Grant, 2016 Genomic Selection with Deep Neural Networks. Graduate Theses and Dissertations. 15973. https://lib.dr.ias- tate.edu/etd/15973.McDowell, R., and Grant, D. 2016. Genomic Selection with Deep Neural Networks. Graduate Theses and Dissertations. 15973. https://lib.dr.iastate.edu/etd/15973.
Ma, "Predicting enhancer-promoter interaction from genomic sequence with deep neural networks," BioRxiv, nov 2016. 17Shashank Singh, Yang Yang, Barnabas Poczos, and Jian Ma. Predicting enhancer-promoter interaction from genomic sequence with deep neural networks. bioRxiv, page 085241, 2016b....
To that end, we introduce Xpresso, a deep convolutional neural network that jointly models promoter sequences and features associated with mRNA stability to predict steady-state mRNA levels. Results An Optimized Deep-Learning Model to Predict mRNA Expression Levels We aspired to train a quantitative ...
Genomic selection with deep neural networks. Graduate Theses and Dissertations; 2016. p. 15973. https://lib.dr.iastate.edu/etd/15973. Google Scholar Rachmatia H, Kusuma WA, Hasibuan LS. Prediction of maize phenotype based on whole-genome single nucleotide polymorphisms using deep belief ...
paper-reading: genomic selection 快乐好难 企鹅不自杀的 1 人赞同了该文章 选择育种的核心便是育种值的估计(estimates of breeding values, EBV)(李恒德等,2012)。育种值估计方法随着科学技术的发展而不断改变,传统上是以表型记录为基础的,包括上世纪中期的选择指数法(Hazel, Lush,1942)以及基于性状和系谱的最佳...
Recently, deep neural networks have been successfully applied in many biological fields. In 2020, a deep learning model AlphaFold won the protein folding competition with predicted structures within the error tolerance of experimental methods. However, this solution to the most prominent bioinformatic cha...
Marker-assisted selection (MAS) was proposed to select individuals with QTL-associated markers. Although MAS can shorten breeding time, it is less suitable for quantitative traits influenced by many genes with small effects [2]. Genomic selection (GS) has been proposed as a promising tool to ...
the response from selection over time is directly proportional to accuracy according to the breeder’s equation [47]. Therefore, in each selection cycle, the genetic gain would be a 2.62% larger with Full_R model than with the UNW model. This increase compounds over time. For example, after...
Remarkable advances in single cell genomics have presented unique challenges and opportunities for interrogating a wealth of biomedical inquiries. High dimensional genomic data are inherently complex because of intertwined relationships among the genes.
To accelerate cancer research that correlates biomarkers with clinical endpoints, methods are needed to ascertain outcomes from electronic health records at scale. Here, we train deep natural language processing (NLP) models to extract outcomes for parti