DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops

2020 ◽  
Author(s):  
Fu-Ying Dao ◽  
Hao Lv ◽  
Dan Zhang ◽  
Zi-Mei Zhang ◽  
Li Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computational Methods ◽  
Learning Algorithm ◽  
Cell Types ◽  
Chromatin Loops ◽  
Deep Learning Algorithm ◽  
Dna Elements ◽  
Chromatin Folding ◽  
User Friendly ◽  
Different Cell Types

Abstract The protein Yin Yang 1 (YY1) could form dimers that facilitate the interaction between active enhancers and promoter-proximal elements. YY1-mediated enhancer–promoter interaction is the general feature of mammalian gene control. Recently, some computational methods have been developed to characterize the interactions between DNA elements by elucidating important features of chromatin folding; however, no computational methods have been developed for identifying the YY1-mediated chromatin loops. In this study, we developed a deep learning algorithm named DeepYY1 based on word2vec to determine whether a pair of YY1 motifs would form a loop. The proposed models showed a high prediction performance (AUCs$\ge$0.93) on both training datasets and testing datasets in different cell types, demonstrating that DeepYY1 has an excellent performance in the identification of the YY1-mediated chromatin loops. Our study also suggested that sequences play an important role in the formation of YY1-mediated chromatin loops. Furthermore, we briefly discussed the distribution of the replication origin site in the loops. Finally, a user-friendly web server was established, and it can be freely accessed at http://lin-group.cn/server/DeepYY1.

User-friendly image-activated microfluidic cell sorting technique using an optimized, fast deep learning algorithm

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Keondo Lee ◽  
Seong-Eun Kim ◽  
Junsang Doh ◽  
Keehoon Kim ◽  
Wan Kyun Chung
Keyword(s):  
Deep Learning ◽  
Piezoelectric Actuator ◽  
Cell Sorting ◽  
High Performance ◽  
Learning Algorithm ◽  
Cell Sorter ◽  
Operational Procedure ◽  
Deep Learning Algorithm ◽  
Sorting Technique ◽  
User Friendly

The image-activated cell sorter employs a significantly simplified operational procedure based on a syringe connected to a piezoelectric actuator and high-performance inference with TensorRT Integration.

scholarly journals INSCT: Integrating millions of single cells using batch-aware triplet neural networks

2020 ◽  
Author(s):  
Lukas M. Simon ◽  
Yin-Ying Wang ◽  
Zhongming Zhao
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Single Cells ◽  
Real Data ◽  
Cell Types ◽  
List Type ◽  
Desktop Computer ◽  
Deep Learning Algorithm ◽  
Computer Environment

AbstractEfficient integration of heterogeneous and increasingly large single cell RNA sequencing (scRNA-seq) data poses a major challenge for analysis and in particular, comprehensive atlasing efforts. Here, we developed a novel deep learning algorithm to overcome batch effects using batch-aware triplet neural networks, called INSCT (“Insight”). Using simulated and real data, we demonstrate that INSCT generates an embedding space which accurately integrates cells across experiments, platforms and species. Our benchmark comparisons with current state-of-the-art scRNA-seq integration methods revealed that INSCT outperforms competing methods in scalability while achieving comparable accuracies. Moreover, using INSCT in semi-supervised mode enables users to classify unlabeled cells by projecting them into a reference collection of annotated cells. To demonstrate scalability, we applied INSCT to integrate more than 2.6 million transcriptomes from four independent studies of mouse brains in less than 1.5 hours using less than 25 gigabytes of memory. This feature empowers researchers to perform atlasing scale data integration in a typical desktop computer environment. INSCT is freely available at https://github.com/lkmklsmn/insct.HighlightsINSCT accurately integrates multiple scRNA-seq datasetsINSCT accurately predicts cell types for an independent scRNA-seq datasetEfficient deep learning framework enables integration of millions of cells on a personal computer

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

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