A naïve Gaussian Bayes classifier for detection of mental activity in gait signature

Prediction of peptide hormones using an ensemble of machine learning and similarity-based methods

Peptide hormones serve as genome-encoded signal transduction molecules that play essential roles in multicellular organisms, and their dysregulation can lead to various health problems. In this study, we propose a method for predicting hormonal peptides with high accuracy. The dataset used for training, testing, and evaluating our models consisted of 1174 hormonal and 1174 non-hormonal peptide sequences. Initially, we developed similarity-based methods utilizing BLAST and MERCI software. Although these similarity-based methods provided a high probability of correct prediction, they had limitations, such as no hits or prediction of limited sequences. To overcome these limitations, we further developed machine and deep learning-based models. Our logistic regression-based model achieved a maximum AUROC of 0.93 with an accuracy of 86% on an independent/validation dataset. To harness the power of similarity-based and machine learning-based models, we developed an ensemble method that achieved an AUROC of 0.96 with an accuracy of 89.79% and a Matthews correlation coefficient (MCC) of 0.8 on the validation set. To facilitate researchers in predicting and designing hormone peptides, we developed a web-based server called HOPPred. This server offers a unique feature that allows the identification of hormone-associated motifs within hormone peptides. The server can be accessed at: https://webs.iiitd.edu.in/raghava/hoppred/.

Single-cell analysis reveals landscape of endometrial cancer response to estrogen and identification of early diagnostic markers

BACKGROUND

The development of endometrial cancer (EC) is closely related to the abnormal activation of the estrogen signaling pathway. Effective diagnostic markers are important for the early detection and treatment of EC.

METHOD

We downloaded single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST) data of EC from public databases. Enrichment scores were calculated for EC cell subpopulations using the "AddModuleScore" function and the AUCell package, respectively. Six predictive models were constructed, including logistic regression (LR), Gaussian naive Bayes (GaussianNB), k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGB), and neural network (NK). Subsequently, receiver-operating characteristics with areas under the curves (AUCs) were used to assess the robustness of the predictive model.

RESULT

We classified EC cell coaggregation into six cell clusters, of which the epithelial, fibroblast and endothelial cell clusters had higher estrogen signaling pathway activity. We founded the epithelial cell subtype Epi cluster1, the fibroblast cell subtype Fib cluster3, and the endothelial cell subtype Endo cluster3 all showed early activation levels of estrogen response. Based on EC cell subtypes, estrogen-responsive early genes, and genes encoding Stage I and para-cancer differentially expressed proteins in EC patients, a total of 24 early diagnostic markers were identified. The AUCs values of all six classifiers were higher than 0.95, which indicates that the early diagnostic markers we screened have superior robustness across different classification algorithms.

CONCLUSION

Our study elucidates the potential biological mechanism of EC response to estrogen at single-cell resolution, which provides a new direction for early diagnosis of EC.

Normalized Mitochondrial DNA Copy Number Can Optimize Pregnancy Outcome Prediction in IVF

The aim of this study is to explore the relationship between mitochondrial DNA (mtDNA) copy number and embryo implantation potential in in vitro fertilization (IVF). A retrospective study of 319 blastocysts from patients undergoing preimplantation genetic testing (PGT) at Reproductive Medicine Center in Tongji Hospital from January 2016 to February 2018 was conducted. We used multiple annealing- and looping-based amplification cycles (MALBAC) technology to amplify the genetic materials from the trophectoderm cells of blastocysts, and next-generation sequencing (NGS) technology to test mitochondrial DNA copy number. Box-Cox transformation was introduced to eliminate the skewness distribution of mtDNA copy number, and the transformed data were defined as adjusted mtDNA. Subsequently, associations between adjusted mtDNA and the clinical characteristics of patients were assessed by univariate analysis and multiple linear regression. In addition, Gaussian Naive Bayes classifier was also used to predict pregnancy outcomes. We observed that only antral follicle count (AFC) was significantly associated with adjusted mtDNA without the influence of multicollinearity. What's more, the distribution of the adjusted mtDNA of blastocysts resulting in live birth was more concentrated than that of others. The area under the curve (AUC) of the prediction model that combined adjusted mtDNA with other clinical characteristics of patients was up to 0.81, higher than that excluded adjusted mtDNA. Among patient clinical characteristics, AFC was significantly associated with adjusted mtDNA. Mitochondrial DNA copy number may help to optimize the pregnancy outcome prediction in IVF.