IEEE Transactions on NanoBioscience最新文献

{"title":"Ontology-Based Data Collection for a Hybrid Outbreak Detection Method Using Social Media","authors":"Ghazaleh Babanejaddehaki;Aijun An;Heidar Davoudi","doi":"10.1109/TNB.2024.3442912","DOIUrl":"10.1109/TNB.2024.3442912","url":null,"abstract":"Given the persistent global challenge presented by rapidly spreading diseases, as evidenced notably by the widespread impact of the COVID-19 pandemic on both human health and economies worldwide, the necessity of developing effective infectious disease prediction models has become of utmost importance. In this context, the utilization of online social media platforms as valuable tools in healthcare settings has gained prominence, offering direct avenues for disseminating critical health information to the public in a timely and accessible manner. Propelled by the ubiquitous accessibility of the internet through computers and mobile devices, these platforms promise to revolutionize traditional detection methods, providing more immediate and reliable epidemiological insights. Leveraging this paradigm shift, our proposed framework harnesses Twitter data associated with infectious disease symptoms, employing ontology to identify and curate relevant tweets. Central to our methodology is a hybrid model that integrates XGBoost and Bidirectional Long Short-Term Memory (BiLSTM) architectures. The integration of XGBoost addresses the challenge of handling small dataset sizes, inherent during outbreaks due to limited time series data. XGBoost serves as a cornerstone for minimizing the loss function and identifying optimal features from our multivariate time series data. Subsequently, the combined dataset, comprising original features and predicted values by XGBoost, is channeled into the BiLSTM for further processing. Through extensive experimentation with a dataset spanning multiple infectious disease outbreaks, our hybrid model demonstrates superior predictive performance compared to state-of-the-art and baseline models. By enhancing forecasting accuracy and outbreak tracking capabilities, our model offers promising prospects for assisting health authorities in mitigating fatalities and proactively preparing for potential outbreaks.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141975609","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}

{"title":"A Controllability Reinforcement Learning Method for Pancreatic Cancer Biomarker Identification","authors":"Yan Wang;Jie Hong;Yuting Lu;Nan Sheng;Yuan Fu;Lili Yang;Lingyu Meng;Lan Huang;Hao Wang","doi":"10.1109/TNB.2024.3441689","DOIUrl":"10.1109/TNB.2024.3441689","url":null,"abstract":"Pancreatic cancer is one of the most malignant cancers with rapid progression and poor prognosis. The use of transcriptional data can be effective in finding new biomarkers for pancreatic cancer. Many network-based methods used to identify cancer biomarkers are proposed, among which the combination of network controllability appears. However, most of the existing methods do not study RNA, rely on priori and mutations information, or can only achieve classification tasks. In this study, we propose a method combined Relational Graph Convolutional Network and Deep Q-Network called RDDriver to identify pancreatic cancer biomarkers based on multi-layer heterogeneous transcriptional regulation network. Firstly, we construct a regulation network containing long non-coding RNA, microRNA, and messenger RNA. Secondly, Relational Graph Convolutional Network is used to learn the node representation. Finally, we use the idea of Deep Q-Network to build a model, which score and prioritize each RNA with the Popov-Belevitch-Hautus criterion. We train RDDriver on three small simulated networks, and calculate the average score after applying the model parameters to the regulation networks separately. To demonstrate the effectiveness of the method, we perform experiments for comparison between RDDriver and other eight methods based on the approximate benchmark of three types cancer drivers RNAs.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10633729","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141971020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TC-DTA: Predicting Drug-Target Binding Affinity With Transformer and Convolutional Neural Networks","authors":"Xiwei Tang;Yiqiang Zhou;Mengyun Yang;Wenjun Li","doi":"10.1109/TNB.2024.3441590","DOIUrl":"10.1109/TNB.2024.3441590","url":null,"abstract":"Bioinformatics is a rapidly evolving field that applies computational methods to analyze and interpret biological data. A key task in bioinformatics is identifying novel drug-target interactions (DTIs), which plays a crucial role in drug discovery. Most computational approaches treat DTI prediction as a binary classification problem, determining whether drug-target pairs interact. However, with the growing availability of drug-target binding affinity data, this binary task can be reframed as a regression problem focused on drug-target affinity (DTA). DTA quantifies the strength of drug-target binding, offering more detailed insights than DTI and serving as a valuable tool for virtual screening in drug discovery. Accurately predicting compound interactions with targets can accelerate the drug development process. In this study, we introduce a deep learning model named TC-DTA for DTA prediction, leveraging convolutional neural networks (CNN) and the encoder module of the transformer architecture. We begin by extracting raw drug SMILES strings and protein amino acid sequences from the dataset, which are then represented using various encoding methods. Subsequently, we employ CNN and the transformer’s encoder module to extract features from the drug SMILES strings and protein sequences, respectively. Finally, the feature information is concatenated and input into a multi-layer perceptron to predict binding affinity scores. We evaluated our model on two benchmark DTA datasets, Davis and KIBA, comparing it with methods such as KronRLS, SimBoost, and DeepDTA. Our model, TC-DTA, outperformed these baseline methods based on evaluation metrics like Mean Squared Error (MSE), Concordance Index (CI), and Regression towards the Mean Index (\u0000<inline-formula> <tex-math>${r}_{m}^{{2}}$ </tex-math></inline-formula>\u0000). These results highlight the effectiveness of the Transformer’s encoder and CNN in extracting meaningful representations from sequences, thereby enhancing DTA prediction accuracy. This deep learning model can accelerate drug discovery by identifying drug candidates with high binding affinity to specific targets. Compared to traditional methods, machine learning technology offers a more effective and efficient approach to drug discovery.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141971022","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}

{"title":"A2HTL: An Automated Hybrid Transformer-Based Learning for Predicting Survival of Esophageal Cancer Using CT Images","authors":"Hailin Yue;Jin Liu;Lina Zhao;Hulin Kuang;Jianhong Cheng;Junjian Li;Mengshen He;Jie Gong;Jianxin Wang","doi":"10.1109/TNB.2024.3441533","DOIUrl":"10.1109/TNB.2024.3441533","url":null,"abstract":"Esophageal cancer is a common malignant tumor, precisely predicting survival of esophageal cancer is crucial for personalized treatment. However, current region of interest (ROI) based methodologies not only necessitate prior medical knowledge for tumor delineation, but may also cause the model to be overly sensitive to ROI. To address these challenges, we develop an automated Hybrid Transformer based learning that integrates a Hybrid Transformer size-aware U-Net with a ranked survival prediction network to enable automatic survival prediction for esophageal cancer. Specifically, we first incorporate the Transformer with shifted windowing multi-head self-attention mechanism (SW-MSA) into the base of the U-Net encoder to capture the long-range dependency in CT images. Furthermore, to alleviate the imbalance between the ROI and the background in CT images, we devise a size-aware coefficient for the segmentation loss. Finally, we also design a ranked pair sorting loss to more comprehensively capture the ranked information inherent in CT images. We evaluate our proposed method on a dataset comprising 759 samples with esophageal cancer. Experimental results demonstrate the superior performance of our proposed method in survival prediction, even without ROI ground truth.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141971021","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}

{"title":"Influence of Red Blood Cells on Channel Characterization in Cylindrical Vasculature.","authors":"Kathan S Joshi, Dhaval K Patel, Shivam Thakker, Miguel Lopez-Benitez, Janne J Lehtomaki","doi":"10.1109/TNB.2024.3436022","DOIUrl":"https://doi.org/10.1109/TNB.2024.3436022","url":null,"abstract":"<p><p>Molecular communication via diffusion (MCvD) expects Brownian motions of the information molecules to transmit information. However, the signal propagation largely depends on the geometric characteristics of the assumed flow model, i.e., the characteristics of the environment, design, and position of the transmitter and receiver, respectively. These characteristics are assumed to be lucid in many ways by either consideration of one-dimensional diffusion, unbounded environment, or constant drift. In reality, diffusion often occurs in blood-vessel-like channels. To this aim, we try to study the effect of the biological environment on channel performance. The Red-Blood Cells (RBCs) found in blood vessels enforces a higher concentration of molecules towards the vessel walls, leading to better reception. Therefore, in this paper we derive an analytical expression of Channel Impulse Response (CIR) for a dispersion-advection-based regime, contemplating the influence of RBCs in the model and considering a point source transmitter and a realistic design of the receiver.</p>","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141901589","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}

{"title":"Deep Learning for the Accurate Prediction of Triggered Drug Delivery.","authors":"Ghaleb A Husseini, Rana Sabouni, Vladimir Puzyrev, Mehdi Ghommem","doi":"10.1109/TNB.2024.3426291","DOIUrl":"https://doi.org/10.1109/TNB.2024.3426291","url":null,"abstract":"<p><p>The need to mitigate the adverse effects of chemotherapy has driven the exploration of innovative drug delivery approaches. One emerging trend in cancer treatment is the utilization of Drug Delivery Systems (DDSs), facilitated by nanotechnology. Nanoparticles, ranging from 1 nm to 1000 nm, act as carriers for chemotherapeutic agents, enabling precise drug delivery. The triggered release of these agents is vital for advancing this novel drug delivery system. Our research investigated this multifaceted delivery capability using liposomes and metal organic frameworks as nanocarriers and utilizing all three targeting techniques: passive, active, and triggered. Liposomes are modified using targeting ligands to render them more targeted toward certain cancers. Moieties are conjugated to the surfaces of these nanocarriers to allow for their binding to receptors overexpressed on cancer cells, thus increasing the accumulation of the agent at the tumor site. A novel class of nanocarriers, namely metal organic frameworks, has emerged, showing promise in cancer treatment. Triggering techniques (both intrinsic and extrinsic) can be used to release therapeutic agents from nanoparticles, thus enhancing the efficacy of drug delivery. In this study, we develop a predictive model combining experimental measurements with deep learning techniques. The model accurately predicts drug release from liposomes and MOFs under various conditions, including low- and high-frequency ultrasound (extrinsic triggering), microwave exposure (extrinsic triggering), ultraviolet light exposure (extrinsic triggering), and different pH levels (intrinsic triggering). The deep learning-based predictions significantly outperform linear predictions, proving the utility of advanced computational methods in drug delivery. Our findings demonstrate the potential of these nanocarriers and highlight the efficacy of deep learning models in predicting drug release behavior, paving the way for enhanced cancer treatment strategies.</p>","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141633393","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}

{"title":"High-Risk Sequence Prediction Model in DNA Storage: The LQSF Method.","authors":"Yitong Ma, Shuai Chen, Xu Qi, Zuhong Lu, Kun Bi","doi":"10.1109/TNB.2024.3424576","DOIUrl":"https://doi.org/10.1109/TNB.2024.3424576","url":null,"abstract":"<p><p>Traditional DNA storage technologies rely on passive filtering methods for error correction during synthesis and sequencing, which result in redundancy and inadequate error correction. Addressing this, the Low Quality Sequence Filter (LQSF) was introduced, an innovative method employing deep learning models to predict high-risk sequences. The LQSF approach leverages a classification model trained on error-prone sequences, enabling efficient pre-sequencing filtration of low-quality sequences and reducing time and resources in subsequent stages. Analysis has demonstrated a clear distinction between high and low-quality sequences, confirming the efficacy of the LQSF method. Extensive training and testing were conducted across various neural networks and test sets. The results showed all models achieving an AUC value above 0.91 on ROC curves and over 0.95 on PR curves across different datasets. Notably, models such as Alexnet, VGG16, and VGG19 achieved a perfect AUC of 1.0 on the Original dataset, highlighting their precision in classification. Further validation using Illumina sequencing data substantiated a strong correlation between model scores and sequence error-proneness, emphasizing the model's applicability. The LQSF method marks a significant advancement in DNA storage technology, introducing active sequence filtering at the encoding stage. This pioneering approach holds substantial promise for future DNA storage research and applications.</p>","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141558613","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}

{"title":"3D Printed Interdigitated Electrodes for Cardiac Biomarker Detection.","authors":"Parvathy Nair, Khairunnisa Amreen, R N Ponnalagu, Sanket Goel","doi":"10.1109/TNB.2024.3423020","DOIUrl":"https://doi.org/10.1109/TNB.2024.3423020","url":null,"abstract":"<p><p>The identification of biomarkers has significant benefits for early disease diagnosis and treatment. Hence, there is an increasing demand for low-cost, disposable point-of-care diagnostic devices for rapid and specific biomarker detection, with good sensitivity and range. Interdigitated electrodes (IDEs) are among the most widely used transducers, especially in chemical and biological sensors, because of their high sensitivity, low cost, and straightforward manufacturing procedure. In this work, a simple 3D printed IDE structure has been developed for cardiac troponin I detection to indicate the risk of acute myocardial infarction (AMI). IDEs have been fabricated using 3D printing technique and the electrically conductive composite polylactic acid (PLA) filament being utilized for the fabrication of electrodes. The demonstrated cardiac troponin I sensor has a calculated quantification limit and detection limit of 0.233 ng ml^-1 and 76.97 pg ml^-1, respectively which are clinically significant ranges for AMI identification. Electrochemical analytical techniques, such as electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV), were carried out for the detection of analyte concentration. Furthermore, using this fabrication methodology IDEs can be fabricated for under US$ 0.4 which can be utilized to detect several other biomarkers.</p>","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141534287","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}

{"title":"A Thermal Study of Terahertz Induced Protein Interactions.","authors":"Hadeel Elayan, Samar Elmaadawy, Andrew W Eckford, Raviraj Adve, Josep Jornet","doi":"10.1109/TNB.2024.3422280","DOIUrl":"https://doi.org/10.1109/TNB.2024.3422280","url":null,"abstract":"<p><p>Proteins can be regarded as thermal nanosensors in an intra-body network. Upon being stimulated by Terahertz (THz) frequencies that match their vibrational modes, protein molecules experience resonant absorption and dissipate their energy as heat, undergoing a thermal process. This paper aims to analyze the effect of THz signaling on the protein heat dissipation mechanism. We therefore deploy a mathematical framework based on the heat diffusion model to characterize how proteins absorb THz-electromagnetic (EM) energy from the stimulating EM fields and subsequently release this energy as heat to their immediate surroundings. We also conduct a parametric study to explain the impact of the signal power, pulse duration, and inter-particle distance on the protein thermal analysis. In addition, we demonstrate the relationship between the change in temperature and the opening probability of thermally-gated ion channels. Our results indicate that a controlled temperature change can be achieved in an intra-body environment by exciting protein particles at their resonant frequencies. We further verify our results numerically using COMSOL Multiphysics^® and introduce an experimental framework that assesses the effects of THz radiation on protein particles. We conclude that under controlled heating, protein molecules can serve as hotspots that impact thermally-gated ion channels. Through the presented work, we infer that the heating process can be engineered on different time and length scales by controlling the THz-EM signal input.</p>","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141491740","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}

{"title":"IEEE Transactions on NanoBioscience Information for Authors","authors":"","doi":"10.1109/TNB.2024.3415195","DOIUrl":"https://doi.org/10.1109/TNB.2024.3415195","url":null,"abstract":"","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10579905","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141495045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}