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Journals IEEE/ACM Transactions on Compu...

IEEE/ACM Transactions on Computational Biology and Bioinformatics

https://read.qxmd.com/read/38363672/mahynet-parallel-hybrid-network-for-rna-protein-binding-sites-prediction-based-on-multi-head-attention-and-expectation-pooling
#21
JOURNAL ARTICLE
Wei Wang, Zhenxi Sun, Dong Liu, Hongjun Zhang, Juntao Li, Xianfang Wang, Yun Zhou
RNA-binding proteins (RBPs) can regulate biological functions by interacting with specific RNAs, and play an important role in many life activities. Therefore, the rapid identification of RNA-protein binding sites is crucial for functional annotation and site-directed mutagenesis. In this work, a new parallel network that integrates the multi-head attention mechanism and the expectation pooling is proposed, named MAHyNet. The left-branch network of MAHyNet hybrids convolutional neural networks (CNNs) and gated recurrent neural network (GRU) to extract the features of one-hot...
February 16, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38358865/gencoder-a-novel-convolutional-neural-network-based-autoencoder-for-genomic-sequence-data-compression
#22
JOURNAL ARTICLE
Sheena K S, Madhu S Nair
Revolutionary advances in DNA sequencing technologies fundamentally change the nature of genomics. Today's sequencing technologies have opened into an outburst in genomic data volume. These data can be used in various applications where long-term storage and analysis of genomic sequence data are required. Data-specific compression algorithms can effectively manage a large volume of data. Genomic sequence data compression has been investigated as a fundamental research topic for many decades. In recent times, deep learning has achieved great success in many compression tools and is gradually being used in genomic sequence compression...
February 15, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38358864/pmdags-predicting-mirna-disease-associations-with-graph-nonlinear-diffusion-convolution-network-and-similarities
#23
JOURNAL ARTICLE
Cheng Yan, Guihua Duan
Many studies have proven that microRNAs (miRNAs) can participate in a wide range of biological processes and can be considered as potential noninvasive biomarkers for disease diagnosis and prognosis. However, it is well-established that identifying potential miRNA-disease associations through wet-lab experimental methods is expensive and time-consuming. Therefore, many computational methods have been developed to reduce the cost of identifying miRNA-disease associations, ultimately enhancing the efficiency of disease diagnosis and treatment...
February 15, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38345958/dinoknot-duplex-interaction-of-nucleic-acids-with-pseudoknots
#24
JOURNAL ARTICLE
Tara Newman, Hiu Fung Kevin Chang, Hosna Jabbari
Interaction of nucleic acid molecules is essential for their functional roles in the cell and their applications in biotechnology. While simple duplex interactions have been studied before, the problem of efficiently predicting the minimum free energy structure of more complex interactions with possibly pseudoknotted structures remains a challenge. In this work, we introduce a novel and efficient algorithm for prediction of Duplex Interaction of Nucleic acids with pseudoKnots, DinoKnot follows the hierarchical folding hypothesis to predict the secondary structure of two interacting nucleic acid strands (both homo- and hetero-dimers)...
February 12, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38335071/slpa-net-a-real-time-recognition-network-for-intelligent-stomata-localization-and-phenotypic-analysis
#25
JOURNAL ARTICLE
Xiao-Hui Yang, Ye-Tong Wang, Ming-Hui Wu, Fan Li, Cheng-Long Zhou, Li-Jun Yang, Chen Zheng, Yong Li, Zhi Li, Si-Yi Guo, Chun-Peng Song
Plant stomatal phenotype traits play an important role in improving crop water use efficiency, stress resistance and yield. However, at present, the acquisition of phenotype traits mainly relies on manual measurement, which is time-consuming and laborious. In order to obtain high-throughput stomatal phenotype traits, we proposed a real-time recognition network SLPA-Net for stomata localization and phenotypic analysis. After locating and identifying stomatal density data, ellipse fitting is used to automatically obtain phenotype data such as apertures...
February 9, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38319777/a-clustering-method-for-single-cell-rna-seq-data-based-on-automatic-weighting-penalty-and-low-rank-representation
#26
JOURNAL ARTICLE
Juan Wang, Zhengchang Wang, Shasha Yuan, Chunhou Zheng, Jinxing Liu, Junliang Shang
Advances in high-throughput single-cell RNA sequencing (scRNA-seq) technology have provided more comprehensive biological information on cell expression. Clustering analysis is a critical step in scRNA-seq research and provides clear knowledge of the cell identity. Unfortunately, the characteristics of scRNA-seq data and the limitations of existing technologies make clustering encounter a considerable challenge. Meanwhile, some existing methods treat different features equally and ignore differences in feature contributions, which leads to a loss of information...
February 6, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38300780/comparison-of-orchard-networks-using-their-extended-%C3%AE-representation
#27
JOURNAL ARTICLE
Gabriel Cardona, Joan Carles Pons, Gerard Ribas, Tomas Martinez Coronado
Phylogenetic networks generalize phylogenetic trees in order to model reticulation events. Although the comparison of phylogenetic trees is well studied, and there are multiple ways to do it in an efficient way, the situation is much different for phylogenetic networks. Some classes of phylogenetic networks, mainly tree-child networks, are known to be classified efficiently by their μ-representation, which essentially counts, for every node, the number of paths to each leaf. In this paper, we introduce the extended μ-representation of networks, where the number of paths to reticulations is also taken into account...
February 1, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38285569/sccan-clustering-with-adaptive-neighbor-based-imputation-method-for-single-cell-rna-seq-data
#28
JOURNAL ARTICLE
Shujie Dong, Yuansheng Liu, Yongshun Gong, Xiangjun Dong, Xiangxiang Zeng
Single-cell RNA sequencing (scRNA-seq) is widely used to study cellular heterogeneity in different samples. However, due to technical deficiencies, dropout events often result in zero gene expression values in the gene expression matrix. In this paper, we propose a new imputation method called scCAN, based on adaptive neighborhood clustering, to estimate the zero value of dropouts. Our method continuously updates cell-cell similarity information by simultaneously learning similarity relationships, clustering structures, and imposing new rank constraints on the Laplacian matrix of the similarity matrix, improving the imputation of dropout zero values...
January 29, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38231821/lmgatcda-graph-neural-network-with-labeling-trick-for-predicting-circrna-disease-associations
#29
JOURNAL ARTICLE
Wenjing Wang, Pengyong Han, Zhengwei Li, Ru Nie, Kangwei Wang, Lei Wang, Hongmei Liao
Previous studies have proven that circular RNAs (circRNAs) are inextricably connected to the etiology and pathophysiology of complicated diseases. Since conventional biological research are frequently small-scale, expensive, and time-consuming, it is essential to establish an efficient and reasonable computation-based method to identify disease-related circRNAs. In this paper, we proposed a novel ensemble model for predicting probable circRNA-disease associations based on multi-source similarity information(LMGATCDA)...
January 17, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38215334/smcc-a-novel-clustering-method-for-single-and-multi-omics-data-based-on-co-regularized-network-fusion
#30
JOURNAL ARTICLE
Sha Tian, Ying Yang, Yushan Qiu, Quan Zou
Clustering is a common technique for statistical data analysis and is essential for developing precision medicine. Numerous computational methods have been proposed for integrating multi-omics data to identify cancer subtypes. However, most existing clustering models based on network fusion fail to preserve the consistency of the distribution of the data before and after fusion. Motivated by this observation, we would like to measure and minimize the distribution difference between networks, which may not be in the same space, to improve the performance of data fusion...
January 12, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38198267/learning-from-an-artificial-neural-network-in-phylogenetics
#31
JOURNAL ARTICLE
Alina F Leuchtenberger, Arndt von Haeseler
We show that an iterative ansatz of deep learning and human intelligence guided simplification may lead to surprisingly simple solutions for a difficult problem in phylogenetics. Distinguishing Farris and Felsenstein trees is a longstanding problem in phylogenetic tree reconstruction. The Artificial Neural Network F-zoneNN solves this problem for 4-taxon alignments evolved under the Jukes-Cantor model. It distinguishes between Farris and Felsenstein trees, but owing to its complexity, lacks transparency in its mechanism of discernment...
January 10, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38194377/a-survey-of-deep-learning-for-detecting-mirna-disease-associations-databases-computational-methods-challenges-and-future-directions
#32
JOURNAL ARTICLE
Nan Sheng, Xuping Xie, Yan Wang, Lan Huang, Shuangquan Zhang, Ling Gao
MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pathogenesis. Traditional laboratory experiments are expensive and time-consuming. Computational models have enabled systematic large-scale prediction of potential MDAs, greatly improving the research efficiency. With recent advances in deep learning, it has become an attractive and powerful technique for uncovering novel MDAs...
January 9, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38194376/flanked-block-interchange-distance-on-strings
#33
JOURNAL ARTICLE
Tiantian Li, Haitao Jiang, Binhai Zhu, Lusheng Wang, Daming Zhu
Rearrangement sorting problems impact profoundly in measuring genome similarities and tracing historic scenarios of species. However, recent studies on genome rearrangement mechanisms disclosed a statistically significant evidence, repeats are situated at the ends of rearrangement relevant segments and stay unchanged before and after rearrangements. To reflect the principle behind this evidence, we propose flanked block-interchange, an operation on strings that exchanges two substrings flanked by identical left and right symbols in a string...
January 9, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38190662/temporal-protein-complex-identification-based-on-dynamic-heterogeneous-protein-information-network-representation-learning
#34
JOURNAL ARTICLE
Zeqian Li, Yijia Zhang, Peixuan Zhou
Protein complexes, as the fundamental units of cellular function and regulation, play a crucial role in understanding the normal physiological functions of cells. Existing methods for protein complex identification attempt to introduce other biological information on top of the protein-protein interaction (PPI) network to assist in evaluating the degree of association between proteins. However, these methods usually treat protein interaction networks as flat homogeneous static networks. They cannot distinguish the roles and importance of different types of biological information, nor can they reflect the dynamic changes of protein complexes...
January 8, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38190661/sadr-self-supervised-graph-learning-with-adaptive-denoising-for-drug-repositioning
#35
JOURNAL ARTICLE
Sichen Jin, Yijia Zhang, Huimin Yu, Mingyu Lu
Traditional drug development is often high-risk and time-consuming. A promising alternative is to reuse or relocate approved drugs. Recently, some methods based on graph representation learning have started to be used for drug repositioning. These models learn the low dimensional embeddings of drug and disease nodes from the drug-disease interaction network to predict the potential association between drugs and diseases. However, these methods have strict requirements for the dataset, and if the dataset is sparse, the performance of these methods will be severely affected...
January 8, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38170658/bioiso-an-objective-oriented-application-for-assisting-the-curation-of-genome-scale-metabolic-models
#36
JOURNAL ARTICLE
Fernando Cruz, Joao Capela, Eugenio C Ferreira, Miguel Rocha, Oscar Dias
As the reconstruction of Genome-Scale Metabolic Models (GEMs) becomes standard practice in systems biology, the number of organisms having at least one metabolic model is peaking at an unprecedented scale. The automation of laborious tasks, such as gap-finding and gap-filling, allowed the development of GEMs for poorly described organisms. However, the quality of these models can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. Biological networks constraint-based In Silico Optimisation (BioISO) is a computational tool aimed at accelerating the reconstruction of GEMs...
January 3, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38153818/gerwr-identifying-the-key-pathogenicity-associated-srnas-of-magnaporthe-oryzae-infection-in-rice-based-on-graph-embedding-and-random-walk-with-restart
#37
JOURNAL ARTICLE
Hao Zhang, Jiao Jiao, Tianheng Zhao, Enshuang Zhao, Lanhui Li, Guihua Li, Borui Zhang, Qing-Ming Qin
Rice blast, caused by Magnaporthe oryzae(M.oryzae), is a destructive rice disease that reduces rice yield by 10% to 30% annually. It also affects other cereal crops such as barley, wheat, rye, millet, sorghum, and maize. Small RNAs (sRNAs) play an essential regulatory role in fungus-plant interaction during the fungal invasion, but studies on pathogenic sRNAs during the fungal invasion of plants based on multi-omics data integration are rare. This paper proposes a novel approach called Graph Embedding combined with Random Walk with Restart (GERWR) to identify pathogenic sRNAs based on multi-omics data integration during M...
December 28, 2023: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38127613/bic-lp-a-hybrid-higher-order-dynamic-bayesian-network-score-function-for-gene-regulatory-network-reconstruction
#38
JOURNAL ARTICLE
Junchang Xin, Mingcan Wang, Luxuan Qu, Qi Chen, Weiyiqi Wang, Zhiqiong Wang
Reconstructing gene regulatory networks(GRNs) is an increasingly hot topic in bioinformatics. Dynamic Bayesian network(DBN) is a stochastic graph model commonly used as a vital model for GRN reconstruction. But probabilistic characteristics of biological networks and the existence of data noise bring great challenges to GRN reconstruction and always lead to many false positive/negative edges. ScoreLasso is a hybrid DBN score function combining DBN and linear regression with good performance. Its performance is, however, limited by first-order assumption and ignorance of the initial network of DBN...
December 21, 2023: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38127612/a-novel-multi-scale-graph-neural-network-for-metabolic-pathway-prediction
#39
JOURNAL ARTICLE
Yuerui Liu, Yongquan Jiang, Fan Zhang, Yan Yang
Predicting the metabolic pathway classes of compounds in the human body is an important problem in drug research and development. For this purpose, we propose a Multi-Scale Graph Neural Network framework, named MSGNN. The framework includes a subgraph encoder, a feature encoder and a global feature processor, and a graph augmentation strategy is adopted. The subgraph encoder is responsible for extracting the local structural features of the compound, the feature encoder learns the characteristics of the atoms, and the global feature processor processes the information from the pre-training model and the two molecular fingerprints, while the graph augmentation strategy is to expand the train set through a scientific and reasonable method...
December 21, 2023: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38109236/genomic-machine-learning-meta-regression-insights-on-associations-of-study-features-with-reported-model-performance
#40
JOURNAL ARTICLE
Eric J Barnett, Daniel G Onete, Asif Salekin, Stephen V Faraone
Many studies have been conducted with the goal of correctly predicting diagnostic status of a disorder using the combination of genomic data and machine learning. It is often hard to judge which components of a study led to better results and whether better reported results represent a true improvement or an uncorrected bias inflating performance. We extracted information about the methods used and other differentiating features in genomic machine learning models. We used these features in linear regressions predicting model performance...
December 18, 2023: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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