scholarly journals Unified Transcriptomic Signature of Arbuscular Mycorrhiza Colonization in Roots of Medicago truncatula by Integration of Machine Learning, Promoter Analysis, and Direct Merging Meta-Analysis

2018 ◽  
Vol 9 ◽  
Author(s):  
Manijeh Mohammadi-Dehcheshmeh ◽  
Ali Niazi ◽  
Mansour Ebrahimi ◽  
Mohammadreza Tahsili ◽  
Zahra Nurollah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Arbuscular Mycorrhiza ◽  
Medicago Truncatula ◽  
Promoter Analysis ◽  
Meta Analysis ◽  
Transcriptomic Signature

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

scholarly journals Transcriptional landscape of PTEN loss in primary prostate cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Eddie Luidy Imada ◽  
Diego Fernando Sanchez ◽  
Wikum Dinalankara ◽  
Thiago Vidotto ◽  
Ericka M. Ebot ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Meta Analysis ◽  
Pten Loss ◽  
Primary Prostate Cancer ◽  
Expression Atlas ◽  
Transcriptomic Signature ◽  
Transcriptional Changes ◽  
New Biomarkers ◽  
Adaptive Immune Systems ◽  
Transcriptional Landscape

Abstract Background PTEN is the most frequently lost tumor suppressor in primary prostate cancer (PCa) and its loss is associated with aggressive disease. However, the transcriptional changes associated with PTEN loss in PCa have not been described in detail. In this study, we highlight the transcriptional changes associated with PTEN loss in PCa. Methods Using a meta-analysis approach, we leveraged two large PCa cohorts with experimentally validated PTEN and ERG status by Immunohistochemistry (IHC), to derive a transcriptomic signature of PTEN loss, while also accounting for potential confounders due to ERG rearrangements. This signature was expanded to lncRNAs using the TCGA quantifications from the FC-R2 expression atlas. Results The signatures indicate a strong activation of both innate and adaptive immune systems upon PTEN loss, as well as an expected activation of cell-cycle genes. Moreover, we made use of our recently developed FC-R2 expression atlas to expand this signature to include many non-coding RNAs recently annotated by the FANTOM consortium. Highlighting potential novel lncRNAs associated with PTEN loss and PCa progression. Conclusion We created a PCa specific signature of the transcriptional landscape of PTEN loss that comprises both the coding and an extensive non-coding counterpart, highlighting potential new players in PCa progression. We also show that contrary to what is observed in other cancers, PTEN loss in PCa leads to increased activation of the immune system. These findings can help the development of new biomarkers and help guide therapy choices.

Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

2021 ◽  
pp. 097215092098485
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Meta Analysis ◽  
Financial Statement ◽  
Research Articles ◽  
Financial Statement Fraud ◽  
Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

scholarly journals Inter-Experiment Machine Learning on APT experiments: New Insights from Meta-Analysis

2021 ◽  
Vol 27 (S1) ◽  
pp. 182-183
Author(s):  
Martin Meier ◽  
Paul Bagot ◽  
Michael Moody ◽  
Daniel Haley
Keyword(s):  
Machine Learning ◽  
Meta Analysis

scholarly journals Meta-analysis cum machine learning approaches address the structure and biogeochemical potential of marine copepod associated bacteriobiomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Balamurugan Sadaiappan ◽  
Chinnamani PrasannaKumar ◽  
V. Uthara Nambiar ◽  
Mahendran Subramanian ◽  
Manguesh U. Gauns
Keyword(s):  
Machine Learning ◽  
Iron Transport ◽  
Meta Analysis ◽  
Zooplankton Community ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
16S Rdna Gene ◽  
Marine Copepod ◽  
Form Of Life ◽  
First Time

AbstractCopepods are the dominant members of the zooplankton community and the most abundant form of life. It is imperative to obtain insights into the copepod-associated bacteriobiomes (CAB) in order to identify specific bacterial taxa associated within a copepod, and to understand how they vary between different copepods. Analysing the potential genes within the CAB may reveal their intrinsic role in biogeochemical cycles. For this, machine-learning models and PICRUSt2 analysis were deployed to analyse 16S rDNA gene sequences (approximately 16 million reads) of CAB belonging to five different copepod genera viz., Acartia spp., Calanus spp., Centropages sp., Pleuromamma spp., and Temora spp.. Overall, we predict 50 sub-OTUs (s-OTUs) (gradient boosting classifiers) to be important in five copepod genera. Among these, 15 s-OTUs were predicted to be important in Calanus spp. and 20 s-OTUs as important in Pleuromamma spp.. Four bacterial s-OTUs Acinetobacter johnsonii, Phaeobacter, Vibrio shilonii and Piscirickettsiaceae were identified as important s-OTUs in Calanus spp., and the s-OTUs Marinobacter, Alteromonas, Desulfovibrio, Limnobacter, Sphingomonas, Methyloversatilis, Enhydrobacter and Coriobacteriaceae were predicted as important s-OTUs in Pleuromamma spp., for the first time. Our meta-analysis revealed that the CAB of Pleuromamma spp. had a high proportion of potential genes responsible for methanogenesis and nitrogen fixation, whereas the CAB of Temora spp. had a high proportion of potential genes involved in assimilatory sulphate reduction, and cyanocobalamin synthesis. The CAB of Pleuromamma spp. and Temora spp. have potential genes accountable for iron transport.

Meta-analysis and machine learning-augmented mixed effects cohort analysis of improved diets among 5,847 medical trainees, providers, and patients

2021 ◽  
pp. 1-27
Author(s):  
Dominique J. Monlezun ◽  
Christopher Carr ◽  
Tianhua Niu ◽  
Francesco Nordio ◽  
Nicole DeValle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
Nutrition Education ◽  
Meta Analysis ◽  
Cohort Analysis ◽  
Cost Effective ◽  
Number Needed To Treat ◽  
Nutrition Counseling ◽  
Medical Trainees ◽  
Hands On

Abstract Objective: We sought to produce the first meta-analysis (of medical trainee competency improvement in nutrition counseling) informing the first cohort study of patient diet improvement through medical trainees and providers counseling patients on nutrition. Design: (Part A) A systematic review and meta-analysis informing (Part B) the intervention analyzed in the world’s largest prospective multi-center cohort study on hands-on cooking and nutrition education for medical trainees, providers, and patients. Settings: (A) Medical educational institutions. (B) Teaching kitchens. Participants: (A) Medical trainees. (B) Trainees, providers, and patients. Results: (A) Of the 212 citations identified (N=1,698 trainees), 11 studies met inclusion criteria. The overall effect size was 9.80 (95%CI 7.15-12.456.87-13.85; p<0.001), comparable to the machine learning (ML)-augmented results. The number needed to treat for the top performing high quality study was 12. (B) The hands-on cooking and nutrition education curriculum from the top performing study was applied for medical trainees and providers who subsequently taught patients in the same curriculum (N=5,847). The intervention compared to standard medical care and education alone significantly increased the odds of superior diets (high/medium versus low Mediterranean diet adherence) for residents/fellows most (OR 10.79, 95%CI 4.94-23.58; p<0.001) followed by students (OR 9.62, 95%CI 5.92-15.63; p<0.001), providers (OR 5.19, 95%CI 3.23-8.32, p<0.001), and patients (OR 2.48, 95%CI 1.38-4.45; p=0.002), results consistent with those from ML. Conclusions: This study suggests that medical trainees and providers can improve patients’ diets with nutrition counseling in a manner that is clinically and cost effective and may simultaneously advance societal equity.

scholarly journals Integrated Gene Expression Profiling Analysis Reveals Potential Molecular Mechanisms and Candidate Biomarkers for Early Risk Stratification and Prediction of STEMI and Post-STEMI Heart Failure Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Jing Xu ◽  
Yuejin Yang
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Operating Characteristics ◽  
Hub Genes ◽  
St Segment Elevation ◽  
Transcriptomic Signature ◽  
Selection Operator ◽  
Logistic Regression Algorithm

Objective: To explore the molecular mechanism and search for the candidate differentially expressed genes (DEGs) with the predictive and prognostic potentiality that is detectable in the whole blood of patients with ST-segment elevation (STEMI) and those with post-STEMI HF.Methods: In this study, we downloaded GSE60993, GSE61144, GSE66360, and GSE59867 datasets from the NCBI-GEO database. DEGs of the datasets were investigated using R. Gene ontology (GO) and pathway enrichment were performed via ClueGO, CluePedia, and DAVID database. A protein interaction network was constructed via STRING. Enriched hub genes were analyzed by Cytoscape software. The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and receiver operating characteristics analyses were performed to build machine learning models for predicting STEMI. Hub genes for further validated in patients with post-STEMI HF from GSE59867.Results: We identified 90 upregulated DEGs and nine downregulated DEGs convergence in the three datasets (|log2FC| ≥ 0.8 and adjusted p &lt; 0.05). They were mainly enriched in GO terms relating to cytokine secretion, pattern recognition receptors signaling pathway, and immune cells activation. A cluster of eight genes including ITGAM, CLEC4D, SLC2A3, BST1, MCEMP1, PLAUR, GPR97, and MMP25 was found to be significant. A machine learning model built by SLC2A3, CLEC4D, GPR97, PLAUR, and BST1 exerted great value for STEMI prediction. Besides, ITGAM and BST1 might be candidate prognostic DEGs for post-STEMI HF.Conclusions: We reanalyzed the integrated transcriptomic signature of patients with STEMI showing predictive potentiality and revealed new insights and specific prospective DEGs for STEMI risk stratification and HF development.

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