IEEE Transactions on NanoBioscience最新文献_第2页

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

IEEE Transactions on NanoBioscience Pub Date : 2024-09-25 DOI: 10.1109/TNB.2024.3467695

Lokendra Chouhan

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

IEEE Transactions on NanoBioscience 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

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

IEEE Transactions on NanoBioscience 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

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

IEEE Transactions on NanoBioscience Pub Date : 2024-09-12 DOI: 10.1109/tnb.2024.3456031

Elham Baradari, Ozgur B Akan

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

IEEE Transactions on NanoBioscience Pub Date : 2024-09-11 DOI: 10.1109/tnb.2024.3457755

Zicheng Wang, Haojie Wang, Yanfeng Wang, Junwei Sun

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

IEEE Transactions on NanoBioscience 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

{"title":"Strategic Multi-Omics Data Integration via Multi-Level Feature Contrasting and Matching","authors":"Jinli Zhang;Hongwei Ren;Zongli Jiang;Zheng Chen;Ziwei Yang;Yasuko Matsubara;Yasushi Sakurai","doi":"10.1109/TNB.2024.3456797","DOIUrl":"10.1109/TNB.2024.3456797","url":null,"abstract":"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%.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IEEE Transactions on NanoBioscience Pub Date : 2024-09-03 DOI: 10.1109/TNB.2024.3453372

Abdullah Baz, Jacob Wekalao, Ngaira Mandela, Shobhit K Patel

{"title":"Design and Performance Evaluation of Machine Learning-based Terahertz Metasurface Chemical Sensor.","authors":"Abdullah Baz, Jacob Wekalao, Ngaira Mandela, Shobhit K Patel","doi":"10.1109/TNB.2024.3453372","DOIUrl":"10.1109/TNB.2024.3453372","url":null,"abstract":"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.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142125606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IEEE Transactions on NanoBioscience Pub Date : 2024-09-03 DOI: 10.1109/TNB.2024.3454079

Chengxin He;Lei Duan;Huiru Zheng;Xinye Wang;Lili Guan;Jiaxuan Xu

{"title":"A Representation Learning Approach for Predicting circRNA Back-Splicing Event via Sequence-Interaction-Aware Dual Encoder","authors":"Chengxin He;Lei Duan;Huiru Zheng;Xinye Wang;Lili Guan;Jiaxuan Xu","doi":"10.1109/TNB.2024.3454079","DOIUrl":"10.1109/TNB.2024.3454079","url":null,"abstract":"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.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142125605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IEEE Transactions on NanoBioscience 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

{"title":"Multiple Heterogeneous Networks Representation With Latent Space for Synthetic Lethality Prediction","authors":"Xiangjin Hu;Haoran Yi;Hao Cheng;Yijing Zhao;Dongqi Zhang;Jinxin Li;Jingjing Ruan;Jin Zhang;Xinguo Lu","doi":"10.1109/TNB.2024.3444922","DOIUrl":"10.1109/TNB.2024.3444922","url":null,"abstract":"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.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IEEE Transactions on NanoBioscience 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

{"title":"An Improved Framework for Drug-Side Effect Associations Prediction via Counterfactual Inference-Based Data Augmentation","authors":"Wenjie Yao;Ankang Wei;Zhen Xiao;Weizhong Zhao;Xianjun Shen;Xingpeng Jiang;Tingting He","doi":"10.1109/TNB.2024.3443244","DOIUrl":"10.1109/TNB.2024.3443244","url":null,"abstract":"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.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141982181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0