As a supplementary of [Xu L. Front. Electr. Electron. Eng. China, 2010, 5（3）： 281-328], this paper outlines current status of efforts made on Bayesian Ying- Yang （BYY） harmony learning, plus gene analysis appli- ...As a supplementary of [Xu L. Front. Electr. Electron. Eng. China, 2010, 5（3）： 281-328], this paper outlines current status of efforts made on Bayesian Ying- Yang （BYY） harmony learning, plus gene analysis appli- cations. At the beginning, a bird＇s-eye view is provided via Gaussian mixture in comparison with typical learn- ing algorithms and model selection criteria. Particularly, semi-supervised learning is covered simply via choosing a scalar parameter. Then, essential topics and demand- ing issues about BYY system design and BYY harmony learning are systematically outlined, with a modern per- spective on Yin-Yang viewpoint discussed, another Yang factorization addressed, and coordinations across and within Ying-Yang summarized. The BYY system acts as a unified framework to accommodate unsupervised, su- pervised, and semi-supervised learning all in one formu- lation, while the best harmony learning provides novelty and strength to automatic model selection. Also, mathe- matical formulation of harmony functional has been ad- dressed as a unified scheme for measuring the proximity to be considered in a BYY system, and used as the best choice among others. Moreover, efforts are made on a number of learning tasks, including a mode-switching factor analysis proposed as a semi-blind learning frame- work for several types of independent factor analysis, a hidden Markov model （HMM） gated temporal fac- tor analysis suggested for modeling piecewise stationary temporal dependence, and a two-level hierarchical Gaus- sian mixture extended to cover semi-supervised learning, as well as a manifold learning modified to facilitate au- tomatic model selection. Finally, studies are applied to the problems of gene analysis, such as genome-wide asso- ciation, exome sequencing analysis, and gene transcrip- tional regulation.展开更多
[目的]建立目标基因测序技术，对后纵韧带骨化（ossification of the posterior longitud inalligament，OPLL）患者的11个已知致病基因进行突变筛查，探讨OPLL与致病基因突变的关系。[方法]提取6例OPLL患者外周血全基因组DNA，利用Gen...[目的]建立目标基因测序技术，对后纵韧带骨化（ossification of the posterior longitud inalligament，OPLL）患者的11个已知致病基因进行突变筛查，探讨OPLL与致病基因突变的关系。[方法]提取6例OPLL患者外周血全基因组DNA，利用GenCap目标基因捕获技术（北京迈基诺公司），设计骨形态发生蛋白-2（bone morphogenetic pro—tein2，BMP2）、骨形态发生蛋白-4（bone morphogenetic protein4，BMP4）、骨形态发生蛋白-9（bone morphogenetic protein 9，BMP9）、Ⅺ型胶原蛋白以（collagen type XI alpha 2，COLllA2）、Ⅵ型胶原蛋白理1（collagen type VIM pha1，COL6A1）、核苷酸焦磷酸酶（ectonucleotide pyrophosphatase／phosphodiesterase 1，ENPPl）、成纤维细胞生长因子2（fibroblast growth factor 2，FGF2）、成纤维细胞生长因子受体1（fibroblast growth factor receptor 1，FGFRl）、成纤维细胞生长因子受体2（fibroblast growth factor receptor2，FGFR2）、转化生长因子一β3（transforming growth factor beta3，TGFB3）和转化生长因子一8受体2（transforming growth factor beta receptorII，TGFBR2）基因外显子区域特异性捕获探针，与基因组DNA文库进行杂交，将目标基因组区域的DNA片段进行富集后，再利用illumina hiseq2000第二代测序仪进行测序，通过数据分析，确定突变位点，对选定的突变位点用Sanger测序法进行验证。[结果]设计合成的目标基因特异性捕获探针可有效地捕捉并富集基因组DNA的目标靶片段。目标区域平均测序深度为426．85～976．15，99．30％-100％目标区域覆盖度。在1例患者中发现COL6A1基因的1个错义突变，此位点检测结果与Sanger测序结果完全一致。[结论]目标基因测序技术成功发现了OPLL患者的致病基因突变。该方法快速而有效，对深入研究OPLL的分子病因学有重要意义。展开更多
文摘As a supplementary of [Xu L. Front. Electr. Electron. Eng. China, 2010, 5（3）： 281-328], this paper outlines current status of efforts made on Bayesian Ying- Yang （BYY） harmony learning, plus gene analysis appli- cations. At the beginning, a bird＇s-eye view is provided via Gaussian mixture in comparison with typical learn- ing algorithms and model selection criteria. Particularly, semi-supervised learning is covered simply via choosing a scalar parameter. Then, essential topics and demand- ing issues about BYY system design and BYY harmony learning are systematically outlined, with a modern per- spective on Yin-Yang viewpoint discussed, another Yang factorization addressed, and coordinations across and within Ying-Yang summarized. The BYY system acts as a unified framework to accommodate unsupervised, su- pervised, and semi-supervised learning all in one formu- lation, while the best harmony learning provides novelty and strength to automatic model selection. Also, mathe- matical formulation of harmony functional has been ad- dressed as a unified scheme for measuring the proximity to be considered in a BYY system, and used as the best choice among others. Moreover, efforts are made on a number of learning tasks, including a mode-switching factor analysis proposed as a semi-blind learning frame- work for several types of independent factor analysis, a hidden Markov model （HMM） gated temporal fac- tor analysis suggested for modeling piecewise stationary temporal dependence, and a two-level hierarchical Gaus- sian mixture extended to cover semi-supervised learning, as well as a manifold learning modified to facilitate au- tomatic model selection. Finally, studies are applied to the problems of gene analysis, such as genome-wide asso- ciation, exome sequencing analysis, and gene transcrip- tional regulation.
文摘[目的]建立目标基因测序技术，对后纵韧带骨化（ossification of the posterior longitud inalligament，OPLL）患者的11个已知致病基因进行突变筛查，探讨OPLL与致病基因突变的关系。[方法]提取6例OPLL患者外周血全基因组DNA，利用GenCap目标基因捕获技术（北京迈基诺公司），设计骨形态发生蛋白-2（bone morphogenetic pro—tein2，BMP2）、骨形态发生蛋白-4（bone morphogenetic protein4，BMP4）、骨形态发生蛋白-9（bone morphogenetic protein 9，BMP9）、Ⅺ型胶原蛋白以（collagen type XI alpha 2，COLllA2）、Ⅵ型胶原蛋白理1（collagen type VIM pha1，COL6A1）、核苷酸焦磷酸酶（ectonucleotide pyrophosphatase／phosphodiesterase 1，ENPPl）、成纤维细胞生长因子2（fibroblast growth factor 2，FGF2）、成纤维细胞生长因子受体1（fibroblast growth factor receptor 1，FGFRl）、成纤维细胞生长因子受体2（fibroblast growth factor receptor2，FGFR2）、转化生长因子一β3（transforming growth factor beta3，TGFB3）和转化生长因子一8受体2（transforming growth factor beta receptorII，TGFBR2）基因外显子区域特异性捕获探针，与基因组DNA文库进行杂交，将目标基因组区域的DNA片段进行富集后，再利用illumina hiseq2000第二代测序仪进行测序，通过数据分析，确定突变位点，对选定的突变位点用Sanger测序法进行验证。[结果]设计合成的目标基因特异性捕获探针可有效地捕捉并富集基因组DNA的目标靶片段。目标区域平均测序深度为426．85～976．15，99．30％-100％目标区域覆盖度。在1例患者中发现COL6A1基因的1个错义突变，此位点检测结果与Sanger测序结果完全一致。[结论]目标基因测序技术成功发现了OPLL患者的致病基因突变。该方法快速而有效，对深入研究OPLL的分子病因学有重要意义。
