Deep learning approaches, such as convolution neural networks (CNNs) and deep recurrent neural networks (RNNs), have been the backbone for predicting protein function, with promising state-of-the-art (SOTA) results. RNNs with an in-built ability (i) focus on past information, (ii) collect both short-and-long range dependency information, and (iii) bi-directional processing offers a strong sequential processing mechanism. CNNs, however, are confined to focusing on short-term information from both the past and the future, although they offer parallelism. Therefore, a novel bi-directional CNN that strictly complies with the sequential processing mechanism of RNNs is introduced and is used for developing a protein function prediction framework, Bi-SeqCNN. This is a sub-sequence-based framework. Further, Bi-SeqCNN $^+$ is an ensemble approach to better the prediction results. To our knowledge, this is the first time bi-directional CNNs are employed for general temporal data analysis and not just for protein sequences. The proposed architecture produces improvements up to +5.5% over contemporary SOTA methods on three benchmark protein sequence datasets. Moreover, it is substantially lighter and attain these results with (0.50–0.70 times) fewer parameters than the SOTA methods.
卷积神经网络（CNN）和深度递归神经网络（RNN）等深度学习方法已成为预测蛋白质功能的中坚力量，并取得了令人鼓舞的先进（SOTA）成果。RNN 具有以下内在能力：(i) 专注于过去的信息；(ii) 同时收集短程和长程依赖信息；(iii) 双向处理，提供了强大的顺序处理机制。而 CNN 虽然提供了并行性，却仅限于关注过去和未来的短期信息。因此，我们引入了一种严格遵守 RNN 顺序处理机制的新型双向 CNN，并将其用于开发蛋白质功能预测框架--Bi-SeqCNN。这是一个基于子序列的框架。此外，Bi-SeqCNN + 是一种集合方法，可以获得更好的预测结果。据我们所知，这是首次将双向 CNN 用于一般时间数据分析，而不仅仅是蛋白质序列。在三个基准蛋白质序列数据集上，所提出的架构比当代的 SOTA 方法提高了 5.5%。此外，与 SOTA 方法相比，它的重量更轻，只需要（0.50-0.70 倍）更少的参数就能获得这些结果。
{"title":"Bi-SeqCNN: A Novel Light-Weight Bi-Directional CNN Architecture for Protein Function Prediction","authors":"Vikash Kumar;Akshay Deepak;Ashish Ranjan;Aravind Prakash","doi":"10.1109/TCBB.2024.3426491","DOIUrl":"10.1109/TCBB.2024.3426491","url":null,"abstract":"Deep learning approaches, such as convolution neural networks (CNNs) and deep recurrent neural networks (RNNs), have been the backbone for predicting protein function, with promising state-of-the-art (SOTA) results. RNNs with an in-built ability (i) focus on past information, (ii) collect both \u0000<i>short-and-long</i>\u0000 range dependency information, and (iii) bi-directional processing offers a strong sequential processing mechanism. CNNs, however, are confined to focusing on \u0000<i>short-term</i>\u0000 information from both the past and the future, although they offer parallelism. Therefore, a novel \u0000<i>bi-directional CNN</i>\u0000 that strictly complies with the sequential processing mechanism of RNNs is introduced and is used for developing a protein function prediction framework, Bi-SeqCNN. This is a sub-sequence-based framework. Further, Bi-SeqCNN\u0000<inline-formula><tex-math>$^+$</tex-math></inline-formula>\u0000 is an ensemble approach to better the prediction results. To our knowledge, this is the first time \u0000<i>bi-directional CNNs</i>\u0000 are employed for general temporal data analysis and not just for protein sequences. The proposed architecture produces improvements up to +5.5% over contemporary SOTA methods on three benchmark protein sequence datasets. Moreover, it is substantially lighter and attain these results with (0.50–0.70 times) fewer parameters than the SOTA methods.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1922-1933"},"PeriodicalIF":3.6,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141590210","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}
Pub Date : 2024-07-08DOI: 10.1109/TCBB.2024.3424400
Wentao Zhu;Zhiqiang Du;Ziang Xu;Defu Yang;Minghan Chen;Qianqian Song
Alzheimer's disease (AD) is the most common neurodegenerative disease, and it consumes considerable medical resources with increasing number of patients every year. Mounting evidence show that the regulatory disruptions altering the intrinsic activity of genes in brain cells contribute to AD pathogenesis. To gain insights into the underlying gene regulation in AD, we proposed a graph learning method, Single-Cell based Regulatory Network (SCRN), to identify the regulatory mechanisms based on single-cell data. SCRN implements the γ-decaying heuristic link prediction based on graph neural networks and can identify reliable gene regulatory networks using locally closed subgraphs. In this work, we first performed UMAP dimension reduction analysis on single-cell RNA sequencing (scRNA-seq) data of AD and normal samples. Then we used SCRN to construct the gene regulatory network based on three well-recognized AD genes (APOE, CX3CR1, and P2RY12). Enrichment analysis of the regulatory network revealed significant pathways including NGF signaling, ERBB2 signaling, and hemostasis. These findings demonstrate the feasibility of using SCRN to uncover potential biomarkers and therapeutic targets related to AD.
阿尔茨海默病（AD）是最常见的神经退行性疾病，每年患者人数不断增加，耗费了大量医疗资源。越来越多的证据表明，改变脑细胞中基因内在活性的调控紊乱是导致阿尔茨海默病发病的原因之一。为了深入了解AD的潜在基因调控，我们提出了一种图学习方法--基于单细胞的调控网络（SCRN），以识别基于单细胞数据的调控机制。SCRN实现了基于图神经网络的γ-衰减启发式链接预测，能利用局部封闭子图识别可靠的基因调控网络。在这项工作中，我们首先对AD和正常样本的单细胞RNA测序（scRNA-seq）数据进行了UMAP降维分析。然后，我们使用 SCRN 构建了基于三个公认的 AD 基因（APOE、CX3CR1 和 P2RY12）的基因调控网络。调控网络的富集分析揭示了包括 NGF 信号转导、ERBB2 信号转导和止血在内的重要通路。这些发现证明了利用 SCRN 发现与 AD 相关的潜在生物标记物和治疗靶点的可行性。
{"title":"SCRN: Single-Cell Gene Regulatory Network Identification in Alzheimer's Disease","authors":"Wentao Zhu;Zhiqiang Du;Ziang Xu;Defu Yang;Minghan Chen;Qianqian Song","doi":"10.1109/TCBB.2024.3424400","DOIUrl":"10.1109/TCBB.2024.3424400","url":null,"abstract":"Alzheimer's disease (AD) is the most common neurodegenerative disease, and it consumes considerable medical resources with increasing number of patients every year. Mounting evidence show that the regulatory disruptions altering the intrinsic activity of genes in brain cells contribute to AD pathogenesis. To gain insights into the underlying gene regulation in AD, we proposed a graph learning method, Single-Cell based Regulatory Network (SCRN), to identify the regulatory mechanisms based on single-cell data. SCRN implements the γ-decaying heuristic link prediction based on graph neural networks and can identify reliable gene regulatory networks using locally closed subgraphs. In this work, we first performed UMAP dimension reduction analysis on single-cell RNA sequencing (scRNA-seq) data of AD and normal samples. Then we used SCRN to construct the gene regulatory network based on three well-recognized AD genes (APOE, CX3CR1, and P2RY12). Enrichment analysis of the regulatory network revealed significant pathways including NGF signaling, ERBB2 signaling, and hemostasis. These findings demonstrate the feasibility of using SCRN to uncover potential biomarkers and therapeutic targets related to AD.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1886-1896"},"PeriodicalIF":3.6,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141558630","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}
Pub Date : 2024-07-04DOI: 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.
{"title":"Improved Fuzzy Cognitive Maps for Gene Regulatory Networks Inference Based on Time Series Data","authors":"Marzieh Emadi;Farsad Zamani Boroujeni;Jamshid Pirgazi","doi":"10.1109/TCBB.2024.3423383","DOIUrl":"10.1109/TCBB.2024.3423383","url":null,"abstract":"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.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1816-1829"},"PeriodicalIF":3.6,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141534365","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}
Pub Date : 2024-07-03DOI: 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.
{"title":"AnglesRefine: Refinement of 3D Protein Structures Using Transformer Based on Torsion Angles.","authors":"Lei Zhang, Junyong Zhu, Sheng Wang, Jie Hou, Dong Si, Renzhi Cao","doi":"10.1109/TCBB.2024.3422288","DOIUrl":"10.1109/TCBB.2024.3422288","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141497925","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}
Pub Date : 2024-07-02DOI: 10.1109/TCBB.2024.3421924
Hailin Feng;Chenchen Ke;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.
{"title":"Prediction of Potential miRNA-Disease Associations Based on a Masked Graph Autoencoder","authors":"Hailin Feng;Chenchen Ke;Quan Zou;Zhechen Zhu;Tongcun Liu","doi":"10.1109/TCBB.2024.3421924","DOIUrl":"10.1109/TCBB.2024.3421924","url":null,"abstract":"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.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1874-1885"},"PeriodicalIF":3.6,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141491810","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}
Pub Date : 2024-07-02DOI: 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.
{"title":"Graph Convolutional Network With Self-Supervised Learning for Brain Disease Classification","authors":"Guangyu Wang;Ying Chu;Qianqian Wang;Limei Zhang;Lishan Qiao;Mingxia Liu","doi":"10.1109/TCBB.2024.3422152","DOIUrl":"10.1109/TCBB.2024.3422152","url":null,"abstract":"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.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1830-1841"},"PeriodicalIF":3.6,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141491809","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}
Pub Date : 2024-07-01DOI: 10.1109/TCBB.2024.3420903
Chris Salahub;Jeffrey Uhlmann
We propose a general method for optimally approximating an arbitrary matrix $mathbf {M}$ by a structured matrix $mathbf {T}$ (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 (Bult et al., 2019). 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。
{"title":"Optimal Structured Matrix Approximation for Robustness to Incomplete Biosequence Data","authors":"Chris Salahub;Jeffrey Uhlmann","doi":"10.1109/TCBB.2024.3420903","DOIUrl":"10.1109/TCBB.2024.3420903","url":null,"abstract":"We propose a general method for optimally approximating an arbitrary matrix \u0000<inline-formula><tex-math>$mathbf {M}$</tex-math></inline-formula>\u0000 by a structured matrix \u0000<inline-formula><tex-math>$mathbf {T}$</tex-math></inline-formula>\u0000 (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 (Bult et al., 2019). 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.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2592-2597"},"PeriodicalIF":3.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141476497","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}
Pub Date : 2024-07-01DOI: 10.1109/TCBB.2024.3421228
Xueqiang Fan;Bing Lin;Jun Hu;Zhongyi Guo
DNA N 6 -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, eXtreme gradient boosting coupled with recursive feature elimination is applied 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。
{"title":"Ense-i6mA: Identification of DNA N6-Methyladenine Sites Using XGB-RFE Feature Selection and Ensemble Machine Learning","authors":"Xueqiang Fan;Bing Lin;Jun Hu;Zhongyi Guo","doi":"10.1109/TCBB.2024.3421228","DOIUrl":"10.1109/TCBB.2024.3421228","url":null,"abstract":"DNA N\u0000<sup>6</sup>\u0000-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, \u0000<italic>K</i>\u0000-mer nucleotide frequency, and \u0000<italic>K</i>\u0000-mer nucleotide frequency with gap, are employed to extract DNA sequence features. Secondly, eXtreme gradient boosting coupled with recursive feature elimination is applied 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.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1842-1854"},"PeriodicalIF":3.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141476496","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}
Pub Date : 2024-06-28DOI: 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 near-singular. 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.
{"title":"Haplotype Frequency Inference From Pooled Genetic Data With a Latent Multinomial Model","authors":"Yong See Foo;Jennifer Flegg","doi":"10.1109/TCBB.2024.3420430","DOIUrl":"10.1109/TCBB.2024.3420430","url":null,"abstract":"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 near-singular. 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.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1864-1873"},"PeriodicalIF":3.6,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141467650","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}
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.
{"title":"Tropical Density Estimation of Phylogenetic Trees","authors":"Ruriko Yoshida;David Barnhill;Keiji Miura;Daniel Howe","doi":"10.1109/TCBB.2024.3420815","DOIUrl":"10.1109/TCBB.2024.3420815","url":null,"abstract":"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.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1855-1863"},"PeriodicalIF":3.6,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10577088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141467651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：