Fast and accurate microRNA search using CNN

Abstract Background There are many different types of microRNAs (miRNAs) and elucidating their functions is still under intensive research. A fundamental step in functional annotation of a new miRNA is to classify it into characterized miRNA families, such as those in Rfam and miRBase. With the accumulation of annotated miRNAs, it becomes possible to use deep learning-based models to classify different types of miRNAs. In this work, we investigate several key issues associated with successful application of deep learning models for miRNA classification. First, as secondary structure conservation is a prominent feature for noncoding RNAs including miRNAs, we examine whether secondary structure-based encoding improves classification accuracy. Second, as there are many more non-miRNA sequences than miRNAs, instead of assigning a negative class for all non-miRNA sequences, we test whether using softmax output can distinguish in-distribution and out-of-distribution samples. Finally, we investigate whether deep learning models can correctly classify sequences from small miRNA families. Results We present our trained convolutional neural network (CNN) models for classifying miRNAs using different types of feature learning and encoding methods. In the first method, we explicitly encode the predicted secondary structure in a matrix. In the second method, we use only the primary sequence information and one-hot encoding matrix. In addition, in order to reject sequences that should not be classified into targeted miRNA families, we use a threshold derived from softmax layer to exclude out-of-distribution sequences, which is an important feature to make this model useful for real transcriptomic data. The comparison with the state-of-the-art ncRNA classification tools such as Infernal shows that our method can achieve comparable sensitivity and accuracy while being significantly faster. Conclusion Automatic feature learning in CNN can lead to better classification accuracy and sensitivity for miRNA classification and annotation. The trained models and also associated codes are freely available at https://github.com/HubertTang/DeepMir.

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MUFFIN: multi-scale feature fusion for drug–drug interaction prediction

Bioinformatics ◽

10.1093/bioinformatics/btab169 ◽

2021 ◽

Author(s):

Yujie Chen ◽

Tengfei Ma ◽

Xixi Yang ◽

Jianmin Wang ◽

Bosheng Song ◽

...

Keyword(s):

Molecular Structure ◽

Deep Learning ◽

Medical Information ◽

Feature Fusion ◽

Molecular Graph ◽

Knowledge Graph ◽

Sequence Information ◽

Learning Models ◽

Scale Feature ◽

Multi Scale

Abstract Motivation Adverse drug–drug interactions (DDIs) are crucial for drug research and mainly cause morbidity and mortality. Thus, the identification of potential DDIs is essential for doctors, patients and the society. Existing traditional machine learning models rely heavily on handcraft features and lack generalization. Recently, the deep learning approaches that can automatically learn drug features from the molecular graph or drug-related network have improved the ability of computational models to predict unknown DDIs. However, previous works utilized large labeled data and merely considered the structure or sequence information of drugs without considering the relations or topological information between drug and other biomedical objects (e.g. gene, disease and pathway), or considered knowledge graph (KG) without considering the information from the drug molecular structure. Results Accordingly, to effectively explore the joint effect of drug molecular structure and semantic information of drugs in knowledge graph for DDI prediction, we propose a multi-scale feature fusion deep learning model named MUFFIN. MUFFIN can jointly learn the drug representation based on both the drug-self structure information and the KG with rich bio-medical information. In MUFFIN, we designed a bi-level cross strategy that includes cross- and scalar-level components to fuse multi-modal features well. MUFFIN can alleviate the restriction of limited labeled data on deep learning models by crossing the features learned from large-scale KG and drug molecular graph. We evaluated our approach on three datasets and three different tasks including binary-class, multi-class and multi-label DDI prediction tasks. The results showed that MUFFIN outperformed other state-of-the-art baselines. Availability and implementation The source code and data are available at https://github.com/xzenglab/MUFFIN.

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Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

Electronics ◽

10.3390/electronics9030445 ◽

2020 ◽

Vol 9 (3) ◽

pp. 445 ◽

Cited By ~ 9

Author(s):

Laith Alzubaidi ◽

Omran Al-Shamma ◽

Mohammed A. Fadhel ◽

Laith Farhan ◽

Jinglan Zhang ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Transfer Learning ◽

Cancer Diagnosis ◽

Classification Accuracy ◽

Cancer Classification ◽

Learning Models ◽

Proposed Model ◽

Domain Transfer ◽

Domain Transfer Learning

Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a reduction in the breast cancer death rate. With the help of a computer-aided diagnosis system, the efficiency increased, and the cost was reduced for the cancer diagnosis. Traditional breast cancer classification techniques are based on handcrafted features techniques, and their performance relies upon the chosen features. They also are very sensitive to different sizes and complex shapes. However, histopathological breast cancer images are very complex in shape. Currently, deep learning models have become an alternative solution for diagnosis, and have overcome the drawbacks of classical classification techniques. Although deep learning has performed well in various tasks of computer vision and pattern recognition, it still has some challenges. One of the main challenges is the lack of training data. To address this challenge and optimize the performance, we have utilized a transfer learning technique which is where the deep learning models train on a task, and then fine-tune the models for another task. We have employed transfer learning in two ways: Training our proposed model first on the same domain dataset, then on the target dataset, and training our model on a different domain dataset, then on the target dataset. We have empirically proven that the same domain transfer learning optimized the performance. Our hybrid model of parallel convolutional layers and residual links is utilized to classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue. To reduce the effect of overfitting, we have augmented the images with different image processing techniques. The proposed model achieved state-of-the-art performance, and it outperformed the latest methods by achieving a patch-wise classification accuracy of 90.5%, and an image-wise classification accuracy of 97.4% on the validation set. Moreover, we have achieved an image-wise classification accuracy of 96.1% on the test set of the microscopy ICIAR-2018 dataset.

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Fusion of Deep Learning Models for Improving Classification Accuracy of Remote Sensing Images

JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES ◽

10.26782/jmcms.2019.10.00015 ◽

2019 ◽

Vol 14 (5) ◽

Author(s):

P Deepan

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Classification Accuracy ◽

Learning Models ◽

Remote Sensing Images

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LSTM-ANN & BiLSTM-ANN: Hybrid deep learning models for enhanced classification accuracy

Procedia Computer Science ◽

10.1016/j.procs.2021.10.013 ◽

2021 ◽

Vol 193 ◽

pp. 131-140

Author(s):

Md. Kowsher ◽

Anik Tahabilder ◽

Md. Zahidul Islam Sanjid ◽

Nusrat Jahan Prottasha ◽

Md. Shihab Uddin ◽

...

Keyword(s):

Deep Learning ◽

Classification Accuracy ◽

Learning Models

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OneHotEncoding and LSTM based Deep Learning Models for Protein Secondary Structure Prediction

10.22541/au.159739202.25582842 ◽

2020 ◽

Author(s):

Vamsidhar Enireddy ◽

C Karthikeyan

Keyword(s):

Deep Learning ◽

Secondary Structure ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Learning Models ◽

Protein Secondary Structure Prediction

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Land Cover/Land Use Mapping of LISS IV Imagery Using Object-Based Convolutional Neural Network with Deep Features

Journal of the Indian Society of Remote Sensing ◽

10.1007/s12524-019-01064-9 ◽

2019 ◽

Vol 48 (1) ◽

pp. 145-154

Author(s):

S. Rajesh ◽

T. Gladima Nisia ◽

S. Arivazhagan ◽

R. Abisekaraj

Keyword(s):

Deep Learning ◽

Classification Accuracy ◽

Urban Areas ◽

Satellite Images ◽

Feature Learning ◽

Remotely Sensed ◽

Learning Method ◽

Deep Feature ◽

Object Based ◽

Remotely Sensed Images

Abstract The paper proposes a new method for classifying the LISS IV satellite images using deep learning method. Deep learning method is to automatically extract many features without any human intervention. The classification accuracy through deep learning is still improved by including object-based segmentation. The object-based deep feature learning method using CNN is used to accurately classify the remotely sensed images. The method is designed with the technique of extracting the deep features and using it for object-based classification. The proposed system extracts deep features using pre-defined filter values, thus increasing the overall performance of the process compared to randomly initialized filter values. The object-based classification method can preserve edge information in complex satellite images. To improve the classification accuracy and to reduce complexity, object-based deep learning technique is used. The proposed object-based deep learning approach is used to drastically increase the classification accuracy. Here, the remotely sensed images were used to classify the urban areas of Ahmadabad and Madurai cities. Experimental results show a better performance with the object-based classification.

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Marginal Deep Architecture: Stacking Feature Learning Modules to Build Deep Learning Models

IEEE Access ◽

10.1109/access.2019.2902631 ◽

2019 ◽

Vol 7 ◽

pp. 30220-30233 ◽

Cited By ~ 3

Author(s):

Guoqiang Zhong ◽

Kang Zhang ◽

Hongxu Wei ◽

Yuchen Zheng ◽

Junyu Dong

Keyword(s):

Deep Learning ◽

Feature Learning ◽

Learning Models ◽

Learning Modules ◽

Deep Architecture

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Comparative Analysis of Three Improved Deep Learning Architectures for Music Genre Classification

International Journal of Information Technology and Computer Science ◽

10.5815/ijitcs.2021.02.01 ◽

2021 ◽

Vol 13 (2) ◽

pp. 1-14

Author(s):

Quazi Ghulam Rafi ◽

◽

Mohammed Noman ◽

Sadia Zahin Prodhan ◽

Sabrina Alam ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Comparative Analysis ◽

Recurrent Neural Network ◽

Classification Accuracy ◽

Neural Net ◽

Learning Models ◽

Genre Classification ◽

Music Genre ◽

Music Genre Classification

Among the many music information retrieval (MIR) tasks, music genre classification is noteworthy. The categorization of music into different groups that came to existence through a complex interplay of cultures, musicians, and various market forces to characterize similarities between compositions and organize collections is known as a music genre. The past researchers extracted various hand-crafted features and developed classifiers based on them. But the major drawback of this approach was the requirement of field expertise. However, in recent times researchers, because of the remarkable classification accuracy of deep learning models, have used similar models for MIR tasks. Convolutional Neural Net- work (CNN), Recurrent Neural Network (RNN), and the hybrid model, Convolutional - Recurrent Neural Network (CRNN), are such prominently used deep learning models for music genre classification along with other MIR tasks and various architectures of these models have achieved state-of-the-art results. In this study, we review and discuss three such architectures of deep learning models, already used for music genre classification of music tracks of length of 29-30 seconds. In particular, we analyze improved CNN, RNN, and CRNN architectures named Bottom-up Broadcast Neural Network (BBNN) [1], Independent Recurrent Neural Network (IndRNN) [2] and CRNN in Time and Frequency dimensions (CRNN- TF) [3] respectively, almost all of the architectures achieved the highest classification accuracy among the variants of their base deep learning model. Hence, this study holds a comparative analysis of the three most impressive architectural variants of the main deep learning models that are prominently used to classify music genre and presents the three architecture, hence the models (CNN, RNN, and CRNN) in one study. We also propose two ways that can improve the performances of the RNN (IndRNN) and CRNN (CRNN-TF) architectures.

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