Embryo classification beyond pregnancy: Early prediction of first trimester miscarriage using machine learning

ABSTRACTObjectiveDevelop a machine learning classifier for predicting the risk of cleavage-stage embryos to undergo first trimester miscarriage based on time-lapse images of preimplantation development.DesignRetrospective study of a 4-year multi-center cohort of women undergoing intra-cytoplasmatic sperm injection (ICSI). The study included embryos with positive indication of clinical implantation based on gestational sac visualization either with first trimester miscarriage or live birth outcome. Miscarriage was determined based on negative fetal heartbeat indication during the first trimester.SettingData were recorded and obtained in hospital setting and research was performed in university setting.Patient(s)Data from 391 women who underwent fresh single or double embryo transfers were included.Intervention(s)None.Main Outcome Measure(s)A minimal subset of six non-redundant morphodynamic features were screened that maintain high prediction capacity. Using this feature subset, XGBoost and Random Forest models were trained following a 100-fold Monte-Carlo cross validation scheme. Feature importance was scored using the SHapley Additive exPlanations (SHAP) methodology. Miscarriage versus live-birth outcome prediction was evaluated using a non-contaminated balanced test set and quantified in terms of the area under the receiver operating characteristic (ROC) curve (AUC), precision-recall curve, positive predictive value (PPV), and confusion matrices.Result(s)Features that account for the distribution of the nucleolus precursor bodies within the small pronucleus and pronuclei dynamics were highly predictive of miscarriage outcome. AUC for miscarriage prediction of validation and test set embryos using both models was 0.68-to-0.69. Clinical utility was tested by setting two classification thresholds accounting for high sensitivity 0.73 with 0.6 specificity and high specificity 0.93 with 0.33 sensitivity.Conclusion(s)We report the development of a decision-support tool for identifying the embryos with high risk of miscarriage. Prioritizing embryos for transfer based on their predicted risk of miscarriage in combination with their predicted implantation potential will improve live-birth rates and shorten time-to-pregnancy.CapsuleThe risk of first trimester miscarriage of cleavage stage embryos is predicted with AUC 68% by screening a minimal subset of six non-redundant morpho-dynamic features and training a machine-learning classifier.

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External validation of an opioid misuse machine learning classifier in hospitalized adult patients

Addiction Science & Clinical Practice ◽

10.1186/s13722-021-00229-7 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Majid Afshar ◽

Brihat Sharma ◽

Sameer Bhalla ◽

Hale M. Thompson ◽

Dmitriy Dligach ◽

...

Keyword(s):

Machine Learning ◽

Screening Program ◽

External Validation ◽

Hospital Setting ◽

Opioid Misuse ◽

Original Cohort ◽

Learning Classifier ◽

Observational Cohort ◽

Drug Abuse Screening Test ◽

Screening Barriers

Abstract Background Opioid misuse screening in hospitals is resource-intensive and rarely done. Many hospitalized patients are never offered opioid treatment. An automated approach leveraging routinely captured electronic health record (EHR) data may be easier for hospitals to institute. We previously derived and internally validated an opioid classifier in a separate hospital setting. The aim is to externally validate our previously published and open-source machine-learning classifier at a different hospital for identifying cases of opioid misuse. Methods An observational cohort of 56,227 adult hospitalizations was examined between October 2017 and December 2019 during a hospital-wide substance use screening program with manual screening. Manually completed Drug Abuse Screening Test served as the reference standard to validate a convolutional neural network (CNN) classifier with coded word embedding features from the clinical notes of the EHR. The opioid classifier utilized all notes in the EHR and sensitivity analysis was also performed on the first 24 h of notes. Calibration was performed to account for the lower prevalence than in the original cohort. Results Manual screening for substance misuse was completed in 67.8% (n = 56,227) with 1.1% (n = 628) identified with opioid misuse. The data for external validation included 2,482,900 notes with 67,969 unique clinical concept features. The opioid classifier had an AUC of 0.99 (95% CI 0.99–0.99) across the encounter and 0.98 (95% CI 0.98–0.99) using only the first 24 h of notes. In the calibrated classifier, the sensitivity and positive predictive value were 0.81 (95% CI 0.77–0.84) and 0.72 (95% CI 0.68–0.75). For the first 24 h, they were 0.75 (95% CI 0.71–0.78) and 0.61 (95% CI 0.57–0.64). Conclusions Our opioid misuse classifier had good discrimination during external validation. Our model may provide a comprehensive and automated approach to opioid misuse identification that augments current workflows and overcomes manual screening barriers.

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Application of Machine Learning Classifier to Candida auris Drug Resistance Analysis

Frontiers in Cellular and Infection Microbiology ◽

10.3389/fcimb.2021.742062 ◽

2021 ◽

Vol 11 ◽

Author(s):

Dingchen Li ◽

Yaru Wang ◽

Wenjuan Hu ◽

Fangyan Chen ◽

Jingya Zhao ◽

...

Keyword(s):

Machine Learning ◽

Drug Resistance ◽

Phylogenetic Tree ◽

Resistance Mutations ◽

Candida Auris ◽

Test Set ◽

Machine Learning Classifiers ◽

Drug Resistance Mutations ◽

Learning Classifier ◽

Learning Classifiers

Candida auris (C. auris) is an emerging fungus associated with high morbidity. It has a unique transmission ability and is often resistant to multiple drugs. In this study, we evaluated the ability of different machine learning models to classify the drug resistance and predicted and ranked the drug resistance mutations of C. auris. Two C. auris strains were obtained. Combined with other 356 strains collected from the European Bioinformatics Institute (EBI) databases, the whole genome sequencing (WGS) data were analyzed by bioinformatics. Machine learning classifiers were used to build drug resistance models, which were evaluated and compared by various evaluation methods based on AUC value. Briefly, two strains were assigned to Clade III in the phylogenetic tree, which was consistent with previous studies; nevertheless, the phylogenetic tree was not completely consistent with the conclusion of clustering according to the geographical location discovered earlier. The clustering results of C. auris were related to its drug resistance. The resistance genes of C. auris were not under additional strong selection pressure, and the performance of different models varied greatly for different drugs. For drugs such as azoles and echinocandins, the models performed relatively well. In addition, two machine learning algorithms, based on the balanced test and imbalanced test, were designed and evaluated; for most drugs, the evaluation results on the balanced test set were better than on the imbalanced test set. The mutations strongly be associated with drug resistance of C. auris were predicted and ranked by Recursive Feature Elimination with Cross-Validation (RFECV) combined with a machine learning classifier. In addition to known drug resistance mutations, some new resistance mutations were predicted, such as Y501H and I466M mutation in the ERG11 gene and R278H mutation in the ERG10 gene, which may be associated with fluconazole (FCZ), micafungin (MCF), and amphotericin B (AmB) resistance, respectively; these mutations were in the “hot spot” regions of the ergosterol pathway. To sum up, this study suggested that machine learning classifiers are a useful and cost-effective method to identify fungal drug resistance-related mutations, which is of great significance for the research on the resistance mechanism of C. auris.

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AIRBP: Accurate identification of RNA-binding proteins using machine learning techniques

10.1101/2020.03.10.985416 ◽

2020 ◽

Author(s):

Avdesh Mishra ◽

Reecha Khanal ◽

Md Tamjidul Hoque

Keyword(s):

Machine Learning ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

Machine Learning Techniques ◽

Valuable Insight ◽

Feature Subset ◽

Accurate Identification ◽

Test Set ◽

Mrna Stabilization

AbstractMotivationIdentification of RNA-binding proteins (RBPs) that bind to ribonucleic acid molecules, is an important problem in Computational Biology and Bioinformatics. It becomes indispensable to identify RBPs as they play crucial roles in post-transcriptional control of RNAs and RNA metabolism as well as have diverse roles in various biological processes such as splicing, mRNA stabilization, mRNA localization, and translation, RNA synthesis, folding-unfolding, modification, processing, and degradation. The existing experimental techniques for identifying RBPs are time-consuming and expensive. Therefore, identifying RBPs directly from the sequence using computational methods can be useful to efficiently annotate RBPs and assist the experimental design. In this work, we present a method, called AIRBP, which is designed using an advanced machine learning technique, called stacking, to effectively predict RBPs by utilizing features extracted from evolutionary information, physiochemical properties, and disordered properties. Moreover, our method, AIRBP is trained on the useful feature-subset identified by the evolutionary algorithm (EA).ResultsThe results show that AIRBP attains Accuracy (ACC), F1-score, and MCC of 95.38%, 0.917, and 0.885, respectively, based on the benchmark dataset, using 10-fold cross-validation (CV). Further evaluation of AIRBP on independent test set reveals that it achieves ACC, F1-score, and MCC of 93.04%, 0.943, and 0.855, for Human test set; 91.60%, 0.942 and 0.789 for S. cerevisiae test set; and 91.67%, 0.953 and 0.594 for A. thaliana test set, respectively. These results indicate that AIRBP outperforms the current state-of-the-art method. Therefore, the proposed top-performing AIRBP can be useful for accurate identification and annotation of RBPs directly from the sequence and help gain valuable insight to treat critical diseases.AvailabilityCode-data is available here: http://cs.uno.edu/~tamjid/Software/AIRBP/code_data.zip

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Development of a Machine Learning Classifier Based on Radiomic Features Extracted From Post-Contrast 3D T1-Weighted MR Images to Distinguish Glioblastoma From Solitary Brain Metastasis

Frontiers in Oncology ◽

10.3389/fonc.2021.638262 ◽

2021 ◽

Vol 11 ◽

Author(s):

Alix de Causans ◽

Alexandre Carré ◽

Alexandre Roux ◽

Arnault Tauziède-Espariat ◽

Samy Ammari ◽

...

Keyword(s):

Machine Learning ◽

Cross Validation ◽

Performance Metrics ◽

Characteristic Curve ◽

Three Dimensional ◽

Mr Images ◽

Post Contrast ◽

Test Set ◽

Learning Classifier ◽

Sensitivity Specificity

ObjectivesTo differentiate Glioblastomas (GBM) and Brain Metastases (BM) using a radiomic features-based Machine Learning (ML) classifier trained from post-contrast three-dimensional T1-weighted (post-contrast 3DT1) MR imaging, and compare its performance in medical diagnosis versus human experts, on a testing cohort.MethodsWe enrolled 143 patients (71 GBM and 72 BM) in a retrospective bicentric study from January 2010 to May 2019 to train the classifier. Post-contrast 3DT1 MR images were performed on a 3-Tesla MR unit and 100 radiomic features were extracted. Selection and optimization of the Machine Learning (ML) classifier was performed using a nested cross-validation. Sensitivity, specificity, balanced accuracy, and area under the receiver operating characteristic curve (AUC) were calculated as performance metrics. The model final performance was cross-validated, then evaluated on a test set of 37 patients, and compared to human blind reading using a McNemar’s test.ResultsThe ML classifier had a mean [95% confidence interval] sensitivity of 85% [77; 94], a specificity of 87% [78; 97], a balanced accuracy of 86% [80; 92], and an AUC of 92% [87; 97] with cross-validation. Sensitivity, specificity, balanced accuracy and AUC were equal to 75, 86, 80 and 85% on the test set. Sphericity 3D radiomic index highlighted the highest coefficient in the logistic regression model. There were no statistical significant differences observed between the performance of the classifier and the experts’ blinded examination.ConclusionsThe proposed diagnostic support system based on radiomic features extracted from post-contrast 3DT1 MR images helps in differentiating solitary BM from GBM with high diagnosis performance and generalizability.

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A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma

10.1101/075382 ◽

2016 ◽

Author(s):

Gregory P. Way ◽

Robert J. Allaway ◽

Stephanie J. Bouley ◽

Camilo E. Fadul ◽

Yolanda Sanchez ◽

...

Keyword(s):

Machine Learning ◽

Tumor Suppressor ◽

Performance Test ◽

Synthetic Lethality ◽

Ensemble Classifier ◽

The Cancer Genome Atlas ◽

Target Cells ◽

Test Set ◽

Mutation Status ◽

Learning Classifier

ABSTRACTBackground:We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of theNF1tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent ofNF1mutation status, photocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules.Results:We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM). On TCGA data, the classifier detectedNF1mutated tumors (test set area under the receiver operating characteristic curve (AUROC) mean = 0.77, 95% quantile = 0.53 – 0.95) over 50 random initializations. On RNA-Seq data transformed into the space of gene expression microarrays, this method produced a classifier with similar performance (test set AUROC mean = 0.77, 95% quantile = 0.53 – 0.96). We applied our ensemble classifier trained on the transformed TCGA data to a microarray validation set of 12 samples with matched RNA and NF1 protein-level measurements. The classifier’s NF1 score was associated with NF1 protein concentration in these samples.Conclusions:We demonstrate that TCGA can be used to train accurate predictors of NF1 inactivation in GBM. The ensemble classifier performed well for samples with very high or very low NF1 protein concentrations but had mixed performance in samples with intermediate NF1 concentrations. Nevertheless, high-performing and validated predictors have the potential to be paired with targeted therapies and personalized medicine.

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Location Detection Method of Detector in Pipeline Using VMD Algorithm and Machine Learning Classifier

Electronics ◽

10.3390/electronics10121436 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1436

Author(s):

Tuoru Li ◽

Senxiang Lu ◽

Enjie Xu

Keyword(s):

Machine Learning ◽

Magnetic Flux ◽

Magnetic Flux Leakage ◽

Intrinsic Mode Functions ◽

Detection Range ◽

Vibration Signals ◽

Feature Vectors ◽

Learning Classifier ◽

Mode Decomposition ◽

Flux Leakage

The internal detector in a pipeline needs to use the ground marker to record the elapsed time for accurate positioning. Most existing ground markers use the magnetic flux leakage testing principle to detect whether the internal detector passes. However, this paper uses the method of detecting vibration signals to track and locate the internal detector. The Variational Mode Decomposition (VMD) algorithm is used to extract features, which solves the defect of large noise and many disturbances of vibration signals. In this way, the detection range is expanded, and some non-magnetic flux leakage internal detectors can also be located. Firstly, the extracted vibration signals are denoised by the VMD algorithm, then kurtosis value and power value are extracted from the intrinsic mode functions (IMFs) to form feature vectors, and finally the feature vectors are input into random forest and Multilayer Perceptron (MLP) for classification. Experimental research shows that the method designed in this paper, which combines VMD with a machine learning classifier, can effectively use vibration signals to locate the internal detector and has the characteristics of high accuracy and good adaptability.

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