The Experts below are selected from a list of 79533 Experts worldwide ranked by ideXlab platform
Yukinori Okada - One of the best experts on this subject based on the ideXlab platform.
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a Deep Learning Method for hla imputation and trans ethnic mhc fine mapping of type 1 diabetes
Nature Communications, 2021Co-Authors: Tatsuhiko Naito, Ken Suzuki, Jun Hirata, Yoichiro Kamatani, Koichi Matsuda, Tatsushi Toda, Yukinori OkadaAbstract:Conventional human leukocyte antigen (HLA) imputation Methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major histocompatibility complex (MHC) fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We develop Deep*HLA, a Deep Learning Method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,122), Deep*HLA achieves the highest accuracies with significant superiority for low-frequency and rare alleles. Deep*HLA is less dependent on distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We apply Deep*HLA to type 1 diabetes GWAS data from BioBank Japan (n = 62,387) and UK Biobank (n = 354,459), and successfully disentangle independently associated class I and II HLA variants with shared risk among diverse populations (the top signal at amino acid position 71 of HLA-DRβ1; P = 7.5 × 10−120). Our study illustrates the value of Deep Learning in genotype imputation and trans-ethnic MHC fine-mapping. Human leukocyte antigen (HLA) genes contribute to risk of many complex traits, yet understanding inter-ethnic heterogeneity is computationally challenging. Here, the authors develop Deep*HLA for imputation of HLA genotypes and show its ability to disentangle HLA variant risk effects in diverse populations.
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a multi task convolutional Deep Learning Method for hla allelic imputation and its application to trans ethnic mhc fine mapping of type 1 diabetes
medRxiv, 2020Co-Authors: Tatsuhiko Naito, Ken Suzuki, Jun Hirata, Yoichiro Kamatani, Koichi Matsuda, Tatsushi Toda, Yukinori OkadaAbstract:Abstract Conventional HLA imputation Methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic MHC fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We developed Deep*HLA, a Deep Learning Method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,122), Deep*HLA achieved the highest accuracies with significant superiority for low-frequency and rare alleles. Deep*HLA was less dependent on distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We applied Deep*HLA to type 1 diabetes GWAS data from BioBank Japan (n = 62,387) and UK Biobank (n = 354,459), and successfully disentangled independently associated class I and II HLA variants with shared risk among diverse populations (the top signal at amino acid position 71 of HLA-DRβ1; P = 7.5 × 10−120). Our study illustrates a value of Deep Learning in genotype imputation and trans-ethnic MHC fine-mapping.
Tatsuhiko Naito - One of the best experts on this subject based on the ideXlab platform.
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a Deep Learning Method for hla imputation and trans ethnic mhc fine mapping of type 1 diabetes
Nature Communications, 2021Co-Authors: Tatsuhiko Naito, Ken Suzuki, Jun Hirata, Yoichiro Kamatani, Koichi Matsuda, Tatsushi Toda, Yukinori OkadaAbstract:Conventional human leukocyte antigen (HLA) imputation Methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major histocompatibility complex (MHC) fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We develop Deep*HLA, a Deep Learning Method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,122), Deep*HLA achieves the highest accuracies with significant superiority for low-frequency and rare alleles. Deep*HLA is less dependent on distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We apply Deep*HLA to type 1 diabetes GWAS data from BioBank Japan (n = 62,387) and UK Biobank (n = 354,459), and successfully disentangle independently associated class I and II HLA variants with shared risk among diverse populations (the top signal at amino acid position 71 of HLA-DRβ1; P = 7.5 × 10−120). Our study illustrates the value of Deep Learning in genotype imputation and trans-ethnic MHC fine-mapping. Human leukocyte antigen (HLA) genes contribute to risk of many complex traits, yet understanding inter-ethnic heterogeneity is computationally challenging. Here, the authors develop Deep*HLA for imputation of HLA genotypes and show its ability to disentangle HLA variant risk effects in diverse populations.
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a multi task convolutional Deep Learning Method for hla allelic imputation and its application to trans ethnic mhc fine mapping of type 1 diabetes
medRxiv, 2020Co-Authors: Tatsuhiko Naito, Ken Suzuki, Jun Hirata, Yoichiro Kamatani, Koichi Matsuda, Tatsushi Toda, Yukinori OkadaAbstract:Abstract Conventional HLA imputation Methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic MHC fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We developed Deep*HLA, a Deep Learning Method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,122), Deep*HLA achieved the highest accuracies with significant superiority for low-frequency and rare alleles. Deep*HLA was less dependent on distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We applied Deep*HLA to type 1 diabetes GWAS data from BioBank Japan (n = 62,387) and UK Biobank (n = 354,459), and successfully disentangled independently associated class I and II HLA variants with shared risk among diverse populations (the top signal at amino acid position 71 of HLA-DRβ1; P = 7.5 × 10−120). Our study illustrates a value of Deep Learning in genotype imputation and trans-ethnic MHC fine-mapping.
Zhipan Wang - One of the best experts on this subject based on the ideXlab platform.
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a self supervised Learning framework for road centerline extraction from high resolution remote sensing images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020Co-Authors: Qing Guo, Zhipan WangAbstract:Road extraction from the high-resolution remote sensing image is significant for the land planning, vehicle navigation, etc. The existing road extraction Methods normally need many preprocessing and subsequent optimization steps. Therefore, an automatic road centerline extraction Method based on the self-supervised Learning framework for high-resolution remote sensing image is proposed. This proposed Method does not need to manually select training samples and other optimization steps, such as the nonroad area removing. First, the positive sample selection Method combining the spectral and shape features is proposed to extract the road sample. Then, the one-class classifier framework is introduced and the random forest positive unlabeled Learning classifier is constructed to get the posterior probability of the pixel belonging to road. The shape feature and the posterior probability are combined to form the final road network in the object-oriented way. Finally, the road centerline is obtained through the tensor voting algorithm. In order to verify the effectiveness of the proposed algorithm, high-resolution remote sensing images and benchmark datasets are used to do experiments. The indexes of the completeness ratio, the correctness ratio, and the detection quality are used for the quantitative accuracy evaluation. Compared with the supervised, the unsupervised, and the one-class classification road extraction algorithms, this proposed algorithm achieves high accuracy and efficiency. For the Deep Learning Method comparison, the Deep Learning Method performs well in most cases especially in the complex urban area. However, the Deep Learning Method needs a large number of samples and a long training time, and our self-supervised Learning framework does not need the training samples.
Yan Xu - One of the best experts on this subject based on the ideXlab platform.
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a Deep Learning Method to more accurately recall known lysine acetylation sites
BMC Bioinformatics, 2019Co-Authors: Meiqi Wu, Yingxi Yang, Hui Wang, Yan XuAbstract:Background Lysine acetylation in protein is one of the most important post-translational modifications (PTMs). It plays an important role in essential biological processes and is related to various diseases. To obtain a comprehensive understanding of regulatory mechanism of lysine acetylation, the key is to identify lysine acetylation sites. Previously, several shallow machine Learning algorithms had been applied to predict lysine modification sites in proteins. However, shallow machine Learning has some disadvantages. For instance, it is not as effective as Deep Learning for processing big data.
Ken Suzuki - One of the best experts on this subject based on the ideXlab platform.
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a Deep Learning Method for hla imputation and trans ethnic mhc fine mapping of type 1 diabetes
Nature Communications, 2021Co-Authors: Tatsuhiko Naito, Ken Suzuki, Jun Hirata, Yoichiro Kamatani, Koichi Matsuda, Tatsushi Toda, Yukinori OkadaAbstract:Conventional human leukocyte antigen (HLA) imputation Methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major histocompatibility complex (MHC) fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We develop Deep*HLA, a Deep Learning Method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,122), Deep*HLA achieves the highest accuracies with significant superiority for low-frequency and rare alleles. Deep*HLA is less dependent on distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We apply Deep*HLA to type 1 diabetes GWAS data from BioBank Japan (n = 62,387) and UK Biobank (n = 354,459), and successfully disentangle independently associated class I and II HLA variants with shared risk among diverse populations (the top signal at amino acid position 71 of HLA-DRβ1; P = 7.5 × 10−120). Our study illustrates the value of Deep Learning in genotype imputation and trans-ethnic MHC fine-mapping. Human leukocyte antigen (HLA) genes contribute to risk of many complex traits, yet understanding inter-ethnic heterogeneity is computationally challenging. Here, the authors develop Deep*HLA for imputation of HLA genotypes and show its ability to disentangle HLA variant risk effects in diverse populations.
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a multi task convolutional Deep Learning Method for hla allelic imputation and its application to trans ethnic mhc fine mapping of type 1 diabetes
medRxiv, 2020Co-Authors: Tatsuhiko Naito, Ken Suzuki, Jun Hirata, Yoichiro Kamatani, Koichi Matsuda, Tatsushi Toda, Yukinori OkadaAbstract:Abstract Conventional HLA imputation Methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic MHC fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We developed Deep*HLA, a Deep Learning Method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,122), Deep*HLA achieved the highest accuracies with significant superiority for low-frequency and rare alleles. Deep*HLA was less dependent on distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We applied Deep*HLA to type 1 diabetes GWAS data from BioBank Japan (n = 62,387) and UK Biobank (n = 354,459), and successfully disentangled independently associated class I and II HLA variants with shared risk among diverse populations (the top signal at amino acid position 71 of HLA-DRβ1; P = 7.5 × 10−120). Our study illustrates a value of Deep Learning in genotype imputation and trans-ethnic MHC fine-mapping.
