Ensemble Deep Learning Based on Multi-level Information Enhancement and Greedy Fuzzy Decision for Plant miRNA–lncRNA Interaction Prediction

Abstract Motivation The studies have indicated that not only microRNAs (miRNAs) or long non-coding RNAs (lncRNAs) play important roles in biological activities, but also their interactions affect the biological process. A growing number of studies focus on the miRNA–lncRNA interactions, while few of them are proposed for plant. The prediction of interactions is significant for understanding the mechanism of interaction between miRNA and lncRNA in plant. Results This article proposes a new method for fulfilling plant miRNA–lncRNA interaction prediction (PmliPred). The deep learning model and shallow machine learning model are trained using raw sequence and manually extracted features, respectively. Then they are hybridized based on fuzzy decision for prediction. PmliPred shows better performance and generalization ability compared with the existing methods. Several new miRNA–lncRNA interactions in Solanum lycopersicum are successfully identified using quantitative real time–polymerase chain reaction from the candidates predicted by PmliPred, which further verifies its effectiveness. Availability and implementation The source code of PmliPred is freely available at http://bis.zju.edu.cn/PmliPred/. Supplementary information Supplementary data are available at Bioinformatics online.

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A deep-learning framework for multi-level peptide–protein interaction prediction

Nature Communications ◽

10.1038/s41467-021-25772-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Yipin Lei ◽

Shuya Li ◽

Ziyi Liu ◽

Fangping Wan ◽

Tingzhong Tian ◽

...

Keyword(s):

Deep Learning ◽

Protein Interaction ◽

Protein Interactions ◽

Comprehensive Evaluation ◽

Interaction Prediction ◽

Learning Framework ◽

Binding Residue ◽

Protein Interaction Prediction ◽

Binding Residues ◽

Multi Level

AbstractPeptide-protein interactions are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. Recently, a number of computational methods have been developed to predict peptide-protein interactions. However, most of the existing prediction approaches heavily depend on high-resolution structure data. Here, we present a deep learning framework for multi-level peptide-protein interaction prediction, called CAMP, including binary peptide-protein interaction prediction and corresponding peptide binding residue identification. Comprehensive evaluation demonstrated that CAMP can successfully capture the binary interactions between peptides and proteins and identify the binding residues along the peptides involved in the interactions. In addition, CAMP outperformed other state-of-the-art methods on binary peptide-protein interaction prediction. CAMP can serve as a useful tool in peptide-protein interaction prediction and identification of important binding residues in the peptides, which can thus facilitate the peptide drug discovery process.

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Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666191113122840 ◽

2020 ◽

Vol 16 (8) ◽

pp. 1004-1021

Author(s):

Xiaoqi Lu ◽

Yu Gu ◽

Lidong Yang ◽

Baohua Zhang ◽

Ying Zhao ◽

...

Keyword(s):

False Positive ◽

Contextual Information ◽

Lung Nodule ◽

Positive Sample ◽

Lung Nodules ◽

Lung Nodule Detection ◽

Nodule Detection ◽

Level Information ◽

Multi Level ◽

Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

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Unsupervised Multi-Level Feature Extraction for Improvement of Hyperspectral Classification

Remote Sensing ◽

10.3390/rs13081602 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1602

Author(s):

Qiaoqiao Sun ◽

Xuefeng Liu ◽

Salah Bourennane

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Spatial Information ◽

Hyperspectral Data ◽

Great Promise ◽

Learning Models ◽

Single Level ◽

Multiple Networks ◽

Multi Level ◽

Hyperspectral Classification

Deep learning models have strong abilities in learning features and they have been successfully applied in hyperspectral images (HSIs). However, the training of most deep learning models requires labeled samples and the collection of labeled samples are labor-consuming in HSI. In addition, single-level features from a single layer are usually considered, which may result in the loss of some important information. Using multiple networks to obtain multi-level features is a solution, but at the cost of longer training time and computational complexity. To solve these problems, a novel unsupervised multi-level feature extraction framework that is based on a three dimensional convolutional autoencoder (3D-CAE) is proposed in this paper. The designed 3D-CAE is stacked by fully 3D convolutional layers and 3D deconvolutional layers, which allows for the spectral-spatial information of targets to be mined simultaneously. Besides, the 3D-CAE can be trained in an unsupervised way without involving labeled samples. Moreover, the multi-level features are directly obtained from the encoded layers with different scales and resolutions, which is more efficient than using multiple networks to get them. The effectiveness of the proposed multi-level features is verified on two hyperspectral data sets. The results demonstrate that the proposed method has great promise in unsupervised feature learning and can help us to further improve the hyperspectral classification when compared with single-level features.

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Multi-level Amplified Iterative Training of Semi-Supervision Deep Learning For Glaucoma Diagnosis

10.1109/bibm52615.2021.9669788 ◽

2021 ◽

Author(s):

Yu Tang ◽

Gang Yang ◽

Dayong Ding ◽

Gangwei Cheng

Keyword(s):

Deep Learning ◽

Glaucoma Diagnosis ◽

Multi Level

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Channel state information–based multi-level fingerprinting for indoor localization with deep learning

International Journal of Distributed Sensor Networks ◽

10.1177/1550147718806719 ◽

2018 ◽

Vol 14 (10) ◽

pp. 155014771880671 ◽

Cited By ~ 2

Author(s):

Tao Li ◽

Hai Wang ◽

Yuan Shao ◽

Qiang Niu

Keyword(s):

Deep Learning ◽

Channel State Information ◽

Deep Neural Networks ◽

Training Phase ◽

Channel State ◽

Positioning Accuracy ◽

State Information ◽

Filtering Method ◽

Multi Level ◽

Better Than

With the rapid growth of indoor positioning requirements without equipment and the convenience of channel state information acquisition, the research on indoor fingerprint positioning based on channel state information is increasingly valued. In this article, a multi-level fingerprinting approach is proposed, which is composed of two-level methods: the first layer is achieved by deep learning and the second layer is implemented by the optimal subcarriers filtering method. This method using channel state information is termed multi-level fingerprinting with deep learning. Deep neural networks are applied in the deep learning of the first layer of multi-level fingerprinting with deep learning, which includes two phases: an offline training phase and an online localization phase. In the offline training phase, deep neural networks are used to train the optimal weights. In the online localization phase, the top five closest positions to the location position are obtained through forward propagation. The second layer optimizes the results of the first layer through the optimal subcarriers filtering method. Under the accuracy of 0.6 m, the positioning accuracy of two common environments has reached, respectively, 96% and 93.9%. The evaluation results show that the positioning accuracy of this method is better than the method based on received signal strength, and it is better than the support vector machine method, which is also slightly improved compared with the deep learning method.

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Boosting compound-protein interaction prediction by deep learning

Methods ◽

10.1016/j.ymeth.2016.06.024 ◽

2016 ◽

Vol 110 ◽

pp. 64-72 ◽

Cited By ~ 65

Author(s):

Kai Tian ◽

Mingyu Shao ◽

Yang Wang ◽

Jihong Guan ◽

Shuigeng Zhou

Keyword(s):

Deep Learning ◽

Protein Interaction ◽

Interaction Prediction ◽

Protein Interaction Prediction

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A deep learning and softmax regression fault diagnosis method for multi-level converter

2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) ◽

10.1109/demped.2017.8062370 ◽

2017 ◽

Cited By ~ 2

Author(s):

Bin Xin ◽

Tianzhen Wang ◽

Tianhao Tang

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Softmax Regression ◽

Diagnosis Method ◽

Multi Level ◽

Level Converter

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GroningenNet: Deep Learning for Low-Magnitude Earthquake Detection on a Multi-Level Sensor Network

Sensors ◽

10.3390/s21238080 ◽

2021 ◽

Vol 21 (23) ◽

pp. 8080

Author(s):

Ahmed Shaheen ◽

Umair bin Waheed ◽

Michael Fehler ◽

Lubos Sokol ◽

Sherif Hanafy

Keyword(s):

Deep Learning ◽

Template Matching ◽

Carbon Capture And Storage ◽

Microseismic Monitoring ◽

Seismic Events ◽

Learning Approaches ◽

Monitoring Networks ◽

Level Sensor ◽

Multi Level ◽

Low Magnitude

Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe. The detection of low-magnitude seismic events is essential for microseismic monitoring of hydraulic fracturing, carbon capture and storage, and geothermal operations for hazard detection and mitigation. Moreover, the detection of micro-earthquakes is crucial to understanding the underlying mechanisms of larger earthquakes. Various algorithms, including deep learning methods, have been proposed over the years to detect such low-magnitude events. However, there is still a need for improving the robustness of these methods in discriminating between local sources of noise and weak seismic events. In this study, we propose a convolutional neural network (CNN) to detect seismic events from shallow borehole stations in Groningen, the Netherlands. We train a CNN model to detect low-magnitude earthquakes, harnessing the multi-level sensor configuration of the G-network in Groningen. Each G-network station consists of four geophones at depths of 50, 100, 150, and 200 m. Unlike prior deep learning approaches that use 3-component seismic records only at a single sensor level, we use records from the entire borehole as one training example. This allows us to train the CNN model using moveout patterns of the energy traveling across the borehole sensors to discriminate between events originating in the subsurface and local noise arriving from the surface. We compare the prediction accuracy of our trained CNN model to that of the STA/LTA and template matching algorithms on a two-month continuous record. We demonstrate that the CNN model shows significantly better performance than STA/LTA and template matching in detecting new events missing from the catalog and minimizing false detections. Moreover, we find that using the moveout feature allows us to effectively train our CNN model using only a fraction of the data that would be needed otherwise, saving plenty of manual labor in preparing training labels. The proposed approach can be easily applied to other microseismic monitoring networks with multi-level sensors.

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Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learning techniques

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v22.i1.pp187-195 ◽

2021 ◽

Vol 22 (1) ◽

pp. 187

Author(s):

Arjun Benagatte Channegowda ◽

H N Prakash

Keyword(s):

Deep Learning ◽

Feature Fusion ◽

Research Work ◽

Recognition Rate ◽

Recognition System ◽

Multimodal Biometrics ◽

Histograms Of Oriented Gradients ◽

Multi Level ◽

Deep Learning Neural Network ◽

Hidden Neurons

Providing security in biometrics is the major challenging task in the current situation. A lot of research work is going on in this area. Security can be more tightened by using complex security systems, like by using more than one biometric trait for recognition. In this paper multimodal biometric models are developed to improve the recognition rate of a person. The combination of physiological and behavioral biometrics characteristics is used in this work. Fingerprint and signature biometrics characteristics are used to develop a multimodal recognition system. Histograms of oriented gradients (HOG) features are extracted from biometric traits and for these feature fusions are applied at two levels. Features of fingerprint and signatures are fused using concatenation, sum, max, min, and product rule at multilevel stages, these features are used to train deep learning neural network model. In the proposed work, multi-level feature fusion for multimodal biometrics with a deep learning classifier is used and results are analyzed by a varying number of hidden neurons and hidden layers. Experiments are carried out on SDUMLA-HMT, machine learning and data mining lab, Shandong University fingerprint datasets, and MCYT signature biometric recognition group datasets, and encouraging results were obtained.

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