DeepANF: A deep attentive neural framework with distributed representation for chromatin accessibility prediction

Guo, Yanbu; Zhou, Dongming; Nie, Rencan; Ruan, Xiaoli; Li, Weihua

doi:10.1016/j.neucom.2019.10.091

Cited by 21 publications

(11 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result of the limited dataset, latest ML methods such as deep learning (DL) were not explored in this study owing to their requirement for large datasets. As more data become available, we shall explore DL methods such as convolutional and recurrent neural networks [54,55]. Nevertheless, conventional machine learning methods are still attractive in this domain given their relative simplicity, cheaper cost, and usefulness for data modeling [56].…”

Section: Discussionmentioning

confidence: 99%

Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines

Eke

Jammeh

et al. 2021

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

The successful development of amyloid-based biomarkers and tests for Alzheimer's disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) > 80%, specificity (SP) > 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using nonamyloid based biomarkers.

show abstract

Section: Discussionmentioning

confidence: 99%

Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines

Eke

Jammeh

et al. 2021

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

show abstract

“…For example, Vaswani et al ( 2017 ) deployed the attention mechanism in the proposed model named Transformer without the operations of convolutions and recurrences, and led a trend of applying Transformer in different fields of machine learning, such as, computer vision, natural language processing. Guo et al ( 2020 ) proposed DeepANF for the prediction of chromatin accessibility by attention mechanism, gated recurrent units and convolutions, learning the deep representations of DNA sequences.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

A Deep Non-negative Matrix Factorization Model for Big Data Representation Learning

et al. 2021

View full text Add to dashboard Cite

Nowadays, deep representations have been attracting much attention owing to the great performance in various tasks. However, the interpretability of deep representations poses a vast challenge on real-world applications. To alleviate the challenge, a deep matrix factorization method with non-negative constraints is proposed to learn deep part-based representations of interpretability for big data in this paper. Specifically, a deep architecture with a supervisor network suppressing noise in data and a student network learning deep representations of interpretability is designed, which is an end-to-end framework for pattern mining. Furthermore, to train the deep matrix factorization architecture, an interpretability loss is defined, including a symmetric loss, an apposition loss, and a non-negative constraint loss, which can ensure the knowledge transfer from the supervisor network to the student network, enhancing the robustness of deep representations. Finally, extensive experimental results on two benchmark datasets demonstrate the superiority of the deep matrix factorization method.

show abstract

“…With the development of deep learning in recent years, a series of fusion algorithms based on deep learning have been proposed and also obtained remarkable results in computer image processing [19] and biomedicine [20]. Moreover, variousdeep learning-based methods have shown their own unique theories and advantages for image fusion.…”

Section: Related Workmentioning

confidence: 99%

UFA-FUSE: A novel deep supervised and hybrid model for multi-focus image fusion

Zang,

Zhou,

Wang

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Traditional and deep learning-based fusion methods generated the intermediate decision map to obtain the fusion image through a series of post-processing procedures. However, the fusion results generated by these methods are easy to lose some source image details or results in artifacts. Inspired by the image reconstruction techniques based on deep learning, we propose a multi-focus image fusion network framework without any post-processing to solve these problems in the end-to-end and supervised learning way. To sufficiently train the fusion model, we have generated a large-scale multi-focus image dataset with ground-truth fusion images. What's more, to obtain a more informative fusion image, we further designed a novel fusion strategy based on unity fusion attention, which is composed of a channel attention module and a spatial attention module. Specifically, the proposed fusion approach mainly comprises three key components: feature extraction, feature fusion and image reconstruction. We firstly utilize seven convolutional blocks to extract the image features from source images. Then, the extracted convolutional features are fused by the proposed fusion strategy in the feature fusion layer. Finally, the fused image features are reconstructed by four convolutional blocks. Experimental results demonstrate that the proposed approach for multi-focus image fusion achieves remarkable fusion performance compared to 19 state-of-the-art fusion methods.

show abstract

DeepANF: A deep attentive neural framework with distributed representation for chromatin accessibility prediction

Cited by 21 publications

References 28 publications

Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines

Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines

A Deep Non-negative Matrix Factorization Model for Big Data Representation Learning

UFA-FUSE: A novel deep supervised and hybrid model for multi-focus image fusion

Contact Info

Product

Resources

About