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Results: CrossPred is introduced as a deep-learning multi-encoder model using contrastive learning to learn a shared embedding space for exmiRs and mRNAs. The shared space enables bidirectional cross-prediction of mRNA from noisy exmiRs and exmiRs from intracellular mRNA. Evaluation on three cancer types shows CrossPred outperforms multiple baseline and generative models. The integrated signals highlight cancer-associated miRNAs and mRNAs, providing new insights into their bidirectional relationship. Availability and implementation: Datasets and tool are provided via Zenodo.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/gene-expression-a-deep-learning-method-to-integrate-extracelluar-mirna-with-mrna-for-cancer-studies-abstract/331523/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/gene-expression-a-deep-learning-method-to-integrate-extracelluar-mirna-with-mrna-for-cancer-studies-abstract/331523.png","ImageObject",300,407,{"name":92,"@type":93},"Quinn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-24","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is integrating extracellular miRNA (exmiR) and intracellular mRNA important for cancer studies?","Question",{"text":112,"@type":113},"exmiRs and mRNAs can both serve as biomarkers and therapeutic targets, but exmiR data are noisy and intracellular mRNA requires invasive procedures. Integrating them helps infer intracellular mRNA expression from noninvasive exmiR data and vice versa.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does CrossPred predict mRNA and exmiR expression?",{"text":117,"@type":113},"CrossPred uses a deep-learning multi-encoder architecture with contrastive learning to create a shared embedding space linking exmiRs and mRNAs. It then performs cross-prediction in both directions using this shared representation.",{"name":119,"@type":110,"acceptedAnswer":120},"What evidence shows CrossPred improves performance over existing models?",{"text":121,"@type":113},"CrossPred is evaluated on three cancer types and is reported to outperform the baseline encoder-decoder model, exmiR- or mRNA-only models, and variational autoencoder models for predicting expression levels.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},331523,1790270065,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":46,"preview_page_count":4},962075114765,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Gene expression  \nA deep learning method to integrate extracelluar miRNA with mRNA for cancer studies  \nTasbiraha Athaya1, Xiaoman Li 2, 􀀃 , Haiyan Hu1,􀀃  \n1 Department of Computer Science, University of Central Florida, 4000 Central Florida BLVD, Orlando, FL, 32816, United States 2 Burnett School of Biomedical Sciences, University of Central Florida, 4000 Central Florida BLVD, Orlando, FL, 32816, United States  \n􀀃 Corresponding authors. Burnett School of Biomedical Science, College of Medicine, University of Central Florida, 4000 Central Florida BLVD, Orlando, FL, United [States. E-mail: xiaoman@mail.ucf.edu](States. E-mail: xiaoman@mail.ucf.edu) (X. L. ); Department of Computer Science, University of Central Florida, 4328 Scorpius Street, Orlando, FL 32816, United States. E-mail: [haihu@cs.ucf.edu](haihu@cs.ucf.edu) (H. H. )  \nAssociate Editor: Anthony Mathelier  \nAbstract  \nMotivation: Extracellular miRNAs (exmiRs) and intracellular mRNAs both can serve as promising biomarkers and therapeutic targets for various diseases. However, exmiR expression data is often noisy, and obtaining intracellular mRNA expression data usually involves intrusive procedures. To gain valuable insights into disease mechanisms, it is thus essential to improve the quality of exmiR expression data and develop noninvasive methods for assessing intracellular mRNA expression.  \nResults: We developed CrossPred, a deep-learning multi-encoder model for the cross-prediction of exmiRs and mRNAs. Utilizing contrastive learning, we created a shared embedding space to integrate exmiRs and mRNAs. This shared embedding was then used to predict intracellular mRNA expression from noisy exmiR data and to predict exmiR expression from intracellular mRNA data. We evaluated CrossPred on three types of cancers and assessed its effectiveness in predicting the expression levels of exmiRs and mRNAs. CrossPred outperformed the baseline encoder-decoder model, exmiR or mRNA-based models, and variational autoencoder models. Moreover, the integration of exmiR and mRNA data uncovered important exmiRs and mRNAs associated with cancer. Our study offers new insights into the bidirectional relationship between mRNAs and exmiRs.  \nAvailability and implementation: The datasets and tool are available at [https://doi.org/10.5281/zenodo.13891508](https://doi.org/10.5281/zenodo.13891508) .  \n1 Introduction  \nmicroRNAs (miRNAs) are 􀀘 22 nucleotides long noncoding RNA that play important roles in gene regulation (Kim et al. 2009, Ding et al. 2018, Talukder et al. 2020) . Extracellular miRNAs (exmiRs) are miRNAs found outside cells, circulating in various body fluids such as blood, urine, and saliva. They have been shown to play pivotal roles in facilitating intercellular communication (Happel et al. 2020) .  \nHigh-throughput sequencing technologies have enabled comprehensive profiling of exmiR expression in a variety of body fluids across different cancer types (Yuan et al. 2016, Gilani et al. 2021) . Analysis of exmiR expression data provides valuable information that contributes to early cancer diagnosis and the customization of therapeutic strategies, significantly benefiting cancer patients (Schwarzenbach et al. 2014) . They have also been shown to be promising biomarkers for other diseases (Cortez et al. 2011) . The noninvasive procedures of using body fluids as test samples eliminate the need for unnecessary repeat biopsies in diagnosing and monitoring the effectiveness of therapy (Happel et al. 2020) . In addition, exmiR-based biomarkers offers advantages in terms of cost and assay simplicity (Schwarzenbach et al. 2014) . However, current exmiR measurements are often  \nnoisy due to the issues of different exmiR profiling protocolsand technical and biological variability (Carnino et al. 2019) .  \nOn the other hand, mRNA expression in tumor tissues has often been used for assessing cancer phenotypes (Mansoori et al. 2019). Various mRNA signatures have been identified for cancer progno","cbCainhC3r57JN3D","https://ap.wps.com/l/cbCainhC3r57JN3D","pdf",2142442,"English","# Abstract\n## Motivation\n## Results\n## Availability and implementation\n# Introduction\n## Background on miRNAs and exmiRs\n## Limitations of current exmiR and mRNA measurements\n## Rationale for integrating exmiR and mRNA\n# Material and Methods\n## mRNA data","[{\"question\":\"Why is integrating extracellular miRNA (exmiR) and intracellular mRNA important for cancer studies?\",\"answer\":\"exmiRs and mRNAs can both serve as biomarkers and therapeutic targets, but exmiR data are noisy and intracellular mRNA requires invasive procedures. Integrating them helps infer intracellular mRNA expression from noninvasive exmiR data and vice versa.\"},{\"question\":\"How does CrossPred predict mRNA and exmiR expression?\",\"answer\":\"CrossPred uses a deep-learning multi-encoder architecture with contrastive learning to create a shared embedding space linking exmiRs and mRNAs. It then performs cross-prediction in both directions using this shared representation.\"},{\"question\":\"What evidence shows CrossPred improves performance over existing models?\",\"answer\":\"CrossPred is evaluated on three cancer types and is reported to outperform the baseline encoder-decoder model, exmiR- or mRNA-only models, and variational autoencoder models for predicting expression levels.\"}]","Gene expression - A deep learning method to integrate extracelluar miRNA with mRNA for cancer studies - Abstract | PDF",1789993720]