Green microalgae in intermittent light: a meta-analysis assisted by machine learning

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.

Download Full-text

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

Global Business Review ◽

10.1177/0972150920984857 ◽

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.

Download Full-text

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

Microscopy and Microanalysis ◽

10.1017/s1431927621001264 ◽

2021 ◽

Vol 27 (S1) ◽

pp. 182-183

Author(s):

Martin Meier ◽

Paul Bagot ◽

Michael Moody ◽

Daniel Haley

Keyword(s):

Machine Learning ◽

Meta Analysis

Download Full-text

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

Scientific Reports ◽

10.1038/s41598-021-82482-z ◽

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.

Download Full-text

Reperfusion Therapy in Acute Ischemic Stroke with Active Cancer: A Meta-Analysis Aided by Machine Learning

Journal of Stroke and Cerebrovascular Diseases ◽

10.1016/j.jstrokecerebrovasdis.2021.105742 ◽

2021 ◽

Vol 30 (6) ◽

pp. 105742

Author(s):

Mi-Yeon Eun ◽

Eun-Tae Jeon ◽

Kwon-Duk Seo ◽

Dongwhane Lee ◽

Jin-Man Jung

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Meta Analysis ◽

Reperfusion Therapy ◽

Active Cancer

Download Full-text

Performances of machine learning in detecting glaucoma using fundus and retinal optical coherence tomography images: A meta-analysis

American Journal of Ophthalmology ◽

10.1016/j.ajo.2021.12.008 ◽

2021 ◽

Author(s):

Jo-Hsuan Wu ◽

Takashi Nishida ◽

Robert N. Weinreb ◽

Jou-Wei Lin

Keyword(s):

Machine Learning ◽

Optical Coherence Tomography ◽

Meta Analysis ◽

Optical Coherence

Download Full-text

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

Public Health Nutrition ◽

10.1017/s1368980021002809 ◽

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.

Download Full-text

Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods

Frontiers in Neuroscience ◽

10.3389/fnins.2021.645998 ◽

2021 ◽

Vol 15 ◽

Author(s):

Shu Zhao ◽

Zhiwei Bao ◽

Xinyi Zhao ◽

Mengxiang Xu ◽

Ming D. Li ◽

...

Keyword(s):

Machine Learning ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Meta Analysis ◽

Single Gene ◽

Support Vector ◽

Svm Classifier ◽

Diagnostic Model ◽

Roc Curve Analysis ◽

Major Depressive

BackgroundMajor depressive disorder (MDD) is a global health challenge that impacts the quality of patients’ lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes.Materials and MethodsWe collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package “MetaOmics” was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection.ResultsOur analysis revealed six differentially expressed genes (AKR1C3, ARG1, KLRB1, MAFG, TPST1, and WWC3) with a false discovery rate (FDR) < 0.05 between MDD patients and control subjects. We then evaluated the diagnostic ability of these genes individually. With single gene prediction, we achieved a corresponding area under the curve (AUC) value of 0.63 ± 0.04, 0.67 ± 0.07, 0.70 ± 0.11, 0.64 ± 0.08, 0.68 ± 0.07, and 0.62 ± 0.09, respectively, for these genes. Next, we constructed the classifiers of SVM, RF, kNN, and NB with an AUC of 0.84 ± 0.09, 0.81 ± 0.10, 0.73 ± 0.11, and 0.83 ± 0.09, respectively, in validation datasets, suggesting that the SVM classifier might be superior for constructing an MDD diagnostic model. The final SVM classifier including 70 feature genes was capable of distinguishing MDD samples from healthy controls and yielded an AUC of 0.78 in an independent dataset.ConclusionThis study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.

Download Full-text

“Just the Way You Are”: Linking Music Listening on Spotify and Personality

Social Psychological and Personality Science ◽

10.1177/1948550620923228 ◽

2020 ◽

pp. 194855062092322

Author(s):

Ian Anderson ◽

Santiago Gil ◽

Clay Gibson ◽

Scott Wolf ◽

Will Shapiro ◽

...

Keyword(s):

Machine Learning ◽

Personality Traits ◽

Meta Analysis ◽

Streaming Data ◽

Self Report ◽

Big Five Personality ◽

Music Listening ◽

Listening Behavior ◽

Listening Behaviors ◽

Musical Preferences

Advances in digital technology have put music libraries at people’s fingertips, giving them immediate access to more music than ever before. Here we overcome limitations of prior research by leveraging ecologically valid streaming data: 17.6 million songs and over 662,000 hr of music listened to by 5,808 Spotify users spanning a 3-month period. Building on interactionist theories, we investigated the link between personality traits and music listening behavior, described by an extensive set of 211 mood, genre, demographic, and behavioral metrics. Findings from machine learning showed that the Big Five personality traits are predicted by musical preferences and habitual listening behaviors with moderate to high accuracy. Importantly, our work contrasts a recent self-report-based meta-analysis, which suggested that personality traits play only a small role in musical preferences; rather, we show with big data and advanced machine learning methods that personality is indeed important and warrants continued rigorous investigation.

Download Full-text