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A knowledge graph-based method for drug-drug interaction prediction with contrastive learning. 基于知识图谱的药物相互作用预测方法与对比学习。
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-10-09 DOI: 10.1109/TCBB.2024.3477410
Jian Zhong, Haochen Zhao, Qichang Zhao, Jianxin Wang

Precisely predicting Drug-Drug Interactions (DDIs) carries the potential to elevate the quality and safety of drug therapies, protecting the well-being of patients, and providing essential guidance and decision support at every stage of the drug development process. In recent years, leveraging large-scale biomedical knowledge graphs has improved DDI prediction performance. However, the feature extraction procedures in these methods are still rough. More refined features may further improve the quality of predictions. To overcome these limitations, we develop a knowledge graph-based method for multi-typed DDI prediction with contrastive learning (KG-CLDDI). In KG-CLDDI, we combine drug knowledge aggregation features from the knowledge graph with drug topological aggregation features from the DDI graph. Additionally, we build a contrastive learning module that uses horizontal reversal and dropout operations to produce high-quality embeddings for drug-drug pairs. The comparison results indicate that KG-CLDDI is superior to state-of-the-art models in both the transductive and inductive settings. Notably, for the inductive setting, KG-CLDDI outperforms the previous best method by 17.49% and 24.97% in terms of AUC and AUPR, respectively. Furthermore, we conduct the ablation analysis and case study to show the effectiveness of KG-CLDDI. These findings illustrate the potential significance of KG-CLDDI in advancing DDI research and its clinical applications. The codes of KG-CLDDI are available at https://github.com/jianzhong123/KG-CLDDI.

精确预测药物间相互作用(DDI)有可能提高药物治疗的质量和安全性,保护患者的健康,并在药物开发过程的各个阶段提供必要的指导和决策支持。近年来,利用大规模生物医学知识图谱提高了 DDI 预测性能。然而,这些方法中的特征提取程序仍然比较粗糙。更精细的特征可能会进一步提高预测质量。为了克服这些局限性,我们开发了一种基于知识图谱的多类型 DDI 预测方法(KG-CLDDI)。在 KG-CLDDI 中,我们将知识图谱中的药物知识聚合特征与 DDI 图谱中的药物拓扑聚合特征相结合。此外,我们还建立了一个对比学习模块,利用水平反转和剔除操作为药物对生成高质量的嵌入。对比结果表明,KG-CLDDI 在转导和归纳环境中都优于最先进的模型。值得注意的是,在归纳环境中,KG-CLDDI 的 AUC 和 AUPR 分别比之前的最佳方法高出 17.49% 和 24.97%。此外,我们还进行了消融分析和案例研究,以显示 KG-CLDDI 的有效性。这些发现说明了 KG-CLDDI 在推动 DDI 研究及其临床应用方面的潜在意义。KG-CLDDI 的代码见 https://github.com/jianzhong123/KG-CLDDI。
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引用次数: 0
Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model. 利用知识增强型生成模型改进分子生成和药物发现。
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-10-09 DOI: 10.1109/TCBB.2024.3477313
Aditya Malusare, Vaneet Aggarwal

Recent advancements in generative models have established state-of-the-art benchmarks in the generation of molecules and novel drug candidates. Despite these successes, a significant gap persists between generative models and the utilization of extensive biomedical knowledge, often systematized within knowledge graphs, whose potential to inform and enhance generative processes has not been realized. In this paper, we present a novel approach that bridges this divide by developing a framework for knowledge-enhanced generative models called KARL. We develop a scalable methodology to extend the functionality of knowledge graphs while preserving semantic integrity, and incorporate this contextual information into a generative framework to guide a diffusion-based model. The integration of knowledge graph embeddings with our generative model furnishes a robust mechanism for producing novel drug candidates possessing specific characteristics while ensuring validity and synthesizability. KARL outperforms state-of-the-art generative models on both unconditional and targeted generation tasks.

生成模型的最新进展为分子和新型候选药物的生成建立了最先进的基准。尽管取得了这些成就,但在生成模型与利用广泛的生物医学知识(通常在知识图谱中系统化)之间仍然存在着巨大的差距,而这些知识为生成过程提供信息和增强生成过程的潜力尚未实现。在本文中,我们提出了一种新颖的方法,通过开发一个名为 KARL 的知识增强生成模型框架来弥合这一鸿沟。我们开发了一种可扩展的方法来扩展知识图谱的功能,同时保持语义的完整性,并将这种上下文信息纳入生成框架,以指导基于扩散的模型。知识图谱嵌入与我们的生成模型相结合,提供了一种稳健的机制,用于生成具有特定特征的新型候选药物,同时确保有效性和可合成性。KARL 在无条件生成和目标生成任务上的表现都优于最先进的生成模型。
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引用次数: 0
RFLP-inator: interactive web platform for in silico simulation and complementary tools of the PCR-RFLP technique. RFLP-inator:用于 PCR-RFLP 技术硅学模拟和补充工具的交互式网络平台。
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-10-08 DOI: 10.1109/TCBB.2024.3476453
Kiefer Andre Bedoya Benites, Wilser Andres Garcia-Quispes

Polymerase chain reaction - Restriction Fragment Length Polymorphism (PCR-RFLP) is an established molecular biology technique leveraging DNA sequence variability for organism identification, genetic disease detection, biodiversity analysis, etc. Traditional PCR-RFLP requires wet-laboratory procedures that can result in technical errors, procedural challenges, and financial costs. With the aim of providing an accessible and efficient PCR-RFLP technique complement, we introduce RFLP-inator. This is a comprehensive web-based platform developed in R using the package Shiny, which simulates the PCR-RFLP technique, integrates analysis capabilities, and offers complementary tools for both pre- and post-evaluation of in vitro results. We developed the RFLP-inator's algorithm independently and our platform offers seven dynamic tools: RFLP simulator, Pattern identifier, Enzyme selector, RFLP analyzer, Multiplex PCR, Restriction map maker, and Gel plotter. Moreover, the software includes a restriction pattern database of more than 250,000 sequences of the bacterial 16S rRNA gene. We successfully validated the core tools against published research findings. This new platform is open access and user-friendly, offering a valuable resource for researchers, educators, and students specializing in molecular genetics. RFLP-inator not only streamlines RFLP technique application but also supports pedagogical efforts in genetics, illustrating its utility and reliability. The software is available for free at https://kodebio.shinyapps.io/RFLP-inator/.

聚合酶链式反应-限制性片段长度多态性(PCR-RFLP)是一种成熟的分子生物学技术,可利用 DNA 序列的变异性进行生物鉴定、遗传病检测和生物多样性分析等。传统的 PCR-RFLP 需要湿法实验室程序,可能导致技术错误、程序挑战和经济成本。为了提供方便、高效的 PCR-RFLP 技术补充,我们推出了 RFLP-inator。这是一个基于 R 的综合网络平台,使用 Shiny 软件包开发,可模拟 PCR-RFLP 技术,集成分析功能,并为体外结果的事前和事后评估提供补充工具。我们独立开发了 RFLP-inator 算法,我们的平台提供七种动态工具:RFLP 模拟器、模式识别器、酶选择器、RFLP 分析器、多重 PCR、限制性图谱制作器和凝胶绘图器。此外,该软件还包括一个包含 25 万多条细菌 16S rRNA 基因序列的限制性模式数据库。我们根据已发表的研究成果成功验证了核心工具。这一新平台具有开放性和用户友好性,为分子遗传学专业的研究人员、教育工作者和学生提供了宝贵的资源。RFLP-inator 不仅简化了 RFLP 技术的应用,还支持遗传学的教学工作,体现了其实用性和可靠性。该软件可在 https://kodebio.shinyapps.io/RFLP-inator/ 免费获取。
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引用次数: 0
LINgroups as a Robust Principled Approach to Compare and Integrate Multiple Bacterial Taxonomies. 将 LINgroups 作为一种可靠的原则性方法来比较和整合多种细菌分类法。
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-10-07 DOI: 10.1109/TCBB.2024.3475917
Reza Mazloom, N Tessa Pierce-Ward, Parul Sharma, Leighton Pritchard, C Titus Brown, Boris A Vinatzer, Lenwood S Heath

As a central organizing principle of biology, bacteria and archaea are classified into a hierarchical structure across taxonomic ranks from kingdom to subspecies. Traditionally, this organization was based on observable characteristics of form and chemistry but recently, bacterial taxonomy has been robustly quantified using comparisons of sequenced genomes, as exemplified in the Genome Taxonomy Database (GTDB). Such genome-based taxonomies resolve genomes down to genera and species and are useful in many contexts yet lack the flexibility and resolution of a fine-grained approach. The Life Identification Number (LIN) approach is a common, quantitative framework to tie existing (and future) bacterial taxonomies together, increase the resolution of genome-based discrimination of taxa, and extend taxonomic identification below the species level in a principled way. Utilizing LINgroup as an organizational concept helps resolve some of the confusion and unforeseen negative effects resulting from nomenclature changes of microorganisms that are closely related by overall genomic similarity (often due to genome-based reclassification). Our experimental results demonstrate the value of LINs and LINgroups in mapping between taxonomies, translating between different nomenclatures, and integrating them into a single taxonomic framework. They also reveal the robustness of LIN assignment to hyper-parameter changes when considering within-species taxonomic groups.

作为生物学的核心组织原则,细菌和古细菌被划分为从王国到亚种的不同分类等级结构。传统上,这种组织结构是基于可观察到的形态和化学特征,但最近,细菌分类学已经通过基因组测序比较得到了有力的量化,基因组分类数据库(GTDB)就是一个例子。这种基于基因组的分类法将基因组细分为属和种,在很多情况下都很有用,但缺乏细粒度方法的灵活性和分辨率。生命识别码(LIN)方法是一种通用的定量框架,可将现有(和未来)的细菌分类法联系在一起,提高基于基因组的分类法的分辨率,并以一种有原则的方式将分类鉴定扩展到物种级别以下。利用 LINgroup 作为一个组织概念,有助于解决因整体基因组相似性(通常是由于基于基因组的重新分类)而密切相关的微生物命名变化所造成的一些混乱和不可预见的负面影响。我们的实验结果表明了 LINs 和 LINgroups 在分类法之间的映射、不同命名法之间的转换以及将它们整合到单一分类框架中的价值。它们还揭示了在考虑物种内分类群时,LIN分配对超参数变化的稳健性。
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引用次数: 0
LEC-Codec: Learning-Based Genome Data Compression. LEC-Codec:基于学习的基因组数据压缩
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-10-03 DOI: 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.

在本文中,我们提出了基于学习的 gEnome 编解码器 (LEC),其设计旨在提高效率和灵活性。LEC 集成了多项先进技术,包括基群(GoB)压缩、多线编码和双向预测,所有这些技术都旨在优化无损压缩中编码复杂性和性能之间的平衡。我们提出的编解码器中应用的模型是数据驱动的,基于深度神经网络来推断每个符号的概率,从而实现完全并行的编码和解码,并为不同的应用配置复杂度。基于压缩比和推理速度的一系列配置,实验结果表明,所提出的方法在压缩性能方面非常高效,并为实际应用提供了更大的灵活性。
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引用次数: 0
MG-TCCA: Tensor Canonical Correlation Analysis across Multiple Groups. MG-TCCA:跨多组的张量典型相关分析。
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-30 DOI: 10.1109/TCBB.2024.3471930
Zhuoping Zhou, Boning Tong, Davoud Ataee Tarzanagh, Bojian Hou, Andrew J Saykin, Qi Long, Li Shen

Tensor Canonical Correlation Analysis (TCCA) is a commonly employed statistical method utilized to examine linear associations between two sets of tensor datasets. However, the existing TCCA models fail to adequately address the heterogeneity present in real-world tensor data, such as brain imaging data collected from diverse groups characterized by factors like sex and race. Consequently, these models may yield biased outcomes. In order to surmount this constraint, we propose a novel approach called Multi-Group TCCA (MG-TCCA), which enables the joint analysis of multiple subgroups. By incorporating a dual sparsity structure and a block coordinate ascent algorithm, our MG-TCCA method effectively addresses heterogeneity and leverages information across different groups to identify consistent signals. This novel approach facilitates the quantification of shared and individual structures, reduces data dimensionality, and enables visual exploration. To empirically validate our approach, we conduct a study focused on investigating correlations between two brain positron emission tomography (PET) modalities (AV-45 and FDG) within an Alzheimer's disease (AD) cohort. Our results demonstrate that MG-TCCA surpasses traditional TCCA and Sparse TCCA (STCCA) in identifying sex-specific cross-modality imaging correlations. This heightened performance of MG-TCCA provides valuable insights for the characterization of multimodal imaging biomarkers in AD.

张量典型相关分析(TCCA)是一种常用的统计方法,用于研究两组张量数据集之间的线性关联。然而,现有的 TCCA 模型未能充分解决现实世界中张量数据存在的异质性问题,例如从不同群体收集的脑成像数据,这些群体的特点是性别和种族等因素。因此,这些模型可能会产生有偏差的结果。为了克服这一限制,我们提出了一种称为多组 TCCA(MG-TCCA)的新方法,它可以对多个子组进行联合分析。我们的 MG-TCCA 方法结合了双重稀疏性结构和块坐标上升算法,能有效解决异质性问题,并利用不同组间的信息来识别一致的信号。这种新方法有助于量化共享结构和个体结构,降低数据维度,并实现可视化探索。为了对我们的方法进行经验验证,我们开展了一项研究,重点调查阿尔茨海默病(AD)队列中两种脑正电子发射断层扫描(PET)模式(AV-45 和 FDG)之间的相关性。我们的研究结果表明,MG-TCCA 在识别性别特异性跨模态成像相关性方面超过了传统 TCCA 和稀疏 TCCA(STCCA)。MG-TCCA 性能的提高为确定 AD 多模态成像生物标记物的特征提供了宝贵的见解。
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引用次数: 0
Enhancing Spatial Domain Identification in Spatially Resolved Transcriptomics Using Graph Convolutional Networks with Adaptively Feature-Spatial Balance and Contrastive Learning. 利用具有自适应特征空间平衡和对比学习功能的图卷积网络增强空间分辨转录组学中的空间域识别能力
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-27 DOI: 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.

空间转录组学(ST)技术的最新进展实现了对基因表达谱的全面测量,同时保留了细胞的空间信息。结合基因表达谱和空间信息一直是识别空间功能域和基因最常用的方法。然而,现有的空间功能域破译方法大多更关注空间相邻结构,未能兼顾斑的自特性和空间结构依赖性。因此,我们提出了一种名为 SpaGCAC 的新型模型,它借助名为 AFSBGCN 的自适应特征空间平衡图卷积网络来识别空间域。AFSBGCN 可以通过在信息聚合过程中自适应地增加或降低自特征的权重,动态学习空间局部拓扑结构与点的自特征之间的关系。此外,为了更好地捕捉点的局部结构,SpaGCAC 采用了局部拓扑结构对比学习策略。同时,SpaGCAC 利用概率分布对比学习策略来提高属于同一类别的点的概率分布的相似性。我们在四个空间转录组数据集上验证了 SpaGCAC 在空间域识别方面的性能。与七种空间域识别方法相比,SpaGCAC在多切片DLPFC数据集上取得了最高的NMI中值0.683和第二高的ARI中值0.559。SpaGCAC 在其他三个单片数据集上都取得了最佳结果。上述结果表明,SpaGCAC 优于大多数现有方法,能更好地洞察组织异质性。
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引用次数: 0
A Protein-Context Enhanced Master Slave Framework for Zero-Shot Drug Target Interaction Prediction. 用于零注射药物靶点相互作用预测的蛋白质-上下文增强型主从框架。
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-27 DOI: 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. The Code and the processed Data will be open once the paper is accepted.

药物靶点相互作用(DTI)预测在硅内药物发现中起着至关重要的作用,尤其是对深度学习(DL)模型而言。根据这一思路,现有方法通常首先从药物和靶蛋白中提取特征,然后使用药物-靶蛋白对训练 DL 模型。然而,这些基于 DL 的方法基本上依赖于大量数据中同源蛋白质所定义的相似结构和模式。当已知的新发现蛋白质及其同源蛋白质的药物-靶标相互作用很少时,预测性能就会明显下降。在本文中,我们提出了一种新颖的蛋白质上下文增强型主从框架(PCMS),用于零次 DTI 预测。该框架有助于为新发现的目标蛋白质高效发现配体,解决了在没有先验数据的情况下预测相互作用的难题。具体来说,PCMS 框架由两个主要部分组成:主学习器和从学习器。主学习器首先学习目标蛋白质的上下文信息,然后自适应地为从学习器生成相应的参数。然后,从属学习器在不同的蛋白质上下文中执行零次 DTI 预测。在两个公开数据集上进行的大量实验验证了我们的 PCMS 在各种指标上与最先进方法相比的有效性。一旦论文被接受,我们将公开代码和处理过的数据。
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引用次数: 0
Game-theoretic Flux Balance Analysis Model for Predicting Stable Community Composition. 预测稳定群落组成的博弈论通量平衡分析模型
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-27 DOI: 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.

微生物相互作用模型试图理解和预测群落中物种间关系的稳态网络,例如对共享代谢物的竞争和通过交叉进食进行的合作。通量平衡分析(FBA)是一种用于模拟特定微生物物种与其环境相互作用的方法。然而,这些方法有两个重要的缺点:首先,必须通过数值求解微分方程来确定稳态;其次,没有可用的方法来分析稳态的稳定性。我们提出了一种基于博弈论的群落 FBA 模型,在该模型中,物种通过竞争最大化各自的增长率,而群落的状态则由由此产生的纳什均衡给出。我们开发了一种计算高效的方法,无需求解微分方程即可直接计算稳态生物量和通量。我们还开发了一种方法来确定稳态对生物量扰动和新物种入侵的稳定性。我们报告了将我们提出的框架应用于一个由四个大肠杆菌突变体组成的小型群落的结果,这四个突变体既竞争外部提供的葡萄糖,又相互合作,因为突变体对其他突变体输出的代谢物具有辅助营养作用;我们还报告了一个由九个物种组成的肠道微生物群的更现实的模型。
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引用次数: 0
Incremental RPN: Hierarchical Region Proposal Network for Apple Leaf Disease Detection in Natural Environments. 增量 RPN:用于自然环境中苹果叶病检测的分层区域建议网络
IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-26 DOI: 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.

苹果叶片病害会严重影响苹果的产量和质量,准确检测苹果叶片病害可以提高病害监测的效率。由于自然生长环境复杂,苹果叶片病害很容易与背景噪声混淆,导致检测效果不佳。本研究提出了一种级联递增区域建议网络(Inc-RPN),用于准确检测自然环境中的苹果叶片病害。所提出的 Inc-RPN 采用双层 RPN 架构,其中前导 RPN 用于生成病叶建议,后继 RPN 侧重于根据病叶建议提取目标病斑。在后继 RPN 中,设计了一个底层特征聚合模块,以充分利用桥接特征并保留目标病斑的语义信息。此外,还利用增量模块提取聚合的病叶特征和目标病斑特征。最后,设计了一个新颖的位置锚点生成器,根据病叶建议生成锚点。实验结果表明,所提出的 Inc-RPN 在 FALD_CED 和苹果叶病数据集上表现出色,表明它能准确地执行苹果叶病检测任务。
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引用次数: 0
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