keras_dna: a wrapper for fast implementation of deep learning models in genomics

2020 ◽  
Author(s):  
Etienne Routhier ◽  
Ayman Bin Kamruddin ◽  
Julien Mozziconacci
Keyword(s):  
Deep Learning ◽  
Dna Sequences ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Learning Models ◽  
Multiple Targets ◽  
Fast Implementation ◽  
Model Training ◽  
High Level

Abstract Summary Prediction of genomic annotations from DNA sequences using deep learning is today becoming a flourishing field with many applications. Nevertheless, there are still difficulties in handling data in order to conveniently build and train models dedicated for specific end-user’s tasks. keras_dna is designed for an easy implementation of Keras models (TensorFlow high level API) for genomics. It can handle standard bioinformatic files formats as inputs such as bigwig, gff, bed, wig, bedGraph or fasta and returns standardized inputs for model training. keras_dna is designed to implement existing models but also to facilitate the development of news models that can have single or multiple targets or inputs. Availability and implementation Freely available with a MIT License using pip install keras_dna or cloning the github repo at https://github.com/etirouthier/keras_dna.git. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models

2020 ◽  
Author(s):  
Yupeng Wang ◽  
Rosario B. Jaime-Lara ◽  
Abhrarup Roy ◽  
Ying Sun ◽  
Xinyue Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Dna Sequences ◽  
Cell Types ◽  
Learning Models ◽  
Cell Type ◽  
Coding Sequences ◽  
Sequence Features ◽  
Cell Type Specific ◽  
Different Cell Types

AbstractWe propose SeqEnhDL, a deep learning framework for classifying cell type-specific enhancers based on sequence features. DNA sequences of “strong enhancer” chromatin states in nine cell types from the ENCODE project were retrieved to build and test enhancer classifiers. For any DNA sequence, sequential k-mer (k=5, 7, 9 and 11) fold changes relative to randomly selected non-coding sequences were used as features for deep learning models. Three deep learning models were implemented, including multi-layer perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). All models in SeqEnhDL outperform state-of-the-art enhancer classifiers including gkm-SVM and DanQ, with regard to distinguishing cell type-specific enhancers from randomly selected non-coding sequences. Moreover, SeqEnhDL is able to directly discriminate enhancers from different cell types, which has not been achieved by other enhancer classifiers. Our analysis suggests that both enhancers and their tissue-specificity can be accurately identified according to their sequence features. SeqEnhDL is publicly available at https://github.com/wyp1125/SeqEnhDL.

Introducing AIDE: a Software Suite for Annotating Images with Deep and Active Learning Assistance

2021 ◽  
Author(s):  
Benjamin Kellenberger ◽  
Devis Tuia ◽  
Dan Morris
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Expert Knowledge ◽  
Semantic Segmentation ◽  
Third Party ◽  
Learning Models ◽  
Web Browser ◽  
Web Based ◽  
Model Training ◽  
Bounding Boxes

<p>Ecological research like wildlife censuses increasingly relies on data on the scale of Terabytes. For example, modern camera trap datasets contain millions of images that require prohibitive amounts of manual labour to be annotated with species, bounding boxes, and the like. Machine learning, especially deep learning [3], could greatly accelerate this task through automated predictions, but involves expansive coding and expert knowledge.</p><p>In this abstract we present AIDE, the Annotation Interface for Data-driven Ecology [2]. In a first instance, AIDE is a web-based annotation suite for image labelling with support for concurrent access and scalability, up to the cloud. In a second instance, it tightly integrates deep learning models into the annotation process through active learning [7], where models learn from user-provided labels and in turn select the most relevant images for review from the large pool of unlabelled ones (Fig. 1). The result is a system where users only need to label what is required, which saves time and decreases errors due to fatigue.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.0402be60f60062057601161/sdaolpUECMynit/12UGE&app=m&a=0&c=131251398e575ac9974634bd0861fadc&ct=x&pn=gnp.elif&d=1" alt=""></p><p><em>Fig. 1: AIDE offers concurrent web image labelling support and uses annotations and deep learning models in an active learning loop.</em></p><p>AIDE includes a comprehensive set of built-in models, such as ResNet [1] for image classification, Faster R-CNN [5] and RetinaNet [4] for object detection, and U-Net [6] for semantic segmentation. All models can be customised and used without having to write a single line of code. Furthermore, AIDE accepts any third-party model with minimal implementation requirements. To complete the package, AIDE offers both user annotation and model prediction evaluation, access control, customisable model training, and more, all through the web browser.</p><p>AIDE is fully open source and available under https://github.com/microsoft/aerial_wildlife_detection.</p><p> </p><p><strong>References</strong></p>

scholarly journals Genesis and Gappa: processing, analyzing and visualizing phylogenetic (placement) data

2020 ◽  
Vol 36 (10) ◽  
pp. 3263-3265 ◽  
Author(s):  
Lucas Czech ◽  
Pierre Barbera ◽  
Alexandros Stamatakis
Keyword(s):  
Phylogenetic Trees ◽  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
Computationally Efficient ◽  
Data Types ◽  
Low Level ◽  
Phylogenetic Placement ◽  
Command Line Tool ◽  
High Level

Abstract Summary We present genesis, a library for working with phylogenetic data, and gappa, an accompanying command-line tool for conducting typical analyses on such data. The tools target phylogenetic trees and phylogenetic placements, sequences, taxonomies and other relevant data types, offer high-level simplicity as well as low-level customizability, and are computationally efficient, well-tested and field-proven. Availability and implementation Both genesis and gappa are written in modern C++11, and are freely available under GPLv3 at http://github.com/lczech/genesis and http://github.com/lczech/gappa. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DEEPrior: a deep learning tool for the prioritization of gene fusions

2020 ◽  
Vol 36 (10) ◽  
pp. 3248-3250
Author(s):  
Marta Lovino ◽  
Maria Serena Ciaburri ◽  
Gianvito Urgese ◽  
Santa Di Cataldo ◽  
Elisa Ficarra
Keyword(s):  
Deep Learning ◽  
Amino Acid ◽  
Gene Fusion ◽  
Supplementary Information ◽  
Gene Fusions ◽  
Supplementary Data ◽  
Learning Tool ◽  
Cancer Driver ◽  
Open Issue ◽  
Passenger Mutation

Abstract Summary In the last decade, increasing attention has been paid to the study of gene fusions. However, the problem of determining whether a gene fusion is a cancer driver or just a passenger mutation is still an open issue. Here we present DEEPrior, an inherently flexible deep learning tool with two modes (Inference and Retraining). Inference mode predicts the probability of a gene fusion being involved in an oncogenic process, by directly exploiting the amino acid sequence of the fused protein. Retraining mode allows to obtain a custom prediction model including new data provided by the user. Availability and implementation Both DEEPrior and the protein fusions dataset are freely available from GitHub at (https://github.com/bioinformatics-polito/DEEPrior). The tool was designed to operate in Python 3.7, with minimal additional libraries. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals On the Depth of Deep Learning Models for Splice Site Identification

2018 ◽  
Author(s):  
Reem Elsousy ◽  
Nagarajan Kathiresan ◽  
Sabri Boughorbel
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Splice Site ◽  
Dna Sequences ◽  
Classification Performance ◽  
Model Parameters ◽  
Learning Models ◽  
Trade Off ◽  
Good Trade ◽  
Site Identification

AbstractThe success of deep learning has been shown in various fields including computer vision, speech recognition, natural language processing and bioinformatics. The advance of Deep Learning in Computer Vision has been an important source of inspiration for other research fields. The objective of this work is to adapt known deep learning models borrowed from computer vision such as VGGNet, Resnet and AlexNet for the classification of biological sequences. In particular, we are interested by the task of splice site identification based on raw DNA sequences. We focus on the role of model architecture depth on model training and classification performance.We show that deep learning models outperform traditional classification methods (SVM, Random Forests, and Logistic Regression) for large training sets of raw DNA sequences. Three model families are analyzed in this work namely VGGNet, AlexNet and ResNet. Three depth levels are defined for each model family. The models are benchmarked using the following metrics: Area Under ROC curve (AUC), Number of model parameters, number of floating operations. Our extensive experimental evaluation show that shallow architectures have an overall better performance than deep models. We introduced a shallow version of ResNet, named S-ResNet. We show that it gives a good trade-off between model complexity and classification performance.Author summaryDeep Learning has been widely applied to various fields in research and industry. It has been also succesfully applied to genomics and in particular to splice site identification. We are interested in the use of advanced neural networks borrowed from computer vision. We explored well-known models and their usability for the problem of splice site identification from raw sequences. Our extensive experimental analysis shows that shallow models outperform deep models. We introduce a new model called S-ResNet, which gives a good trade-off between computational complexity and classification accuracy.

scholarly journals AUTOMATED MARINE OIL SPILL DETECTION USING DEEP LEARNING INSTANCE SEGMENTATION MODEL

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1271-1276
Author(s):  
S. T. Yekeen ◽  
A.-L. Balogun
Keyword(s):  
Deep Learning ◽  
Oil Spill ◽  
Semantic Segmentation ◽  
Learning Models ◽  
Marine Oil ◽  
Conventional Machine ◽  
Feature Pyramid ◽  
Model Training ◽  
Instance Segmentation ◽  
Better Than

Abstract. This study developed a novel deep learning oil spill instance segmentation model using Mask-Region-based Convolutional Neural Network (Mask R-CNN) model which is a state-of-the-art computer vision model. A total of 2882 imageries containing oil spill, look-alike, ship, and land area after conducting different pre-processing activities were acquired. These images were subsequently sub-divided into 88% training and 12% for testing, equating to 2530 and 352 images respectively. The model training was conducted using transfer learning on a pre-trained ResNet 101 with COCO data as a backbone in combination with Feature Pyramid Network (FPN) architecture for the extraction of features at 30 epochs with 0.001 learning rate. The model’s performance was evaluated using precision, recall, and F1-measure which shows a higher performance than other existing models with value of 0.964, 0.969 and 0.968 respectively. As a specialized task, the study concluded that the developed deep learning instance segmentation model (Mask R-CNN) performs better than conventional machine learning models and semantic segmentation deep learning models in detection and segmentation of marine oil spill.

scholarly journals Inference of Brain States under Anesthesia with Meta Learning Based Deep Learning Models

2021 ◽  
Author(s):  
Qihang Wang ◽  
Feng Liu ◽  
Guihong Wan ◽  
Ying Chen
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Short Term Memory ◽  
Signal To Noise Ratio ◽  
General Anesthetics ◽  
Learning Models ◽  
Multi Stage ◽  
Meta Learning ◽  
Brain States ◽  
High Level

AbstractMonitoring the depth of unconsciousness during anesthesia is useful in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anesthetics affect cerebral electrical activities in different ways. However, the performance of conventional machine learning models on EEG data is unsatisfactory due to the low Signal to Noise Ratio (SNR) in the EEG signals, especially in the office-based anesthesia EEG setting. Deep learning models have been used widely in the field of Brain Computer Interface (BCI) to perform classification and pattern recognition tasks due to their capability of good generalization and handling noises. Compared to other BCI applications, where deep learning has demonstrated encouraging results, the deep learning approach for classifying different brain consciousness states under anesthesia has been much less investigated. In this paper, we propose a new framework based on meta-learning using deep neural networks, named Anes-MetaNet, to classify brain states under anesthetics. The Anes-MetaNet is composed of Convolutional Neural Networks (CNN) to extract power spectrum features, and a time consequence model based on Long Short-Term Memory (LSTM) Networks to capture the temporal dependencies, and a meta-learning framework to handle large cross-subject variability. We used a multi-stage training paradigm to improve the performance, which is justified by visualizing the high-level feature mapping. Experiments on the office-based anesthesia EEG dataset demonstrate the effectiveness of our proposed Anes-MetaNet by comparison of existing methods.

scholarly journals Structured crowdsourcing enables convolutional segmentation of histology images

2019 ◽  
Vol 35 (18) ◽  
pp. 3461-3467 ◽  
Author(s):  
Mohamed Amgad ◽  
Habiba Elfandy ◽  
Hagar Hussein ◽  
Lamees A Atteya ◽  
Mai A T Elsebaie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Digital Slide ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Annotation Data ◽  
Whole Slide Images

Abstract Motivation While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. Results We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. Availability and Implementation Dataset is freely available at: https://goo.gl/cNM4EL. Supplementary information Supplementary data are available at Bioinformatics online.

DeepMSPeptide: peptide detectability prediction using deep learning

2019 ◽  
Author(s):  
Guillermo Serrano ◽  
Elizabeth Guruceaga ◽  
Victor Segura
Keyword(s):  
Deep Learning ◽  
Protein Detection ◽  
Amino Acid Sequences ◽  
Supplementary Information ◽  
Learning Method ◽  
Supplementary Data ◽  
Stochastic Nature ◽  
Bioinformatic Tool ◽  
Peptide Detectability ◽  
Detection And Quantification

Abstract Summary The protein detection and quantification using high-throughput proteomic technologies is still challenging due to the stochastic nature of the peptide selection in the mass spectrometer, the difficulties in the statistical analysis of the results and the presence of degenerated peptides. However, considering in the analysis only those peptides that could be detected by mass spectrometry, also called proteotypic peptides, increases the accuracy of the results. Several approaches have been applied to predict peptide detectability based on the physicochemical properties of the peptides. In this manuscript, we present DeepMSPeptide, a bioinformatic tool that uses a deep learning method to predict proteotypic peptides exclusively based on the peptide amino acid sequences. Availability and implementation DeepMSPeptide is available at https://github.com/vsegurar/DeepMSPeptide. Supplementary information Supplementary data are available at Bioinformatics online.

CATHER: a novel threading algorithm with predicted contacts

2019 ◽  
Vol 36 (7) ◽  
pp. 2119-2125 ◽  
Author(s):  
Zongyang Du ◽  
Shuo Pan ◽  
Qi Wu ◽  
Zhenling Peng ◽  
Jianyi Yang
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Structure Prediction ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Contact Map ◽  
Test Set ◽  
Benchmark Tests ◽  
Independent Test ◽  
Push Forward

Abstract Motivation Threading is one of the most effective methods for protein structure prediction. In recent years, the increasing accuracy in protein contact map prediction opens a new avenue to improve the performance of threading algorithms. Several preliminary studies suggest that with predicted contacts, the performance of threading algorithms can be improved greatly. There is still much room to explore to make better use of predicted contacts. Results We have developed a new contact-assisted threading algorithm named CATHER using both conventional sequential profiles and contact map predicted by a deep learning-based algorithm. Benchmark tests on an independent test set and the CASP12 targets demonstrated that CATHER made significant improvement over other methods which only use either sequential profile or predicted contact map. Our method was ranked at the Top 10 among all 39 participated server groups on the 32 free modeling targets in the blind tests of the CASP13 experiment. These data suggest that it is promising to push forward the threading algorithms by using predicted contacts. Availability and implementation http://yanglab.nankai.edu.cn/CATHER/. Supplementary information Supplementary data are available at Bioinformatics online.

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