Pub Date : 2024-10-03DOI: 10.1109/TCBB.2024.3473899
Zhenhao Sun;Meng Wang;Shiqi Wang;Sam Kwong
In this paper, we propose a Learning-based gEnome Codec (LEC), which is designed for high efficiency and enhanced flexibility. The LEC integrates several advanced technologies, including Group of Bases (GoB) compression, multi-stride coding and bidirectional prediction, all of which are aimed at optimizing the balance between coding complexity and performance in lossless compression. The model applied in our proposed codec is data-driven, based on deep neural networks to infer probabilities for each symbol, enabling fully parallel encoding and decoding with configured complexity for diverse applications. Based upon a set of configurations on compression ratios and inference speed, experimental results show that the proposed method is very efficient in terms of compression performance and provides improved flexibility in real-world applications.
{"title":"LEC-Codec: Learning-Based Genome Data Compression","authors":"Zhenhao Sun;Meng Wang;Shiqi Wang;Sam Kwong","doi":"10.1109/TCBB.2024.3473899","DOIUrl":"10.1109/TCBB.2024.3473899","url":null,"abstract":"In this paper, we propose a Learning-based gEnome Codec (LEC), which is designed for high efficiency and enhanced flexibility. The LEC integrates several advanced technologies, including Group of Bases (GoB) compression, multi-stride coding and bidirectional prediction, all of which are aimed at optimizing the balance between coding complexity and performance in lossless compression. The model applied in our proposed codec is data-driven, based on deep neural networks to infer probabilities for each symbol, enabling fully parallel encoding and decoding with configured complexity for diverse applications. Based upon a set of configurations on compression ratios and inference speed, experimental results show that the proposed method is very efficient in terms of compression performance and provides improved flexibility in real-world applications.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2447-2458"},"PeriodicalIF":3.6,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142371724","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 : 2024-09-27DOI: 10.1109/TCBB.2024.3469164
Xuena Liang;Junliang Shang;Jin-Xing Liu;Chun-Hou Zheng;Juan Wang
Recent advancements in spatially transcriptomics (ST) technologies have enabled the comprehensive measurement of gene expression profiles while preserving the spatial information of cells. Combining gene expression profiles and spatial information has been the most commonly used method to identify spatial functional domains and genes. However, most existing spatial domain decipherer methods are more focused on spatially neighboring structures and fail to take into account balancing the self-characteristics and the spatial structure dependency of spots. Therefore, we propose a novel model called SpaGCAC, which recognizes spatial domains with the help of an adaptive feature-spatial balanced graph convolutional network named AFSBGCN. The AFSBGCN can dynamically learn the relationship between spatial local topology structures and the self-characteristics of spots by adaptively increasing or declining the weight on the self-characteristics during message aggregation. Moreover, to better capture the local structures of spots, SpaGCAC exploits a local topology structure contrastive learning strategy. Meanwhile, SpaGCAC utilizes a probability distribution contrastive learning strategy to increase the similarity of probability distributions for points belonging to the same category. We validate the performance of SpaGCAC for spatial domain identification on four spatial transcriptomic datasets. In comparison with seven spatial domain recognition methods, SpaGCAC achieved the highest NMI median of 0.683 and the second highest ARI median of 0.559 on the multi-slice DLPFC dataset. SpaGCAC achieved the best results on all three other single-slice datasets. The above-mentioned results show that SpaGCAC outperforms most existing methods, providing enhanced insights into tissue heterogeneity.
{"title":"Enhancing Spatial Domain Identification in Spatially Resolved Transcriptomics Using Graph Convolutional Networks With Adaptively Feature-Spatial Balance and Contrastive Learning","authors":"Xuena Liang;Junliang Shang;Jin-Xing Liu;Chun-Hou Zheng;Juan Wang","doi":"10.1109/TCBB.2024.3469164","DOIUrl":"10.1109/TCBB.2024.3469164","url":null,"abstract":"Recent advancements in spatially transcriptomics (ST) technologies have enabled the comprehensive measurement of gene expression profiles while preserving the spatial information of cells. Combining gene expression profiles and spatial information has been the most commonly used method to identify spatial functional domains and genes. However, most existing spatial domain decipherer methods are more focused on spatially neighboring structures and fail to take into account balancing the self-characteristics and the spatial structure dependency of spots. Therefore, we propose a novel model called SpaGCAC, which recognizes spatial domains with the help of an adaptive feature-spatial balanced graph convolutional network named AFSBGCN. The AFSBGCN can dynamically learn the relationship between spatial local topology structures and the self-characteristics of spots by adaptively increasing or declining the weight on the self-characteristics during message aggregation. Moreover, to better capture the local structures of spots, SpaGCAC exploits a local topology structure contrastive learning strategy. Meanwhile, SpaGCAC utilizes a probability distribution contrastive learning strategy to increase the similarity of probability distributions for points belonging to the same category. We validate the performance of SpaGCAC for spatial domain identification on four spatial transcriptomic datasets. In comparison with seven spatial domain recognition methods, SpaGCAC achieved the highest NMI median of 0.683 and the second highest ARI median of 0.559 on the multi-slice DLPFC dataset. SpaGCAC achieved the best results on all three other single-slice datasets. The above-mentioned results show that SpaGCAC outperforms most existing methods, providing enhanced insights into tissue heterogeneity.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2406-2417"},"PeriodicalIF":3.6,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345922","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 : 2024-09-27DOI: 10.1109/TCBB.2024.3468434
Yuyang Xu;Jingbo Zhou;Haochao Ying;Jintai Chen;Wei Chen;Danny Z. Chen;Jian Wu
Drug Target Interaction (DTI) prediction plays a crucial role in in-silico drug discovery, especially for deep learning (DL) models. Along this line, existing methods usually first extract features from drugs and target proteins, and use drug-target pairs to train DL models. However, these DL-based methods essentially rely on similar structures and patterns defined by the homologous proteins from a large amount of data. When few drug-target interactions are known for a newly discovered protein and its homologous proteins, prediction performance can suffer notable reduction. In this paper, we propose a novel Protein-Context enhanced Master/Slave Framework (PCMS), for zero-shot DTI prediction. This framework facilitates the efficient discovery of ligands for newly discovered target proteins, addressing the challenge of predicting interactions without prior data. Specifically, the PCMS framework consists of two main components: a Master Learner and a Slave Learner. The Master Learner first learns the target protein context information, and then adaptively generates the corresponding parameters for the Slave Learner. The Slave Learner then perform zero-shot DTI prediction in different protein contexts. Extensive experiments verify the effectiveness of our PCMS compared to state-of-the-art methods in various metrics on two public datasets.
{"title":"A Protein-Context Enhanced Master Slave Framework for Zero-Shot Drug Target Interaction Prediction","authors":"Yuyang Xu;Jingbo Zhou;Haochao Ying;Jintai Chen;Wei Chen;Danny Z. Chen;Jian Wu","doi":"10.1109/TCBB.2024.3468434","DOIUrl":"10.1109/TCBB.2024.3468434","url":null,"abstract":"Drug Target Interaction (DTI) prediction plays a crucial role in in-silico drug discovery, especially for deep learning (DL) models. Along this line, existing methods usually first extract features from drugs and target proteins, and use drug-target pairs to train DL models. However, these DL-based methods essentially rely on similar structures and patterns defined by the homologous proteins from a large amount of data. When few drug-target interactions are known for a newly discovered protein and its homologous proteins, prediction performance can suffer notable reduction. In this paper, we propose a novel Protein-Context enhanced Master/Slave Framework (PCMS), for zero-shot DTI prediction. This framework facilitates the efficient discovery of ligands for newly discovered target proteins, addressing the challenge of predicting interactions without prior data. Specifically, the PCMS framework consists of two main components: a Master Learner and a Slave Learner. The Master Learner first learns the target protein context information, and then adaptively generates the corresponding parameters for the Slave Learner. The Slave Learner then perform zero-shot DTI prediction in different protein contexts. Extensive experiments verify the effectiveness of our PCMS compared to state-of-the-art methods in various metrics on two public datasets.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2359-2370"},"PeriodicalIF":3.6,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345921","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 : 2024-09-27DOI: 10.1109/TCBB.2024.3470592
Garud Iyengar;Mitch Perry
Models for microbial interactions attempt to understand and predict the steady state network of inter-species relationships in a community, e.g. competition for shared metabolites, and cooperation through cross-feeding. Flux balance analysis (FBA) is an approach that was introduced to model the interaction of a particular microbial species with its environment. This approach has been extended to analyzing interactions in a community of microbes; however, these approaches have two important drawbacks: first, one has to numerically solve a differential equation to identify the steady state, and second, there are no methods available to analyze the stability of the steady state. We propose a game theory based community FBA model wherein species compete to maximize their individual growth rate, and the state of the community is given by the resulting Nash equilibrium. We develop a computationally efficient method for directly computing the steady state biomasses and fluxes without solving a differential equation. We also develop a method to determine the stability of a steady state to perturbations in the biomasses and to invasion by new species. We report the results of applying our proposed framework to a small community of four E. coli mutants that compete for externally supplied glucose, as well as cooperate since the mutants are auxotrophic for metabolites exported by other mutants, and a more realistic model for a gut microbiome consisting of nine species.
{"title":"Game-Theoretic Flux Balance Analysis Model for Predicting Stable Community Composition","authors":"Garud Iyengar;Mitch Perry","doi":"10.1109/TCBB.2024.3470592","DOIUrl":"10.1109/TCBB.2024.3470592","url":null,"abstract":"Models for microbial interactions attempt to understand and predict the steady state network of inter-species relationships in a community, e.g. competition for shared metabolites, and cooperation through cross-feeding. Flux balance analysis (FBA) is an approach that was introduced to model the interaction of a particular microbial species with its environment. This approach has been extended to analyzing interactions in a community of microbes; however, these approaches have two important drawbacks: first, one has to numerically solve a differential equation to identify the steady state, and second, there are no methods available to analyze the stability of the steady state. We propose a game theory based community FBA model wherein species compete to maximize their individual growth rate, and the state of the community is given by the resulting Nash equilibrium. We develop a computationally efficient method for directly computing the steady state biomasses and fluxes without solving a differential equation. We also develop a method to determine the stability of a steady state to perturbations in the biomasses and to invasion by new species. We report the results of applying our proposed framework to a small community of four \u0000<italic>E. coli</i>\u0000 mutants that compete for externally supplied glucose, as well as cooperate since the mutants are auxotrophic for metabolites exported by other mutants, and a more realistic model for a gut microbiome consisting of nine species.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2394-2405"},"PeriodicalIF":3.6,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345923","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 : 2024-09-26DOI: 10.1109/TCBB.2024.3469178
Haixi Zhang;Jiahui Yang;Chenyan Lv;Xing Wei;Haibin Han;Bin Liu
Apple leaf diseases can seriously affect apple production and quality, and accurately detecting them can improve the efficiency of disease monitoring. Owing to the complex natural growth environment, apple leaf lesions may be easily confused with background noise, leading to poor performance. In this study, a cascaded Incremental Region Proposal Network (Inc-RPN) is proposed to accurately detect apple leaf diseases in natural environments. The proposed Inc-RPN has a two-layer RPN architecture, where the precursor RPN is leveraged to generate diseased leaf proposals, and the successor RPN focuses on extracting target disease spots based on diseased leaf proposals. In the successor RPN, a low-level feature aggregation module is designed to fully utilize the bridged features and preserve the semantic information of the target disease spots. An incremental module is also leveraged to extract aggregated diseased leaf features and target disease spot features. Finally, a novel position anchor generator is designed to generate anchors based on diseased leaf proposals. The experimental results show that the proposed Inc-RPN performs very well on the FALD_CED and Apple Leaf Disease datasets, showing that it can accurately perform apple leaf disease detection tasks.
{"title":"Incremental RPN: Hierarchical Region Proposal Network for Apple Leaf Disease Detection in Natural Environments","authors":"Haixi Zhang;Jiahui Yang;Chenyan Lv;Xing Wei;Haibin Han;Bin Liu","doi":"10.1109/TCBB.2024.3469178","DOIUrl":"10.1109/TCBB.2024.3469178","url":null,"abstract":"Apple leaf diseases can seriously affect apple production and quality, and accurately detecting them can improve the efficiency of disease monitoring. Owing to the complex natural growth environment, apple leaf lesions may be easily confused with background noise, leading to poor performance. In this study, a cascaded Incremental Region Proposal Network (Inc-RPN) is proposed to accurately detect apple leaf diseases in natural environments. The proposed Inc-RPN has a two-layer RPN architecture, where the precursor RPN is leveraged to generate diseased leaf proposals, and the successor RPN focuses on extracting target disease spots based on diseased leaf proposals. In the successor RPN, a low-level feature aggregation module is designed to fully utilize the bridged features and preserve the semantic information of the target disease spots. An incremental module is also leveraged to extract aggregated diseased leaf features and target disease spot features. Finally, a novel position anchor generator is designed to generate anchors based on diseased leaf proposals. The experimental results show that the proposed Inc-RPN performs very well on the FALD_CED and Apple Leaf Disease datasets, showing that it can accurately perform apple leaf disease detection tasks.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2418-2431"},"PeriodicalIF":3.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345927","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}
AutoDock Vina and its derivatives have established themselves as a prevailing pipeline for virtual screening in contemporary drug discovery. Our Vina-GPU method leverages the parallel computing power of GPUs to accelerate AutoDock Vina, and Vina-GPU 2.0 further enhances the speed of AutoDock Vina and its derivatives. Given the prevalence of large virtual screens in modern drug discovery, the improvement of speed and accuracy in virtual screening has become a longstanding challenge. In this study, we propose Vina-GPU 2.1, aimed at enhancing the docking speed and precision of AutoDock Vina and its derivatives through the integration of novel algorithms to facilitate improved docking and virtual screening outcomes. Building upon the foundations laid by Vina-GPU 2.0, we introduce a novel algorithm, namely Reduced Iteration and Low Complexity BFGS (RILC-BFGS), designed to expedite the most time-consuming operation. Additionally, we implement grid cache optimization to further enhance the docking speed. Furthermore, we employ optimal strategies to individually optimize the structures of ligands, receptors, and binding pockets, thereby enhancing the docking precision. To assess the performance of Vina-GPU 2.1, we conduct extensive virtual screening experiments on three prominent targets, utilizing two fundamental compound libraries and seven docking tools. Our results demonstrate that Vina-GPU 2.1 achieves an average 4.97-fold acceleration in docking speed and an average 342% improvement in EF1% compared to Vina-GPU 2.0.
{"title":"Vina-GPU 2.1: Towards Further Optimizing Docking Speed and Precision of AutoDock Vina and Its Derivatives","authors":"Shidi Tang;Ji Ding;Xiangyu Zhu;Zheng Wang;Haitao Zhao;Jiansheng Wu","doi":"10.1109/TCBB.2024.3467127","DOIUrl":"10.1109/TCBB.2024.3467127","url":null,"abstract":"AutoDock Vina and its derivatives have established themselves as a prevailing pipeline for virtual screening in contemporary drug discovery. Our Vina-GPU method leverages the parallel computing power of GPUs to accelerate AutoDock Vina, and Vina-GPU 2.0 further enhances the speed of AutoDock Vina and its derivatives. Given the prevalence of large virtual screens in modern drug discovery, the improvement of speed and accuracy in virtual screening has become a longstanding challenge. In this study, we propose Vina-GPU 2.1, aimed at enhancing the docking speed and precision of AutoDock Vina and its derivatives through the integration of novel algorithms to facilitate improved docking and virtual screening outcomes. Building upon the foundations laid by Vina-GPU 2.0, we introduce a novel algorithm, namely Reduced Iteration and Low Complexity BFGS (RILC-BFGS), designed to expedite the most time-consuming operation. Additionally, we implement grid cache optimization to further enhance the docking speed. Furthermore, we employ optimal strategies to individually optimize the structures of ligands, receptors, and binding pockets, thereby enhancing the docking precision. To assess the performance of Vina-GPU 2.1, we conduct extensive virtual screening experiments on three prominent targets, utilizing two fundamental compound libraries and seven docking tools. Our results demonstrate that Vina-GPU 2.1 achieves an average 4.97-fold acceleration in docking speed and an average 342% improvement in EF1% compared to Vina-GPU 2.0.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2382-2393"},"PeriodicalIF":3.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345933","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 : 2024-09-25DOI: 10.1109/TCBB.2024.3467093
Runchang Jia;Zhijie He;Cong Wang;Xudong Guo;Fuyi Li
Protein-metal ion interactions play a central role in the onset of numerous diseases. When amino acid changes lead to missense mutations in metal-binding sites, the disrupted interaction with metal ions can compromise protein function, potentially causing severe human ailments. Identifying these disease-associated mutation sites within metal-binding regions is paramount for understanding protein function and fostering innovative drug development. While some computational methods aim to tackle this challenge, they often fall short in accuracy, commonly due to manual feature extraction and the absence of structural data. We introduce MetalPrognosis, an innovative, alignment-free solution that predicts disease-associated mutations within metal-binding sites of metalloproteins with heightened precision. Rather than relying on manual feature extraction, MetalPrognosis employs sliding window sequences as input, extracting deep semantic insights from pre-trained protein language models. These insights are then incorporated into a convolutional neural network, facilitating the derivation of intricate features. Comparative evaluations show MetalPrognosis outperforms leading methodologies like MCCNN and M-Ionic across various metalloprotein test sets. Furthermore, an ablation study reiterates the effectiveness of our model architecture.
{"title":"MetalPrognosis: A Biological Language Model-Based Approach for Disease-Associated Mutations in Metal-Binding Site Prediction","authors":"Runchang Jia;Zhijie He;Cong Wang;Xudong Guo;Fuyi Li","doi":"10.1109/TCBB.2024.3467093","DOIUrl":"10.1109/TCBB.2024.3467093","url":null,"abstract":"Protein-metal ion interactions play a central role in the onset of numerous diseases. When amino acid changes lead to missense mutations in metal-binding sites, the disrupted interaction with metal ions can compromise protein function, potentially causing severe human ailments. Identifying these disease-associated mutation sites within metal-binding regions is paramount for understanding protein function and fostering innovative drug development. While some computational methods aim to tackle this challenge, they often fall short in accuracy, commonly due to manual feature extraction and the absence of structural data. We introduce MetalPrognosis, an innovative, alignment-free solution that predicts disease-associated mutations within metal-binding sites of metalloproteins with heightened precision. Rather than relying on manual feature extraction, MetalPrognosis employs sliding window sequences as input, extracting deep semantic insights from pre-trained protein language models. These insights are then incorporated into a convolutional neural network, facilitating the derivation of intricate features. Comparative evaluations show MetalPrognosis outperforms leading methodologies like MCCNN and M-Ionic across various metalloprotein test sets. Furthermore, an ablation study reiterates the effectiveness of our model architecture.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2340-2348"},"PeriodicalIF":3.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345928","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}
Alignment-free analysis of sequences has revolutionized the high-throughput processing of sequencing data within numerous bioinformatics pipelines. Hashing $k$ -mers represents a common function across various alignment-free applications, serving as a crucial tool for indexing, querying, and rapid similarity searching. More recently, spaced seeds, a specialized pattern that accommodates errors or mutations, have become a standard choice over traditional $k$ -mers. Spaced seeds offer enhanced sensitivity in many applications when compared to $k$ -mers. However, it's important to note that hashing spaced seeds significantly increases computational time. Furthermore, if multiple spaced seeds are employed, accuracy can be further improved, albeit at the expense of longer processing times. This paper addresses the challenge of efficiently hashing multiple spaced seeds. The proposed algorithms leverage the similarity of adjacent spaced seed hash values within an input sequence, allowing for the swift computation of subsequent hashes. Our experimental results, conducted across various tests, demonstrate a remarkable performance improvement over previously suggested algorithms, with potential speedups of up to 20 times. Additionally, we apply these efficient spaced seed hashing algorithms to a metagenomic application, specifically the classification of reads using Clark-S (Ounit and Lonardi, 2016). Our findings reveal a substantial speedup, effectively mitigating the slowdown caused by the utilization of multiple spaced seeds.
{"title":"MISSH: Fast Hashing of Multiple Spaced Seeds","authors":"Eleonora Mian;Enrico Petrucci;Cinzia Pizzi;Matteo Comin","doi":"10.1109/TCBB.2024.3467368","DOIUrl":"10.1109/TCBB.2024.3467368","url":null,"abstract":"Alignment-free analysis of sequences has revolutionized the high-throughput processing of sequencing data within numerous bioinformatics pipelines. Hashing \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-mers represents a common function across various alignment-free applications, serving as a crucial tool for indexing, querying, and rapid similarity searching. More recently, spaced seeds, a specialized pattern that accommodates errors or mutations, have become a standard choice over traditional \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-mers. Spaced seeds offer enhanced sensitivity in many applications when compared to \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-mers. However, it's important to note that hashing spaced seeds significantly increases computational time. Furthermore, if multiple spaced seeds are employed, accuracy can be further improved, albeit at the expense of longer processing times. This paper addresses the challenge of efficiently hashing multiple spaced seeds. The proposed algorithms leverage the similarity of adjacent spaced seed hash values within an input sequence, allowing for the swift computation of subsequent hashes. Our experimental results, conducted across various tests, demonstrate a remarkable performance improvement over previously suggested algorithms, with potential speedups of up to 20 times. Additionally, we apply these efficient spaced seed hashing algorithms to a metagenomic application, specifically the classification of reads using Clark-S (Ounit and Lonardi, 2016). Our findings reveal a substantial speedup, effectively mitigating the slowdown caused by the utilization of multiple spaced seeds.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2330-2339"},"PeriodicalIF":3.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345930","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 : 2024-09-24DOI: 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.
{"title":"Reinforced Metapath Optimization in Heterogeneous Information Networks for Drug-Target Interaction Prediction","authors":"Ben Xu;Jianping Chen;Yunzhe Wang;Qiming Fu;You Lu","doi":"10.1109/TCBB.2024.3467135","DOIUrl":"10.1109/TCBB.2024.3467135","url":null,"abstract":"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.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2315-2329"},"PeriodicalIF":3.6,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345932","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}
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。
{"title":"Identification of Cancer Driver Genes based on Dynamic Incentive Model","authors":"Zhipeng Hu;Gaoshi Li;Xinlong Luo;Wei Peng;Jiafei Liu;Xiaoshu Zhu;Jingli Wu","doi":"10.1109/TCBB.2024.3467119","DOIUrl":"10.1109/TCBB.2024.3467119","url":null,"abstract":"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.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2371-2381"},"PeriodicalIF":3.6,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345924","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}
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