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/...
batch_yintrain_loader:optimizer.zero_grad()outputs=model(batch_X.unsqueeze(1))loss=criterion(outputs,batch_y.unsqueeze(1))loss.backward()optimizer.step()# Evaluate the modelmodel.eval()withtorch.no_grad():forbatch_X
Predicting enhancer-promoter interaction from genomic sequence with deep neural networks. bioRxiv. 2016 Jan 1:085241.Singh, S., Y. Yang, B. Poczos, and J. Ma, 2018 Predicting enhancer- promoter interaction from genomic sequence with deep neural networks. bioRxiv ....
Deep Neural Pursuit trained a small-sized sub-network and computed gradients with low variance for feature selection [21]. Although there are variant architectures in deep learning, most conventional deep neural networks consist of multiple fully-connected layers for analyzing structure data, which ...
Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks Genome Res., 26 (2016), pp. 990-999 Google Scholar Kelley et al., 2018 D.R. Kelley, Y.A. Reshef, M. Bileschi, D. Belanger, C.Y. McLean, J. Snoek Sequential regulatory activity predic...
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.
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 ...
Ensemble learning has been widely utilized in genome selection, such as Ma et al. [36], who assembles two basic methods and trains the weights with PSO algorithm; however, it has several disadvantages, (1) it assumes the phenotypes of testing individuals have been known, so that it is onl...
genome selection in dairy cattle[189]. In food production, the segment of food processing aids, i.e. industrialenzymesenhanced by the use of genomics and biotechnology, has proven invaluable in the production of enzymes with greater purity and flexibility, while ensuring a sustainable and cheap ...
Hyperparameter selection To choose the representation size of our model, we performed an ablation analysis. We computed the average mAP across all downstream tasks with the Hi-C-LSTM model which consists of a single layer, unidirectional LSTM with layer norm in the absence of dropout106 for odd...