Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery

Multiple-model machine learning identifies potential functional genes in dilated cardiomyopathy

Introduction

Machine learning (ML) has gained intensive popularity in various fields, such as disease diagnosis in healthcare. However, it has limitation for single algorithm to explore the diagnosing value of dilated cardiomyopathy (DCM). We aim to develop a novel overall normalized sum weight of multiple-model MLs to assess the diagnosing value in DCM.

Methods

Gene expression data were selected from previously published databases (six sets of eligible microarrays, 386 samples) with eligible criteria. Two sets of microarrays were used as training; the others were studied in the testing sets (ratio 5:1). Totally, we identified 20 differently expressed genes (DEGs) between DCM and control individuals (7 upregulated and 13 down-regulated).

Results

We developed six classification ML methods to identify potential candidate genes based on their overall weights. Three genes, serine proteinase inhibitor A3 (SERPINA3), frizzled-related proteins (FRPs) 3 (FRZB), and ficolin 3 (FCN3) were finally identified as the receiver operating characteristic (ROC). Interestingly, we found all three genes correlated considerably with plasma cells. Importantly, not only in training sets but also testing sets, the areas under the curve (AUCs) for SERPINA3, FRZB, and FCN3 were greater than 0.88. The ROC of SERPINA3 was significantly high (0.940 in training and 0.918 in testing sets), indicating it is a potentially functional gene in DCM. Especially, the plasma levels in DCM patients of SERPINA3, FCN, and FRZB were significant compared with healthy control.

Discussion

SERPINA3, FRZB, and FCN3 might be potential diagnosis targets for DCM, Further verification work could be implemented.

Nextcast: A software suite to analyse and model toxicogenomics data

The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).

New Model for Estimation of the Age at Onset in Spinocerebellar Ataxia Type 3

OBJECTIVES

The aim of this study was to develop an appropriate parametric survival model to predict patient's age at onset (AAO) for spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) populations from mainland China.

METHODS

We compared the efficiency and performance of 6 parametric survival analysis methods (exponential, weibull, log-gaussian, gaussian, log-logistic, and logistic) based on cytosine-adenine-guanine (CAG) repeat length at ATXN3 to predict the probability of AAO in the largest cohort of patients with SCA3/MJD. A set of evaluation criteria, including -2 log-likelihood statistic, Akaike information criterion (AIC), bayesian information criterion (BIC), Nagelkerke R-squared (Nagelkerke R^2), and Cox-Snell residual plot, were used to identify the best model.

RESULTS

Among these 6 parametric survival models, the logistic model had the lowest -2 log-likelihood (6,560.12), AIC (6,566.12), and BIC (6,566.14) and the highest value of Nagelkerke R^2 (0.54), with the closest graph to the bisector Cox-Snell residual graph. Therefore, the logistic survival model was the best fit to the studied data. Using the optimal logistic survival model, we indicated the age-specific probability distribution of AAO according to the CAG repeat size and current age.

CONCLUSIONS

We first demonstrated that the logistic survival model provided the best fit for AAO prediction in patients with SCA3/MJD from mainland China. This optimal model can be valuable in clinical and research. However, the rigorous clinical testing and practice of other independent cohorts are needed for its clinical application. A unified model across multiethnic cohorts is worth further exploration by identifying regional differences and significant modifiers in AAO determination.

Identification of whole blood mRNA and microRNA biomarkers of tissue damage and immune function resulting from amphetamine exposure or heat stroke in adult male rats

This work extends the understanding of how toxic exposures to amphetamine (AMPH) adversely affect the immune system and lead to tissue damage. Importantly, it determines which effects of AMPH are and are not due to pronounced hyperthermia. Whole blood messenger RNA (mRNA) and whole blood and serum microRNA (miRNA) transcripts were identified in adult male Sprague-Dawley rats after exposure to toxic AMPH under normothermic conditions, AMPH when it produces pronounced hyperthermia, or environmentally-induced hyperthermia (EIH). mRNA transcripts with large increases in fold-change in treated relative to control rats and very low expression in the control group were a rich source of organ-specific transcripts in blood. When severe hyperthermia was produced by either EIH or AMPH, significant increases in circulating organ-specific transcripts for liver (Alb, Fbg, F2), pancreas (Spink1), bronchi/lungs (F3, Cyp4b1), bone marrow (Np4, RatNP-3b), and kidney (Cesl1, Slc22a8) were observed. Liver damage was suggested also by increased miR-122 levels in the serum. Increases in muscle/heart-enriched transcripts were produced by AMPH even in the absence of hyperthermia. Expression increases in immune-related transcripts, particularly Cd14 and Vcan, indicate that AMPH can activate the innate immune system in the absence of hyperthermia. Most transcripts specific for T-cells decreased 50–70% after AMPH exposure or EIH, with the noted exception of Ccr5 and Chst12. This is probably due to T-cells leaving the circulation and down-regulation of these genes. Transcript changes specific for B-cells or B-lymphoblasts in the AMPH and EIH groups ranged widely from decreasing ≈ 40% (Cd19, Cd180) to increasing 30 to 100% (Tk1, Ahsa1) to increasing ≥500% (Stip1, Ackr3). The marked increases in Ccr2, Ccr5, Pld1, and Ackr3 produced by either AMPH or EIH observed in vivo provide further insight into the initial immune system alterations that result from methamphetamine and AMPH abuse and could modify risk for HIV and other viral infections.