A diagnostic miRNA panel to detect recurrence of ovarian cancer through artificial intelligence approaches

Aghayousefi, Reyhaneh; Khatibi, Seyed Mahdi Hosseiniyan; Vahed, Sepideh Zununi; Bastami, Milad; Pirmoradi, Saeed; Teshnehlab, Mohammad

doi:10.1007/s00432-022-04468-2

Cited by 7 publications

(3 citation statements)

References 84 publications

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“…For the normalization of the training data, two approaches were applied during (i) feature selection and (ii) classification; Min–max for the former and Z-score for the latter as described previously 22 . In order to address the side-effects of imbalanced data, we applied random duplicate oversampling technique to bring parity between minority and majority classes, only for the training part of the data of course.…”

Section: Methodsmentioning

confidence: 99%

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Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method

Pirmoradi,

Hosseiniyan Khatibi,

Zununi Vahed

et al. 2023

Sci Rep

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Severe asthma is a chronic inflammatory airway disease with great therapeutic challenges. Understanding the genetic and molecular mechanisms of severe asthma may help identify therapeutic strategies for this complex condition. RNA expression data were analyzed using a combination of artificial intelligence methods to identify novel genes related to severe asthma. Through the ANOVA feature selection approach, 100 candidate genes were selected among 54,715 mRNAs in blood samples of patients with severe asthmatic and healthy groups. A deep learning model was used to validate the significance of the candidate genes. The accuracy, F1-score, AUC-ROC, and precision of the 100 genes were 83%, 0.86, 0.89, and 0.9, respectively. To discover hidden associations among selected genes, association rule mining was applied. The top 20 genes including the PTBP1, RAB11FIP3, APH1A, and MYD88 were recognized as the most frequent items among severe asthma association rules. The PTBP1 was found to be the most frequent gene associated with severe asthma among those 20 genes. PTBP1 was the gene most frequently associated with severe asthma among candidate genes. Identification of master genes involved in the initiation and development of asthma can offer novel targets for its diagnosis, prognosis, and targeted-signaling therapy.

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Section: Methodsmentioning

confidence: 99%

“…During this study, a serial combination method containing the analysis of variance (ANOVA) and association rule mining 22 is used to select features in gene expression data as illustrated in Fig. 1 .…”

Section: Methodsmentioning

confidence: 99%

Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method

Pirmoradi,

Hosseiniyan Khatibi,

Zununi Vahed

et al. 2023

Sci Rep

Self Cite

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show abstract

“…Aghayousefi et al ( 107 ) applied a DL model to screen microRNAs (miRNAs/miRs) related to OC occurrence, and found that miR-1914, miR-203, miR-135a-2, miR-149 and miR-9-1 were the risk factors associated with OC with the highest frequency. Moreover, the study suggested that the miRNAs may participate in the epithelial-mesenchymal transformation of cancer cells, as well as the heterogeneous and adaptive processes of tumors.…”

Section: Other Ai-based Omics In Ocmentioning

confidence: 99%

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

Wang,

Lin,

Zhuang

et al. 2024

Oncol Rep

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Artificial intelligence (AI) has emerged as a crucial technique for extracting high-throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI-based radiomics has proven to be a non-invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI-based multi-omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.

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Preoperative CECT-Based Multitask Model Predicts Peritoneal Recurrence and Disease-Free Survival in Advanced Ovarian Cancer: A Multicenter Study

Yin,

Dou,

Wang

et al. 2024

Academic Radiology

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A diagnostic miRNA panel to detect recurrence of ovarian cancer through artificial intelligence approaches

Cited by 7 publications

References 84 publications

Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method

Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

Preoperative CECT-Based Multitask Model Predicts Peritoneal Recurrence and Disease-Free Survival in Advanced Ovarian Cancer: A Multicenter Study

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