MSF-PFP: A Novel Multisource Feature Fusion Model for Protein Function Prediction.

Protein function prediction is essential for disease treatment and drug development; yet, traditional biological experimental methods are less efficient in annotating protein function, and existing automated methods fail to fully leverage protein multisource data. Here, we present MSF-PFP, a computational framework that fuses multisource data features to predict protein function with high accuracy. Our framework designs specific models for feature extraction based on the characteristics of various data sources, including a global-local-individual strategy for local location features. MSF-PFP then integrates extracted features through a multisource feature fusion model, ultimately categorizing protein functions. Experimental results demonstrate that MSF-PFP outperforms eight state-of-the-art models, achieving FMax scores of 0.542, 0.675, and 0.624 for the biological process (BP), molecular function (MF), and cellular component (CC), respectively. The source code and data set for MSF-PFP are available at https://swanhub.co/TianGua/MSF-PFP, facilitating further exploration and validation of the proposed framework. This study highlights the potential of multisource data fusion in enhancing protein function prediction, contributing to improved disease therapy and medication discovery strategies.

Devosia aquimaris sp. nov., isolated from seawater of the Changjiang River estuary of China.

A gram-stain-negative, aerobic, rod-shaped bacterium strain CJK-A8-3T was isolated from a polyamine-enriched seawater sample collected from the Changjiang River estuary of China. The colonies were white and circular. Strain CJK-A8-3T grew optimally at 35 °C, pH 7.0 and 1.5% NaCl. Its polar lipids contained phosphatidylglycerol, phosphatidic acid, unidentified glycolipids, and a combination of phospholipids and glycolipids. The respiratory quinone was ubiquinone-10, and its main fatty acids were C16:0, 11-methyl C18:1ω7c and Summed Feature 8 (including C18:1ω7c/C18:1ω6c). The phylogenetic tree based on 16S rRNA genes placed strain CJK-A8-3T in a new linage within the genus Devosia. 16S rRNA gene sequence of strain CJK-A8-3T showed identities of 98.50% with Devosia beringensis S02T, 98.15% with D. oryziradicis, and 98.01% with D. submarina JCM 18935T. The genome size of strain CJK-A8-3T was 3.81 Mb with the DNA G + C content 63.9%, higher than those of the reference strains (60.4-63.8%). The genome contained genes functional in the metabolism of terrigenous aromatic compounds, alkylphosphonate and organic nitrogen, potentially beneficial for nutrient acquirement and environmental remediation. It also harbored genes functional in antibiotics resistance and balance of osmotic pressure, enhancing their adaptation to estuarine environments. Both genomic investigation and experimental verification showed that strain CJK-A8-3T could be versatile and efficient to use diverse organic nitrogen compounds as carbon and nitrogen sources. Based on phenotypic, chemotaxonomic, phylogenetic and genomic characteristics, strain CJK-A8-3T was identified as a novel Devosia species, named as Devosia aquimaris sp. nov. The type strain is CJK-A8-3T (= MCCC 1K06953T = KCTC 92162T).

Membranihabitans maritimus sp. nov., a marine bacterium isolated from deep seawater.

A Gram-stain-negative, non-motile, rod-shaped bacterial strain, designated C281T, was isolated from seawater sampled at the Marshallese seamount chain. Results of 16S rRNA gene analysis revealed that strain C281T was most closely related to Membranihabitans marinus CZ-AZ5T with 92.7 % sequence similarity. Phylogenetic analysis indicated that the new isolate represented a novel species by forming a distinctive lineage within the family Saprospiraceae. The DNA G+C content of strain C281T was 38.4 mol%. The genome sizes of strain C281T and the reference strain M. marinus CZ-AZ5T were 5 962 917 and 5 395 999 bp, respectively. The average nucleotide identity and in silico DNA-DNA hybridization values between strains C281T and M. marinus CZ-AZ5T were found to be low (69.3 and 17.6 %, respectively). Different functional genes were found in the genome of strain C281T, such as CZC CBA, polysaccharide utilization loci and linear azol(in)e-containing peptide cluster coding genes. The NaCl range for growth was 0.5-15.0 %. Positive results were obtained for hydrolysis of Tween 60 and urease. MK-7 was the sole respiratory quinone. The major fatty acids were C16 : 1 ω6c and/or C16 : 1 ω7c, iso-C15 : 0 and iso-C15 : 1 F. The major polar lipids of strain C281T were phosphatidylethanolamine, phosphatidylglycerol, two unidentified lipids and five unidentified glycolipids. On the basis of its taxonomic characteristics, the isolate represents a novel species of the genus Membranihabitans, for which the name Membranihabitans maritimus sp. nov. (type strain C281T=KCTC 92171T=MCCC M27001T) is proposed.

Pansharpening Model of Transferable Remote Sensing Images Based on Feature Fusion and Attention Modules.

The purpose of the panchromatic sharpening of remote sensing images is to generate high-resolution multispectral images through software technology without increasing economic expenditure. The specific method is to fuse the spatial information of a high-resolution panchromatic image and the spectral information of a low-resolution multispectral image. This work proposes a novel model for generating high-quality multispectral images. This model uses the feature domain of the convolution neural network to fuse multispectral and panchromatic images so that the fused images can generate new features so that the final fused features can restore clear images. Because of the unique feature extraction ability of convolution neural networks, we use the core idea of convolution neural networks to extract global features. To extract the complementary features of the input image at a deeper level, we first designed two subnetworks with the same structure but different weights, and then used single-channel attention to optimize the fused features to improve the final fusion performance. We select the public data set widely used in this field to verify the validity of the model. The experimental results on the GaoFen-2 and SPOT6 data sets show that this method has a better effect in fusing multi-spectral and panchromatic images. Compared with the classical and the latest methods in this field, our model fusion obtained panchromatic sharpened images from both quantitative and qualitative analysis has achieved better results. In addition, to verify the transferability and generalization of our proposed model, we directly apply it to multispectral image sharpening, such as hyperspectral image sharpening. Experiments and tests have been carried out on Pavia Center and Botswana public hyperspectral data sets, and the results show that the model has also achieved good performance in hyperspectral data sets.

Predicting circRNA-drug sensitivity associations via graph attention auto-encoder.

BACKGROUND: Circular RNAs (circRNAs) play essential roles in cancer development and therapy resistance. Many studies have shown that circRNA is closely related to human health. The expression of circRNAs also affects the sensitivity of cells to drugs, thereby significantly affecting the efficacy of drugs. However, traditional biological experiments are time-consuming and expensive to validate drug-related circRNAs. Therefore, it is an important and urgent task to develop an effective computational method for predicting unknown circRNA-drug associations. RESULTS: In this work, we propose a computational framework (GATECDA) based on graph attention auto-encoder to predict circRNA-drug sensitivity associations. In GATECDA, we leverage multiple databases, containing the sequences of host genes of circRNAs, the structure of drugs, and circRNA-drug sensitivity associations. Based on the data, GATECDA employs Graph attention auto-encoder (GATE) to extract the low-dimensional representation of circRNA/drug, effectively retaining critical information in sparse high-dimensional features and realizing the effective fusion of nodes' neighborhood information. Experimental results indicate that GATECDA achieves an average AUC of 89.18% under 10-fold cross-validation. Case studies further show the excellent performance of GATECDA. CONCLUSIONS: Many experimental results and case studies show that our proposed GATECDA method can effectively predict the circRNA-drug sensitivity associations.

Graph Neural Network with Self-Supervised Learning for Noncoding RNA-Drug Resistance Association Prediction.

Noncoding RNA(ncRNA) is closely related to drug resistance. Identifying the association between ncRNA and drug resistance is of great significance for drug development. Methods based on biological experiments are often time-consuming and small-scale. Therefore, developing computational methods to distinguish the association between ncRNA and drug resistance is urgent. We develop a computational framework called GSLRDA to predict the association between ncRNA and drug resistance in this work. First, the known ncRNA-drug resistance associations are modeled as a bipartite graph of ncRNA and drug. Then, GSLRDA uses the light graph convolutional network (lightGCN) to learn the vector representation of ncRNA and drug from the ncRNA-drug bipartite graph. In addition, GSLRDA uses different data augmentation methods to generate different views for ncRNA and drug nodes and performs self-supervised learning, further improving the quality of learned ncRNA and drug vector representations through contrastive learning between nodes. Finally, GSLRDA uses the inner product to predict the association between ncRNA and drug resistance. To the best of our knowledge, GSLRDA is the first to apply self-supervised learning in association prediction tasks in the field of bioinformatics. The experimental results show that GSLRDA takes an AUC value of 0.9101, higher than the other eight state-of-the-art models. In addition, case studies including two drugs further illustrate the effectiveness of GSLRDA in predicting the association between ncRNA and drug resistance. The code and data sets of GSLRDA are available at https://github.com/JJZ-code/GSLRDA.

Aerial Video Trackers Review.

Target tracking technology that is based on aerial videos is widely used in many fields; however, this technology has challenges, such as image jitter, target blur, high data dimensionality, and large changes in the target scale. In this paper, the research status of aerial video tracking and the characteristics, background complexity and tracking diversity of aerial video targets are summarized. Based on the findings, the key technologies that are related to tracking are elaborated according to the target type, number of targets and applicable scene system. The tracking algorithms are classified according to the type of target, and the target tracking algorithms that are based on deep learning are classified according to the network structure. Commonly used aerial photography datasets are described, and the accuracies of commonly used target tracking methods are evaluated in an aerial photography dataset, namely, UAV123, and a long-video dataset, namely, UAV20L. Potential problems are discussed, and possible future research directions and corresponding development trends in this field are analyzed and summarized.

PASTFNet: a paralleled attention spatio-temporal fusion network for micro-expression recognition.

Micro-expressions (MEs) play such an important role in predicting a person's genuine emotions, as to make micro-expression recognition such an important resea rch focus in recent years. Most recent researchers have made efforts to recognize MEs with spatial and temporal information of video clips. However, because of their short duration and subtle intensity, capturing spatio-temporal features of micro-expressions remains challenging. To effectively promote the recognition performance, this paper presents a novel paralleled dual-branch attention-based spatio-temporal fusion network (PASTFNet). We jointly extract short- and long-range spatial relationships in spatial branch. Inspired by the composite architecture of the convolutional neural network (CNN) and long short-term memory (LSTM) for temporal modeling, we propose a novel attention-based multi-scale feature fusion network (AMFNet) to encode features of sequential frames, which can learn more expressive facial-detailed features for it implements the integrated use of attention and multi-scale feature fusion, then design an aggregation block to aggregate and acquire temporal features. At last, the features learned by the above two branches are fused to accomplish expression recognition with outstanding effect. Experiments on two MER datasets (CASMEII and SAMM) show that the PASTFNet model achieves promising ME recognition performance compared with other methods.

XGBCDA: a multiple heterogeneous networks-based method for predicting circRNA-disease associations.

BACKGROUND: Biological experiments have demonstrated that circRNA plays an essential role in various biological processes and human diseases. However, it is time-consuming and costly to merely conduct biological experiments to detect the association between circRNA and diseases. Accordingly, developing an efficient computational model to predict circRNA-disease associations is urgent. METHODS: In this research, we propose a multiple heterogeneous networks-based method, named XGBCDA, to predict circRNA-disease associations. The method first extracts original features, namely statistical features and graph theory features, from integrated circRNA similarity network, disease similarity network and circRNA-disease association network, and then sends these original features to the XGBoost classifier for training latent features. The method utilizes the tree learned by the XGBoost model, the index of leaf that instance finally falls into, and the 1 of K coding to represent the latent features. Finally, the method combines the latent features from the XGBoost with the original features to train the final model for predicting the association between the circRNA and diseases. RESULTS: The tenfold cross-validation results of the XGBCDA method illustrate that the area under the ROC curve reaches 0.9860. In addition, the method presents a striking performance in the case studies of colorectal cancer, gastric cancer and cervical cancer. CONCLUSION: With fabulous performance in predicting potential circRNA-disease associations, the XGBCDA method has the promising ability to assist biomedical researchers in terms of circRNA-disease association prediction.

Prediction of circRNA-miRNA Association using Singular Value Decomposition and Graph Neural Networks.

A large number of experimental studies have shown that circRNAs can act as molecular sponges of microRNAs, interacting with miRNAs to regulate gene expression levels, thereby affecting the development of human diseases. Exploring the potential associations between circRNAs and miRNAs can help understand complex disease mechanisms. Considering that biological experiments are time-consuming and labor-intensive, this study proposes a computational model using a graph neural network and singular value decomposition (CMASG) for circRNA-miRNA association prediction. Specifically, graph neural networks are used to learn nonlinear feature representations of nodes, followed by matrix factorization algorithms to learn linear feature representations of nodes, and then combined feature representations learned from different perspectives. Finally, the lightGBM algorithm was used for circRNA-miRNA association prediction. The proposed CMASG model achieved an AUC value of 0.8804. The experimental results demonstrate the superiority and effectiveness of the CMASG model in predicting circRNA-miRNA association tasks.

Parallel label propagation algorithm based on weight and random walk.

Community detection is a complex and meaningful process, which plays an important role in studying the characteristics of complex networks. In recent years, the discovery and analysis of community structures in complex networks has attracted the attention of many scholars, and many community discovery algorithms have been proposed. Many existing algorithms are only suitable for small-scale data, not for large-scale data, so it is necessary to establish a stable and efficient label propagation algorithm to deal with massive data and complex social networks. In this paper, we propose a novel label propagation algorithm, called WRWPLPA (Parallel Label Propagation Algorithm based on Weight and Random Walk). WRWPLPA proposes a new similarity calculation method combining weights and random walks. It uses weights and similarities to update labels in the process of label propagation, improving the accuracy and stability of community detection. First, weight is calculated by combining the neighborhood index and the position index, and the weight is used to distinguish the importance of the nodes in the network. Then, use random walk strategy to describe the similarity between nodes, and the label of nodes are updated by combining the weight and similarity. Finally, parallel propagation is comprehensively proposed to utilize label probability efficiently. Experiment results on artificial network datasets and real network datasets show that our algorithm has improved accuracy and stability compared with other label propagation algorithms.

Friend closeness based user matching cross social networks.

The typical aim of user matching is to detect the same individuals cross different social networks. The existing efforts in this field usually focus on the users' attributes and network embedding, but these methods often ignore the closeness between the users and their friends. To this end, we present a friend closeness based user matching algorithm (FCUM). It is a semi-supervised and end-to-end cross networks user matching algorithm. Attention mechanism is used to quantify the closeness between users and their friends. We considers both individual similarity and their close friends similarity by jointly optimize them in a single objective function. Quantification of close friends improves the generalization ability of the FCUM. Due to the expensive costs of labeling new match users for training FCUM, we also design a bi-directional matching strategy. Experiments on real datasets illustrate that FCUM outperforms other state-of-the-art methods that only consider the individual similarity.