scholarly journals MicrographCleaner: a python package for cryo-EM micrograph cleaning using deep learning

2019 ◽  
Author(s):  
Ruben Sanchez-Garcia ◽  
Joan Segura ◽  
David Maluenda ◽  
C.O.S. Sorzano ◽  
J.M. Carazo
Keyword(s):  
Deep Learning ◽  
Source Code ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Particle Analysis ◽  
Single Particle Analysis ◽  
Reconstruction Process ◽  
Different Types ◽  
Deep Learning Model ◽  
Python Package

AbstractCryo-EM Single Particle Analysis workflows require from tens of thousands of high-quality particle projections to unveil the three-dimensional structure of macromolecules. Conventional methods for automatic particle picking tend to suffer from high false-positive rates, hurdling the reconstruction process. One common cause of this problem is the presence of carbon and different types of high-contrast contaminations. In order to overcome this limitation, we have developed MicrographCleaner, a deep learning package designed to discriminate which regions of micrographs are suitable for particle picking and which are not in an automatic fashion. MicrographCleaner implements a U-net-like deep learning model trained on a manually curated dataset compiled from over five hundred micrographs. The benchmarking, carried out on about one hundred independent micrographs, shows that MicrographCleaner is a very efficient approach for micrograph preprocessing. MicrographCleaner (micrograph_cleaner_em) package is available at PyPI and Anaconda Cloud and also as a Scipion/Xmipp protocol. Source code is available at https://github.com/rsanchezgarc/micrograph_cleaner_em.

scholarly journals A Deep Learning Model for Predicting Tumor Suppressor Genes and Oncogenes from PDB Structure

2017 ◽  
Author(s):  
Amirhossein Tavanaei ◽  
Nishanth Anandanadarajah ◽  
Anthony Maida ◽  
Rasiah Loganantharaj
Keyword(s):  
Deep Learning ◽  
Tumor Suppression ◽  
Protein Structures ◽  
Three Dimensional ◽  
Learning Model ◽  
Dimensional Structure ◽  
Regulatory Function ◽  
Cancer Genes ◽  
Feature Map ◽  
Deep Learning Model

AbstractWhile cancer is a heterogeneous complex of distinct diseases, the common underlying mechanism for uncontrolled tumor growth is due to mutations in proto-oncogenes and the loss of the regulatory function of tumor suppression genes. In this paper we propose a novel deep learning model for predicting tumor suppression genes (TSGs) and proto-oncogenes (OGs) from their Protein Data Bank (PDB) three dimensional structures. Specifically, we develop a convolutional neural network (CNN) to classify the feature map sets extracted from the tertiary protein structures. Each feature map set represents particular biological features associated with the atomic coordinates appearing on the outer surface of protein’s three dimensional structure. The experimental results on the collected dataset for classifying TSGs and OGs demonstrate promising performance with 82.57% accuracy and 0.89 area under ROC curve. The initial success of the proposed model warrants further study to develop a comprehensive model to identify the cancer driver genes or events using the principle cancer genes (TSG and OG).

scholarly journals A Climate Downscaling Deep Learning Model considering the Multiscale Spatial Correlations and Chaos of Meteorological Events

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Bin Mu ◽  
Bo Qin ◽  
Shijin Yuan ◽  
Xiaoyun Qin
Keyword(s):  
Deep Learning ◽  
General Circulation ◽  
Dynamical Downscaling ◽  
General Circulation Models ◽  
Learning Model ◽  
Spatial Correlations ◽  
Reconstruction Process ◽  
Daily Precipitation Data ◽  
Climate Downscaling ◽  
Deep Learning Model

Climate downscaling is a way to provide finer resolution data at local scales, which has been widely used in meteorological research. The two main approaches for climate downscaling are dynamical and statistical. The traditional dynamical downscaling methods are quite time- and resource-consuming based on general circulation models (GCMs). Recently, more and more researchers construct a statistical deep learning model for climate downscaling motivated by the single-image superresolution (SISR) process in computer vision (CV). This is an approach that uses historical climate observations to learn a low-resolution to high-resolution mapping and produces great enhancements in terms of efficiency and effectiveness. Therefore, it has provided an appreciable new insight and successful downscaling solution to multiple climate phenomena. However, most existing models only make a simple analogy between climate downscaling and SISR and ignore the underlying dynamical mechanisms, which leads to the overaveraged downscaling results lacking crucial physical details. In this paper, we incorporate the a priori meteorological knowledge into a deep learning formalization for climate downscaling. More specifically, we consider the multiscale spatial correlations and the chaos in multiple climate events. Depending on two characteristics, we build up a two-stage deep learning model containing a stepwise reconstruction process and ensemble inference, which is named climate downscaling network (CDN). It can extract more local/remote spatial dependencies and provide more comprehensive captures of extreme conditions. We evaluate our model based on two datasets: climate science dataset (CSD) and benchmark image dataset (BID). The results demonstrate that our model shows the effectiveness and superiority in downscaling daily precipitation data from 2.5 degrees to 0.5 degrees over Asia and Europe. In addition, our model exhibits better performance than the other traditional approaches and state-of-the-art deep learning models.

scholarly journals Prediction of Merchandise Sales on E-Commerce Platforms Based on Data Mining and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaoting Yin ◽  
Xiaosha Tao
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Research Process ◽  
Deep Learning Algorithm ◽  
Sales Prediction ◽  
Different Types ◽  
Online Business ◽  
Product Sales ◽  
Deep Learning Model

Online business has grown exponentially during the last decade, and the industries are focusing on online business more than before. However, just setting up an online store and starting selling might not work. Different machine learning and data mining techniques are needed to know the users’ preferences and know what would be best for business. According to the decision-making needs of online product sales, combined with the influencing factors of online product sales in various industries and the advantages of deep learning algorithm, this paper constructs a sales prediction model suitable for online products and focuses on evaluating the adaptability of the model in different types of online products. In the research process, the full connection model is compared with the training results of CNN, which proves the accuracy and generalization ability of CNN model. By selecting the non-deep learning model as the comparison baseline, the performance advantages of CNN model under different categories of products are proved. In addition, the experiment concludes that the unsupervised pretrained CNN model is more effective and adaptable in sales forecasting.

scholarly journals Three Dimensional Reconstruction of CFTR Chloride Channel Using Single Particle Analysis

2009 ◽  
Vol 96 (3) ◽  
pp. 468a
Author(s):  
Kazuhiro Mio ◽  
Toshihiko Ogura ◽  
Muneyo Mio ◽  
Hiroyasu Shimizu ◽  
Tzyh-Chang Hwang ◽  
...  
Keyword(s):  
Single Particle ◽  
Chloride Channel ◽  
Three Dimensional ◽  
Particle Analysis ◽  
Single Particle Analysis ◽  
Three Dimensional Reconstruction ◽  
Dimensional Reconstruction

scholarly journals DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins

2020 ◽  
Vol 16 (5) ◽  
pp. 448-454 ◽  
Author(s):  
Meenal Chaudhari ◽  
Niraj Thapa ◽  
Kaushik Roy ◽  
Robert H. Newman ◽  
Hiroto Saigo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Source Code ◽  
Protein Sequences ◽  
Learning Model ◽  
Arginine Methylation ◽  
Deep Learning Model

DeepRMethylSite is an ensemble-based deep learning model that takes protein sequences as input and predicts sites of Arginine methylation. The implementation and source code are provided at https://github.com/dukkakc/DeepRMethylSite.

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