Here, we present a deep learning model (DLM) for predicting Next-Generation Sequencing (NGS) depth from DNA probe sequences. Our DLM includes a bidirectional recurrent neural network that takes as input both DNA nucleotide identities as well as the calculated probability of the nucleotide being ...
16. Deep learning (DL), with convolutional neural networks, has been developed for the automated detection of DR from retinal photographs17,18,19,20. There are, however, very few studies with retinal image-based DL systems to prospectively predict the risk of DR15,21. Moreover, there are...
我们知道 GBDT 擅长处理的是稠密的数值型变量,而对稀疏的分类变量效果较差;相反,Deep Learning 擅长处理的是稀疏的分类变量,而对稠密的数值型变量效果较差。那能不能将两者的长处相结合呢?我们看看 DeepGBM 是怎么做的。 1. 模型架构 DeepGBM 模型包含两部分,GatNN 处理的是稀疏的分类变量,GBDT2NN处理的是稠密的...
阅读笔记:Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correl Dr.Whale 2 人赞同了该文章 一句话概述:提出了一种通过图神经网络以分层的时间方式实现了来自金融数据,社交媒体和股票间关系的混沌时间信号的有效融合的模型结构。 Abstract意思同上。 Intruduction 股票价格具...
Using ensemble-based deep learning with big data fused from multiple sources we developed a PM2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008–2017). We leveraged autoencoder-based full residual...
这个问题是2013年提问的，时间来到2019年，deep learning不仅能够来做推荐系统，而且已经成为推荐系统的主流...
Using Deep Learning to Predict Short Term Traffic Flow: A Systematic Literature Review This paper systematically reviews Deep Learning-based methods for traffic flow prediction. We extracted 26 articles using a concrete methodology and reviewed them from two perspectives: first, the deep learning archite...
(50kb/10kb) contact maps of the same sample, you want to use the high-resolution map to find more precise breakpoint coordinates for these SVs, rather than perform a genome-wide SV prediction on the high-resolution map. With thepredictSV-single-resolutioncommand, you can easily get this ...
Also, this study is the first investigation that compares the performance of a deep learning model to that of pathologists regarding EBVaGC prediction. The AUROC of EBVNet was significantly better than that of all pathologists. Note that the method of calculating the AUROC has been used in dicho...
These parameters were estimated for the entire population, as was the case for the LSTM model, and were used to obtain model accuracy by comparing the model’s prediction of the participants’ choices. This model is extensively used to model behaviour in such learning and decision making tasks...