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Reinforced Metapath Optimization in Heterogeneous Information Networks for Drug-Target Interaction Prediction. 异构信息网络中用于药物-靶点相互作用预测的强化元路径优化。
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-24 DOI: 10.1109/TCBB.2024.3467135
Ben Xu, Jianping Chen, Yunzhe Wang, Qiming Fu, You Lu

Graph neural networks offer an effective avenue for predicting drug-target interactions. In this domain, researchers have found that constructing heterogeneous information networks based on metapaths using diverse biological datasets enhances prediction performance. However, the performance of such methods is closely tied to the selection of metapaths and the compatibility between metapath subgraphs and graph neural networks. Most existing approaches still rely on fixed strategies for selecting metapaths and often fail to fully exploit node information along the metapaths, limiting the improvement in model performance. This paper introduces a novel method for predicting drug-target interactions by optimizing metapaths in heterogeneous information networks. On one hand, the method formulates the metapath optimization problem as a Markov decision process, using the enhancement of downstream network performance as a reward signal. Through iterative training of a reinforcement learning agent, a high-quality set of metapaths is learned. On the other hand, to fully leverage node information along the metapaths, the paper constructs subgraphs based on nodes along the metapaths. Different depths of subgraphs are processed using different graph convolutional neural network. The proposed method is validated using standard heterogeneous biological benchmark datasets. Experimental results on standard datasets show significant advantages over traditional methods.

图神经网络为预测药物-靶点相互作用提供了有效途径。在这一领域,研究人员发现,利用不同的生物数据集构建基于元图谱的异构信息网络可以提高预测性能。然而,这些方法的性能与元图的选择以及元图子图和图神经网络之间的兼容性密切相关。现有的大多数方法仍然依赖于固定的元路径选择策略,往往不能充分利用元路径上的节点信息,从而限制了模型性能的提高。本文介绍了一种在异构信息网络中通过优化元径预测药物-靶点相互作用的新方法。一方面,该方法将元路径优化问题表述为马尔可夫决策过程,将下游网络性能的提升作为奖励信号。通过强化学习代理的迭代训练,可以学习到一组高质量的元路径。另一方面,为了充分利用元路径上的节点信息,本文根据元路径上的节点构建子图。使用不同的图卷积神经网络处理不同深度的子图。本文使用标准异构生物基准数据集对所提出的方法进行了验证。标准数据集上的实验结果表明,该方法与传统方法相比具有显著优势。
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引用次数: 0
Identification of cancer driver genes based on dynamic incentive model. 基于动态激励模型的癌症驱动基因识别。
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-24 DOI: 10.1109/TCBB.2024.3467119
Zhipeng Hu, Gaoshi Li, Xinlong Luo, Wei Peng, Jiafei Liu, Xiaoshu Zhu, Jingli Wu

Cancer is a complex genomic mutation disease, and identifying cancer driver genes promotes the development of targeted drugs and personalized therapies. The current computational method takes less consideration of the relationship among features and the effect of noise in protein-protein interaction(PPI) data, resulting in a low recognition rate. In this paper, we propose a cancer driver genes identification method based on dynamic incentive model, DIM. This method firstly constructs a hypergraph to reduce the impact of false positive data in PPI. Then, the importance of genes in each hyperedge in hypergraph is considered from three perspectives, network and functional score(NFS) is proposed. By analyzing the relation among features, the dynamic incentive model is proposed to fuse NFS, the differential expression score of mRNA and the differential expression score of miRNA. DIM is compared with some classical methods on breast cancer, lung cancer, prostate cancer, and pan-cancer datasets. The results show that DIM has the best performance on statistical evaluation indicators, functional consistency and the partial area under the ROC curve, and has good cross-cancer capability.

癌症是一种复杂的基因组突变疾病,识别癌症驱动基因有助于靶向药物和个性化疗法的开发。目前的计算方法较少考虑蛋白质-蛋白质相互作用(PPI)数据中特征之间的关系和噪声的影响,导致识别率较低。本文提出了一种基于动态激励模型(DIM)的癌症驱动基因识别方法。该方法首先构建了一个超图,以减少 PPI 中假阳性数据的影响。然后,从网络和功能得分(NFS)三个角度考虑超图中每个超边中基因的重要性。通过分析特征之间的关系,提出了融合 NFS、mRNA 差异表达得分和 miRNA 差异表达得分的动态激励模型。在乳腺癌、肺癌、前列腺癌和泛癌症数据集上,将 DIM 与一些经典方法进行了比较。结果表明,DIM 在统计评价指标、功能一致性和 ROC 曲线下部分面积方面表现最佳,并具有良好的跨癌症能力。
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引用次数: 0
Partition Based Algorithms for Rearrangement Distances with Flexible Intergenic Regions. 基于分区的灵活基因间重排距离算法
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-24 DOI: 10.1109/TCBB.2024.3467033
Gabriel Siqueira, Alexsandro Oliveira Alexandrino, Andre Rodrigues Oliveira, Geraldine Jean, Guillaume Fertin, Zanoni Dias

Genome Rearrangement distance problems are used in Computational Biology to estimate the evolutionary distance between genomes. These problems consist of minimizing the number of rearrangement events necessary to transform one genome into another. Two commonly used rearrangement events are reversal and transposition. The first studied problems ignored nucleotides outside genes (called intergenic regions), or assumed that genomes have a single copy of each gene. Recent works made advancements in more general problems considering the number of nucleotides in intergenic regions, and replicated genes. Nevertheless, genomes tend to have wildly different quantities of nucleotides on their intergenic regions, which poses a problem when comparing these regions exactly. To overcome this limitation, our work considers some flexibility when matching intergenic regions that do not have the same number of nucleotides. We propose new problems seeking the minimum number of reversals, or reversals and transpositions, necessary to transform one genome into another, while considering flexible intergenic region information. We show approximations for these problems by exploring their relationship with the Signed Minimum Common Flexible Intergenic String Partition problem. We also present different heuristics for the partition problem, and conduct experimental tests on simulated genomes to assess the performance of our algorithms.

基因组重排距离问题在计算生物学中用于估算基因组之间的进化距离。这些问题包括将一个基因组转化为另一个基因组所需的重排事件数量最小化。两种常用的重排事件是反转和转座。最初研究的问题忽略了基因外的核苷酸(称为基因间区),或假设基因组中每个基因只有一个拷贝。最近的研究在考虑基因间区核苷酸数量和复制基因等更一般的问题上取得了进展。然而,基因组在基因间区的核苷酸数量往往相差很大,这就给精确比较这些区域带来了问题。为了克服这一局限,我们的研究在匹配核苷酸数量不一致的基因间区时考虑了一定的灵活性。我们提出了新的问题,即在考虑灵活的基因间区域信息的同时,寻求将一个基因组转化为另一个基因组所需的最小反转或反转和转座次数。我们通过探讨这些问题与符号最小通用灵活基因间字符串分割问题的关系,展示了这些问题的近似值。我们还针对分割问题提出了不同的启发式算法,并在模拟基因组上进行了实验测试,以评估我们算法的性能。
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引用次数: 0
Improving Antifreeze Proteins Prediction with Protein Language Models and Hybrid Feature Extraction Networks. 利用蛋白质语言模型和混合特征提取网络改进抗冻蛋白预测。
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-24 DOI: 10.1109/TCBB.2024.3467261
Jiashun Wu, Yan Liu, Yiheng Zhu, Dong-Jun Yu

Accurate identification of antifreeze proteins (AFPs) is crucial in developing biomimetic synthetic anti-icing materials and low-temperature organ preservation materials. Although numerous machine learning-based methods have been proposed for AFPs prediction, the complex and diverse nature of AFPs limits the prediction performance of existing methods. In this study, we propose AFP-Deep, a new deep learning method to predict antifreeze proteins by integrating embedding from protein sequences with pre-trained protein language models and evolutionary contexts with hybrid feature extraction networks. The experimental results demonstrated that the main advantage of AFP-Deep is its utilization of pre-trained protein language models, which can extract discriminative global contextual features from protein sequences. Additionally, the hybrid deep neural networks designed for protein language models and evolutionary context feature extraction enhance the correlation between embeddings and antifreeze pattern. The performance evaluation results show that AFP-Deep achieves superior performance compared to state-of-the-art models on benchmark datasets, achieving an AUPRC of 0.724 and 0.924, respectively.

准确鉴定防冻蛋白(AFPs)对于开发仿生合成防冰材料和低温器官保存材料至关重要。虽然已经提出了许多基于机器学习的 AFPs 预测方法,但 AFPs 的复杂性和多样性限制了现有方法的预测性能。在本研究中,我们提出了一种新的深度学习方法AFP-Deep,通过将蛋白质序列的嵌入与预训练的蛋白质语言模型和进化上下文与混合特征提取网络相结合来预测防冻蛋白质。实验结果表明,AFP-Deep 的主要优势在于它利用了预训练的蛋白质语言模型,可以从蛋白质序列中提取具有区分性的全局上下文特征。此外,为蛋白质语言模型和进化上下文特征提取设计的混合深度神经网络增强了嵌入与防冻模式之间的相关性。性能评估结果表明,AFP-Deep 在基准数据集上的性能优于最先进的模型,AUPRC 分别达到 0.724 和 0.924。
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引用次数: 0
GenoM7GNet: An Efficient N7-Methylguanosine Site Prediction Approach Based on a Nucleotide Language Model. GenoM7GNet:基于核苷酸语言模型的高效 N7-甲基鸟苷位点预测方法
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-20 DOI: 10.1109/TCBB.2024.3459870
Chuang Li, Heshi Wang, Yanhua Wen, Rui Yin, Xiangxiang Zeng, Keqin Li

N7 -methylguanosine (m7G), one of the mainstream post-transcriptional RNA modifications, occupies an exceedingly significant place in medical treatments. However, classic approaches for identifying m7G sites are costly both in time and equipment. Meanwhile, the existing machine learning methods extract limited hidden information from RNA sequences, thus making it difficult to improve the accuracy. Therefore, we put forward to a deep learning network, called "GenoM7GNet," for m7G site identification. This model utilizes a Bidirectional Encoder Representation from Transformers (BERT) and is pretrained on nucleotide sequences data to capture hidden patterns from RNA sequences for m7G site prediction. Moreover, through detailed comparative experiments with various deep learning models, we discovered that the one-dimensional convolutional neural network (CNN) exhibits outstanding performance in sequence feature learning and classification. The proposed GenoM7GNet model achieved 0.953in accuracy, 0.932in sensitivity, 0.976in specificity, 0.907in Matthews Correlation Coefficient and 0.984in Area Under the receiver operating characteristic Curve on performance evaluation. Extensive experimental results further prove that our GenoM7GNet model markedly surpasses other state-of-the-art models in predicting m7G sites, exhibiting high computing performance.

N7 -甲基鸟苷(m7G)是转录后 RNA 修饰的主流之一,在医学治疗中占有极其重要的地位。然而,识别 m7G 位点的传统方法在时间和设备上都很昂贵。同时,现有的机器学习方法从 RNA 序列中提取的隐藏信息有限,因此很难提高准确率。因此,我们提出了一种用于识别 m7G 位点的深度学习网络,称为 "GenoM7GNet"。该模型利用双向变换器编码器表征(BERT),并在核苷酸序列数据上进行预训练,以捕捉 RNA 序列中的隐藏模式,用于 m7G 位点预测。此外,通过与各种深度学习模型的详细对比实验,我们发现一维卷积神经网络(CNN)在序列特征学习和分类方面表现出色。所提出的 GenoM7GNet 模型在性能评估上取得了 0.953 的准确率、0.932 的灵敏度、0.976 的特异性、0.907 的马修斯相关系数和 0.984 的接收者工作特征曲线下面积。广泛的实验结果进一步证明,我们的 GenoM7GNet 模型在预测 m7G 位点方面明显超越了其他最先进的模型,表现出很高的计算性能。
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引用次数: 0
Topological-Similarity Based Canonical Representations for Biological Link Prediction 基于拓扑相似性的生物链接预测典型表示法
IF 4.5 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-17 DOI: 10.1109/tcbb.2024.3462730
Mengzhen Li, Mustafa Coşkun, Mehmet Koyutürk
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引用次数: 0
Accurate Flow Decomposition via Robust Integer Linear Programming 通过稳健整数线性规划实现精确流量分解
IF 4.5 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-13 DOI: 10.1109/tcbb.2024.3433523
Fernando H. C. Dias, Alexandru I. Tomescu
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引用次数: 0
A New Graph Autoencoder-Based Multi-level Kernel Subspace Fusion Framework for Single-cell Type Identification 基于图自动编码器的单细胞类型识别多级核子空间融合新框架
IF 4.5 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-12 DOI: 10.1109/tcbb.2024.3459960
Juan Wang, Tian-Jing Qiao, Chun-Hou Zheng, Jin-Xing Liu, Jun-Liang Shang
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引用次数: 0
Using Multi-Encoder Semi-Implicit Graph Variational Autoencoder to Analyze Single-Cell RNA Sequencing Data 使用多编码器半隐式图变自动编码器分析单细胞 RNA 测序数据
IF 4.5 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-10 DOI: 10.1109/tcbb.2024.3458170
Shengwen Tian, Cunmei Ji, Jiancheng Ni, Yutian Wang, Chunhou Zheng
{"title":"Using Multi-Encoder Semi-Implicit Graph Variational Autoencoder to Analyze Single-Cell RNA Sequencing Data","authors":"Shengwen Tian, Cunmei Ji, Jiancheng Ni, Yutian Wang, Chunhou Zheng","doi":"10.1109/tcbb.2024.3458170","DOIUrl":"https://doi.org/10.1109/tcbb.2024.3458170","url":null,"abstract":"","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
APMG: 3D Molecule Generation Driven by Atomic Chemical Properties APMG:由原子化学性质驱动的三维分子生成
IF 4.5 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-10 DOI: 10.1109/tcbb.2024.3457807
Yang Hua, Zhenhua Feng, Xiaoning Song, Hui Li, Tianyang Xu, Xiao-Jun Wu, Dong-Jun Yu
{"title":"APMG: 3D Molecule Generation Driven by Atomic Chemical Properties","authors":"Yang Hua, Zhenhua Feng, Xiaoning Song, Hui Li, Tianyang Xu, Xiao-Jun Wu, Dong-Jun Yu","doi":"10.1109/tcbb.2024.3457807","DOIUrl":"https://doi.org/10.1109/tcbb.2024.3457807","url":null,"abstract":"","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
IEEE/ACM Transactions on Computational Biology and Bioinformatics
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