ArticleNon-coding RNA2017
PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants.
Article in Non-coding RNA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Epigenetic Alterations in DCIS Progression: What Can lncRNAs Teach Us?International journal of molecular sciences · 2023Review
- CircPCBL: Identification of Plant CircRNAs with a CNN-BiGRU-GLT Model.Plants (Basel, Switzerland) · 2023Article
- CicerSpTEdb: A web-based database for high-resolution genome-wide identification of transposable elements in Cicer species.PloS one · 2021Article
- Analysis of Soybean Long Non-Coding RNAs Reveals a Subset of Small Peptide-Coding Transcripts.Plant physiology · 2020Article
- From Trash to Luxury: The Potential Role of Plant LncRNA in DNA Methylation During Abiotic Stress.Frontiers in plant science · 2020Review
- The computational approaches of lncRNA identification based on coding potential:Computational and structural biotechnology journal · 2020Review
- Article
- Roles of Non-Coding RNA in Sugarcane-Microbe Interaction.Non-coding RNA · 2017Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-coding RNAs (ncRNAs) constitute an important set of transcripts produced in the cells of organisms. Among them, there is a large amount of a particular class of long ncRNAs that are difficult to predict, the so-called long intergenic ncRNAs (lincRNAs), which might play essential roles in gene regulation and other cellular processes. Despite the importance of these lincRNAs, there is still a lack of biological knowledge and, currently, the few computational methods considered are so specific that they cannot be successfully applied to other species different from those that they have been originally designed to. Prediction of lncRNAs have been performed with machine learning techniques. Particularly, for lincRNA prediction, supervised learning methods have been explored in recent literature. As far as we know, there are no methods nor workflows specially designed to predict lincRNAs in plants. In this context, this work proposes a workflow to predict lincRNAs on plants, considering a workflow that includes known bioinformatics tools together with machine learning techniques, here a support vector machine (SVM). We discuss two case studies that allowed to identify novel lincRNAs, in sugarcane (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.