SpineNet-6mA: A Novel Deep Learning Tool for Predicting DNA N6-Methyladenine Sites in Genomes

Abbas, Zeeshan; Tayara, Hilal; Chong, Kil To

doi:10.1109/access.2020.3036090

Cited by 43 publications

(35 citation statements)

References 42 publications

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“…All our networks were very prone to over tting due to the limited amount of data that was available to train with. To reduce over tting, we've also employed Data Augmentation [20],[6], [21], [22] to arti cially increase the size of the database by using label-preserving image transformations [14]. Data Augmentation works on the principle of mathematical transformations such as scaling, rotation, translation, cropping, ipping the image on both the horizontal and the vertical axis, off-center randomized zoom.…”

Section: Methodsmentioning

confidence: 99%

“…Transfer Learning is an investigating technique utilized in Machine Learning to use data learnt in previous problems by applying it in related existing problems. This concept that vies for the cross-utilization of knowledge to tackle related, novel problems is inspired from the intrinsic ability of the human brain to transfer knowledge across different tasks [21]. Transfer Learning is touted to be the next big driver of the professional success of Machine Learning by many eminent individuals in the deep learning community [10], [11], [18].…”

Section: Learning With the Training Wheels Onmentioning

confidence: 99%

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Image Classification using ImageNet Classifiers in Environments with Limited Data

Sharma

Singh

Choudhury

et al. 2021

Preprint

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In this research, we compare and contrast various image classification algorithms and how effective they are in specific problem sets where data might be scarce such as prediction of rare phenomena (for example, natural calamities), enterprise solutions etc. We have employed various state-of-the-art algorithms in this study credited to have been some of the best classifiers at the time of their inception. These classifiers have also been suspected to fall prey to overfitting on the datasets they were initially tested on viz. ImageNet and Common Objects in Context (COCO); we test to what extent these classifiers tend to generalize to the new data provided by us in a transfer learning framework. We utilize transfer learning on the ImageNet classifiers to adapt to our smaller dataset and examine various techniques such as data augmentation, batch normalization, dropout etc. to mitigate overfitting. All the classifiers follow a standard fully connected architecture. The end result should provide the reader with an overall analysis of which algorithm or approach to use in conditions where data might be limited while also giving a brief overview of the progress of image classification algorithms since their advent. We also provide an analysis on the effectiveness of data augmentation in limited datasets by providing results achieved with and without utilizing data augmentation. In our case, we found the MobileNet (with its lightweight nature contributing to low computational costs) and InceptionV3 (owing to its lower training time) to be the best performing classifiers for applying transfer learning to limited datasets out of the classifiers we have used for our study. This paper aims to establish preemptive standards that can be used to evaluate the models which can be used in object recognition, and image classification for problems containing limited amounts of data.

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Section: Methodsmentioning

confidence: 99%

Section: Learning With the Training Wheels Onmentioning

confidence: 99%

Image Classification using ImageNet Classifiers in Environments with Limited Data

Sharma

Singh

Choudhury

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…These models have accomplished outstanding results in different fields, generally because of the ongoing improvement of convolutional neural networks. CNNs achieved record-breaking results in medical image processing [ 29 , 30 ] and in computational biology [ 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. Moreover, there are several remarkable examples of the use of CNNs to produce a prediction system that can identify the effects of genetic variation.…”

Section: Proposed Methodologymentioning

confidence: 99%

pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters

Shujaat

Wahab

Tayara

et al. 2020

Genes

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A promoter is a small region within the DNA structure that has an important role in initiating transcription of a specific gene in the genome. Different types of promoters are recognized by their different functions. Due to the importance of promoter functions, computational tools for the prediction and classification of a promoter are highly desired. Promoters resemble each other; therefore, their precise classification is an important challenge. In this study, we propose a convolutional neural network (CNN)-based tool, the pcPromoter-CNN, for application in the prediction of promotors and their classification into subclasses σ70, σ54, σ38, σ32, σ28 and σ24. This CNN-based tool uses a one-hot encoding scheme for promoter classification. The tools architecture was trained and tested on a benchmark dataset. To evaluate its classification performance, we used four evaluation metrics. The model exhibited notable improvement over that of existing state-of-the-art tools.

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“…In the past few years, various deep learning models for computer vision tasks have been proposed, such as VGGNet [ 13 ], ResNet [ 14 ], and DenseNet [ 15 ]. Deep Neural Networks have the strong ability to automatically extract the discriminant features; therefore, they are widely used in the field of medical imaging and bioinformatics [ 16 , 17 , 18 , 19 , 20 ]. Similarly, the use of deep learning for computer-aided diagnosis of brain tumor has gathered extensive attention.…”

Section: Related Workmentioning

confidence: 99%

BrainSeg-Net: Brain Tumor MR Image Segmentation via Enhanced Encoder–Decoder Network

Rehman

Cho

Kim³

et al. 2021

Diagnostics

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Efficient segmentation of Magnetic Resonance (MR) brain tumor images is of the utmost value for the diagnosis of tumor region. In recent years, advancement in the field of neural networks has been used to refine the segmentation performance of brain tumor sub-regions. The brain tumor segmentation has proven to be a complicated task even for neural networks, due to the small-scale tumor regions. These small-scale tumor regions are unable to be identified, the reason being their tiny size and the huge difference between area occupancy by different tumor classes. In previous state-of-the-art neural network models, the biggest problem was that the location information along with spatial details gets lost in deeper layers. To address these problems, we have proposed an encoder–decoder based model named BrainSeg-Net. The Feature Enhancer (FE) block is incorporated into the BrainSeg-Net architecture which extracts the middle-level features from low-level features from the shallow layers and shares them with the dense layers. This feature aggregation helps to achieve better performance of tumor identification. To address the problem associated with imbalance class, we have used a custom-designed loss function. For evaluation of BrainSeg-Net architecture, three benchmark datasets are utilized: BraTS2017, BraTS 2018, and BraTS 2019. Segmentation of Enhancing Core (EC), Whole Tumor (WT), and Tumor Core (TC) is carried out. The proposed architecture have exhibited good improvement when compared with existing baseline and state-of-the-art techniques. The MR brain tumor segmentation by BrainSeg-Net uses enhanced location and spatial features, which performs better than the existing plethora of brain MR image segmentation approaches.

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SpineNet-6mA: A Novel Deep Learning Tool for Predicting DNA N6-Methyladenine Sites in Genomes

Cited by 43 publications

References 42 publications

Image Classification using ImageNet Classifiers in Environments with Limited Data

Image Classification using ImageNet Classifiers in Environments with Limited Data

pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters

BrainSeg-Net: Brain Tumor MR Image Segmentation via Enhanced Encoder–Decoder Network

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