Human transcripts without protein-coding potential are known as noncoding RNAs(ncRNAs), including long noncoding RNAs (lncRNAs), circular RNA (circRNA), microRNA(miRNAs), and small nucleolar RNA (snoRNAs), which have been recently reported to be responsible for the development of drug resistance in cancer treatments. The cross-talk between different types of noncoding RNA can form the key regulation network in drug resistance, elucidating its potential clinical implications. Increasing attention has been paid to the emerging field of noncoding RNA-based therapies providing valuable advancements in comparison to current pharmacotherapy. Discovering the links between the modes of noncoding RNA expression and drug resistance in specific disease mechanisms helps to assess gene expression profiles in personalized treatment. However, our understanding of such information is still the tip of the iceberg.
Noncoding RNAs play regulatory roles in almost all biological processes by modulating gene expression at the transcriptional and posttranscriptional levels. For most of them, the pathways where they influence the individuals’ drug response are largely unknown. The past decade has witnessed the upsurge of research interest in noncoding RNA with substantial efforts on function annotation, signaling pathway identification and etc. At present, the treatment data have also been collected for revealing the noncoding RNA’s expression in various drug resistance events. Such data describe a knowledge graph of noncoding RNA-induced drug resistance, providing an information resource for building machine learning models to identify the association between noncoding RNA and drug resistance on a large scale.
This research topic will encourage the authors to submit original research, review articles, and opinions. This will provide a deeper insight into the application of machine learning techniques to drug resistance analysis with the use of large amounts of relevant data. We welcome reviews, mini-reviews, and original research articles that provide an in-depth understanding of, but are not limited to, the following subthemes:
• Computational methodologies to predict the association between noncoding RNA and drug resistance
• Identification of noncoding RNA’s functional sets for specific drug resistances
• Exploration of the role of noncoding RNA in disease mechanism and drug treatment
• Single-cell sequencing data analysis for drug response prediction
• Function annotation of noncoding RNA with clinical result analysis
• Text Ming Tool and database for detecting noncoding RNA-mediated drug resistance
Articles acceptable for this Research Topic are limited to:
• Articles unambiguously related to pharmacogenetics, pharmacogenomics, drug metabolism, or drug transport.
• If patient data are analyzed, a comprehensive description of the patients including sex, age, diagnostic criteria, inclusion and exclusion criteria, disease stage, therapy received, comorbidities as well as additional clinical information and assessment of drug response/effects should be included.
• If genetic, proteomics, metabolomics, or other omics data are analyzed, a comprehensive description of the methods and the rationale for the selection of the specific data studied should be provided.
• Only articles involving highly purified, chemically characterized compounds will be included. Studies related to natural compounds, herbal extracts, or traditional medicine products, will not be included in this Research Topic.
Human transcripts without protein-coding potential are known as noncoding RNAs(ncRNAs), including long noncoding RNAs (lncRNAs), circular RNA (circRNA), microRNA(miRNAs), and small nucleolar RNA (snoRNAs), which have been recently reported to be responsible for the development of drug resistance in cancer treatments. The cross-talk between different types of noncoding RNA can form the key regulation network in drug resistance, elucidating its potential clinical implications. Increasing attention has been paid to the emerging field of noncoding RNA-based therapies providing valuable advancements in comparison to current pharmacotherapy. Discovering the links between the modes of noncoding RNA expression and drug resistance in specific disease mechanisms helps to assess gene expression profiles in personalized treatment. However, our understanding of such information is still the tip of the iceberg.
Noncoding RNAs play regulatory roles in almost all biological processes by modulating gene expression at the transcriptional and posttranscriptional levels. For most of them, the pathways where they influence the individuals’ drug response are largely unknown. The past decade has witnessed the upsurge of research interest in noncoding RNA with substantial efforts on function annotation, signaling pathway identification and etc. At present, the treatment data have also been collected for revealing the noncoding RNA’s expression in various drug resistance events. Such data describe a knowledge graph of noncoding RNA-induced drug resistance, providing an information resource for building machine learning models to identify the association between noncoding RNA and drug resistance on a large scale.
This research topic will encourage the authors to submit original research, review articles, and opinions. This will provide a deeper insight into the application of machine learning techniques to drug resistance analysis with the use of large amounts of relevant data. We welcome reviews, mini-reviews, and original research articles that provide an in-depth understanding of, but are not limited to, the following subthemes:
• Computational methodologies to predict the association between noncoding RNA and drug resistance
• Identification of noncoding RNA’s functional sets for specific drug resistances
• Exploration of the role of noncoding RNA in disease mechanism and drug treatment
• Single-cell sequencing data analysis for drug response prediction
• Function annotation of noncoding RNA with clinical result analysis
• Text Ming Tool and database for detecting noncoding RNA-mediated drug resistance
Articles acceptable for this Research Topic are limited to:
• Articles unambiguously related to pharmacogenetics, pharmacogenomics, drug metabolism, or drug transport.
• If patient data are analyzed, a comprehensive description of the patients including sex, age, diagnostic criteria, inclusion and exclusion criteria, disease stage, therapy received, comorbidities as well as additional clinical information and assessment of drug response/effects should be included.
• If genetic, proteomics, metabolomics, or other omics data are analyzed, a comprehensive description of the methods and the rationale for the selection of the specific data studied should be provided.
• Only articles involving highly purified, chemically characterized compounds will be included. Studies related to natural compounds, herbal extracts, or traditional medicine products, will not be included in this Research Topic.