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Improved Fuzzy Cognitive Maps for Gene Regulatory Networks Inference based on time series data. 基于时间序列数据的基因调控网络推断的改进型模糊认知图。
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-07-04 DOI: 10.1109/TCBB.2024.3423383
Marzieh Emadi, Farsad Zamani Boroujeni, Jamshid Pirgazi

Microarray data provide lots of information regarding gene expression levels. Due to the large amount of such data, their analysis requires sufficient computational methods for identifying and analyzing gene regulation networks; however, researchers in this field are faced with numerous challenges such as consideration for too many genes and at the same time, the limited number of samples and their noisy nature of the data. In this paper, a hybrid method base on fuzzy cognitive map and compressed sensing is used to identify interactions between genes. For this purpose, in inference of the gene regulation network, the Ensemble Kalman filtered compressed sensing is used to learn the fuzzy cognitive map. Using the Ensemble Kalman filter and compressed sensing, the fuzzy cognitive map will be robust against noise. The proposed algorithm is evaluated using several metrics and compared with several well know methods such as LASSOFCM, KFRegular, CMI2NI. The experimental results show that the proposed method outperforms methods proposed in recent years in terms of SSmean, Data Error and accuracy.

微阵列数据提供了大量有关基因表达水平的信息。然而,该领域的研究人员面临着诸多挑战,如需要考虑的基因数量过多,同时样本数量有限以及数据的噪声特性。本文采用基于模糊认知图谱和压缩传感的混合方法来识别基因之间的相互作用。为此,在推断基因调控网络时,使用了集合卡尔曼滤波压缩传感来学习模糊认知图。利用组合卡尔曼滤波和压缩传感,模糊认知图谱将对噪声具有鲁棒性。我们使用多个指标对所提出的算法进行了评估,并将其与 LASSOFCM、KFRegular、CMI2NI 等几种已知方法进行了比较。实验结果表明,所提出的方法在 SSmean、数据误差和准确性方面都优于近年来提出的方法。
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引用次数: 0
AnglesRefine: Refinement of 3D Protein Structures Using Transformer Based on Torsion Angles. AnglesRefine:利用基于扭转角的变换器完善三维蛋白质结构
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-07-03 DOI: 10.1109/TCBB.2024.3422288
Lei Zhang, Junyong Zhu, Sheng Wang, Jie Hou, Dong Si, Renzhi Cao

The goal of protein structure refinement is to enhance the precision of predicted protein models, particularly at the residue level of the local structure. Existing refinement approaches primarily rely on physics, whereas molecular simulation methods are resource-intensive and time-consuming. In this study, we employ deep learning methods to extract structural constraints from protein structure residues to assist in protein structure refinement. We introduce a novel method, AnglesRefine, which focuses on a protein's secondary structure and employs transformer to refine various protein structure angles (psi, phi, omega, CA_C_N_angle, C_N_CA_angle, N_CA_C_angle), ultimately generating a superior protein model based on the refined angles. We evaluate our approach against other cutting-edge methods using the CASP11-14 and CASP15 datasets. Experimental outcomes indicate that our method generally surpasses other techniques on the CASP11-14 test dataset, while performing comparably or marginally better on the CASP15 test dataset. Our method consistently demonstrates the least likelihood of model quality degradation, e.g., the degradation percentage of our method is less than 10%, while other methods are about 50%. Furthermore, as our approach eliminates the need for conformational search and sampling, it significantly reduces computational time compared to existing refinement methods.

蛋白质结构精细化的目标是提高预测蛋白质模型的精度,尤其是在局部结构的残基水平上。现有的细化方法主要依赖物理学,而分子模拟方法则需要大量资源和时间。在本研究中,我们采用深度学习方法从蛋白质结构残基中提取结构约束,以协助蛋白质结构的细化。我们引入了一种新方法--AnglesRefine,该方法专注于蛋白质的二级结构,并利用变换器来细化各种蛋白质结构角度(psi、phi、ω、CA_C_N_angle、C_N_CA_angle、N_CA_C_angle),最终根据细化后的角度生成优秀的蛋白质模型。我们利用 CASP11-14 和 CASP15 数据集对我们的方法与其他先进方法进行了评估。实验结果表明,在 CASP11-14 测试数据集上,我们的方法总体上超越了其他技术,而在 CASP15 测试数据集上,我们的方法表现相当或略胜一筹。我们的方法始终是模型质量退化可能性最小的方法,例如,我们的方法的退化百分比低于 10%,而其他方法的退化百分比约为 50%。此外,由于我们的方法无需进行构象搜索和采样,因此与现有的完善方法相比,大大缩短了计算时间。
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引用次数: 0
Prediction of Potential miRNA-Disease Associations Based on a Masked Graph Autoencoder. 基于屏蔽图自动编码器的潜在 miRNA 与疾病关联预测
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-07-02 DOI: 10.1109/TCBB.2024.3421924
Chenchen Ke, Hailin Feng, Quan Zou, Zhechen Zhu, Tongcun Liu

Biomedical evidence has demonstrated the relevance of microRNA (miRNA) dysregulation in complex human diseases, and determining the relationship between miRNAs and diseases can aid in the early detection and prevention of diseases. Traditional biological experimental methods have the disadvantages of high cost and low efficiency, which are well compensated by computational methods. However, many computational methods have the challenge of excessively focusing on the neighbor relationship, ignoring the structural information of the graph, and belittling the redundant information of the graph structure. This study proposed a computational model based on a graph-masking autoencoder named MGAEMDA. MGAEMDA is an asymmetric framework in which the encoder maps partially observed graphs into latent representations. The decoder reconstructs the masked structural information based on the edge and node levels and combines it with linear matrices to obtain the result. The empirical results on the two datasets reveal that the MGAEMDA model performs better than its counterparts. We also demonstrated the predictive performance of MGAEMDA using a case study of four diseases, and all the top 30 predicted miRNAs were validated in the database, providing further evidence of the excellent performance of the model.

生物医学证据表明,微RNA(miRNA)失调与复杂的人类疾病有关,确定miRNA与疾病的关系有助于疾病的早期检测和预防。传统的生物学实验方法具有成本高、效率低的缺点,而计算方法可以很好地弥补这些缺点。然而,许多计算方法都存在过度关注邻接关系、忽视图的结构信息、轻视图结构冗余信息等难题。本研究提出了一种基于图屏蔽自动编码器的计算模型,命名为 MGAEMDA。MGAEMDA 是一个非对称框架,其中编码器将部分观察到的图映射为潜在表示。解码器根据边缘和节点水平重建被掩蔽的结构信息,并将其与线性矩阵相结合,从而得到结果。在两个数据集上的实证结果表明,MGAEMDA 模型的性能优于同类模型。我们还利用四种疾病的案例研究证明了MGAEMDA的预测性能,所有预测的前30个miRNA都在数据库中得到了验证,进一步证明了该模型的卓越性能。
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引用次数: 0
Graph Convolutional Network with Self-supervised Learning for Brain Disease Classification. 基于自我监督学习的图卷积网络用于脑疾病分类
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-07-02 DOI: 10.1109/TCBB.2024.3422152
Guangyu Wang, Ying Chu, Qianqian Wang, Limei Zhang, Lishan Qiao, Mingxia Liu

Brain functional network (BFN) analysis has become a popular method for identifying neurological diseases at their early stages and revealing sensitive biomarkers related to these diseases. Due to the fact that BFN is a graph with complex structure, graph convolutional networks (GCNs) can be naturally used in the identification of BFN, and can generally achieve an encouraging performance if given large amounts of training data. In practice, however, it is very difficult to obtain sufficient brain functional data, especially from subjects with brain disorders. As a result, GCNs usually fail to learn a reliable feature representation from limited BFNs, leading to overfitting issues. In this paper, we propose an improved GCN method to classify brain diseases by introducing a self-supervised learning (SSL) module for assisting the graph feature representation. We conduct experiments to classify subjects with mild cognitive impairment (MCI) and autism spectrum disorder (ASD) respectively from normal controls (NCs). Experimental results on two benchmark databases demonstrate that our proposed scheme tends to obtain higher classification accuracy than the baseline methods.

脑功能网络(BFN)分析已成为一种流行的方法,用于在早期阶段识别神经系统疾病并揭示与这些疾病相关的敏感生物标记物。由于脑功能网络是一个结构复杂的图,因此图卷积网络(GCN)可以很自然地用于脑功能网络的识别,如果给定大量的训练数据,通常可以取得令人鼓舞的性能。但在实际应用中,很难获得足够的脑功能数据,尤其是来自脑部疾病受试者的数据。因此,GCN 通常无法从有限的 BFN 中学习到可靠的特征表示,从而导致过拟合问题。在本文中,我们提出了一种改进的 GCN 方法,通过引入自我监督学习(SSL)模块来辅助图特征表示,从而对脑部疾病进行分类。我们通过实验将轻度认知障碍(MCI)和自闭症谱系障碍(ASD)患者分别与正常对照组(NCs)进行了分类。在两个基准数据库上的实验结果表明,与基线方法相比,我们提出的方案往往能获得更高的分类准确率。
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引用次数: 0
Optimal Structured Matrix Approximation for Robustness to Incomplete Biosequence Data. 针对不完整生物序列数据的最佳结构化矩阵近似。
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-07-01 DOI: 10.1109/TCBB.2024.3420903
Chris Salahub, Jeffrey Uhlmann

We propose a general method for optimally approximating an arbitrary matrix M by a structured matrix T (circulant, Toeplitz/Hankel, etc.) and examine its use for estimating the spectra of genomic linkage disequilibrium matrices. This application is prototypical of a variety of genomic and proteomic problems that demand robustness to incomplete biosequence information. We perform a simulation study and corroborative test of our method using real genomic data from the Mouse Genome Database [1]. The results confirm the predicted utility of the method and provide strong evidence of its potential value to a wide range of bioinformatics applications. Our optimal general matrix approximation method is expected to be of independent interest to an even broader range of applications in applied mathematics and engineering.

我们提出了一种用结构矩阵 T(环状、Toeplitz/Hankel 等)优化近似任意矩阵 M 的通用方法,并研究了该方法在估计基因组连锁不平衡矩阵频谱中的应用。这一应用是各种基因组和蛋白质组问题的原型,这些问题要求对不完整的生物序列信息具有鲁棒性。我们使用小鼠基因组数据库 [1] 中的真实基因组数据对我们的方法进行了模拟研究和确证测试。结果证实了该方法的预期效用,并有力地证明了它在广泛的生物信息学应用中的潜在价值。我们的最优通用矩阵近似方法有望在应用数学和工程学的更广泛应用中产生独立的兴趣。
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引用次数: 0
Ense-i6mA: Identification of DNA N6-methyl-adenine Sites Using XGB-RFE Feature Se-lection and Ensemble Machine Learning. Ense-i6mA:利用 XGB-RFE 特征选择和集合机器学习识别 DNA N6-甲基腺嘌呤位点。
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-07-01 DOI: 10.1109/TCBB.2024.3421228
Xue-Qiang Fan, Bing Lin, Jun Hu, Zhong-Yi Guo

DNA N6-methyladenine (6mA) is an important epigenetic modification that plays a vital role in various cellular processes. Accurate identification of the 6mA sites is fundamental to elucidate the biological functions and mechanisms of modification. However, experimental methods for detecting 6mA sites are high-priced and time-consuming. In this study, we propose a novel computational method, called Ense-i6mA, to predict 6mA sites. Firstly, five encoding schemes, i.e., one-hot encoding, gcContent, Z-Curve, K-mer nucleotide frequency, and K-mer nucleotide frequency with gap, are employed to extract DNA sequence features. Secondly, to our knowledge, it is the first time that eXtreme gradient boosting coupled with recursive feature elimination is applied to 6mA sites prediction domain to remove noisy features for avoiding over-fitting, reducing computing time and complexity. Then, the best subset of features is fed into base-classifiers composed of Extra Trees, eXtreme Gradient Boosting, Light Gradient Boosting Machine, and Support Vector Machine. Finally, to minimize generalization errors, the prediction probabilities of the base-classifiers are aggregated by averaging for inferring the final 6mA sites results. We conduct experiments on two species, i.e., Arabidopsis thaliana and Drosophila melanogaster, to compare the performance of Ense-i6mA against the recent 6mA sites prediction methods. The experimental results demonstrate that the proposed Ense-i6mA achieves area under the receiver operating characteristic curve values of 0.967 and 0.968, accuracies of 91.4% and 92.0%, and Mathew's correlation coefficient values of 0.829 and 0.842 on two benchmark datasets, respectively, and outperforms several existing state-of-the-art methods.

DNA N6-甲基腺嘌呤(6mA)是一种重要的表观遗传修饰,在各种细胞过程中发挥着至关重要的作用。准确鉴定 6mA 位点是阐明生物功能和修饰机制的基础。然而,检测 6mA 位点的实验方法既昂贵又耗时。在本研究中,我们提出了一种名为 Ense-i6mA 的新型计算方法来预测 6mA 位点。首先,我们采用了五种编码方案,即单次编码、gcContent、Z-Curve、K-mer核苷酸频率和带间隙的K-mer核苷酸频率,来提取DNA序列特征。其次,据我们所知,这是首次在 6mA 位点预测领域采用极限梯度提升和递归特征消除相结合的方法来去除噪声特征,以避免过度拟合,减少计算时间和复杂度。然后,将最佳特征子集输入由 Extra Trees、eXtreme Gradient Boosting、Light Gradient Boosting Machine 和 Support Vector Machine 组成的基础分类器。最后,为了最大限度地减少泛化误差,基分类器的预测概率通过平均值进行汇总,以推断 6mA 站点的最终结果。我们在拟南芥和黑腹果蝇这两个物种上进行了实验,以比较 Ense-i6mA 与最近的 6mA 位点预测方法的性能。实验结果表明,所提出的Ense-i6mA在两个基准数据集上的接收者操作特征曲线下面积值分别为0.967和0.968,准确率分别为91.4%和92.0%,马修相关系数分别为0.829和0.842,优于现有的几种先进方法。
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引用次数: 0
Haplotype frequency inference from pooled genetic data with a latent multinomial model. 利用潜在多项式模型从集合遗传数据中推断单倍型频率。
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-28 DOI: 10.1109/TCBB.2024.3420430
Yong See Foo, Jennifer Flegg

In genetic association studies, haplotype data provide more refined information than data about separate genetic markers. However, large-scale studies that genotype hundreds to thousands of individuals may only provide results of pooled data. Methods for inferring haplotype frequencies from pooled genetic data that scale well with pool size rely on a normal approximation, which we observe to produce unreliable inference when applied to real data. We illustrate cases where the approximation fails, due to the normal covariance matrix being nearsingular. As an alternative to approximate methods, in this paper we propose two exact methods to infer haplotype frequencies from pooled genetic data based on a latent multinomial model, where the pooled results are considered integer combinations of latent, unobserved haplotype counts. One of our methods, latent count sampling via Markov bases, achieves approximately linear runtime with respect to pool size. Our exact methods produce more accurate inference over existing approximate methods for synthetic data and for haplotype data from the 1000 Genomes Project. We also demonstrate how our methods can be applied to time-series of pooled genetic data, as a proof of concept of how our methods are relevant to more complex hierarchical settings, such as spatiotemporal models.

在遗传关联研究中,单体型数据比单独的遗传标记数据能提供更精细的信息。然而,对成百上千的个体进行基因分型的大规模研究可能只能提供集合数据的结果。从集合遗传数据中推断单倍型频率的方法可以很好地随着集合规模的扩大而扩展,但这种方法依赖于正态近似值,我们观察到这种近似值在应用于实际数据时会产生不可靠的推断。我们举例说明了由于正态协方差矩阵是近似值而导致近似失效的情况。作为近似方法的替代方法,我们在本文中提出了两种基于潜伏多叉模型的精确方法,用于从集合遗传数据中推断单倍型频率,其中集合结果被视为潜伏的、未观察到的单倍型计数的整数组合。我们的方法之一是通过马尔可夫基进行潜伏计数采样,其运行时间与集合大小近似线性关系。对于合成数据和来自 1000 基因组计划的单倍型数据,我们的精确方法比现有的近似方法能产生更精确的推断。我们还演示了如何将我们的方法应用于集合遗传数据的时间序列,以此证明我们的方法如何适用于更复杂的分层设置,如时空模型。
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引用次数: 0
Tropical Density Estimation of Phylogenetic Trees. 系统发生树的热带密度估计
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-28 DOI: 10.1109/TCBB.2024.3420815
Ruriko Yoshida, David Barnhill, Keiji Miura, Daniel Howe

Much evidence from biological theory and empirical data indicates that, gene trees, phylogenetic trees reconstructed from different genes (loci), do not have to have exactly the same tree topologies. Such incongruence between gene trees might be caused by some "unusual" evolutionary events, such as meiotic sexual recombination in eukaryotes or horizontal transfers of genetic material in prokaryotes. However, most of the gene trees are constrained by the tree topology of the underlying species tree, that is, the phylogenetic tree depicting the evolutionary history of the set of species under consideration. In order to discover "outlying" gene trees which do not follow the "main distribution(s)" of trees, we propose to apply the "tropical metric" with the max-plus algebra from tropical geometry to a non-parametric estimation of gene trees over the space of phylogenetic trees. In this research we apply the "tropical metric," a well-defined metric over the space of phylogenetic trees under the max-plus algebra, to non-parametric estimation of gene trees distribution over the tree space. Kernel density estimator (KDE) is one of the most popular non-parametric estimation of a distribution from a given sample, and we propose an analogue of the classical KDE in the setting of tropical geometry with the tropical metric which measures the length of an intrinsic geodesic between trees over the tree space. We estimate the probability of an observed tree by empirical frequencies of nearby trees, with the level of influence determined by the tropical metric. Then, with simulated data generated from the multispecies coalescent model, we show that the non-parametric estimation of the gene tree distribution using the tropical metric performs better than one using the Billera-Holmes-Vogtmann (BHV) metric developed by Weyenberg et al. in terms of computational times and accuracy. We then apply it to Apicomplexa data.

来自生物学理论和经验数据的大量证据表明,基因树,即由不同基因(位点)重建的系统发生树,并不一定具有完全相同的树拓扑结构。基因树之间的这种不一致性可能是由一些 "不寻常 "的进化事件造成的,如真核生物中的减数分裂性重组或原核生物中遗传物质的水平转移。然而,大多数基因树都受到底层物种树(即描述所研究物种进化历史的系统发生树)拓扑结构的限制。为了发现不遵循 "主要分布 "的 "离群 "基因树,我们建议将 "热带度量 "与热带几何中的 max-plus 代数应用于系统发生树空间上基因树的非参数估计。在这项研究中,我们将 "热带度量 "这一在系统发育树空间中定义明确的度量与 max-plus 代数结合起来,用于对基因树在树空间中的分布进行非参数估计。核密度估算器(KDE)是从给定样本中对分布进行估算的最常用的非参数估计方法之一,我们提出了在热带几何环境中使用热带度量来类比经典的 KDE,热带度量测量的是树空间中树与树之间的内在大地线的长度。我们通过附近树木的经验频率来估计观察到的树木的概率,影响程度由热带度量决定。然后,通过多物种凝聚模型产生的模拟数据,我们证明使用热带度量对基因树分布的非参数估计在计算时间和准确性方面优于使用韦恩伯格等人开发的比勒拉-霍姆斯-沃格曼(Billera-Holmes-Vogtmann,BHV)度量。然后,我们将其应用于 Apicomplexa 数据。
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引用次数: 0
Analyzing Large-Scale Single-Cell RNA-Seq Data Using Coreset. 使用 Coreset 分析大规模单细胞 RNA-Seq 数据。
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-24 DOI: 10.1109/TCBB.2024.3418078
Khalid Usman, Fangping Wan, Dan Zhao, Jian Peng, Jianyang Zeng

The recent boom in single-cell sequencing technologies provides valuable insights into the transcriptomes of individual cells. Through single-cell data analyses, a number of biological discoveries, such as novel cell types, developmental cell lineage trajectories, and gene regulatory networks, have been uncovered. However, the massive and increasingly accumulated single-cell datasets have also posed a seriously computational and analytical challenge for researchers. To address this issue, one typically applies dimensionality reduction approaches to reduce the large-scale datasets. However, these approaches are generally computationally infeasible for tall matrices. In addition, the downstream data analysis tasks such as clustering still take a large time complexity even on the dimension-reduced datasets. We present single-cell Coreset (scCoreset), a data summarization framework that extracts a small weighted subset of cells from a huge sparse single-cell RNA-seq data to facilitate the downstream data analysis tasks. Single-cell data analyses run on the extracted subset yield similar results to those derived from the original uncompressed data. Tests on various single-cell datasets show that scCoreset outperforms the existing data summarization approaches for common downstream tasks such as visualization and clustering. We believe that scCoreset can serve as a useful plug-in tool to improve the efficiency of current single-cell RNA-seq data analyses.

近年来,单细胞测序技术的蓬勃发展为人们提供了了解单个细胞转录组的宝贵信息。通过单细胞数据分析,人们发现了许多生物学新发现,如新型细胞类型、发育细胞系轨迹和基因调控网络等。然而,海量且日益积累的单细胞数据集也给研究人员带来了严重的计算和分析挑战。为了解决这个问题,人们通常采用降维方法来减少大规模数据集。然而,这些方法通常对高矩阵的计算不可行。此外,即使在降维后的数据集上,聚类等下游数据分析任务仍然需要耗费大量的时间复杂度。我们提出的单细胞核心集(scCoreset)是一个数据汇总框架,它能从庞大的稀疏单细胞 RNA-seq 数据中提取一小部分加权细胞子集,以方便下游数据分析任务。在提取的子集上运行单细胞数据分析,会得到与原始未压缩数据类似的结果。对各种单细胞数据集的测试表明,在可视化和聚类等常见下游任务方面,scCoreset 优于现有的数据汇总方法。我们相信,scCoreset 可以作为一种有用的插件工具,提高当前单细胞 RNA-seq 数据分析的效率。
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引用次数: 0
BLAM6A-Merge: Leveraging Attention Mechanisms and Feature Fusion Strategies to Improve the Identification of RNA N6-methyladenosine Sites. BLAM6A-Merge:利用注意机制和特征融合策略改进 RNA N6-甲基腺苷位点的鉴定。
IF 3.6 3区 生物学 Q1 Mathematics Pub Date : 2024-06-24 DOI: 10.1109/TCBB.2024.3418490
Yunpeng Xia, Ying Zhang, Dian Liu, Yi-Heng Zhu, Zhikang Wang, Jiangning Song, Dong-Jun Yu

RNA N6-methyladenosine is a prevalent and abundant type of RNA modification that exerts significant influence on diverse biological processes. To date, numerous computational approaches have been developed for predicting methylation, with most of them ignoring the correlations of different encoding strategies and failing to explore the adaptability of various attention mechanisms for methylation identification. To solve the above issues, we proposed an innovative framework for predicting RNA m6A modification site, termed BLAM6A-Merge. Specifically, it utilized a multimodal feature fusion strategy to combine the classification results of four features and Blastn tool. Apart from this, different attention mechanisms were employed for extracting higher-level features on specific features after the screening process. Extensive experiments on 12 benchmarking datasets demonstrated that BLAM6A-Merge achieved superior performance (average AUC: 0.849 for the full transcript mode and 0.784 for the mature mRNA mode). Notably, the Blastn tool was employed for the first time in the identification of methylation sites. The data and code can be accessed at https://github.com/DoraemonXia/BLAM6A-Merge.

RNA N6-甲基腺苷是一种普遍而丰富的 RNA 修饰类型,对多种生物过程具有重要影响。迄今为止,用于预测甲基化的计算方法层出不穷,但大多数方法都忽略了不同编码策略之间的相关性,也未能探索各种注意机制对甲基化鉴定的适应性。为了解决上述问题,我们提出了一种预测 RNA m6A 修饰位点的创新框架,称为 BLAM6A-Merge。具体来说,它利用多模态特征融合策略,将四个特征的分类结果与 Blastn 工具结合起来。除此之外,在筛选过程之后,还采用了不同的关注机制,以提取特定特征上的高层次特征。在 12 个基准数据集上进行的广泛实验表明,BLAM6A-Merge 取得了卓越的性能(全转录本的平均 AUC:全转录本模式为 0.849,成熟 mRNA 模式为 0.784)。值得注意的是,Blastn 工具首次被用于甲基化位点的鉴定。数据和代码可在 https://github.com/DoraemonXia/BLAM6A-Merge 上获取。
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引用次数: 0
期刊
IEEE/ACM Transactions on Computational Biology and Bioinformatics
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