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Impact of Anomalous Diffusion Phenomenon on Molecular Information Delivery in Bounded Environment. 反常扩散现象对有界环境中分子信息传递的影响
IF 3.7 4区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-25 DOI: 10.1109/TNB.2024.3467695
Lokendra Chouhan

Through this paper, a three-dimensional molecular communication (MC) inside a cuboid container is considered. Instead of normal diffusion phenomenon, the anomalous diffusion phenomenon is incorporated which enhances the practicability of the model. The Fick's law is re-defined for the considering rectangular coordinate system in which information carrying molecules (ICMs) diffuse anomalously in the environment. The impact of flow of the fluid along the +x direction in the environment is also considered. Moreover, considering free propagator phenomenon, the expressions of spatio-temporal probability density function (PDF) of the ICMs is derived for the considered model. Further, the novel closed-form expressions for first arrival time density (FATD) of the ICM, survival probability (SP) at any time, and its corresponding first arrival probability (FAP) are also derived. Furthermore, the considered MC model is also analyzed in terms of minimum bit-error-rate (BER) using log-likelihood ratio test (LLRT) optimal detector. The derived expressions are verified using MATLAB based particle-based and Monte-Carlo simulations.

本文考虑了立方体容器内的三维分子通讯(MC)。模型中不再使用正常扩散现象,而是加入了反常扩散现象,从而提高了模型的实用性。在考虑到信息携带分子(ICM)在环境中异常扩散的矩形坐标系中,重新定义了菲克定律。还考虑了流体沿 +x 方向在环境中流动的影响。此外,考虑到自由传播者现象,推导出了所考虑模型中 ICM 的时空概率密度函数 (PDF) 表达式。此外,还推导出了 ICM 的首次到达时间密度 (FATD)、任意时间的存活概率 (SP) 及其相应的首次到达概率 (FAP) 的新型闭式表达式。此外,还使用对数似然比检验(LLRT)最佳检测器分析了所考虑的 MC 模型的最小误码率(BER)。推导出的表达式通过基于 MATLAB 的粒子模拟和蒙特卡罗模拟进行了验证。
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引用次数: 0
PCF-based Sensors for Biomedical Applications-A Review 基于 PCF 的生物医学应用传感器--综述
IF 3.9 4区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-17 DOI: 10.1109/tnb.2024.3462748
Sushma Sawraj, Dharmendra Kumar, Ram Pravesh, Vijay Shanker Chaudhary, Bramha Prasad Pandey, Sneha Sharma, Santosh Kumar
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引用次数: 0
A novel framework for tongue feature extraction framework based on sublingual vein segmentation 基于舌下静脉分割的新型舌头特征提取框架
IF 3.9 4区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-17 DOI: 10.1109/tnb.2024.3462461
Xiaohua Wan, Yulong Hu, Dehui Qiu, Juan Zhang, Xiaotong Wang, Fa Zhang, Bin Hu
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引用次数: 0
Molecular Communication-Based Intelligent Dopamine Rate Modulator for Parkinson’s Disease Treatment 用于帕金森病治疗的基于分子通讯的智能多巴胺速率调节器
IF 3.9 4区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-12 DOI: 10.1109/tnb.2024.3456031
Elham Baradari, Ozgur B Akan
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引用次数: 0
State Observer Synchronization of Three-dimensional Chaotic Oscillatory Systems Based on DNA Strand Displacement 基于 DNA 链位移的三维混沌振荡系统的状态观测器同步化
IF 3.9 4区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-11 DOI: 10.1109/tnb.2024.3457755
Zicheng Wang, Haojie Wang, Yanfeng Wang, Junwei Sun
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引用次数: 0
Strategic Multi-Omics Data Integration via Multi-Level Feature Contrasting and Matching 通过多层次特征对比和匹配实现战略性多传感器数据整合
IF 3.7 4区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-10 DOI: 10.1109/TNB.2024.3456797
Jinli Zhang;Hongwei Ren;Zongli Jiang;Zheng Chen;Ziwei Yang;Yasuko Matsubara;Yasushi Sakurai
The analysis and comprehension of multi-omics data has emerged as a prominent topic in the field of bioinformatics and data science. However, the sparsity characteristics and high dimensionality of omics data pose difficulties in terms of extracting meaningful information. Moreover, the heterogeneity inherent in multiple omics sources makes the effective integration of multi-omics data challenging To tackle these challenges, we propose MFCC-SAtt, a multi-level feature contrast clustering model based on self-attention to extract informative features from multi-omics data. MFCC-SAtt treats each omics type as a distinct modality and employs autoencoders with self-attention for each modality to integrate and compress their respective features into a shared feature space. By utilizing a multi-level feature extraction framework along with incorporating a semantic information extractor, we mitigate optimization conflicts arising from different learning objectives. Additionally, MFCC-SAtt guides deep clustering based on multi-level features which further enhances the quality of output labels. By conducting extensive experiments on multi-omics data, we have validated the exceptional performance of MFCC-SAtt. For instance, in a pan-cancer clustering task, MFCC-SAtt achieved an accuracy of over 80.38%.
多组学数据的分析和理解已成为生物信息学和数据科学领域的一个重要课题。然而,omics 数据的稀疏性和高维性给提取有意义的信息带来了困难。为了应对这些挑战,我们提出了基于自我关注的多级特征对比聚类模型 MFCC-SAtt,以从多组学数据中提取信息特征。MFCC-SAtt 将每种 omics 类型视为一种不同的模态,并针对每种模态采用具有自我注意功能的自动编码器,将它们各自的特征整合并压缩到一个共享特征空间中。通过利用多层次特征提取框架和语义信息提取器,我们缓解了不同学习目标带来的优化冲突。此外,MFCC-SAtt 还能引导基于多层次特征的深度聚类,从而进一步提高输出标签的质量。通过在多组学数据上进行大量实验,我们验证了 MFCC-SAtt 的卓越性能。例如,在泛癌症聚类任务中,MFCC-SAtt 的准确率超过了 80.38%。
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引用次数: 0
Design and Performance Evaluation of Machine Learning-based Terahertz Metasurface Chemical Sensor. 基于机器学习的太赫兹元表面化学传感器的设计与性能评估。
IF 3.7 4区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-03 DOI: 10.1109/TNB.2024.3453372
Abdullah Baz, Jacob Wekalao, Ngaira Mandela, Shobhit K Patel

This paper presents a terahertz metasurface based sensor design incorporating graphene and other plasmonic materials for highly sensitive detection of different chemicals. The proposed sensor employs the combination of multiple resonator designs - including circular and square ring resonators - to attain enhanced sensitivity among other performance parameters. Machine learning techniques like Random Forest regression, are employed to enhance the sensor design and predict its performance. The optimized sensor demonstrates excellent sensitivity of 417 GHzRIU-1 and a low detection limit of 0.264 RIU for ethanol and benzene detection. Furthermore, the integration of machine learning cuts down the simulation time and computational requirements by approximately 90% without compromising accuracy. The sensor's unique design and performance characteristics, including its high-quality factor of 14.476, position it as a promising candidate for environmental monitoring and chemical sensing applications. Moreover, it also demonstrates potential for 2-bit encoding applications through strategic modulation of graphene chemical potential values. On the other hand, it also shows prospects of 2-bit encoding applications via the modulation of graphene chemical. This work provides a major advancement to the terahertz sensing application by proposing new materials, structures, and methods in computation in order to develop a high-performance chemical sensor.

本文介绍了一种基于太赫兹元表面的传感器设计,其中结合了石墨烯和其他等离子材料,用于高灵敏度地检测不同的化学物质。所提出的传感器采用了多种谐振器设计(包括圆形和方形环形谐振器)的组合,以提高灵敏度和其他性能参数。采用随机森林回归等机器学习技术来增强传感器设计并预测其性能。优化后的传感器灵敏度高达 417 GHzRIU-1,乙醇和苯的检测限低至 0.264 RIU。此外,在不影响精度的情况下,机器学习的集成将模拟时间和计算要求减少了约 90%。该传感器独特的设计和性能特点,包括 14.476 的高质量系数,使其成为环境监测和化学传感应用的理想候选产品。此外,通过对石墨烯化学势值进行策略性调制,它还展示了 2 位编码应用的潜力。另一方面,它还展示了通过调制石墨烯化学势值进行 2 位编码的应用前景。这项工作通过提出新材料、新结构和新计算方法,为太赫兹传感应用提供了重大进展,从而开发出高性能化学传感器。
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引用次数: 0
A Representation Learning Approach for Predicting circRNA Back-Splicing Event via Sequence-Interaction-Aware Dual Encoder 通过序列交互感知双编码器预测 circRNA 回接事件的表征学习方法
IF 3.7 4区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-03 DOI: 10.1109/TNB.2024.3454079
Chengxin He;Lei Duan;Huiru Zheng;Xinye Wang;Lili Guan;Jiaxuan Xu
Circular RNAs (circRNAs) play a crucial role in gene regulation and association with diseases because of their unique closed continuous loop structure, which is more stable and conserved than ordinary linear RNAs. As fundamental work to clarify their functions, a large number of computational approaches for identifying circRNA formation have been proposed. However, these methods fail to fully utilize the important characteristics of back-splicing events, i.e., the positional information of the splice sites and the interaction features of its flanking sequences, for predicting circRNAs. To this end, we hereby propose a novel approach called SIDE for predicting circRNA back-splicing events using only raw RNA sequences. Technically, SIDE employs a dual encoder to capture global and interactive features of the RNA sequence, and then a decoder designed by the contrastive learning to fuse out discriminative features improving the prediction of circRNAs formation. Empirical results on three real-world datasets show the effectiveness of SIDE. Further analysis also reveals that the effectiveness of SIDE.
环状 RNA(circRNA)因其独特的闭合连续环状结构而在基因调控和疾病相关方面发挥着至关重要的作用,这种结构比普通线性 RNA 更稳定、更保守。作为阐明其功能的基础性工作,人们提出了大量识别 circRNA 形成的计算方法。然而,这些方法未能充分利用反向剪接事件的重要特征,即剪接位点的位置信息及其侧翼序列的相互作用特征来预测 circRNA。为此,我们提出了一种名为 SIDE 的新方法,仅利用原始 RNA 序列预测 circRNA 的反向剪接事件。在技术上,SIDE 采用双重编码器捕捉 RNA 序列的全局和交互特征,然后通过对比学习设计解码器,融合出辨别特征,从而提高 circRNA 形成的预测能力。在三个真实世界数据集上的实证结果表明了 SIDE 的有效性。进一步的分析还显示了 SIDE 的有效性。
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引用次数: 0
Multiple Heterogeneous Networks Representation With Latent Space for Synthetic Lethality Prediction 利用潜空间的多重异构网络表示法进行合成致死率预测
IF 3.7 4区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-08-16 DOI: 10.1109/TNB.2024.3444922
Xiangjin Hu;Haoran Yi;Hao Cheng;Yijing Zhao;Dongqi Zhang;Jinxin Li;Jingjing Ruan;Jin Zhang;Xinguo Lu
Computational synthetic lethality (SL) method has become a promising strategy to identify SL gene pairs for targeted cancer therapy and cancer medicine development. Feature representation for integrating various biological networks is crutial to improve the identification performance. However, previous feature representation, such as matrix factorization and graph neural network, projects gene features onto latent variables by keeping a specific geometric metric. There is a lack of models of gene representational latent space with considerating multiple dimentionalities correlation and preserving latent geometric structures in both sample and feature spaces. Therefore, we propose a novel method to model gene Latent Space using matrix Tri-Factorization (LSTF) to obtain gene representation with embedding variables resulting from the potential interpretation of synthetic lethality. Meanwhile, manifold subspace regularization is applied to the tri-factorization to capture the geometrical manifold structure in the latent space with gene PPI functional and GO semantic embeddings. Then, SL gene pairs are identified by the reconstruction of the associations with gene representations in the latent space. The experimental results illustrate that LSTF is superior to other state-of-the-art methods. Case study demonstrate the effectiveness of the predicted SL associations.
计算合成致死率(SL)方法已成为为癌症靶向治疗和癌症药物开发识别SL基因对的一种有前途的策略。整合各种生物网络的特征表示对于提高识别性能至关重要。然而,以往的特征表示方法,如矩阵因式分解和图神经网络,都是通过保持特定的几何度量将基因特征投射到潜在变量上。目前还缺乏同时考虑多维度相关性和保留样本空间与特征空间中潜在几何结构的基因表征潜在空间模型。因此,我们提出了一种利用矩阵三因子化(LSTF)对基因潜空间进行建模的新方法,以获得具有合成致死率潜在解释所产生的嵌入变量的基因表征。同时,将流形子空间正则化应用于三因子化,以捕捉潜空间中带有基因 PPI 功能嵌入和 GO 语义嵌入的几何流形结构。然后,通过重建潜空间中与基因表征的关联来识别 SL 基因对。实验结果表明,LSTF 优于其他最先进的方法。案例研究证明了预测 SL 关联的有效性。
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引用次数: 0
An Improved Framework for Drug-Side Effect Associations Prediction via Counterfactual Inference-Based Data Augmentation 通过基于反事实推理的数据扩充,改进药物副作用关联预测框架。
IF 3.7 4区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-08-14 DOI: 10.1109/TNB.2024.3443244
Wenjie Yao;Ankang Wei;Zhen Xiao;Weizhong Zhao;Xianjun Shen;Xingpeng Jiang;Tingting He
Detecting side effects of drugs is a fundamental task in drug development. With the expansion of publicly available biomedical data, researchers have proposed many computational methods for predicting drug-side effect associations (DSAs), among which network-based methods attract wide attention in the biomedical field. However, the problem of data scarcity poses a great challenge for existing DSAs prediction models. Although several data augmentation methods have been proposed to address this issue, most of existing methods employ a random way to manipulate the original networks, which ignores the causality of existence of DSAs, leading to the poor performance on the task of DSAs prediction. In this paper, we propose a counterfactual inference-based data augmentation method for improving the performance of the task. First, we construct a heterogeneous information network (HIN) by integrating multiple biomedical data. Based on the community detection on the HIN, a counterfactual inference-based method is designed to derive augmented links, and an augmented HIN is obtained accordingly. Then, a meta-path-based graph neural network is applied to learn high-quality representations of drugs and side effects, on which the predicted DSAs are obtained. Finally, comprehensive experiments are conducted, and the results demonstrate the effectiveness of the proposed counterfactual inference-based data augmentation for the task of DSAs prediction.
检测药物的副作用是药物开发的一项基本任务。随着公开生物医学数据的增加,研究人员提出了许多预测药物副作用关联(DSAs)的计算方法,其中基于网络的方法在生物医学领域受到广泛关注。然而,数据稀缺问题给现有的 DSAs 预测模型带来了巨大挑战。虽然已经有多种数据增强方法被提出来解决这一问题,但现有方法大多采用随机的方式来处理原始网络,忽略了 DSA 存在的因果关系,导致 DSA 预测效果不佳。本文提出了一种基于反事实推理的数据增强方法,以提高任务的性能。首先,我们通过整合多个生物医学数据构建了一个异构信息网络(HIN)。在对 HIN 进行群落检测的基础上,我们设计了一种基于反事实推理的方法来推导增强链接,并相应地得到了一个增强的 HIN。然后,应用基于元路径的图神经网络学习药物和副作用的高质量表征,并在此基础上获得预测的 DSA。最后,我们进行了综合实验,结果证明了所提出的基于反事实推理的数据增强方法在 DSAs 预测任务中的有效性。
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IEEE Transactions on NanoBioscience
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