Applications of Machine Learning in miRNA Discovery and Target Prediction

Parveen, Alisha; Mustafa, Syed Husain; Yadav, Pankaj; Kumar, Abhishek

doi:10.2174/1389202921666200106111813

Cited by 24 publications

(16 citation statements)

References 33 publications

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“…In recent years, miRNAs have been identified in some plants species through cloning and computational approaches (Saliminejad et al, 2019;Smoczynska et al, 2019), and it has also been shown that miRNAs may be predicted using modern machine learning approaches (Parveen et al, 2019;Esposito et al, 2020). Recent reports have identified hundreds of miRNAs in several species, including Fragaria vesca (Han et al, 2019), cardamom (Anjali et al, 2019), sweet cherry (Wang et al, 2019), and Brazilian pine (Galdino et al, 2019) through highthroughput sequencing.…”

Section: Introductionmentioning

confidence: 99%

Identification of Known and Novel MicroRNAs in Raspberry Organs Through High-Throughput Sequencing

et al. 2020

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MicroRNAs (miRNAs) are a class of small endogenous RNAs that play important regulatory roles in plants by negatively affecting gene expression. Studies on the identification of miRNAs and their functions in various plant species and organs have significantly contributed to plant development research. In the current study, we utilized high-throughput sequencing to detect the miRNAs in the root, stem, and leaf tissues of raspberry (Rubus idaeus). A total of more than 35 million small RNA reads ranging in size from 18 to 35 nucleotides were obtained, with 147 known miRNAs and 542 novel miRNAs identified among the three organs. Sequence verification and the relative expression profiles of the six known miRNAs were investigated by stemloop quantitative real-time PCR. Furthermore, the potential target genes of the known and novel miRNAs were predicted and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway annotation. Enrichment analysis of the GO-associated biological processes and molecular functions revealed that these target genes were potentially involved in a wide range of metabolic pathways and developmental processes. Moreover, the miRNA target prediction revealed that most of the targets predicted as transcription factor-coding genes are involved in cellular and metabolic processes. This report is the first to identify miRNAs in raspberry. The detected miRNAs were analyzed by cluster analysis according to their expression, which revealed that these conservative miRNAs are necessary for plant functioning. The results add novel miRNAs to the raspberry transcriptome, providing a useful resource for the further elucidation of the functional roles of miRNAs in raspberry growth and development.

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Section: Introductionmentioning

confidence: 99%

Identification of Known and Novel MicroRNAs in Raspberry Organs Through High-Throughput Sequencing

et al. 2020

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“…Interestingly, ML is not programmed to necessitate stringent seed matches in the 3′-UTRs, but it learns by provided examples with biological relevance, so it also allows identifying non-canonical binding sites, including those within the coding regions. On the other hand, ML learns exclusively from the provided examples and consequently it is only able to find results similar to those examples [ 35 ].…”

Section: Mirna Target Predictionmentioning

confidence: 99%

“…Conversely, if the negative and the positive datasets are very similar to each other, the ML approach will not be able to distinguish between them. Moreover, an excess of positive or negative dataset can cause underfitting or overfitting models, respectively [ 35 ]. Finally, the choice of the validation set is critical for ML: to verify its performance, a model should be assessed on a test dataset that is completely different from the dataset used for the training step (training dataset).…”

Section: Mirna Target Predictionmentioning

confidence: 99%

miRNA Targets: From Prediction Tools to Experimental Validation

Riolo

Cantara

Marzocchi

et al. 2020

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141

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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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“…Clinical diagnostics, for example, in ophthalmology, can also be supported significantly by methods of advanced data science [ 4 , 5 ]. In addition to the application of ML in diagnostics, there are also promising approaches in such different fields as robotic-assisted surgery [ 6 ], human genomics [ 7 , 8 ] or prevention [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

The ethics of machine learning-based clinical decision support: an analysis through the lens of professionalisation theory

Heyen

Salloch

2021

BMC Med Ethics

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Background Machine learning-based clinical decision support systems (ML_CDSS) are increasingly employed in various sectors of health care aiming at supporting clinicians’ practice by matching the characteristics of individual patients with a computerised clinical knowledge base. Some studies even indicate that ML_CDSS may surpass physicians’ competencies regarding specific isolated tasks. From an ethical perspective, however, the usage of ML_CDSS in medical practice touches on a range of fundamental normative issues. This article aims to add to the ethical discussion by using professionalisation theory as an analytical lens for investigating how medical action at the micro level and the physician–patient relationship might be affected by the employment of ML_CDSS. Main text Professionalisation theory, as a distinct sociological framework, provides an elaborated account of what constitutes client-related professional action, such as medical action, at its core and why it is more than pure expertise-based action. Professionalisation theory is introduced by presenting five general structural features of professionalised medical practice: (i) the patient has a concern; (ii) the physician deals with the patient’s concern; (iii) s/he gives assistance without patronising; (iv) s/he regards the patient in a holistic manner without building up a private relationship; and (v) s/he applies her/his general expertise to the particularities of the individual case. Each of these five key aspects are then analysed regarding the usage of ML_CDSS, thereby integrating the perspectives of professionalisation theory and medical ethics. Conclusions Using ML_CDSS in medical practice requires the physician to pay special attention to those facts of the individual case that cannot be comprehensively considered by ML_CDSS, for example, the patient’s personality, life situation or cultural background. Moreover, the more routinized the use of ML_CDSS becomes in clinical practice, the more that physicians need to focus on the patient’s concern and strengthen patient autonomy, for instance, by adequately integrating digital decision support in shared decision-making.

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Applications of Machine Learning in miRNA Discovery and Target Prediction

Cited by 24 publications

References 33 publications

Identification of Known and Novel MicroRNAs in Raspberry Organs Through High-Throughput Sequencing

Identification of Known and Novel MicroRNAs in Raspberry Organs Through High-Throughput Sequencing

miRNA Targets: From Prediction Tools to Experimental Validation

The ethics of machine learning-based clinical decision support: an analysis through the lens of professionalisation theory

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