Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network

Abstract Background Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA–protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs. Results We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement. Conclusions This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver (http://csbg-jlu.site/lpc/predict) is developed to be convenient for users.

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LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning

Genes ◽

10.3390/genes12111689 ◽

2021 ◽

Vol 12 (11) ◽

pp. 1689

Author(s):

Lan Huang ◽

Shaoqing Jiao ◽

Sen Yang ◽

Shuangquan Zhang ◽

Xiaopeng Zhu ◽

...

Keyword(s):

Fourier Transform ◽

Deep Learning ◽

Protein Interactions ◽

Noncoding Rna ◽

State Of The Art ◽

Experimental Methods ◽

Biological Processes ◽

Sequence Composition ◽

Novel Method ◽

Global And Local

Long noncoding RNA (lncRNA) plays a crucial role in many critical biological processes and participates in complex human diseases through interaction with proteins. Considering that identifying lncRNA–protein interactions through experimental methods is expensive and time-consuming, we propose a novel method based on deep learning that combines raw sequence composition features and hand-designed features, called LGFC-CNN, to predict lncRNA–protein interactions. The two sequence preprocessing methods and CNN modules (GloCNN and LocCNN) are utilized to extract the raw sequence global and local features. Meanwhile, we select hand-designed features by comparing the predictive effect of different lncRNA and protein features combinations. Furthermore, we obtain the structure features and unifying the dimensions through Fourier transform. In the end, the four types of features are integrated to comprehensively predict the lncRNA–protein interactions. Compared with other state-of-the-art methods on three lncRNA–protein interaction datasets, LGFC-CNN achieves the best performance with an accuracy of 94.14%, on RPI21850; an accuracy of 92.94%, on RPI7317; and an accuracy of 98.19% on RPI1847. The results show that our LGFC-CNN can effectively predict the lncRNA–protein interactions by combining raw sequence composition features and hand-designed features.

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BRWSP: Predicting circRNA-Disease Associations Based on Biased Random Walk to Search Paths on a Multiple Heterogeneous Network

Complexity ◽

10.1155/2019/5938035 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Xiujuan Lei ◽

Wenxiang Zhang

Keyword(s):

Random Walk ◽

Heterogeneous Network ◽

State Of The Art ◽

Circular Rnas ◽

Biological Processes ◽

Biased Random Walk ◽

Random Walk Algorithm ◽

Disease Associations ◽

Walk Algorithm ◽

Better Than

The circular RNAs (circRNAs) have significant effects on a variety of biological processes, the dysfunction of which is closely related to the emergence and development of diseases. Therefore, identification of circRNA-disease associations will contribute to analysing the pathogenesis of diseases. Here, we present a computational model called BRWSP to predict circRNA-disease associations, which searches paths on a multiple heterogeneous network based on biased random walk. Firstly, BRWSP constructs a multiple heterogeneous network by using circRNAs, diseases, and genes. Then, the biased random walk algorithm runs on the multiple heterogeneous network to search paths between circRNAs and diseases. Finally, the performance of BRWSP is significantly better than the state-of-the-art algorithms. Furthermore, BRWSP further contributes to the discovery of novel circRNA-disease associations.

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GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009655 ◽

2021 ◽

Vol 17 (12) ◽

pp. e1009655

Author(s):

Lei Li ◽

Yu-Tian Wang ◽

Cun-Mei Ji ◽

Chun-Hou Zheng ◽

Jian-Cheng Ni ◽

...

Keyword(s):

Cross Validation ◽

State Of The Art ◽

Human Diseases ◽

Biological Processes ◽

Proposed Model ◽

Disease Associations ◽

Convolutional Autoencoder ◽

Non Coding Rnas ◽

Novel Model ◽

Better Than

microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related miRNAs as potential biomarkers, which could greatly contribute to understanding the mechanisms of complex diseases and benefit the prevention, detection, diagnosis and treatment of extraordinary diseases. In this study, we presented a novel model named Graph Convolutional Autoencoder for miRNA-Disease Association Prediction (GCAEMDA). In the proposed model, we utilized miRNA-miRNA similarities, disease-disease similarities and verified miRNA-disease associations to construct a heterogeneous network, which is applied to learn the embeddings of miRNAs and diseases. In addition, we separately constructed miRNA-based and disease-based sub-networks. Combining the embeddings of miRNAs and diseases, graph convolution autoencoder (GCAE) is utilized to calculate association scores of miRNA-disease on two sub-networks, respectively. Furthermore, we obtained final prediction scores between miRNAs and diseases by adopting an average ensemble way to integrate the prediction scores from two types of subnetworks. To indicate the accuracy of GCAEMDA, we applied different cross validation methods to evaluate our model whose performance were better than the state-of-the-art models. Case studies on a common human diseases were also implemented to prove the effectiveness of GCAEMDA. The results demonstrated that GCAEMDA were beneficial to infer potential associations of miRNA-disease.

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Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers

Plants ◽

10.3390/plants9101319 ◽

2020 ◽

Vol 9 (10) ◽

pp. 1319

Author(s):

Muhammad Hammad Saleem ◽

Johan Potgieter ◽

Khalid Mahmood Arif

Keyword(s):

Deep Learning ◽

Comparative Evaluation ◽

Plant Disease ◽

Performance Metrics ◽

State Of The Art ◽

Disease Classification ◽

Agricultural Applications ◽

Learning Architectures ◽

Better Than

Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes.

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An Integrated Prediction Method for Identifying Protein-Protein Interactions

Current Proteomics ◽

10.2174/1570164616666190306152318 ◽

2020 ◽

Vol 17 (4) ◽

pp. 271-286

Author(s):

Chang Xu ◽

Limin Jiang ◽

Zehua Zhang ◽

Xuyao Yu ◽

Renhai Chen ◽

...

Keyword(s):

Protein Interactions ◽

Feature Vector ◽

Prediction Method ◽

Feature Representation ◽

Protein Interaction Networks ◽

Learning Approach ◽

Biological Processes ◽

Integrated Learning ◽

Protein Protein Interactions ◽

Training Process

Background: Protein-Protein Interactions (PPIs) play a key role in various biological processes. Many methods have been developed to predict protein-protein interactions and protein interaction networks. However, many existing applications are limited, because of relying on a large number of homology proteins and interaction marks. Methods: In this paper, we propose a novel integrated learning approach (RF-Ada-DF) with the sequence-based feature representation, for identifying protein-protein interactions. Our method firstly constructs a sequence-based feature vector to represent each pair of proteins, viaMultivariate Mutual Information (MMI) and Normalized Moreau-Broto Autocorrelation (NMBAC). Then, we feed the 638- dimentional features into an integrated learning model for judging interaction pairs and non-interaction pairs. Furthermore, this integrated model embeds Random Forest in AdaBoost framework and turns weak classifiers into a single strong classifier. Meanwhile, we also employ double fault detection in order to suppress over-adaptation during the training process. Results: To evaluate the performance of our method, we conduct several comprehensive tests for PPIs prediction. On the H. pyloridataset, our method achieves 88.16% accuracy and 87.68% sensitivity, the accuracy of our method is increased by 0.57%. On the S. cerevisiaedataset, our method achieves 95.77% accuracy and 93.36% sensitivity, the accuracy of our method is increased by 0.76%. On the Humandataset, our method achieves 98.16% accuracy and 96.80% sensitivity, the accuracy of our method is increased by 0.6%. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. The datasets and codes are available at https://github.com/guofei-tju/RF-Ada-DF.git.

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In vivo interactome profiling by enzyme‐catalyzed proximity labeling

Cell & Bioscience ◽

10.1186/s13578-021-00542-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yangfan Xu ◽

Xianqun Fan ◽

Yang Hu

Keyword(s):

State Of The Art ◽

Catalytic Efficiency ◽

Biological Processes ◽

Protein Protein Interaction ◽

Current State ◽

Protein Interactome ◽

Potential Applications ◽

Protein Protein Interaction Networks ◽

Temporal And Spatial

AbstractEnzyme-catalyzed proximity labeling (PL) combined with mass spectrometry (MS) has emerged as a revolutionary approach to reveal the protein-protein interaction networks, dissect complex biological processes, and characterize the subcellular proteome in a more physiological setting than before. The enzymatic tags are being upgraded to improve temporal and spatial resolution and obtain faster catalytic dynamics and higher catalytic efficiency. In vivo application of PL integrated with other state of the art techniques has recently been adapted in live animals and plants, allowing questions to be addressed that were previously inaccessible. It is timely to summarize the current state of PL-dependent interactome studies and their potential applications. We will focus on in vivo uses of newer versions of PL and highlight critical considerations for successful in vivo PL experiments that will provide novel insights into the protein interactome in the context of human diseases.

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A new approach based on graph matching and evolutionary approach for sport scheduling problem

Intelligent Decision Technologies ◽

10.3233/idt-190114 ◽

2020 ◽

pp. 1-16

Author(s):

Meriem Khelifa ◽

Dalila Boughaci ◽

Esma Aïmeur

Keyword(s):

Graph Matching ◽

State Of The Art ◽

Travel Cost ◽

Round Robin ◽

New Approach ◽

Traveling Tournament Problem ◽

Significant Interest ◽

National League ◽

Better Than

The Traveling Tournament Problem (TTP) is concerned with finding a double round-robin tournament schedule that minimizes the total distances traveled by the teams. It has attracted significant interest recently since a favorable TTP schedule can result in significant savings for the league. This paper proposes an original evolutionary algorithm for TTP. We first propose a quick and effective constructive algorithm to construct a Double Round Robin Tournament (DRRT) schedule with low travel cost. We then describe an enhanced genetic algorithm with a new crossover operator to improve the travel cost of the generated schedules. A new heuristic for ordering efficiently the scheduled rounds is also proposed. The latter leads to significant enhancement in the quality of the schedules. The overall method is evaluated on publicly available standard benchmarks and compared with other techniques for TTP and UTTP (Unconstrained Traveling Tournament Problem). The computational experiment shows that the proposed approach could build very good solutions comparable to other state-of-the-art approaches or better than the current best solutions on UTTP. Further, our method provides new valuable solutions to some unsolved UTTP instances and outperforms prior methods for all US National League (NL) instances.

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Fighting Together against the Pandemic: Learning Multiple Models on Tomography Images for COVID-19 Diagnosis

AI ◽

10.3390/ai2020016 ◽

2021 ◽

Vol 2 (2) ◽

pp. 261-273

Author(s):

Mario Manzo ◽

Simone Pellino

Keyword(s):

Network Architecture ◽

State Of The Art ◽

Ensemble Classification ◽

Effective Vaccine ◽

Rt Pcr ◽

Neural Network Architecture ◽

Experimental Phase ◽

Different Types ◽

Polymerase Chain ◽

Better Than

COVID-19 has been a great challenge for humanity since the year 2020. The whole world has made a huge effort to find an effective vaccine in order to save those not yet infected. The alternative solution is early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) tests or thorax Computer Tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for image analysis. They optimize the classification design task, which is essential for an automatic approach with different types of images, including medical. In this paper, we adopt a pretrained deep convolutional neural network architecture in order to diagnose COVID-19 disease from CT images. Our idea is inspired by what the whole of humanity is achieving, as the set of multiple contributions is better than any single one for the fight against the pandemic. First, we adapt, and subsequently retrain for our assumption, some neural architectures that have been adopted in other application domains. Secondly, we combine the knowledge extracted from images by the neural architectures in an ensemble classification context. Our experimental phase is performed on a CT image dataset, and the results obtained show the effectiveness of the proposed approach with respect to the state-of-the-art competitors.

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Cache-efficient sweeping-based interval joins for extended Allen relation predicates

The VLDB Journal ◽

10.1007/s00778-020-00650-5 ◽

2021 ◽

Author(s):

Danila Piatov ◽

Sven Helmer ◽

Anton Dignös ◽

Fabio Persia

Keyword(s):

Data Structure ◽

Experimental Evaluation ◽

State Of The Art ◽

Temporal Databases ◽

Access Method ◽

Wide Range ◽

Interval Relation ◽

Cache Efficient ◽

Join Algorithms ◽

Better Than

AbstractWe develop a family of efficient plane-sweeping interval join algorithms for evaluating a wide range of interval predicates such as Allen’s relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-of-the-art techniques, while being much better suited for real-time event processing.

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Video Frame Interpolation via Deformable Separable Convolution

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6634 ◽

2020 ◽

Vol 34 (07) ◽

pp. 10607-10614 ◽

Cited By ~ 2

Author(s):

Xianhang Cheng ◽

Zhenzhong Chen

Keyword(s):

State Of The Art ◽

Video Frame ◽

Kernel Size ◽

Frame Interpolation ◽

Interpolation Methods ◽

Video Frames ◽

Convolution Process ◽

Strong Performance ◽

Existing Frames ◽

Better Than

Learning to synthesize non-existing frames from the original consecutive video frames is a challenging task. Recent kernel-based interpolation methods predict pixels with a single convolution process to replace the dependency of optical flow. However, when scene motion is larger than the pre-defined kernel size, these methods yield poor results even though they take thousands of neighboring pixels into account. To solve this problem in this paper, we propose to use deformable separable convolution (DSepConv) to adaptively estimate kernels, offsets and masks to allow the network to obtain information with much fewer but more relevant pixels. In addition, we show that the kernel-based methods and conventional flow-based methods are specific instances of the proposed DSepConv. Experimental results demonstrate that our method significantly outperforms the other kernel-based interpolation methods and shows strong performance on par or even better than the state-of-the-art algorithms both qualitatively and quantitatively.

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