Pub Date : 2023-12-22DOI: 10.1109/TMBMC.2023.3345590
{"title":"2023 Index IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Vol.9","authors":"","doi":"10.1109/TMBMC.2023.3345590","DOIUrl":"https://doi.org/10.1109/TMBMC.2023.3345590","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"9 4","pages":"499-510"},"PeriodicalIF":2.2,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10372180","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139034111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-22DOI: 10.1109/TMBMC.2023.3342731
O. Tansel Baydas;Ozgur B. Akan
Molecular communication (MC) is a paradigm that employs molecules as information carriers, hence, requiring unconventional transceivers and detection techniques for the Internet of Bio-Nano Things (IoBNT). In this study, we provide a novel MC model that incorporates a spherical transmitter and receiver with partial absorption. This model offers a more realistic representation than receiver architectures in literature, e.g., passive or entirely absorbing configurations. An optimization-based technique utilizing particle swarm optimization (PSO) is employed to accurately estimate the cumulative number of molecules received. This technique yields nearly constant correction parameters and demonstrates a significant improvement of 5 times in terms of root mean square error (RMSE) compared to the literature. The estimated channel model provides an approximate analytical impulse response; hence, it is used for estimating channel parameters such as distance, diffusion coefficient, or a combination of both. The iterative maximum likelihood estimation (MLE) is applied for the parameter estimation, which gives consistent errors compared to the estimated Cramer-Rao Lower Bound (CLRB).
分子通信（MC）是一种利用分子作为信息载体的范例，因此，生物纳米物联网（IoBNT）需要非常规的收发器和检测技术。在本研究中，我们提供了一种新颖的 MC 模型，该模型包含具有部分吸收功能的球形发射器和接收器。与文献中的接收器架构（如无源或完全吸收配置）相比，该模型提供了更真实的表示。利用粒子群优化（PSO）的优化技术，可以准确估计接收到的分子累积数量。该技术可获得几乎不变的校正参数，与文献相比，在均方根误差 (RMSE) 方面显著提高了 5 倍。估算出的信道模型提供了近似的分析脉冲响应，因此可用于估算距离、扩散系数或两者的组合等信道参数。参数估计采用迭代最大似然估计 (MLE)，与估计的克拉默-拉奥下限 (CLRB) 相比，误差一致。
{"title":"Received Signal and Channel Parameter Estimation in Molecular Communications","authors":"O. Tansel Baydas;Ozgur B. Akan","doi":"10.1109/TMBMC.2023.3342731","DOIUrl":"https://doi.org/10.1109/TMBMC.2023.3342731","url":null,"abstract":"Molecular communication (MC) is a paradigm that employs molecules as information carriers, hence, requiring unconventional transceivers and detection techniques for the Internet of Bio-Nano Things (IoBNT). In this study, we provide a novel MC model that incorporates a spherical transmitter and receiver with partial absorption. This model offers a more realistic representation than receiver architectures in literature, e.g., passive or entirely absorbing configurations. An optimization-based technique utilizing particle swarm optimization (PSO) is employed to accurately estimate the cumulative number of molecules received. This technique yields nearly constant correction parameters and demonstrates a significant improvement of 5 times in terms of root mean square error (RMSE) compared to the literature. The estimated channel model provides an approximate analytical impulse response; hence, it is used for estimating channel parameters such as distance, diffusion coefficient, or a combination of both. The iterative maximum likelihood estimation (MLE) is applied for the parameter estimation, which gives consistent errors compared to the estimated Cramer-Rao Lower Bound (CLRB).","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"10 1","pages":"92-97"},"PeriodicalIF":2.2,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-21DOI: 10.1109/TMBMC.2023.3345145
Pranab Das;Dilwar Hussain Mazumder
Drug function study is vital in current drug discovery, design, and development. Determining the drug functions of a novel drug is time-consuming, complicated, expensive, and requires many experts and clinical testing phases. The computational-based drug function prediction activity has recently become more attractive due to its capability to reduce drug development design complexity, time, human resources, cost, chemical waste, and the risk of failure. The evolution of the computational model has advanced as an effective tool for predicting and analyzing drug functions, which are derived from Medical Subject Headings (MeSH). However, predicting drug functions still faces several difficulties. Therefore, an exhaustive literature survey was conducted that discusses the application of computational methods to predict drug functions in the past decade. Additionally, this paper discusses the utilization of drug functions as an input feature to predict adverse drug reactions and disease classification. This work also provides an overview of the computational models with their performance, multi-label problem transformation methods, drug properties, and their sources needed for the task of drug function prediction. Finally, unsolved issues, research gaps, and difficulties with the drug function prediction task have been summarized.
{"title":"Advances in Predicting Drug Functions: A Decade-Long Survey in Drug Discovery Research","authors":"Pranab Das;Dilwar Hussain Mazumder","doi":"10.1109/TMBMC.2023.3345145","DOIUrl":"https://doi.org/10.1109/TMBMC.2023.3345145","url":null,"abstract":"Drug function study is vital in current drug discovery, design, and development. Determining the drug functions of a novel drug is time-consuming, complicated, expensive, and requires many experts and clinical testing phases. The computational-based drug function prediction activity has recently become more attractive due to its capability to reduce drug development design complexity, time, human resources, cost, chemical waste, and the risk of failure. The evolution of the computational model has advanced as an effective tool for predicting and analyzing drug functions, which are derived from Medical Subject Headings (MeSH). However, predicting drug functions still faces several difficulties. Therefore, an exhaustive literature survey was conducted that discusses the application of computational methods to predict drug functions in the past decade. Additionally, this paper discusses the utilization of drug functions as an input feature to predict adverse drug reactions and disease classification. This work also provides an overview of the computational models with their performance, multi-label problem transformation methods, drug properties, and their sources needed for the task of drug function prediction. Finally, unsolved issues, research gaps, and difficulties with the drug function prediction task have been summarized.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"10 1","pages":"75-91"},"PeriodicalIF":2.2,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-18DOI: 10.1109/TMBMC.2023.3326009
{"title":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Publication Information","authors":"","doi":"10.1109/TMBMC.2023.3326009","DOIUrl":"https://doi.org/10.1109/TMBMC.2023.3326009","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"9 4","pages":"C2-C2"},"PeriodicalIF":2.2,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10364886","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138739585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-18DOI: 10.1109/TMBMC.2023.3326011
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Pub Date : 2023-12-04DOI: 10.1109/TMBMC.2023.3338950
Hanlin Xiao;Kamela Dokaj;Ozgur B. Akan
Molecular communication, as implied by its name, uses molecules as information carriers for communication between objects. It has an advantage over traditional electromagnetic-wave-based communication in that molecule-based systems could be biocompatible, operable in challenging environments, and energetically undemanding. Consequently, they are envisioned to have a broad range of applications, such as in the Internet of Bio-Nano Things, targeted drug delivery, and agricultural monitoring. Despite the rapid development of the field, with an increasing number of theoretical models and experimental testbeds established by researchers, a fundamental aspect of the field has often been sidelined, namely, the nature of the molecule in molecular communication. The potential information molecules could exhibit a wide range of properties, making them require drastically different treatments when being modeled and experimented upon. Therefore, in this paper, we delve into the intricacies of commonly used information molecules, examining their fundamental physical characteristics, associated communication systems, and potential applications in a more realistic manner, focusing on the influence of their own properties. Through this comprehensive survey, we aim to offer a novel yet essential perspective on molecular communication, thereby bridging the current gap between theoretical research and real-world applications.
{"title":"What Really is “Molecule” in Molecular Communications? The Quest for Physics of Particle-Based Information Carriers","authors":"Hanlin Xiao;Kamela Dokaj;Ozgur B. Akan","doi":"10.1109/TMBMC.2023.3338950","DOIUrl":"10.1109/TMBMC.2023.3338950","url":null,"abstract":"Molecular communication, as implied by its name, uses molecules as information carriers for communication between objects. It has an advantage over traditional electromagnetic-wave-based communication in that molecule-based systems could be biocompatible, operable in challenging environments, and energetically undemanding. Consequently, they are envisioned to have a broad range of applications, such as in the Internet of Bio-Nano Things, targeted drug delivery, and agricultural monitoring. Despite the rapid development of the field, with an increasing number of theoretical models and experimental testbeds established by researchers, a fundamental aspect of the field has often been sidelined, namely, the nature of the molecule in molecular communication. The potential information molecules could exhibit a wide range of properties, making them require drastically different treatments when being modeled and experimented upon. Therefore, in this paper, we delve into the intricacies of commonly used information molecules, examining their fundamental physical characteristics, associated communication systems, and potential applications in a more realistic manner, focusing on the influence of their own properties. Through this comprehensive survey, we aim to offer a novel yet essential perspective on molecular communication, thereby bridging the current gap between theoretical research and real-world applications.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"10 1","pages":"43-74"},"PeriodicalIF":2.2,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139234475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-29DOI: 10.1109/TMBMC.2023.3336259
Guodong Yue;Guoying Lin;Qiang Liu;Kun Yang
Molecular communication (MC) is a significant technology in the field of nano-biology, which uses molecules as message carriers to transmit information. Diffusion channel model is the most common channel model base on Brownian motion in molecular communication since molecules can diffuse to the destination without the need of extra energy supply. However, the random Brownian motion brings high delay and uncertainty to the communication process and thus modulation methods are required to improve the communication performance. The molecular communication system in the SISO (Single Input Single Output) scenario will be seriously affected by ISI (Inter Symbol Interference). In MIMO (Multi-Input Multi-Output) scenario, since there are multiple transmitters and receivers, in addition to ISI, there will be ILI (Inter Link Interference) as well. At present, most modulations are based on the concentration, type, time and space of molecules and only focus on SISO scenario. In this study, inspired by the MoSK (Molecule Shift Keying) modulation method, we proposed a new joint modulation method for MIMO communication in order to minimize the effect of ISI and ILI. Numerical results show that compared with the current modulation scheme, the proposed scheme allows the MIMO system achieve better BER (Bit error rate) performance and transmission rate.
分子通讯（MC）是纳米生物学领域的一项重要技术，它利用分子作为信息载体来传输信息。扩散信道模型是分子通讯中最常见的基于布朗运动的信道模型，因为分子可以扩散到目的地而不需要额外的能量供应。然而，随机布朗运动会给通信过程带来高延迟和不确定性，因此需要采用调制方法来提高通信性能。在 SISO（单输入单输出）情况下，分子通信系统会受到 ISI（符号间干扰）的严重影响。在 MIMO（多输入多输出）情况下，由于有多个发射器和接收器，除了 ISI 外，还会出现 ILI（链路间干扰）。目前，大多数调制都是基于分子的浓度、类型、时间和空间，并且只关注 SISO 场景。本研究受 MoSK（分子移频键控）调制方法的启发，提出了一种用于 MIMO 通信的新型联合调制方法，以最大限度地降低 ISI 和 ILI 的影响。数值结果表明，与当前的调制方案相比，所提出的方案能使多输入多输出系统获得更好的误码率（BER）性能和传输速率。
{"title":"Diffusion-Based Anti-Interference Joint Modulation in MIMO Molecular Communication","authors":"Guodong Yue;Guoying Lin;Qiang Liu;Kun Yang","doi":"10.1109/TMBMC.2023.3336259","DOIUrl":"https://doi.org/10.1109/TMBMC.2023.3336259","url":null,"abstract":"Molecular communication (MC) is a significant technology in the field of nano-biology, which uses molecules as message carriers to transmit information. Diffusion channel model is the most common channel model base on Brownian motion in molecular communication since molecules can diffuse to the destination without the need of extra energy supply. However, the random Brownian motion brings high delay and uncertainty to the communication process and thus modulation methods are required to improve the communication performance. The molecular communication system in the SISO (Single Input Single Output) scenario will be seriously affected by ISI (Inter Symbol Interference). In MIMO (Multi-Input Multi-Output) scenario, since there are multiple transmitters and receivers, in addition to ISI, there will be ILI (Inter Link Interference) as well. At present, most modulations are based on the concentration, type, time and space of molecules and only focus on SISO scenario. In this study, inspired by the MoSK (Molecule Shift Keying) modulation method, we proposed a new joint modulation method for MIMO communication in order to minimize the effect of ISI and ILI. Numerical results show that compared with the current modulation scheme, the proposed scheme allows the MIMO system achieve better BER (Bit error rate) performance and transmission rate.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"10 1","pages":"112-121"},"PeriodicalIF":2.2,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cells communicate with each other exploiting a variety of chemical signals. Among them, Extracellular Vesicles (EVs) have attracted large interest by the scientific community. In fact, thanks to the advances in bio-nano-technology and the possibility of engineering EVs, they are envisioned as a perfect means for distributing biological information among receiving cells. However, deciphering the molecular mechanisms that regulate the delivery of EV cargo is, today, a necessary, yet challenging, step toward the exploitation of EV signaling to support innovative and efficient therapeutic protocols, alternative to current drug delivery technologies. In particular, very little information is currently available on the processes of EV fusion, which is the EV internalization process occurring when the EV membrane dissolves into the plasma membrane of the target cell, and the EV content is released into the cytosol. In order to understand the dynamics of this process, this paper introduces an analytical model of the evolution of the fusion process. Moreover, since the measurement of the biological parameters driving the fusion process is far to be achieved, in this paper we use the model as a tool to infer likely values of such parameters from parameters that are measurable with current technology.
细胞之间利用各种化学信号进行交流。其中，细胞外囊泡（EVs）引起了科学界的极大兴趣。事实上，得益于生物纳米技术的进步以及对 EVs 进行工程化的可能性，EVs 被认为是在接收细胞间传播生物信息的完美手段。然而，破译调控 EV 货物递送的分子机制是当今利用 EV 信号支持创新和高效治疗方案、替代当前药物递送技术的一个必要但极具挑战性的步骤。特别是，目前有关 EV 融合过程的信息非常少，而 EV 融合是指 EV 膜溶解到靶细胞的质膜上，EV 内容释放到细胞质中的 EV 内化过程。为了了解这一过程的动态，本文介绍了融合过程演变的分析模型。此外，由于对驱动融合过程的生物参数的测量远未实现，我们在本文中将该模型作为一种工具，从现有技术可测量的参数中推断出这些参数的可能值。
{"title":"Intercellular Chemical Communication Through EV Exchange: Evaluation of the EV Fusion Process Parameters at the Receiving Cell","authors":"Alfio Lombardo;Giacomo Morabito;Carla Panarello;Fabrizio Pappalardo","doi":"10.1109/TMBMC.2023.3336322","DOIUrl":"https://doi.org/10.1109/TMBMC.2023.3336322","url":null,"abstract":"Cells communicate with each other exploiting a variety of chemical signals. Among them, Extracellular Vesicles (EVs) have attracted large interest by the scientific community. In fact, thanks to the advances in bio-nano-technology and the possibility of engineering EVs, they are envisioned as a perfect means for distributing biological information among receiving cells. However, deciphering the molecular mechanisms that regulate the delivery of EV cargo is, today, a necessary, yet challenging, step toward the exploitation of EV signaling to support innovative and efficient therapeutic protocols, alternative to current drug delivery technologies. In particular, very little information is currently available on the processes of EV fusion, which is the EV internalization process occurring when the EV membrane dissolves into the plasma membrane of the target cell, and the EV content is released into the cytosol. In order to understand the dynamics of this process, this paper introduces an analytical model of the evolution of the fusion process. Moreover, since the measurement of the biological parameters driving the fusion process is far to be achieved, in this paper we use the model as a tool to infer likely values of such parameters from parameters that are measurable with current technology.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"10 1","pages":"21-31"},"PeriodicalIF":2.2,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10330635","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-28DOI: 10.1109/TMBMC.2023.3336254
Grant Greenberg;Ilan Shomorony
The goal of metagenomics is to study the composition of microbial communities, typically using high-throughput shotgun sequencing. In the metagenomic binning problem, we observe random substrings (called contigs) from a mixture of genomes and aim to cluster them according to their genome of origin. Based on the empirical observation that genomes of different bacterial species can be distinguished based on their tetranucleotide frequencies, we model this task as the problem of clustering ${N}$ sequences generated by ${M}$ distinct Markov processes, where $M ll N$ . Utilizing the large-deviation principle for Markov processes, we establish the information-theoretic limit for perfect binning. Specifically, we show that the length of the contigs must scale with the inverse of the Chernoff divergence rate between the two most similar species. Furthermore, our result implies that contigs should be binned using the KL divergence rate as a measure of distance, as opposed to the Euclidean distance often used in practice.
{"title":"The Metagenomic Binning Problem: Clustering Markov Sequences","authors":"Grant Greenberg;Ilan Shomorony","doi":"10.1109/TMBMC.2023.3336254","DOIUrl":"https://doi.org/10.1109/TMBMC.2023.3336254","url":null,"abstract":"The goal of metagenomics is to study the composition of microbial communities, typically using high-throughput shotgun sequencing. In the metagenomic binning problem, we observe random substrings (called contigs) from a mixture of genomes and aim to cluster them according to their genome of origin. Based on the empirical observation that genomes of different bacterial species can be distinguished based on their tetranucleotide frequencies, we model this task as the problem of clustering \u0000<inline-formula> <tex-math>${N}$ </tex-math></inline-formula>\u0000 sequences generated by \u0000<inline-formula> <tex-math>${M}$ </tex-math></inline-formula>\u0000 distinct Markov processes, where \u0000<inline-formula> <tex-math>$M ll N$ </tex-math></inline-formula>\u0000. Utilizing the large-deviation principle for Markov processes, we establish the information-theoretic limit for perfect binning. Specifically, we show that the length of the contigs must scale with the inverse of the Chernoff divergence rate between the two most similar species. Furthermore, our result implies that contigs should be binned using the KL divergence rate as a measure of distance, as opposed to the Euclidean distance often used in practice.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"10 1","pages":"32-42"},"PeriodicalIF":2.2,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-09DOI: 10.1109/TMBMC.2023.3331579
Wenfeng Wu;Luping Xiang;Qiang Liu;Kun Yang
In the current era, DeoxyriboNucleic Acid (DNA) based data storage emerges as an intriguing approach, garnering substantial academic interest and investigation. This paper introduces a novel deep joint source-channel coding (DJSCC) scheme for DNA image storage, designated as DJSCC-DNA. This paradigm distinguishes itself from conventional DNA storage techniques through three key modifications: 1) it employs advanced deep learning methodologies, employing convolutional neural networks for DNA encoding and decoding processes; 2) it seamlessly integrates DNA polymerase chain reaction (PCR) amplification into the network architecture, thereby augmenting data recovery precision; and 3) it restructures the loss function by targeting biological constraints for optimization. The performance of the proposed model is demonstrated via numerical results from specific channel testing, suggesting that it surpasses conventional deep learning methodologies in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Additionally, the model effectively ensures positive constraints on both homopolymer run-length and GC content.
当今时代，基于脱氧核糖核酸（DNA）的数据存储成为一种引人入胜的方法，引起了学术界的极大兴趣和研究。本文介绍了一种用于 DNA 图像存储的新型深度联合源信道编码（DJSCC）方案，命名为 DJSCC-DNA。该范例通过三个关键修改将自己与传统的 DNA 存储技术区分开来：1）它采用了先进的深度学习方法，在 DNA 编码和解码过程中使用卷积神经网络；2）它将 DNA 聚合酶链式反应（PCR）扩增无缝集成到网络架构中，从而提高了数据恢复精度；3）它通过针对生物约束进行优化来重组损失函数。通过特定信道测试的数值结果证明了所提模型的性能，表明它在峰值信噪比（PSNR）和结构相似性指数（SSIM）方面超越了传统的深度学习方法。此外，该模型还有效地确保了对均聚物运行长度和 GC 含量的正向约束。
{"title":"Deep Joint Source-Channel Coding for DNA Image Storage: A Novel Approach With Enhanced Error Resilience and Biological Constraint Optimization","authors":"Wenfeng Wu;Luping Xiang;Qiang Liu;Kun Yang","doi":"10.1109/TMBMC.2023.3331579","DOIUrl":"10.1109/TMBMC.2023.3331579","url":null,"abstract":"In the current era, DeoxyriboNucleic Acid (DNA) based data storage emerges as an intriguing approach, garnering substantial academic interest and investigation. This paper introduces a novel deep joint source-channel coding (DJSCC) scheme for DNA image storage, designated as DJSCC-DNA. This paradigm distinguishes itself from conventional DNA storage techniques through three key modifications: 1) it employs advanced deep learning methodologies, employing convolutional neural networks for DNA encoding and decoding processes; 2) it seamlessly integrates DNA polymerase chain reaction (PCR) amplification into the network architecture, thereby augmenting data recovery precision; and 3) it restructures the loss function by targeting biological constraints for optimization. The performance of the proposed model is demonstrated via numerical results from specific channel testing, suggesting that it surpasses conventional deep learning methodologies in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Additionally, the model effectively ensures positive constraints on both homopolymer run-length and GC content.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"9 4","pages":"461-471"},"PeriodicalIF":2.2,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135562109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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