Dataset Information

Association mapping in biomedical time series via statistically significant shapelet mining.

ABSTRACT:

Motivation

Most modern intensive care units record the physiological and vital signs of patients. These data can be used to extract signatures, commonly known as biomarkers, that help physicians understand the biological complexity of many syndromes. However, most biological biomarkers suffer from either poor predictive performance or weak explanatory power. Recent developments in time series classification focus on discovering shapelets, i.e. subsequences that are most predictive in terms of class membership. Shapelets have the advantage of combining a high predictive performance with an interpretable component-their shape. Currently, most shapelet discovery methods do not rely on statistical tests to verify the significance of individual shapelets. Therefore, identifying associations between the shapelets of physiological biomarkers and patients that exhibit certain phenotypes of interest enables the discovery and subsequent ranking of physiological signatures that are interpretable, statistically validated and accurate predictors of clinical endpoints.

Results

We present a novel and scalable method for scanning time series and identifying discriminative patterns that are statistically significant. The significance of a shapelet is evaluated while considering the problem of multiple hypothesis testing and mitigating it by efficiently pruning untestable shapelet candidates with Tarone's method. We demonstrate the utility of our method by discovering patterns in three of a patient's vital signs: heart rate, respiratory rate and systolic blood pressure that are indicators of the severity of a future sepsis event, i.e. an inflammatory response to an infective agent that can lead to organ failure and death, if not treated in time.

Availability and implementation

We make our method and the scripts that are required to reproduce the experiments publicly available at https://github.com/BorgwardtLab/S3M.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Bock C

PROVIDER: S-EPMC6022601 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Association mapping in biomedical time series via statistically significant shapelet mining.

Bock Christian C Gumbsch Thomas T Moor Michael M Rieck Bastian B Roqueiro Damian D Borgwardt Karsten K

Bioinformatics (Oxford, England) 20180701 13

<h4>Motivation</h4>Most modern intensive care units record the physiological and vital signs of patients. These data can be used to extract signatures, commonly known as biomarkers, that help physicians understand the biological complexity of many syndromes. However, most biological biomarkers suffer from either poor predictive performance or weak explanatory power. Recent developments in time series classification focus on discovering shapelets, i.e. subsequences that are most predictive in ter ...[more]

PMID: 29949972

Similar Datasets

Project description:BackgroundMarker detection is an important task in complex disease studies. Here we provide an association rule mining (ARM) based approach for identifying integrated markers through mutual information (MI) based statistically significant feature extraction, and apply it to acute myeloid leukemia (AML) and prostate carcinoma (PC) gene expression and methylation profiles.MethodsWe first collect the genes having both expression and methylation values in AML as well as PC. Next, we run Jarque-Bera normality test on the expression/methylation data to divide the whole dataset into two parts: one that ollows normal distribution and the other that does not follow normal distribution. Thus, we have now four parts of the dataset: normally distributed expression data, normally distributed methylation data, non-normally distributed expression data, and non-normally distributed methylated data. A feature-extraction technique, "mRMR" is then utilized on each part. This results in a list of top-ranked genes. Next, we apply Welch t-test (parametric test) and Shrink t-test (non-parametric test) on the expression/methylation data for the top selected normally distributed genes and non-normally distributed genes, respectively. We then use a recent weighted ARM method, "RANWAR" to combine all/specific resultant genes to generate top oncogenic rules along with respective integrated markers. Finally, we perform literature search as well as KEGG pathway and Gene-Ontology (GO) analyses using Enrichr database for in silico validation of the prioritized oncogenes as the markers and labeling the markers as existing or novel.ResultsThe novel markers of AML are {ABCB11↑∪KRT17↓} (i.e., ABCB11 as up-regulated, & KRT17 as down-regulated), and {AP1S1-∪KRT17↓∪NEIL2-∪DYDC1↓}) (i.e., AP1S1 and NEIL2 both as hypo-methylated, & KRT17 and DYDC1 both as down-regulated). The novel marker of PC is {UBIAD1¶∪APBA2‡∪C4orf31‡} (i.e., UBIAD1 as up-regulated and hypo-methylated, & APBA2 and C4orf31 both as down-regulated and hyper-methylated).ConclusionThe identified novel markers might have critical roles in AML as well as PC. The approach can be applied to other complex disease.

Project description:BackgroundGuyana reported a significant rise in malaria between 2008 and 2014. As there was no evidence of impairment of national malaria control strategies, public health authorities attributed the surge to a temporal increase in gold mining activity in forested regions. However, systematic analysis of this association is lacking because of the difficulties associated with collecting reliable data for both malaria and mining. We aimed to investigate the association between the international gold price and Plasmodium falciparum malaria transmission in Guyana between 2007 and 2019. We also aimed to evaluate the association between P falciparum cases and the El Niño-Southern Oscillation pattern, which has previously been suggested as a major driver of malaria.MethodsWe used national malaria surveillance data from Guyana to estimate the correlation over time between the international gold price and reported P falciparum infections in individuals who were likely to be involved in mining activities (ie, men and boys aged between 15 and 50 years who were living in mining regions) for each month between 2007 and 2019. We compared the estimates with those obtained from individuals who were unlikely to be directly involved in mining activities (ie, women, children aged 12 years and younger, and adults aged over 70 years) and estimates obtained from individuals living in non-mining regions. We also evaluated the correlation between P falciparum infections and the El Niño-Southern Oscillation pattern in the same subpopulations and time period. Lastly, we evaluated the performance of a statistical model formulated to estimate P falciparum infections in real time using the international gold price as the predictor variable.FindingsThe proportion of P falciparum malaria cases in temporary residents, which was used as a proxy for circulating individuals involved in gold mining, was highest during the years of peak gold price (ie, between 2008 and 2014). Cases of malaria in all demographic groups showed a strong positive correlation with the gold price, but only in regions with mining camps (0·88 [95% CI 0·84-0·89] for boys and men aged between 15 and 50 years and 0·80 [0·73-0·85] for the aggregated population of women, children aged 12 years and younger, and adults older than 70 years). The highest correlation occurred earlier in men and boys aged between 15 and 50 years, the demographic most likely to be miners, suggesting that transmission in mining camps is followed by infections in the community. On the basis of these findings, we were able to reliably forecast P falciparum malaria trends using only the gold price as the predictor variable. A 1% increase in gold price was associated with a 2·13% increase in P falciparum infections after 1 month in the mining populations, and with a 1·63% increase after 2 months in the non-mining populations. Lastly, La Niña climatic events showed an additional, smaller positive correlation with malaria transmission.InterpretationOur analysis provides evidence that the P falciparum malaria surge observed in Guyana between 2008 and 2014 was likely to have been driven mainly by an increase in gold mining, while climate factors might have contributed synergistically. We propose that the international gold price over time is a useful indicator of malaria trends. We conclude that the feasibility of malaria elimination in Guyana, and in other areas in the Amazon where malaria and gold mining overlap, should be evaluated against the challenges posed by rapidly rising gold prices.FundingRamón Areces Foundation, National Institutes of Health, and National Institute of General Medical Sciences.

Dataset Information

Association mapping in biomedical time series via statistically significant shapelet mining.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Association mapping in biomedical time series via statistically significant shapelet mining.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets