Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome

Metselaar, Paula I; Mendoza‐Maldonado, Lucero; Yim, Andrew Y. Li; Abarkan, Ilias; Henneman, Peter; Velde, Anje A. te; Schönhuth, Alexander; Bosch, Jos A.; Kraneveld, Aletta D.; Lopez-Rincon, Alejandro

doi:10.1038/s41598-021-83660-9

Cited by 21 publications

(29 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To date, Moustafa et al [65] is the only team to have attempted to build a blood-based miRNA diagnostic signature for endometriosis composed of six miRNAs based on Random Forest analysis. In agreement with previous studies [40][41][42], it would appear illusory that so few miRNAs could reflect the diversity of a multifactorial disorder such as endometriosis, which involves multiple and poorly known signaling pathways. Therefore, we hypothesized the value of (i) analyzing a specific selection of miRNAs, which resulted in a selection of 109; (ii) reducing the number of features to improve the final accuracy; and finally, (iii) using Random Forest model with high accuracy, which supports the value of AI technology.…”

Section: Discussionsupporting

confidence: 89%

“…The study and data analysis followed the Standards for Reporting of Diagnostic Accuracy (STARD) reporting guidelines [33] (Annex S1). The study consisted of two parts: (i) identification of a biomarker based on genome-wide miRNA expression profiling by small RNA sequencing using next-generation sequencing (NGS) and (ii) development of a saliva-based miRNA diagnostic signature according to expression and accuracy profiling using an ML algorithm [17,21,[34][35][36][37][38][39][40][41][42].…”

Section: Ethics Statementmentioning

confidence: 99%

See 1 more Smart Citation

Salivary MicroRNA Signature for Diagnosis of Endometriosis

Bendifallah

Suisse²,

Puchar

et al. 2022

JCM

View full text Add to dashboard Cite

Background: Endometriosis diagnosis constitutes a considerable economic burden for the healthcare system with diagnostic tools often inconclusive with insufficient accuracy. We sought to analyze the human miRNAome to define a saliva-based diagnostic miRNA signature for endometriosis. Methods: We performed a prospective ENDO-miRNA study involving 200 saliva samples obtained from 200 women with chronic pelvic pain suggestive of endometriosis collected between January and June 2021. The study consisted of two parts: (i) identification of a biomarker based on genome-wide miRNA expression profiling by small RNA sequencing using next-generation sequencing (NGS) and (ii) development of a saliva-based miRNA diagnostic signature according to expression and accuracy profiling using a Random Forest algorithm. Results: Among the 200 patients, 76.5% (n = 153) were diagnosed with endometriosis and 23.5% (n = 47) without (controls). Small RNA-seq of 200 saliva samples yielded ~4642 M raw sequencing reads (from ~13.7 M to ~39.3 M reads/sample). Quantification of the filtered reads and identification of known miRNAs yielded ~190 M sequences that were mapped to 2561 known miRNAs. Of the 2561 known miRNAs, the feature selection with Random Forest algorithm generated after internally cross validation a saliva signature of endometriosis composed of 109 miRNAs. The respective sensitivity, specificity, and AUC for the diagnostic miRNA signature were 96.7%, 100%, and 98.3%. Conclusions: The ENDO-miRNA study is the first prospective study to report a saliva-based diagnostic miRNA signature for endometriosis. This could contribute to improving early diagnosis by means of a non-invasive tool easily available in any healthcare system.

show abstract

Section: Discussionsupporting

confidence: 89%

Section: Ethics Statementmentioning

confidence: 99%

Salivary MicroRNA Signature for Diagnosis of Endometriosis

Bendifallah

Suisse²,

Puchar

et al. 2022

JCM

View full text Add to dashboard Cite

show abstract

“…The study and data analysis followed the STAndards for the Reporting of Diagnostic accuracy studies (STARD) guidelines 27 (Annex 1 ). The study consisted of two parts: (i) biomarker discovery based on genome-wide miRNA expression profiling by small RNA sequencing using next generation sequencing (NGS), and (ii) development of a miRNA diagnostic signature according to expression and accuracy profiling using an ML algorithm 28 – 38 .…”

Section: Methodsmentioning

confidence: 99%

MicroRNome analysis generates a blood-based signature for endometriosis

Bendifallah

Dabi

Suisse

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Endometriosis, characterized by endometrial-like tissue outside the uterus, is thought to affect 2–10% of women of reproductive age: representing about 190 million women worldwide. Numerous studies have evaluated the diagnostic value of blood biomarkers but with disappointing results. Thus, the gold standard for diagnosing endometriosis remains laparoscopy. We performed a prospective trial, the ENDO-miRNA study, using both Artificial Intelligence (AI) and Machine Learning (ML), to analyze the current human miRNome to differentiate between patients with and without endometriosis, and to develop a blood-based microRNA (miRNA) diagnostic signature for endometriosis. Here, we present the first blood-based diagnostic signature obtained from a combination of two robust and disruptive technologies merging the intrinsic quality of miRNAs to condense the endometriosis phenotype (and its heterogeneity) with the modeling power of AI. The most accurate signature provides a sensitivity, specificity, and Area Under the Curve (AUC) of 96.8%, 100%, and 98.4%, respectively, and is sufficiently robust and reproducible to replace the gold standard of diagnostic surgery. Such a diagnostic approach for this debilitating disorder could impact recommendations from national and international learned societies.

show abstract

“…For the open field data, we assigned the label ‘0’ for ≥10 and ‘1’ for <10 number of entries into the zone. A feature selection algorithm for recursive ensemble feature selection (REFS) algorithm [ 71 ] was run based on the Borda Method [ 72 ]. In this algorithm, 8 different classifiers were used from the sci-kit learning toolbox [ 73 ]: Bagging, Random Forest, Logistic Regression, Gradient Boosting, Support Vector, Stochastic Gradient Descent, Passive Aggressive and Ridge.…”

Section: Methodsmentioning

confidence: 99%

Dietary Supplementation throughout Life with Non-Digestible Oligosaccharides and/or n-3 Poly-Unsaturated Fatty Acids in Healthy Mice Modulates the Gut–Immune System–Brain Axis

et al. 2021

Self Cite

View full text Add to dashboard Cite

The composition and activity of the intestinal microbial community structures can be beneficially modulated by nutritional components such as non-digestible oligosaccharides and omega-3 poly-unsaturated fatty acids (n-3 PUFAs). These components affect immune function, brain development and behaviour. We investigated the additive effect of a dietary combination of scGOS:lcFOS and n-3 PUFAs on caecal content microbial community structures and development of the immune system, brain and behaviour from day of birth to early adulthood in healthy mice. Male BALB/cByJ mice received a control or enriched diet with a combination of scGOS:lcFOS (9:1) and 6% tuna oil (n-3 PUFAs) or individually scGOS:lcFOS (9:1) or 6% tuna oil (n-3 PUFAs). Behaviour, caecal content microbiota composition, short-chain fatty acid levels, brain monoamine levels, enterochromaffin cells and immune parameters in the mesenteric lymph nodes (MLN) and spleen were assessed. Caecal content microbial community structures displayed differences between the control and dietary groups, and between the dietary groups. Compared to control diet, the scGOS:lcFOS and combination diets increased caecal saccharolytic fermentation activity. The diets enhanced the number of enterochromaffin cells. The combination diet had no effects on the immune cells. Although the dietary effect on behaviour was limited, serotonin and serotonin metabolite levels in the amygdala were increased in the combination diet group. The combination and individual interventions affected caecal content microbial profiles, but had limited effects on behaviour and the immune system. No apparent additive effect was observed when scGOS:lcFOS and n-3 PUFAs were combined. The results suggest that scGOS:lcFOS and n-3 PUFAs together create a balance—the best of both in a healthy host.

show abstract

Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome

Cited by 21 publications

References 55 publications

Salivary MicroRNA Signature for Diagnosis of Endometriosis

Salivary MicroRNA Signature for Diagnosis of Endometriosis

MicroRNome analysis generates a blood-based signature for endometriosis

Dietary Supplementation throughout Life with Non-Digestible Oligosaccharides and/or n-3 Poly-Unsaturated Fatty Acids in Healthy Mice Modulates the Gut–Immune System–Brain Axis

Contact Info

Product

Resources

About