DeepMotifSyn: a deep learning approach to synthesize heterodimeric DNA motifs

AbstractMotivationThe cooperativity of transcription factors (TFs) is a widespread phenomenon in the gene regulation system. However, the interaction patterns between TF binding motifs remain elusive. The recent high-throughput assays, CAP-SELEX, have identified over 600 composite DNA sites (i.e. heterodimeric motifs) bound by cooperative TF pairs. However, there are over 25,000 inferentially effective heterodimeric TFs in human cell. It is not practically feasible to validate all heterodimeric motifs due to cost and labour. Therefore, it is highly demanding to develop a fast and accurate computational tool for heterodimeric motif synthesis.ResultsWe introduce DeepMotifSyn, a deep-learning-based tool for synthesizing heterodimeric motifs from monomeric motif pairs. Specifically, DeepMotifSyn is composed of heterodimeric motif generator and evaluator. The generator is a U-Net-based neural network that can synthesize heterodimeric motifs from aligned motif pairs. The evaluator is a machine-learning-based model that can score the generated heterodimeric motif candidates based on the motif sequence features. Systematic evaluations on CAP-SELEX data illustrates that DeepMotif-Syn significantly outperforms the current state-of-the-art predictors. In addition, DeepMotifSyn can synthesize multiple heterodimeric motifs with different orientation and spacing settings. Such a feature can address the shortcomings of previous models. We believe Deep-MotifSyn is a more practical and reliable model than current predictors on heterodimeric motif synthesis.Availability and implementationThe software is freely available at https://github.com/JasonLinjc/deepMotifSyn.

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A Survey of Graphical Page Object Detection with Deep Neural Networks

10.20944/preprints202104.0739.v1 ◽

2021 ◽

Author(s):

Jwalin Bhatt ◽

Khurram Azeem Hashmi ◽

Muhammad Zeshan Afzal ◽

Didier Stricker

Keyword(s):

Deep Learning ◽

Object Detection ◽

Conceptual Understanding ◽

Deep Neural Networks ◽

State Of The Art ◽

Learning Approaches ◽

Document Images ◽

Essential Information ◽

Current State ◽

High Level

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that makes digitization of documents viable. Since the advent of deep learning, the performance of deep learning-based object detection has improved many folds. In this work, we outline and summarize the deep learning approaches for detecting graphical page objects in the document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.

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A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

Computational Intelligence and Neuroscience ◽

10.1155/2021/9923491 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Juan F. Ramirez Rochac ◽

Nian Zhang ◽

Lara A. Thompson ◽

Tolessa Deksissa

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Mineral Exploration ◽

Classification Models ◽

Noise Resistance ◽

Deep Convolutional Neural Networks ◽

Current State ◽

Feature Augmentation ◽

Active Research ◽

Hyperspectral Classification

Hyperspectral imaging is an area of active research with many applications in remote sensing, mineral exploration, and environmental monitoring. Deep learning and, in particular, convolution-based approaches are the current state-of-the-art classification models. However, in the presence of noisy hyperspectral datasets, these deep convolutional neural networks underperform. In this paper, we proposed a feature augmentation approach to increase noise resistance in imbalanced hyperspectral classification. Our method calculates context-based features, and it uses a deep convolutional neuronet (DCN). We tested our proposed approach on the Pavia datasets and compared three models, DCN, PCA + DCN, and our context-based DCN, using the original datasets and the datasets plus noise. Our experimental results show that DCN and PCA + DCN perform well on the original datasets but not on the noisy datasets. Our robust context-based DCN was able to outperform others in the presence of noise and was able to maintain a comparable classification accuracy on clean hyperspectral images.

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Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning

Methods ◽

10.1016/j.ymeth.2018.07.007 ◽

2018 ◽

Vol 151 ◽

pp. 41-54 ◽

Cited By ~ 23

Author(s):

Nicholas Cummins ◽

Alice Baird ◽

Björn W. Schuller

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Speech Analysis ◽

Current State

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Training a neural network to learn other dimensionality reduction removes data size restrictions in bioinformatics and provides a new route to exploring data representations

10.1101/2020.09.03.269555 ◽

2020 ◽

Cited By ~ 1

Author(s):

Alex Dexter ◽

Spencer A. Thomas ◽

Rory T. Steven ◽

Kenneth N. Robinson ◽

Adam J. Taylor ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Dimensionality Reduction ◽

Computational Analysis ◽

New Technologies ◽

State Of The Art ◽

Current State ◽

Data Representations ◽

Non Linear ◽

Linear Dimensionality Reduction

AbstractHigh dimensionality omics and hyperspectral imaging datasets present difficult challenges for feature extraction and data mining due to huge numbers of features that cannot be simultaneously examined. The sample numbers and variables of these methods are constantly growing as new technologies are developed, and computational analysis needs to evolve to keep up with growing demand. Current state of the art algorithms can handle some routine datasets but struggle when datasets grow above a certain size. We present a training deep learning via neural networks on non-linear dimensionality reduction, in particular t-distributed stochastic neighbour embedding (t-SNE), to overcome prior limitations of these methods.One Sentence SummaryAnalysis of prohibitively large datasets by combining deep learning via neural networks with non-linear dimensionality reduction.

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Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series

Remote Sensing ◽

10.3390/rs13224599 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4599

Author(s):

Félix Quinton ◽

Loic Landrieu

Keyword(s):

Time Series ◽

Deep Learning ◽

Crop Rotation ◽

Large Scale ◽

State Of The Art ◽

Crop Rotations ◽

Learning Approach ◽

Type Mapping ◽

Current State ◽

Crop Type

While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated satellite data to propose to model simultaneously the inter- and intra-annual agricultural dynamics of yearly parcel classification with a deep learning approach. Along with simple training adjustments, our model provides an improvement of over 6.3% mIoU over the current state-of-the-art of crop classification, and a reduction of over 21% of the error rate. Furthermore, we release the first large-scale multi-year agricultural dataset with over 300,000 annotated parcels.

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Mobile-PolypNet : Light-weight Colon Polyp Segmentation Network for Low Resources Settings

10.21203/rs.3.rs-1175121/v1 ◽

2021 ◽

Author(s):

Ranit Karmakar ◽

Saeid Nooshabadi

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Colorectal Polyp ◽

P Value ◽

Colon Polyps ◽

Current State ◽

Model Size ◽

Ablation Study ◽

Small Clump ◽

Deep Learning Model

Abstract Colon polyps, small clump of cells on the lining of the colon can lead to Colorectal cancer (CRC), one of the leading types of cancer globally. Hence, early detection of these polyps is crucial in the prevention of CRC. This paper proposes a lightweight deep learning model for colorectal polyp segmentation that achieved state-of-the-art accuracy while significantly reducing the model size and complexity. The proposed deep learning autoencoder model employs a set of state-of-the-art architectural blocks and optimization objective functions to achieve the desired efficiency. The model is trained and tested on five publicly available colorectal polyp segmentation datasets (CVC-ClinicDB, CVC-ColonDB, EndoScene, Kvasir, and ETIS). We also performed ablation testing on the model to test various aspects of the autoencoder architecture. We performed the model evaluation using most of the common image segmentation metrics. The backbone model achieved a dice score of 0.935 on the Kvasir dataset and 0.945 on the CVC-ClinicDB dataset improving the accuracy by 4.12% and 5.12% respectively over the current state-of-the-art network, while using 88 times fewer parameters, 40 times less storage space, and being computationally 17 times more efficient. Our ablation study showed that the addition of ConvSkip in the autoencoder slightly improves the model’s performance but it was not significant (p-value=0.815).

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COVID19 detection from Radiographs: Is Deep Learning able to handle the crisis?

10.36227/techrxiv.12476426 ◽

2020 ◽

Author(s):

Muhammad Saqib ◽

Saeed Anwar ◽

Abbas Anwar ◽

Lars petersson ◽

Michael Blumenstein

Keyword(s):

Deep Learning ◽

Viral Infection ◽

Viral Infections ◽

State Of The Art ◽

World Health ◽

Screening Tests ◽

Early Screening ◽

Current State ◽

Infection Types ◽

Health Organization

The COVID-19 is a highly contagious viral infection which played havoc on everyone's life in many different ways. According to the world health organization and scientists, more testing potentially helps governments and disease control organizations in containing the spread of the virus. The use of chest radiographs is one of the early screening tests to determine the onset of disease, as the infection affects the lungs severely. This study will investigate and automate the process of testing by using state-of-the-art CNN classifiers to detect the COVID19 infection. However, the viral could of many different types; therefore, we only regard for COVID19 while the other viral infection types are treated as non-COVID19 in the radiographs of various viral infections. The classification task is challenging due to the limited number of scans available for COVID19 and the minute variations in the viral infections. We aim to employ current state-of-the-art CNN architectures, compare their results, and determine whether deep learning algorithms can handle the crisis appropriately. All trained models are available at https://github.com/saeed-anwar/COVID19-Baselines

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Bayesian U-Net: Estimating Uncertainty in Semantic Segmentation of Earth Observation Images

Remote Sensing ◽

10.3390/rs13193836 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3836

Author(s):

Clément Dechesne ◽

Pierre Lassalle ◽

Sébastien Lefèvre

Keyword(s):

Deep Learning ◽

Satellite Images ◽

State Of The Art ◽

Qualitative Evaluation ◽

Semantic Segmentation ◽

High Accuracy ◽

Earth Observation ◽

Current State ◽

Learning Techniques ◽

Reference Databases

In recent years, numerous deep learning techniques have been proposed to tackle the semantic segmentation of aerial and satellite images, increase trust in the leaderboards of main scientific contests and represent the current state-of-the-art. Nevertheless, despite their promising results, these state-of-the-art techniques are still unable to provide results with the level of accuracy sought in real applications, i.e., in operational settings. Thus, it is mandatory to qualify these segmentation results and estimate the uncertainty brought about by a deep network. In this work, we address uncertainty estimations in semantic segmentation. To do this, we relied on a Bayesian deep learning method, based on Monte Carlo Dropout, which allows us to derive uncertainty metrics along with the semantic segmentation. Built on the most widespread U-Net architecture, our model achieves semantic segmentation with high accuracy on several state-of-the-art datasets. More importantly, uncertainty maps are also derived from our model. While they allow for the performance of a sounder qualitative evaluation of the segmentation results, they also include valuable information to improve the reference databases.

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A Deep Learning Based Approach for Localization and Recognition of Pakistani Vehicle License Plates

Sensors ◽

10.3390/s21227696 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7696

Author(s):

Umair Yousaf ◽

Ahmad Khan ◽

Hazrat Ali ◽

Fiaz Gul Khan ◽

Zia ur Rehman ◽

...

Keyword(s):

Deep Learning ◽

State Of The Art ◽

The Other ◽

License Plate ◽

Standard Size ◽

Current State ◽

Bounding Box ◽

Proposed Model ◽

Plate Area

License plate localization is the process of finding the license plate area and drawing a bounding box around it, while recognition is the process of identifying the text within the bounding box. The current state-of-the-art license plate localization and recognition approaches require license plates of standard size, style, fonts, and colors. Unfortunately, in Pakistan, license plates are non-standard and vary in terms of the characteristics mentioned above. This paper presents a deep-learning-based approach to localize and recognize Pakistani license plates with non-uniform and non-standardized sizes, fonts, and styles. We developed a new Pakistani license plate dataset (PLPD) to train and evaluate the proposed model. We conducted extensive experiments to compare the accuracy of the proposed approach with existing techniques. The results show that the proposed method outperformed the other methods to localize and recognize non-standard license plates.

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