A Novel Integrative Approach for Non-coding RNA Classification Based on Deep Learning

Background: Molecular biomarkers show new ways to understand many disease processes. Noncoding RNAs as biomarkers play a crucial role in several cellular activities, which are highly correlated to many human diseases especially cancer. The classification and the identification of ncRNAs have become a critical issue due to their application, such as biomarkers in many human diseases. Objective: Most existing computational tools for ncRNA classification are mainly used for classifying only one type of ncRNA. They are based on structural information or specific known features. Furthermore, these tools suffer from a lack of significant and validated features. Therefore, the performance of these methods is not always satisfactory. Methods: We propose a novel approach named imCnC for ncRNA classification based on multisource deep learning, which integrates several data sources such as genomic and epigenomic data to identify several ncRNA types. Also, we propose an optimization technique to visualize the extracted features pattern from the multisource CNN model to measure the epigenomics features of each ncRNA type. Results: The computational results using a dataset of 16 human ncRNA classes downloaded from RFAM show that imCnC outperforms the existing tools. Indeed, imCnC achieved an accuracy of 94,18%. In addition, our method enables to discover new ncRNA features using an optimization technique to measure and visualize the features pattern of the imCnC classifier.

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Deep Learning through Convolutional Neural Networks for Classification of Image A Novel Approach Using Hyper Filter

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i6.164168 ◽

2019 ◽

Vol 7 (6) ◽

pp. 164-168

Author(s):

Kshitij Tripathi ◽

Rajendra G. Vyas ◽

Anil K. Gupta

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Novel Approach

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A Novel Approach based on PSO Optimized K-Means in MRI Brain Image Segmentation

10.36227/techrxiv.12310100 ◽

2020 ◽

Author(s):

Abhisha Mano

Keyword(s):

Image Segmentation ◽

Optimization Technique ◽

Brain Image ◽

Mri Brain ◽

Novel Approach ◽

Mri Brain Image ◽

Brain Image Segmentation

A brain image segmentation by an optimization technique.<br>

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Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review

Protein and Peptide Letters ◽

10.2174/0929866526666191025104043 ◽

2020 ◽

Vol 27 (5) ◽

pp. 385-391

Author(s):

Lin Zhong ◽

Zhong Ming ◽

Guobo Xie ◽

Chunlong Fan ◽

Xue Piao

Keyword(s):

Supervised Learning ◽

Protein Interactions ◽

Computational Models ◽

Prediction Models ◽

Chromatin Modification ◽

Computational Prediction ◽

Human Diseases ◽

Future Research ◽

Non Coding Rna ◽

Long Non Coding Rna

: In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

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Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽

10.3390/s21061962 ◽

2021 ◽

Vol 21 (6) ◽

pp. 1962

Author(s):

Enrico Buratto ◽

Adriano Simonetto ◽

Gianluca Agresti ◽

Henrik Schäfer ◽

Pietro Zanuttigh

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Light Response ◽

Real Data ◽

Depth Image ◽

Learning Approach ◽

Multiple Reflections ◽

Noisy Input ◽

Novel Approach ◽

Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

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Identification of the conserved long non-coding RNAs in myogenesis

BMC Genomics ◽

10.1186/s12864-021-07615-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Anupam Bhattacharya ◽

Simang Champramary ◽

Tanya Tripathi ◽

Debajit Thakur ◽

Ilya Ioshikhes ◽

...

Keyword(s):

Gene Regulation ◽

Muscle Development ◽

Three Dimensional ◽

C2c12 Cells ◽

Mouse Muscle ◽

Rna Molecules ◽

Expression Of Genes ◽

Novel Approach ◽

Non Coding Rna ◽

Topologically Associated Domains

Abstract Background Our understanding of genome regulation is ever-evolving with the continuous discovery of new modes of gene regulation, and transcriptomic studies of mammalian genomes have revealed the presence of a considerable population of non-coding RNA molecules among the transcripts expressed. One such non-coding RNA molecule is long non-coding RNA (lncRNA). However, the function of lncRNAs in gene regulation is not well understood; moreover, finding conserved lncRNA across species is a challenging task. Therefore, we propose a novel approach to identify conserved lncRNAs and functionally annotate these molecules. Results In this study, we exploited existing myogenic transcriptome data and identified conserved lncRNAs in mice and humans. We identified the lncRNAs expressing differentially between the early and later stages of muscle development. Differential expression of these lncRNAs was confirmed experimentally in cultured mouse muscle C2C12 cells. We utilized the three-dimensional architecture of the genome and identified topologically associated domains for these lncRNAs. Additionally, we correlated the expression of genes in domains for functional annotation of these trans-lncRNAs in myogenesis. Using this approach, we identified conserved lncRNAs in myogenesis and functionally annotated them. Conclusions With this novel approach, we identified the conserved lncRNAs in myogenesis in humans and mice and functionally annotated them. The method identified a large number of lncRNAs are involved in myogenesis. Further studies are required to investigate the reason for the conservation of the lncRNAs in human and mouse while their sequences are dissimilar. Our approach can be used to identify novel lncRNAs conserved in different species and functionally annotated them.

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Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices

Sensors ◽

10.3390/s21082834 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2834

Author(s):

Billur Kazaz ◽

Subhadipto Poddar ◽

Saeed Arabi ◽

Michael A. Perez ◽

Anuj Sharma ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Pollution Prevention ◽

Aerial Images ◽

Unmanned Aerial Systems ◽

Construction Sites ◽

State Regulations ◽

Novel Approach ◽

Model Training ◽

Aerial Systems

Construction activities typically create large amounts of ground disturbance, which can lead to increased rates of soil erosion. Construction stormwater practices are used on active jobsites to protect downstream waterbodies from offsite sediment transport. Federal and state regulations require routine pollution prevention inspections to ensure that temporary stormwater practices are in place and performing as intended. This study addresses the existing challenges and limitations in the construction stormwater inspections and presents a unique approach for performing unmanned aerial system (UAS)-based inspections. Deep learning-based object detection principles were applied to identify and locate practices installed on active construction sites. The system integrates a post-processing stage by clustering results. The developed framework consists of data preparation with aerial inspections, model training, validation of the model, and testing for accuracy. The developed model was created from 800 aerial images and was used to detect four different types of construction stormwater practices at 100% accuracy on the Mean Average Precision (MAP) with minimal false positive detections. Results indicate that object detection could be implemented on UAS-acquired imagery as a novel approach to construction stormwater inspections and provide accurate results for site plan comparisons by rapidly detecting the quantity and location of field-installed stormwater practices.

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A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

Scientific Reports ◽

10.1038/s41598-021-85254-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Carlo Donadio ◽

Massimo Brescia ◽

Alessia Riccardo ◽

Giuseppe Angora ◽

Michele Delli Veneri ◽

...

Keyword(s):

Deep Learning ◽

Morphological Variations ◽

Control Factors ◽

Novel Approach ◽

Drainage Networks ◽

Key Characteristics ◽

Intrinsic Complexity ◽

Solar System Bodies ◽

Manual Extraction

AbstractSeveral approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields.

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Compression of Phase-Only Holograms with JPEG Standard and Deep Learning

Applied Sciences ◽

10.3390/app8081258 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1258 ◽

Cited By ~ 16

Author(s):

Shuming Jiao ◽

Zhi Jin ◽

Chenliang Chang ◽

Changyuan Zhou ◽

Wenbin Zou ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

High Frequency ◽

Critical Issue ◽

Compression Process ◽

Compression Scheme ◽

Quality Degradation ◽

Digital Format ◽

Enormous Amount ◽

Frequency Features

It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format. In photograph compression, the JPEG standard is commonly supported by almost every system and device. It will be favorable if JPEG standard is applicable to hologram compression, with advantages of universal compatibility. However, the reconstructed image from a JPEG compressed hologram suffers from severe quality degradation since some high frequency features in the hologram will be lost during the compression process. In this work, we employ a deep convolutional neural network to reduce the artifacts in a JPEG compressed hologram. Simulation and experimental results reveal that our proposed “JPEG + deep learning” hologram compression scheme can achieve satisfactory reconstruction results for a computer-generated phase-only hologram after compression.

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