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

Genes ◽  
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.

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

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ying Li ◽  
Hang Sun ◽  
Shiyao Feng ◽  
Qi Zhang ◽  
Siyu Han ◽  
...  
Keyword(s):  
Protein Interactions ◽  
State Of The Art ◽  
Recognition Performance ◽  
Feature Learning ◽  
Biological Processes ◽  
Multimodal Features ◽  
Learning Architectures ◽  
Motif Information ◽  
Experimental Comparisons ◽  
Better Than

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.

Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions

2019 ◽  
Vol 26 (8) ◽  
pp. 601-619 ◽  
Author(s):  
Amit Sagar ◽  
Bin Xue
Keyword(s):  
Machine Learning ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Experimental Methods ◽  
Machine Learning Techniques ◽  
Computational Techniques ◽  
Biological Processes ◽  
Complete Spectrum ◽  
Future Developments ◽  
Learning Techniques

The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.

scholarly journals SeDAR: Reading Floorplans Like a Human—Using Deep Learning to Enable Human-Inspired Localisation

2019 ◽  
Vol 128 (5) ◽  
pp. 1286-1310 ◽  
Author(s):  
Oscar Mendez ◽  
Simon Hadfield ◽  
Nicolas Pugeault ◽  
Richard Bowden
Keyword(s):  
Deep Learning ◽  
Semantic Information ◽  
State Of The Art ◽  
Depth Information ◽  
Semantic Maps ◽  
Novel Method ◽  
Rgb Images ◽  
High Level ◽  
Robotic Tasks ◽  
And Robotics

Abstract The use of human-level semantic information to aid robotic tasks has recently become an important area for both Computer Vision and Robotics. This has been enabled by advances in Deep Learning that allow consistent and robust semantic understanding. Leveraging this semantic vision of the world has allowed human-level understanding to naturally emerge from many different approaches. Particularly, the use of semantic information to aid in localisation and reconstruction has been at the forefront of both fields. Like robots, humans also require the ability to localise within a structure. To aid this, humans have designed high-level semantic maps of our structures called floorplans. We are extremely good at localising in them, even with limited access to the depth information used by robots. This is because we focus on the distribution of semantic elements, rather than geometric ones. Evidence of this is that humans are normally able to localise in a floorplan that has not been scaled properly. In order to grant this ability to robots, it is necessary to use localisation approaches that leverage the same semantic information humans use. In this paper, we present a novel method for semantically enabled global localisation. Our approach relies on the semantic labels present in the floorplan. Deep Learning is leveraged to extract semantic labels from RGB images, which are compared to the floorplan for localisation. While our approach is able to use range measurements if available, we demonstrate that they are unnecessary as we can achieve results comparable to state-of-the-art without them.

DISCOVERING PROTEIN–PROTEIN INTERACTIONS

2004 ◽  
Vol 01 (04) ◽  
pp. 711-741 ◽  
Author(s):  
SEE-KIONG NG ◽  
SOON-HENG TAN
Keyword(s):  
Protein Interactions ◽  
Experimental Methods ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
Comprehensive Description ◽  
New Genes ◽  
Genomics And Proteomics ◽  
Living Organisms

The ongoing genomics and proteomics efforts have helped identify many new genes and proteins in living organisms. However, simply knowing the existence of genes and proteins does not tell us much about the biological processes in which they participate. Many major biological processes are controlled by protein interaction networks. A comprehensive description of protein–protein interactions is therefore necessary to understand the genetic program of life. In this tutorial, we provide an overview of the various current high-throughput methods for discovering protein–protein interactions, covering both the conventional experimental methods and new computational approaches.

scholarly journals Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation

2021 ◽  
Author(s):  
Gaode Chen ◽  
Xinghua Zhang ◽  
Yanyan Zhao ◽  
Cong Xue ◽  
Ji Xiang
Keyword(s):  
Real World ◽  
Information Overload ◽  
State Of The Art ◽  
Recommendation Systems ◽  
Time Interval ◽  
Interest Representation ◽  
Novel Method ◽  
Real World Datasets ◽  
Item Representation ◽  
Global And Local

Sequential recommendation systems alleviate the problem of information overload, and have attracted increasing attention in the literature. Most prior works usually obtain an overall representation based on the user’s behavior sequence, which can not sufficiently reflect the multiple interests of the user. To this end, we propose a novel method called PIMI to mitigate this issue. PIMI can model the user’s multi-interest representation effectively by considering both the periodicity and interactivity in the item sequence. Specifically, we design a periodicity-aware module to utilize the time interval information between user’s behaviors. Meanwhile, an ingenious graph is proposed to enhance the interactivity between items in user’s behavior sequence, which can capture both global and local item features. Finally, a multi-interest extraction module is applied to describe user’s multiple interests based on the obtained item representation. Extensive experiments on two real-world datasets Amazon and Taobao show that PIMI outperforms state-of-the-art methods consistently.

scholarly journals Global and Local Attention-Based Free-Form Image Inpainting

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3204
Author(s):  
S. M. Nadim Uddin ◽  
Yong Ju Jung
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Image Inpainting ◽  
Experimental Results ◽  
Free Form ◽  
Local Similarity ◽  
Global And Local ◽  
Similarity Information

Deep-learning-based image inpainting methods have shown significant promise in both rectangular and irregular holes. However, the inpainting of irregular holes presents numerous challenges owing to uncertainties in their shapes and locations. When depending solely on convolutional neural network (CNN) or adversarial supervision, plausible inpainting results cannot be guaranteed because irregular holes need attention-based guidance for retrieving information for content generation. In this paper, we propose two new attention mechanisms, namely a mask pruning-based global attention module and a global and local attention module to obtain global dependency information and the local similarity information among the features for refined results. The proposed method is evaluated using state-of-the-art methods, and the experimental results show that our method outperforms the existing methods in both quantitative and qualitative measures.

scholarly journals DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning

Cells ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1756 ◽  
Author(s):  
Abdul Wahab ◽  
Omid Mahmoudi ◽  
Jeehong Kim ◽  
Kil To Chong
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
State Of The Art ◽  
Critical Role ◽  
Regulation Of Gene Expression ◽  
The State ◽  
Superior Performance ◽  
Training Dataset ◽  
Conformation Stability ◽  
Deep Learning Model

N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic performance such as protein interactions, conformation, stability in DNA as well as the regulation of gene expression same cell developmental and genomic imprinting. Some different 4mC site identifiers have been proposed for various species. Herein, we proposed a computational model, DNC4mC-Deep, including six encoding techniques plus a deep learning model to predict 4mC sites in the genome of F. vesca, R. chinensis, and Cross-species dataset. It was demonstrated by the 10-fold cross-validation test to get superior performance. The DNC4mC-Deep obtained 0.829 and 0.929 of MCC on F. vesca and R. chinensis training dataset, respectively, and 0.814 on cross-species. This means the proposed method outperforms the state-of-the-art predictors at least 0.284 and 0.265 on F. vesca and R. chinensis training dataset in turn. Furthermore, the DNC4mC-Deep achieved 0.635 and 0.565 of MCC on F. vesca and R. chinensis independent dataset, respectively, and 0.562 on cross-species which shows it can achieve the best performance to predict 4mC sites as compared to the state-of-the-art predictor.

scholarly journals Opportunities and obstacles for deep learning in biology and medicine

2018 ◽  
Vol 15 (141) ◽  
pp. 20170387 ◽  
Author(s):  
Travers Ching ◽  
Daniel S. Himmelstein ◽  
Brett K. Beaulieu-Jones ◽  
Alexandr A. Kalinin ◽  
Brian T. Do ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Extensive Literature ◽  
Patient Classification ◽  
Biological Processes ◽  
Health Records ◽  
Learning Techniques ◽  
Privacy Constraints ◽  
Prior State

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

scholarly journals LABEL-EFFICIENT DEEP LEARNING-BASED SEMANTIC SEGMENTATION OF BUILDING POINT CLOUDS AT LOD3 LEVEL

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 449-456
Author(s):  
Y. Cao ◽  
M. Scaioni
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Training Data ◽  
Second Step ◽  
Dynamic Graph ◽  
Input Point ◽  
Supervised Methods ◽  
Global And Local

Abstract. In recent research, fully supervised Deep Learning (DL) techniques and large amounts of pointwise labels are employed to train a segmentation network to be applied to buildings’ point clouds. However, fine-labelled buildings’ point clouds are hard to find and manually annotating pointwise labels is time-consuming and expensive. Consequently, the application of fully supervised DL for semantic segmentation of buildings’ point clouds at LoD3 level is severely limited. To address this issue, we propose a novel label-efficient DL network that obtains per-point semantic labels of LoD3 buildings’ point clouds with limited supervision. In general, it consists of two steps. The first step (Autoencoder – AE) is composed of a Dynamic Graph Convolutional Neural Network-based encoder and a folding-based decoder, designed to extract discriminative global and local features from input point clouds by reconstructing them without any label. The second step is semantic segmentation. By supplying a small amount of task-specific supervision, a segmentation network is proposed for semantically segmenting the encoded features acquired from the pre-trained AE. Experimentally, we evaluate our approach based on the ArCH dataset. Compared to the fully supervised DL methods, we find that our model achieved state-of-the-art results on the unseen scenes, with only 10% of labelled training data from fully supervised methods as input.

CoCoNet: an efficient deep learning tool for viral metagenome binning

2021 ◽  
Author(s):  
Cédric G Arisdakessian ◽  
Olivia D Nigro ◽  
Grieg F Steward ◽  
Guylaine Poisson ◽  
Mahdi Belcaid
Keyword(s):  
Deep Learning ◽  
Viral Genome ◽  
High Performance ◽  
Source Code ◽  
Supplementary Information ◽  
Biological Processes ◽  
Bacterial Genomes ◽  
Large Dataset ◽  
Sequence Composition ◽  
Rigorous Framework

Abstract Motivation Metagenomic approaches hold the potential to characterize microbial communities and unravel the intricate link between the microbiome and biological processes. Assembly is one of the most critical steps in metagenomics experiments. It consists of transforming overlapping DNA sequencing reads into sufficiently accurate representations of the community’s genomes. This process is computationally difficult and commonly results in genomes fragmented across many contigs. Computational binning methods are used to mitigate fragmentation by partitioning contigs based on their sequence composition, abundance or chromosome organization into bins representing the community’s genomes. Existing binning methods have been principally tuned for bacterial genomes and do not perform favorably on viral metagenomes. Results We propose Composition and Coverage Network (CoCoNet), a new binning method for viral metagenomes that leverages the flexibility and the effectiveness of deep learning to model the co-occurrence of contigs belonging to the same viral genome and provide a rigorous framework for binning viral contigs. Our results show that CoCoNet substantially outperforms existing binning methods on viral datasets. Availability and implementation CoCoNet was implemented in Python and is available for download on PyPi (https://pypi.org/). The source code is hosted on GitHub at https://github.com/Puumanamana/CoCoNet and the documentation is available at https://coconet.readthedocs.io/en/latest/index.html. CoCoNet does not require extensive resources to run. For example, binning 100k contigs took about 4 h on 10 Intel CPU Cores (2.4 GHz), with a memory peak at 27 GB (see Supplementary Fig. S9). To process a large dataset, CoCoNet may need to be run on a high RAM capacity server. Such servers are typically available in high-performance or cloud computing settings. Supplementary information Supplementary data are available at Bioinformatics online.

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