, Yan Liang
Background: Hypertension is a chronic disease with high morbidity and high mortality in the world. Its pathogenesis is complicated and its molecular mechanism has not been fully elucidated, which seriously threatens human life and health. The purpose of this paper was to the molecular study of hypertension, explore candidate biomarkers affecting the occurrence of hypertension from the perspective of weighted network, and provide the theoretical and practical basis for the prevention and treatment of hypertension. Materials and methods: The hypertension gene expression dataset of GSE75360 were downloaded from the Gene Expression Omnibus (GEO). The “limma” package of R was utilized to screen the differentially expressed genes (DEGs) between the sample group with and without high blood pressure. Next, Weight Gene co-expression Network Analysis (WGCNA) algorithm was used to establish a co-expression network of the DEGs and to detect hypertension-related gene modules. And DAVID was utilized to perform Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, we proposed the hierarchical fusion method to screen hub genes. Results: We identified 2 key gene modules that were significantly associated with hypertension, named Mlightcyan and Mgreenyellow. In addition, 18 hub genes (RPS28, LOC730288/RPS28P6, LOC645968/ RPS3AP25, LOC727826/RPS11P5, RPL21, LOC653079/ RPL36P14, LOC441743/RPL23AP5, LOC651453/RPL36P14, LPPR2, NSMCE4A, FKBP1A, RAB5C, MAN2B1, FURIN, TBXAS1, RPS6KA4, PARN, LOC642489/FKBP1C) relating to hypertension were identified form the two key gene modules. Conclusions: In this study, we identified two key gene modules and 18 hub genes, which were associated with the mechanisms of hypertension. These findings will provide references that improve the understanding of the pathogenesis of hypertension. The hub genes might can serve as therapeutic targets for diagnosis of hypertension in the future.
{"title":"Identification of Key Gene Modules and Hub Genes of Hypertension Based on WGCNA Algorithm","authors":"Zongjin Li, Changxin Song, Zeyu Jia, Dong Chen, Yan Liang","doi":"10.1145/3469678.3469701","DOIUrl":"https://doi.org/10.1145/3469678.3469701","url":null,"abstract":"Background: Hypertension is a chronic disease with high morbidity and high mortality in the world. Its pathogenesis is complicated and its molecular mechanism has not been fully elucidated, which seriously threatens human life and health. The purpose of this paper was to the molecular study of hypertension, explore candidate biomarkers affecting the occurrence of hypertension from the perspective of weighted network, and provide the theoretical and practical basis for the prevention and treatment of hypertension. Materials and methods: The hypertension gene expression dataset of GSE75360 were downloaded from the Gene Expression Omnibus (GEO). The “limma” package of R was utilized to screen the differentially expressed genes (DEGs) between the sample group with and without high blood pressure. Next, Weight Gene co-expression Network Analysis (WGCNA) algorithm was used to establish a co-expression network of the DEGs and to detect hypertension-related gene modules. And DAVID was utilized to perform Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, we proposed the hierarchical fusion method to screen hub genes. Results: We identified 2 key gene modules that were significantly associated with hypertension, named Mlightcyan and Mgreenyellow. In addition, 18 hub genes (RPS28, LOC730288/RPS28P6, LOC645968/ RPS3AP25, LOC727826/RPS11P5, RPL21, LOC653079/ RPL36P14, LOC441743/RPL23AP5, LOC651453/RPL36P14, LPPR2, NSMCE4A, FKBP1A, RAB5C, MAN2B1, FURIN, TBXAS1, RPS6KA4, PARN, LOC642489/FKBP1C) relating to hypertension were identified form the two key gene modules. Conclusions: In this study, we identified two key gene modules and 18 hub genes, which were associated with the mechanisms of hypertension. These findings will provide references that improve the understanding of the pathogenesis of hypertension. The hub genes might can serve as therapeutic targets for diagnosis of hypertension in the future.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90742886","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}
This paper focuses on adolescents with low vision and indicates that prevention and treatment of adolescents with low vision through standardization methods is urgently needed. By investigating relevant standards and information released through professional organizations such as the International Organization for Standardization (ISO) , the American Optometry Association (AOA), the American Academy of Ophthalmology (AAO), and the International Commission on Illumination (ICI), combined with current status of development of relevant Chinese national standards, sector standards and local standards, the problems of technical standards in development status quo of prevention and control related to adolescents with low vision has been raised in this article. Three aspects can be improved include lacking of environmental standards, the scope of product standards need to be expanded and the Traditional Chinese Medicine standards need to be developed for better prevention and control of low vision among adolescents. It has been pointed out that a standard system with clear goals, complete sets, appropriate levels and clear divisions continuous optimizing and adjusting following social, economic and technical development needs to be established and it can play an important role in promoting the prevention and control of adolescents with low vision.
{"title":"Research on Technical Standards of Prevention and Control of Low Vision among Adolescents","authors":"Wei Pan","doi":"10.1145/3469678.3469680","DOIUrl":"https://doi.org/10.1145/3469678.3469680","url":null,"abstract":"This paper focuses on adolescents with low vision and indicates that prevention and treatment of adolescents with low vision through standardization methods is urgently needed. By investigating relevant standards and information released through professional organizations such as the International Organization for Standardization (ISO) , the American Optometry Association (AOA), the American Academy of Ophthalmology (AAO), and the International Commission on Illumination (ICI), combined with current status of development of relevant Chinese national standards, sector standards and local standards, the problems of technical standards in development status quo of prevention and control related to adolescents with low vision has been raised in this article. Three aspects can be improved include lacking of environmental standards, the scope of product standards need to be expanded and the Traditional Chinese Medicine standards need to be developed for better prevention and control of low vision among adolescents. It has been pointed out that a standard system with clear goals, complete sets, appropriate levels and clear divisions continuous optimizing and adjusting following social, economic and technical development needs to be established and it can play an important role in promoting the prevention and control of adolescents with low vision.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88634105","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}
Enhancers are a small region of DNA that can bind with protein. After binding with protein, gene transcription will be strengthened. It is time-consuming and expensive to identify enhancers using traditional biological experimental methods. However, with the development of computer technology, more and more computer technology is applied to gene identification. There are two innovations in this study. First, a new feature information PCWM is proposed, which combines the normalized frequency information of k-tuple nucleotide in DNA sequence as weight and the physicochemical properties of k-tuple nucleotide to obtain DNA sequence features. Second, a two-layer model is proposed to process the acquired sequence feature information to predict the enhancer and its strength. The independent set test results show that this new feature method effectively improves the prediction accuracy of enhancers and their strengths, obtaining accuracy of 77.0% and 69.5%, respectively. Compared with the classical two feature methods, the new feature method shows greater advantages, and has greater improvement than the prediction results of the existing literature. This method is an effective supplement to the existing research methods.
{"title":"Identifying Enhancers and Their Strength Based on PCWM Feature by A Two-Layer Predictor","authors":"Huan Yang, Shunfang Wang","doi":"10.1145/3469678.3469707","DOIUrl":"https://doi.org/10.1145/3469678.3469707","url":null,"abstract":"Enhancers are a small region of DNA that can bind with protein. After binding with protein, gene transcription will be strengthened. It is time-consuming and expensive to identify enhancers using traditional biological experimental methods. However, with the development of computer technology, more and more computer technology is applied to gene identification. There are two innovations in this study. First, a new feature information PCWM is proposed, which combines the normalized frequency information of k-tuple nucleotide in DNA sequence as weight and the physicochemical properties of k-tuple nucleotide to obtain DNA sequence features. Second, a two-layer model is proposed to process the acquired sequence feature information to predict the enhancer and its strength. The independent set test results show that this new feature method effectively improves the prediction accuracy of enhancers and their strengths, obtaining accuracy of 77.0% and 69.5%, respectively. Compared with the classical two feature methods, the new feature method shows greater advantages, and has greater improvement than the prediction results of the existing literature. This method is an effective supplement to the existing research methods.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74318446","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}
Localization of the epileptic focus is crucial for epilepsy surgery. Pre-ictal EEG and interictal epileptic discharges are commonly used to localize the focus. Post-ictal scalp EEG may provide useful information for localizing the epileptic focus. This study proposed a non-invasive procedure to localize the epileptic focus via the EEG source imaging (ESI) and epileptic network analysis. Scalp EEG from two patients with drug-resistant epilepsy were used, and two segments of post-ictal EEG were analyzed. The sLORETA algorithm was applied to obtain signals in source space using the patient specific head model. Then we extracted the representative source signals of each brain area by singular value decomposition (SVD). The epileptic networks of different frequency bands in the source space were constructed by Granger causality analysis. The results showed that the regions identified by in-degree feature of low-frequency post-ictal epileptic network were concordant with surgical resected areas. The preliminary result indicates that post-ictal epileptic network in low frequency may potentially be used to identify the ictal focus for surgical planning.
{"title":"Localizing Epileptic Focus of Patients with Epilepsy Using Post-Ictal Scalp EEG","authors":"M. Yao, Chunsheng Li","doi":"10.1145/3469678.3469684","DOIUrl":"https://doi.org/10.1145/3469678.3469684","url":null,"abstract":"Localization of the epileptic focus is crucial for epilepsy surgery. Pre-ictal EEG and interictal epileptic discharges are commonly used to localize the focus. Post-ictal scalp EEG may provide useful information for localizing the epileptic focus. This study proposed a non-invasive procedure to localize the epileptic focus via the EEG source imaging (ESI) and epileptic network analysis. Scalp EEG from two patients with drug-resistant epilepsy were used, and two segments of post-ictal EEG were analyzed. The sLORETA algorithm was applied to obtain signals in source space using the patient specific head model. Then we extracted the representative source signals of each brain area by singular value decomposition (SVD). The epileptic networks of different frequency bands in the source space were constructed by Granger causality analysis. The results showed that the regions identified by in-degree feature of low-frequency post-ictal epileptic network were concordant with surgical resected areas. The preliminary result indicates that post-ictal epileptic network in low frequency may potentially be used to identify the ictal focus for surgical planning.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73868078","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}
Depression is a mental illness and considered the main cause of disability worldwide. Further study is still needed to enhance the accuracy of depression detection. The aim of this study was to explore the potential EEG biomarker for cortical dysfunction to help the diagnosis with depression clinically. In this study, symbolic transfer entropy (STE) of five sleep periods (Wake, REM, N1, N2, N3) and four frequency bands (δ, θ, α, β) were obtained from six sleep EEG channels. Significant differences between the two groups were found. The average STE values in the patients with depression were lower than those of normal participants in all sleep periods and frequency bands. These findings indicated the lower complexity of brain and abnormalities in sleep cortical activity in patients with depression. It may provide insights into the influence of depression on cognitive function and important indicators for studying depression pathological mechanisms.
{"title":"Abnormal Symbolic Transfer Entropy in Depression","authors":"Yangting Zhang, Yu-xi Luo","doi":"10.1145/3469678.3469718","DOIUrl":"https://doi.org/10.1145/3469678.3469718","url":null,"abstract":"Depression is a mental illness and considered the main cause of disability worldwide. Further study is still needed to enhance the accuracy of depression detection. The aim of this study was to explore the potential EEG biomarker for cortical dysfunction to help the diagnosis with depression clinically. In this study, symbolic transfer entropy (STE) of five sleep periods (Wake, REM, N1, N2, N3) and four frequency bands (δ, θ, α, β) were obtained from six sleep EEG channels. Significant differences between the two groups were found. The average STE values in the patients with depression were lower than those of normal participants in all sleep periods and frequency bands. These findings indicated the lower complexity of brain and abnormalities in sleep cortical activity in patients with depression. It may provide insights into the influence of depression on cognitive function and important indicators for studying depression pathological mechanisms.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79810415","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}
The diagnosis and treatment of depression is very important since it brings a heavy burden to family and society. Because of the high sensitivity, relatively low cost, and convenient recording, electroencephalogram (EEG) has become an important tool for monitoring brain activity and is gradually being used in the auxiliary diagnosis of mental diseases. EEG signals are typically non-linear and non-stationary. Therefore, they are suitable to be dealt with by time-frequency analysis technique. In this paper, we propose a strategy that combines the time-frequency analysis technique and temporal convolution network for depression recognition. Firstly, we use the method of intrinsic time-scale decomposition to decompose each EEG recording to several components. And secondly, some statistical indices are calculated from the instantaneous amplitudes and instantaneous frequencies of these components to form the feature vectors. Thirdly, an improved temporal convolution network (TCN) is used to detect the depression from normal controls. Temporal convolution network is not only suitable for the sequence model, but also retains the parallel computing characteristics of the convolutional neural network. To improve the model performance, we further modify the original softmax loss of TCN as L-softmax. Experiments show the effectiveness of the proposed model. Furthermore, we find that the depressive patients and normal controls shows different patterns through functional connectivity analysis. Our analysis results can be used as an auxiliary tool to help psychiatrists diagnose patients with depression.
{"title":"EEG-Based Depression Recognition Using Intrinsic Time-scale Decomposition and Temporal Convolution Network","authors":"Yixin Wang, Fengrui Liu, Lijun Yang","doi":"10.1145/3469678.3469683","DOIUrl":"https://doi.org/10.1145/3469678.3469683","url":null,"abstract":"The diagnosis and treatment of depression is very important since it brings a heavy burden to family and society. Because of the high sensitivity, relatively low cost, and convenient recording, electroencephalogram (EEG) has become an important tool for monitoring brain activity and is gradually being used in the auxiliary diagnosis of mental diseases. EEG signals are typically non-linear and non-stationary. Therefore, they are suitable to be dealt with by time-frequency analysis technique. In this paper, we propose a strategy that combines the time-frequency analysis technique and temporal convolution network for depression recognition. Firstly, we use the method of intrinsic time-scale decomposition to decompose each EEG recording to several components. And secondly, some statistical indices are calculated from the instantaneous amplitudes and instantaneous frequencies of these components to form the feature vectors. Thirdly, an improved temporal convolution network (TCN) is used to detect the depression from normal controls. Temporal convolution network is not only suitable for the sequence model, but also retains the parallel computing characteristics of the convolutional neural network. To improve the model performance, we further modify the original softmax loss of TCN as L-softmax. Experiments show the effectiveness of the proposed model. Furthermore, we find that the depressive patients and normal controls shows different patterns through functional connectivity analysis. Our analysis results can be used as an auxiliary tool to help psychiatrists diagnose patients with depression.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78188230","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}
Recent years, RNA secondary structure prediction has attracted much attention of many researchers, which is an important way to grasp the biochemical function of RNA. However, it is very difficult to predict the RNA secondary structure including pseudoknot, which has been identified to be an NP-complete problem. In this paper, a novel multimodal optimization evolutionary algorithm is proposed to optimize the decision space based on the minimum free energy to predict the secondary structure of RNA. Because there exist multiple equivalent secondary structures which represent the same minimum free energy, our algorithm maintain diversity in decision space to find multiple sets of secondary structure simultaneously. The performance of our algorithm is evaluated by PseudoBase instances and compared with some good prediction algorithms. The comparison results show that our method has higher accuracy in RNA secondary structure prediction.
{"title":"Multimodal Optimization Evolutionary Algorithm for RNA Secondary Structure Prediction","authors":"Yunfei Hu, Kai Zhang","doi":"10.1145/3469678.3469714","DOIUrl":"https://doi.org/10.1145/3469678.3469714","url":null,"abstract":"Recent years, RNA secondary structure prediction has attracted much attention of many researchers, which is an important way to grasp the biochemical function of RNA. However, it is very difficult to predict the RNA secondary structure including pseudoknot, which has been identified to be an NP-complete problem. In this paper, a novel multimodal optimization evolutionary algorithm is proposed to optimize the decision space based on the minimum free energy to predict the secondary structure of RNA. Because there exist multiple equivalent secondary structures which represent the same minimum free energy, our algorithm maintain diversity in decision space to find multiple sets of secondary structure simultaneously. The performance of our algorithm is evaluated by PseudoBase instances and compared with some good prediction algorithms. The comparison results show that our method has higher accuracy in RNA secondary structure prediction.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89874953","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}
Tianhe Zhang, Le Ao, Bo Sun, Chuyi Zhang, W. Su, Xingfeng Du, Changlu Guo, Yu Yang
This research taking epilepsy as the research object, uses network database resources to inquire and sort out the relevant genes, and uses the biological pathways involved in genes to successfully construct a local gene network. The coupling of the metabolic module proves that there is a complex connection between epilepsy and the nutritional metabolism module. To a certain extent, it reflects the important influence of nutritional metabolism on epilepsy, and also provides a basis for the prevention and treatment of epilepsy through diet and the development of new therapeutic drugs.
{"title":"Preliminary Study of Local Gene Network in Epilepsy","authors":"Tianhe Zhang, Le Ao, Bo Sun, Chuyi Zhang, W. Su, Xingfeng Du, Changlu Guo, Yu Yang","doi":"10.1145/3469678.3469688","DOIUrl":"https://doi.org/10.1145/3469678.3469688","url":null,"abstract":"This research taking epilepsy as the research object, uses network database resources to inquire and sort out the relevant genes, and uses the biological pathways involved in genes to successfully construct a local gene network. The coupling of the metabolic module proves that there is a complex connection between epilepsy and the nutritional metabolism module. To a certain extent, it reflects the important influence of nutritional metabolism on epilepsy, and also provides a basis for the prevention and treatment of epilepsy through diet and the development of new therapeutic drugs.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79302886","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}
To understand the melt-based electrohydrodynamic (EHD) bioprinting, fabrication process related parameters such as melting temperature of the EHD ink solution and environmental factors within the fabrication chamber requires careful consideration and comprehensive experiments. The main purpose of this paper is to study the control of the heating and melting process of ink solutions, and environmental factors (temperature and humidity). Besides, a power module control unit is proposed to cut off the high voltage module output under abnormal conditions. These designs and testing can improve the current EHD scaffold printing system, which will be very helpful for stable scaffold fabrication in tissue engineering.
{"title":"Melt-based Electrohydrodynamic Bioprinting:: Heating Unit, Ambient Control, and Power Module Control","authors":"Yu-lin Dong, Jie Sun","doi":"10.1145/3469678.3469697","DOIUrl":"https://doi.org/10.1145/3469678.3469697","url":null,"abstract":"To understand the melt-based electrohydrodynamic (EHD) bioprinting, fabrication process related parameters such as melting temperature of the EHD ink solution and environmental factors within the fabrication chamber requires careful consideration and comprehensive experiments. The main purpose of this paper is to study the control of the heating and melting process of ink solutions, and environmental factors (temperature and humidity). Besides, a power module control unit is proposed to cut off the high voltage module output under abnormal conditions. These designs and testing can improve the current EHD scaffold printing system, which will be very helpful for stable scaffold fabrication in tissue engineering.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79564178","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}
Genome-wide association studies (GWAS) is an effective way to reveal the pathogenic genes of complex diseases by analyzing the genotype information and related disease phenotype information on the SNP loci of the whole genome of a large number of living organisms. Machine learning (ML) is a method that allows computers to simulate human cognitive processes to solve problems. The advantage of using machine learning methods to carry out genome-wide association analysis research is that it does not require false anchor points or gene-gene interaction models in advance Instead of exhaustive search, computer algorithms that simulate human cognitive processes can learn from a large amount of data to discover the ability of nonlinear high-dimensional gene-gene interactions. In recent years, a large number of machine learning methods have been used in the study of genome-wide association analysis. This article will briefly introduct these methods.
全基因组关联研究(genome -wide association studies, GWAS)是通过分析大量生物体全基因组SNP位点上的基因型信息和相关疾病表型信息，揭示复杂疾病致病基因的有效途径。机器学习(ML)是一种允许计算机模拟人类认知过程来解决问题的方法。利用机器学习方法开展全基因组关联分析研究的优势在于，它不需要事先建立假锚点或基因-基因相互作用模型，而是通过模拟人类认知过程的计算机算法，从大量数据中学习，发现非线性高维基因-基因相互作用的能力。近年来，大量的机器学习方法被用于全基因组关联分析的研究。本文将简要介绍这些方法。
{"title":"Overview of Machine Learning Methods for Genome-Wide Association Analysis","authors":"Minzhu Xie, Fang Liu","doi":"10.1145/3469678.3469682","DOIUrl":"https://doi.org/10.1145/3469678.3469682","url":null,"abstract":"Genome-wide association studies (GWAS) is an effective way to reveal the pathogenic genes of complex diseases by analyzing the genotype information and related disease phenotype information on the SNP loci of the whole genome of a large number of living organisms. Machine learning (ML) is a method that allows computers to simulate human cognitive processes to solve problems. The advantage of using machine learning methods to carry out genome-wide association analysis research is that it does not require false anchor points or gene-gene interaction models in advance Instead of exhaustive search, computer algorithms that simulate human cognitive processes can learn from a large amount of data to discover the ability of nonlinear high-dimensional gene-gene interactions. In recent years, a large number of machine learning methods have been used in the study of genome-wide association analysis. This article will briefly introduct these methods.","PeriodicalId":22513,"journal":{"name":"The Fifth International Conference on Biological Information and Biomedical Engineering","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77108186","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}
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