Cnngeno: A High-Precision Deep Learning Based Strategy for the Calling of Structural Variation Genotype

Motivation: Integrative modeling of macromolecular structures usually results in an ensemble of models that satisfy the input information. The model precision, or variability among these models is estimated globally, i.e., a single precision value is reported for the model. However, it would be useful to identify regions of high and low precision. For instance, low-precision regions can suggest where the next experiments could be performed and high-precision regions can be used for further analysis, e.g., suggesting mutations. Results: We develop PrISM (Precision for Integrative Structural Models), using autoencoders to efficiently and accurately annotate precision for integrative models. The method is benchmarked and tested on five examples of binary protein complexes and five examples of large protein assemblies. The annotated precision is shown to be consistent with, and more informative than localization densities. The generated networks are also interpreted by gradient-based attention analysis. Availability: Source code is at https://github.com/isblab/prism.

Download Full-text

Dissimilarity-Based Regularized Learning of Charts

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3458884 ◽

2021 ◽

Vol 17 (4) ◽

pp. 1-23

Author(s):

Prerna Mishra ◽

Santosh Kumar ◽

Mithilesh Kumar Chaube

Keyword(s):

Deep Learning ◽

Loss Function ◽

Structural Variation ◽

Learning Model ◽

Discriminative Power ◽

Learning Models ◽

Dissimilarity Index ◽

Regularization Parameters ◽

Learned Features

Chart images exhibit significant variabilities that make each image different from others even though they belong to the same class or categories. Classification of charts is a major challenge because each chart class has variations in features, structure, and noises. However, due to the lack of affiliation between the dissimilar features and the structure of the chart, it is a challenging task to model these variations for automatic chart recognition. In this article, we present a novel dissimilarity-based learning model for similar structured but diverse chart classification. Our approach jointly learns the features of both dissimilar and similar regions. The model is trained by an improved loss function, which is fused by a structural variation-aware dissimilarity index and incorporated with regularization parameters, making the model more prone toward dissimilar regions. The dissimilarity index enhances the discriminative power of the learned features not only from dissimilar regions but also from similar regions. Extensive comparative evaluations demonstrate that our approach significantly outperforms other benchmark methods, including both traditional and deep learning models, over publicly available datasets.

Download Full-text

Detection of chromosome structural variation by targeted next-generation sequencing and a deep learning application

Scientific Reports ◽

10.1038/s41598-019-40364-5 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 4

Author(s):

Hosub Park ◽

Sung-Min Chun ◽

Jooyong Shim ◽

Ji-Hye Oh ◽

Eun Jeong Cho ◽

...

Keyword(s):

Deep Learning ◽

Next Generation Sequencing ◽

Structural Variation ◽

Next Generation ◽

Targeted Next Generation Sequencing ◽

Generation Sequencing

Download Full-text

An Improved Faster R-CNN Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images

Remote Sensing ◽

10.3390/rs13112052 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2052

Author(s):

Dongchuan Yan ◽

Guoqing Li ◽

Xiangqiang Li ◽

Hao Zhang ◽

Hua Lei ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

High Precision ◽

Large Scale ◽

Dam Failure ◽

Remote Sensing Images ◽

Feature Maps ◽

Tailings Pond ◽

Tailings Ponds

Dam failure of tailings ponds can result in serious casualties and environmental pollution. Therefore, timely and accurate monitoring is crucial for managing tailings ponds and preventing damage from tailings pond accidents. Remote sensing technology facilitates the regular extraction and monitoring of tailings pond information. However, traditional remote sensing techniques are inefficient and have low levels of automation, which hinders the large-scale, high-frequency, and high-precision extraction of tailings pond information. Moreover, research into the automatic and intelligent extraction of tailings pond information from high-resolution remote sensing images is relatively rare. However, the deep learning end-to-end model offers a solution to this problem. This study proposes an intelligent and high-precision method for extracting tailings pond information from high-resolution images, which improves deep learning target detection model: faster region-based convolutional neural network (Faster R-CNN). A comparison study is conducted and the model input size with the highest precision is selected. The feature pyramid network (FPN) is adopted to obtain multiscale feature maps with rich context information, the attention mechanism is used to improve the FPN, and the contribution degrees of feature channels are recalibrated. The model test results based on GoogleEarth high-resolution remote sensing images indicate a significant increase in the average precision (AP) and recall of tailings pond detection from that of Faster R-CNN by 5.6% and 10.9%, reaching 85.7% and 62.9%, respectively. Considering the current rapid increase in high-resolution remote sensing images, this method will be important for large-scale, high-precision, and intelligent monitoring of tailings ponds, which will greatly improve the decision-making efficiency in tailings pond management.

Download Full-text

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

Scientific Reports ◽

10.1038/s41598-021-84499-w ◽

2021 ◽

Vol 11 (1) ◽

Cited By ~ 2

Author(s):

Ruoqian Lin ◽

Rui Zhang ◽

Chunyang Wang ◽

Xiao-Qing Yang ◽

Huolin L. Xin

Keyword(s):

Deep Learning ◽

High Precision ◽

Super Resolution ◽

Training Data ◽

Atomic Resolution ◽

Thickness Variation ◽

Learning Models ◽

Scanning Transmission ◽

Gaussian Fit ◽

Atom Detection

AbstractAtom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.

Download Full-text