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Nazarov PV, Kreis S. Integrative approaches for analysis of mRNA and microRNA high-throughput data. Comput Struct Biotechnol J 2021; 19:1154-1162. [PMID: 33680358 PMCID: PMC7895676 DOI: 10.1016/j.csbj.2021.01.029] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/19/2021] [Accepted: 01/20/2021] [Indexed: 12/11/2022] Open
Abstract
Review on tools and databases linking miRNA and its mRNA targetome. Databases show little overlap in miRNA targetome predictions suggesting strong contextual effects. Deconvolution and deep learning approaches are promising new approaches to improve miRNA targetome predictions.
Advanced sequencing technologies such as RNASeq provide the means for production of massive amounts of data, including transcriptome-wide expression levels of coding RNAs (mRNAs) and non-coding RNAs such as miRNAs, lncRNAs, piRNAs and many other RNA species. In silico analysis of datasets, representing only one RNA species is well established and a variety of tools and pipelines are available. However, attaining a more systematic view of how different players come together to regulate the expression of a gene or a group of genes requires a more intricate approach to data analysis. To fully understand complex transcriptional networks, datasets representing different RNA species need to be integrated. In this review, we will focus on miRNAs as key post-transcriptional regulators summarizing current computational approaches for miRNA:target gene prediction as well as new data-driven methods to tackle the problem of comprehensively and accurately dissecting miRNome-targetome interactions.
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Key Words
- CCA, canonical correlation analysis
- CDS, coding sequence
- CLASH, cross-linking, ligation and sequencing of hybrids
- CLIP, cross-linking immunoprecipitation
- CNN, convolutional neural network
- Data integration
- GO, gene ontology
- ICA, independent component analysis
- Matrix factorization
- NGS, next-generation sequencing
- NMF, non-negative matrix factorization
- PCA, principal component analysis
- RNASeq, high-throughput RNA sequencing
- TDMD, target RNA-directed miRNA degradation
- TF, transcription factors
- Target prediction
- Transcriptomics
- circRNA, circular RNA
- lncRNA, long non-coding RNA
- mRNA, messenger RNA
- miRNA, microRNA
- microRNA
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Affiliation(s)
- Petr V Nazarov
- Multiomics Data Science Research Group, Department of Oncology & Quantitative Biology Unit, Luxembourg Institute of Health (LIH), Strassen L-1445, Luxembourg
| | - Stephanie Kreis
- Signal Transduction Group, Department of Life Sciences and Medicine, University of Luxembourg, Belvaux L-4367, Luxembourg
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Riolo G, Cantara S, Marzocchi C, Ricci C. miRNA Targets: From Prediction Tools to Experimental Validation. Methods Protoc 2020; 4:1. [PMID: 33374478 PMCID: PMC7839038 DOI: 10.3390/mps4010001] [Citation(s) in RCA: 134] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 12/17/2020] [Accepted: 12/22/2020] [Indexed: 12/12/2022] Open
Abstract
MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests.
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Affiliation(s)
| | | | | | - Claudia Ricci
- Department of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, Italy; (G.R.); (S.C.); (C.M.)
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Kang Q, Meng J, Cui J, Luan Y, Chen M. PmliPred: a method based on hybrid model and fuzzy decision for plant miRNA-lncRNA interaction prediction. Bioinformatics 2020; 36:2986-2992. [PMID: 32087005 DOI: 10.1093/bioinformatics/btaa074] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 12/18/2019] [Accepted: 01/27/2020] [Indexed: 12/28/2022] Open
Abstract
MOTIVATION The studies have indicated that not only microRNAs (miRNAs) or long non-coding RNAs (lncRNAs) play important roles in biological activities, but also their interactions affect the biological process. A growing number of studies focus on the miRNA-lncRNA interactions, while few of them are proposed for plant. The prediction of interactions is significant for understanding the mechanism of interaction between miRNA and lncRNA in plant. RESULTS This article proposes a new method for fulfilling plant miRNA-lncRNA interaction prediction (PmliPred). The deep learning model and shallow machine learning model are trained using raw sequence and manually extracted features, respectively. Then they are hybridized based on fuzzy decision for prediction. PmliPred shows better performance and generalization ability compared with the existing methods. Several new miRNA-lncRNA interactions in Solanum lycopersicum are successfully identified using quantitative real time-polymerase chain reaction from the candidates predicted by PmliPred, which further verifies its effectiveness. AVAILABILITY AND IMPLEMENTATION The source code of PmliPred is freely available at http://bis.zju.edu.cn/PmliPred/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qiang Kang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Jun Meng
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Jun Cui
- School of Bioengineering, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Yushi Luan
- School of Bioengineering, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Ming Chen
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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54
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Computational discovery and modeling of novel gene expression rules encoded in the mRNA. Biochem Soc Trans 2020; 48:1519-1528. [PMID: 32662820 DOI: 10.1042/bst20191048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 06/15/2020] [Accepted: 06/17/2020] [Indexed: 11/17/2022]
Abstract
The transcript is populated with numerous overlapping codes that regulate all steps of gene expression. Deciphering these codes is very challenging due to the large number of variables involved, the non-modular nature of the codes, biases and limitations in current experimental approaches, our limited knowledge in gene expression regulation across the tree of life, and other factors. In recent years, it has been shown that computational modeling and algorithms can significantly accelerate the discovery of novel gene expression codes. Here, we briefly summarize the latest developments and different approaches in the field.
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Yoshida K, Yokoi A, Kato T, Ochiya T, Yamamoto Y. The clinical impact of intra- and extracellular miRNAs in ovarian cancer. Cancer Sci 2020; 111:3435-3444. [PMID: 32750177 PMCID: PMC7541008 DOI: 10.1111/cas.14599] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 07/17/2020] [Accepted: 07/28/2020] [Indexed: 12/12/2022] Open
Abstract
Ovarian cancer is the most lethal gynecological cancer due to lack of early screening methods and acquired drug resistance. MicroRNAs (miRNAs) are effective post‐transcriptional regulators that are transferred by extracellular vesicles, such as exosomes. Numerous studies have revealed that miRNAs are differentially expressed in epithelial ovarian cancer and act either as oncogenes or tumor suppressor genes. Cancer cells secrete exosomes containing miRNAs, which exert various effects on the components of the tumor microenvironment, including cancer‐associated fibroblasts, macrophages, and adipocytes. Conversely, cancer cells also receive exosomes from these cells. As a result of cell‐to‐cell communication, epithelial ovarian cancer acquires a more aggressive phenotype and resistance to multiple drugs. In addition, some circulating miRNAs are protected from RNase degradation in the peripheral blood and can be potential non‐invasive biomarkers. In particular, the combination of several circulating miRNAs enhances the accuracy of cancer screening. Likewise, comprehensive analyses revealed specific miRNA signatures in non‐epithelial ovarian tumors and several miRNAs contributing to alterations of carcinogenic pathways. Overall, miRNAs play a crucial role in ovarian cancer progression. In this review, we discuss the emerging roles of intra‐ and extracellular miRNAs in ovarian cancers. In the near future, miRNAs will be practical biomarkers and computational deep learning will help in the clinical application of miRNAs. Moreover, miRNAs are potential therapeutic targets and agents, and there are ongoing clinical trials of miRNA replacement therapy. Therefore, accelerating research on miRNA might improve the prognosis of patients with ovarian cancer.
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Affiliation(s)
- Kosuke Yoshida
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Division of Cellular Signaling, National Cancer Center Research Institute, Tokyo, Japan
| | - Akira Yokoi
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Tomoyasu Kato
- Department of Gynecology, National Cancer Center Hospital, Tokyo, Japan
| | - Takahiro Ochiya
- Department of Molecular and Cellular Medicine, Institute of Medical Science, Tokyo Medical University, Tokyo, Japan
| | - Yusuke Yamamoto
- Division of Cellular Signaling, National Cancer Center Research Institute, Tokyo, Japan
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Kyrollos DG, Reid B, Dick K, Green JR. RPmirDIP: Reciprocal Perspective improves miRNA targeting prediction. Sci Rep 2020; 10:11770. [PMID: 32678114 PMCID: PMC7366700 DOI: 10.1038/s41598-020-68251-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 06/15/2020] [Indexed: 12/16/2022] Open
Abstract
MicroRNAs (miRNAs) are short, non-coding RNAs that interact with messenger RNA (mRNA) to accomplish critical cellular activities such as the regulation of gene expression. Several machine learning methods have been developed to improve classification accuracy and reduce validation costs by predicting which miRNA will target which gene. Application of these predictors to large numbers of unique miRNA-gene pairs has resulted in datasets comprising tens of millions of scored interactions; the largest among these is mirDIP. We here demonstrate that miRNA target prediction can be significantly improved ([Formula: see text]) through the application of the Reciprocal Perspective (RP) method, a cascaded, semi-supervised machine learning method originally developed for protein-protein interaction prediction. The RP method, aptly named RPmirDIP, augments the original mirDIP prediction scores by leveraging local thresholds from the two complimentary views available to each miRNA-gene pair, rather than apply a traditional global decision threshold. Application of this novel RPmirDIP predictor promises to help identify new, unexpected miRNA-gene interactions. A dataset of RPmirDIP-scored interactions are made available to the scientific community at cu-bic.ca/RPmirDIP and https://doi.org/10.5683/SP2/LD8JKJ.
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Affiliation(s)
- Daniel G Kyrollos
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada
| | - Bradley Reid
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada
| | - Kevin Dick
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada
- Institute of Data Science, Carleton University, Ottawa, Canada
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada.
- Institute of Data Science, Carleton University, Ottawa, Canada.
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Zheng X, Chen L, Li X, Zhang Y, Xu S, Huang X. Prediction of miRNA targets by learning from interaction sequences. PLoS One 2020; 15:e0232578. [PMID: 32369518 PMCID: PMC7199961 DOI: 10.1371/journal.pone.0232578] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 04/17/2020] [Indexed: 12/14/2022] Open
Abstract
MicroRNAs (miRNAs) are involved in a diverse variety of biological processes through regulating the expression of target genes in the post-transcriptional level. So, it is of great importance to discover the targets of miRNAs in biological research. But, due to the short length of miRNAs and limited sequence complementarity to their gene targets in animals, it is challenging to develop algorithms to predict the targets of miRNA accurately. Here we developed a new miRNA target prediction algorithm using a multilayer convolutional neural network. Our model learned automatically the interaction patterns of the experiment-validated miRNA:target-site chimeras from the raw sequence, avoiding hand-craft selection of features by domain experts. The performance on test dataset is inspiring, indicating great generalization ability of our model. Moreover, considering the stability of miRNA:target-site duplexes, our method also showed good performance to predict the target transcripts of miRNAs.
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Affiliation(s)
- Xueming Zheng
- Department of Biochemistry and Molecular Biology, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, P. R. China
- Department of Clinical Laboratory, the First People’s Hospital of Zhangjiagang, Zhangjiagang, Jiangsu, P. R. China
| | - Long Chen
- Department of Clinical Laboratory, the First People’s Hospital of Zhangjiagang, Zhangjiagang, Jiangsu, P. R. China
| | - Xiuming Li
- School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, P. R. China
| | - Ying Zhang
- Department of Biochemistry and Molecular Biology, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, P. R. China
| | - Shungao Xu
- Department of Biochemistry and Molecular Biology, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, P. R. China
| | - Xinxiang Huang
- Department of Biochemistry and Molecular Biology, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, P. R. China
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Jiang H, Yang M, Chen X, Li M, Li Y, Wang J. miRTMC: A miRNA Target Prediction Method Based on Matrix Completion Algorithm. IEEE J Biomed Health Inform 2020; 24:3630-3641. [PMID: 32287029 DOI: 10.1109/jbhi.2020.2987034] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
microRNAs (miRNAs) are small non-coding RNAs which modulate the stability of gene targets and their rates of translation into proteins at transcriptional level and post-transcriptional level. miRNA dysfunctions can lead to human diseases because of dysregulation of their targets. Correct miRNA target prediction will lead to better understanding of the mechanisms of human diseases and provide hints on curing them. In recent years, computational miRNA target prediction methods have been proposed according to the interaction rules between miRNAs and targets. However, these methods suffer from high false positive rates due to the complicated relationship between miRNAs and their targets. The rapidly growing number of experimentally validated miRNA targets enables predicting miRNA targets with high precision via accurate data analysis. Taking advantage of these known miRNA targets, a novel recommendation system model (miRTMC) for miRNA target prediction is established using a new matrix completion algorithm. In miRTMC, a heterogeneous network is constructed by integrating the miRNA similarity network, the gene similarity network, and the miRNA-gene interaction network. Our assumption is that the latent factors determining whether a gene is the target of miRNA or not are highly correlated, i.e., the adjacency matrix of the heterogeneous network is low-rank, which is then completed by using a nuclear norm regularized linear least squares model under non-negative constraints. Alternating direction method of multipliers (ADMM) is adopted to numerically solve the matrix completion problem. Our results show that miRTMC outperforms the competing methods in terms of various evaluation metrics. Our software package is available at https://github.com/hjiangcsu/miRTMC.
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Schäfer M, Ciaudo C. Prediction of the miRNA interactome - Established methods and upcoming perspectives. Comput Struct Biotechnol J 2020; 18:548-557. [PMID: 32211130 PMCID: PMC7082591 DOI: 10.1016/j.csbj.2020.02.019] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 02/21/2020] [Accepted: 02/27/2020] [Indexed: 01/21/2023] Open
Abstract
MicroRNAs (miRNAs) are well-studied small noncoding RNAs involved in post-transcriptional gene regulation in a wide range of organisms, including mammals. Their function is mediated by base pairing with their target RNAs. Although many features required for miRNA-mediated repression have been described, the identification of functional interactions is still challenging. In the last two decades, numerous Machine Learning (ML) models have been developed to predict their putative targets. In this review, we summarize the biological knowledge and the experimental data used to develop these ML models. Recently, Deep Neural Network-based models have also emerged in miRNA interaction modeling. We thus outline established and emerging models to give a perspective on the future developments needed to improve the identification of genes directly regulated by miRNAs.
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Affiliation(s)
- Moritz Schäfer
- Swiss Federal Institute of Technology Zurich, Department of Biology, Institute of Molecular Health Sciences, CH-8093 Zurich, Switzerland
- Life Science Zurich Graduate School, Systems Biology Program, University of Zurich, CH-8047 Zurich, Switzerland
| | - Constance Ciaudo
- Swiss Federal Institute of Technology Zurich, Department of Biology, Institute of Molecular Health Sciences, CH-8093 Zurich, Switzerland
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Mégret L, Nair SS, Dancourt J, Aaronson J, Rosinski J, Neri C. Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington's disease mice. BMC Bioinformatics 2020; 21:75. [PMID: 32093602 PMCID: PMC7041117 DOI: 10.1186/s12859-020-3418-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 02/17/2020] [Indexed: 12/12/2022] Open
Abstract
Background MicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative diseases. Several approaches can be used for modeling miRNA regulation. However, their precision may be limited for analyzing multidimensional data. Here, we addressed this question by integrating shape analysis and feature selection into miRAMINT, a methodology that we used for analyzing multidimensional RNA-seq and proteomic data from a knock-in mouse model (Hdh mice) of Huntington’s disease (HD), a disease caused by CAG repeat expansion in huntingtin (htt). This dataset covers 6 CAG repeat alleles and 3 age points in the striatum and cortex of Hdh mice. Results Remarkably, compared to previous analyzes of this multidimensional dataset, the miRAMINT approach retained only 31 explanatory striatal miRNA-mRNA pairs that are precisely associated with the shape of CAG repeat dependence over time, among which 5 pairs with a strong change of target expression levels. Several of these pairs were previously associated with neuronal homeostasis or HD pathogenesis, or both. Such miRNA-mRNA pairs were not detected in cortex. Conclusions These data suggest that miRNA regulation has a limited global role in HD while providing accurately-selected miRNA-target pairs to study how the brain may compute molecular responses to HD over time. These data also provide a methodological framework for researchers to explore how shape analysis can enhance multidimensional data analytics in biology and disease.
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Affiliation(s)
- Lucile Mégret
- Sorbonne Université, CNRS UMR8256, INSERM ERL U1164, Brain-C Lab, Paris, France.
| | | | - Julia Dancourt
- Sorbonne Université, CNRS UMR8256, INSERM ERL U1164, Brain-C Lab, Paris, France
| | | | | | - Christian Neri
- Sorbonne Université, CNRS UMR8256, INSERM ERL U1164, Brain-C Lab, Paris, France.
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Fan X, Ming W, Zeng H, Zhang Z, Lu H. Deep learning-based component identification for the Raman spectra of mixtures. Analyst 2019; 144:1789-1798. [PMID: 30672931 DOI: 10.1039/c8an02212g] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Raman spectroscopy is widely used as a fingerprint technique for molecular identification. However, Raman spectra contain molecular information from multiple components and interferences from noise and instrumentation. Thus, component identification using Raman spectra is still challenging, especially for mixtures. In this study, a novel approach entitled deep learning-based component identification (DeepCID) was proposed to solve this problem. Convolution neural network (CNN) models were established to predict the presence of components in mixtures. Comparative studies showed that DeepCID could learn spectral features and identify components in both simulated and real Raman spectral datasets of mixtures with higher accuracy and significantly lower false positive rates. In addition, DeepCID showed better sensitivity when compared with the logistic regression (LR) with L1-regularization, k-nearest neighbor (kNN), random forest (RF) and back propagation artificial neural network (BP-ANN) models for ternary mixture spectral datasets. In conclusion, DeepCID is a promising method for solving the component identification problem in the Raman spectra of mixtures.
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Affiliation(s)
- Xiaqiong Fan
- College of Chemistry and Chemical Engineering, Central South University, Changsha, China.
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