Detection of high-risk subjects in acute myocardial infarction (AMI) by noninvasive means would reduce the need for intracardiac catheterization and associated complications. Liver enzymes are ...associated with cardiovascular disease risk. A potential predictive value for liver serum markers for the severity of stenosis in AMI was analyzed.Patients with AMI undergoing percutaneous coronary intervention (PCI; n = 437) were retrospectively evaluated. Minimal lumen diameter (MLD) and percent stenosis diameter (SD) were determined from quantitative coronary angiography. Patients were classified according to the severity of stenosis (SD ≥ 50%, n = 357; SD < 50%, n = 80). Routine heart and liver parameters were associated with SD using random forests (RF). A prediction model (M10) was developed based on parameter importance analysis in RF.Age, alkaline phosphatase (AP), aspartate aminotransferase (AST), and MLD differed significantly between SD ≥ 50 and SD < 50. Age, AST, alanine aminotransferase (ALT), and troponin correlated significantly with SD, whereas MLD correlated inversely with SD. M10 (age, BMI, AP, AST, ALT, gamma-glutamyltransferase, creatinine, troponin) reached an AUC of 69.7% (CI 63.8-75.5%, P < 0.0001).Routine liver parameters are associated with SD in AMI. A small set of noninvasively determined parameters can identify SD in AMI, and might avoid unnecessary coronary angiography in patients with low risk. The model can be accessed via http://stenosis.heiderlab.de.
Biomarker discovery methods are essential to identify a minimal subset of features (e.g., serum markers in predictive medicine) that are relevant to develop prediction models with high accuracy. By ...now, there exist diverse feature selection methods, which either are embedded, combined, or independent of predictive learning algorithms. Many preceding studies showed the defectiveness of single feature selection results, which cause difficulties for professionals in a variety of fields (e.g., medical practitioners) to analyze and interpret the obtained feature subsets. Whereas each of these methods is highly biased, an ensemble feature selection has the advantage to alleviate and compensate for such biases. Concerning the reliability, validity, and reproducibility of these methods, we examined eight different feature selection methods for binary classification datasets and developed an ensemble feature selection system.
By using an ensemble of feature selection methods, a quantification of the importance of the features could be obtained. The prediction models that have been trained on the selected features showed improved prediction performance.
Antiretroviral therapy is essential for human immunodeficiency virus (HIV) infected patients to inhibit viral replication and therewith to slow progression of disease and prolong a patient's life. ...However, the high mutation rate of HIV can lead to a fast adaptation of the virus under drug pressure and thereby to the evolution of resistant variants. In turn, these variants will lead to the failure of antiretroviral treatment. Moreover, these mutations cannot only lead to resistance against single drugs, but also to cross-resistance, i.e., resistance against drugs that have not yet been applied.
662 protease sequences and 715 reverse transcriptase sequences with complete resistance profiles were analyzed using machine learning techniques, namely binary relevance classifiers, classifier chains, and ensembles of classifier chains.
In our study, we applied multi-label classification models incorporating cross-resistance information to predict drug resistance for two of the major drug classes used in antiretroviral therapy for HIV-1, namely protease inhibitors (PIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs). By means of multi-label learning, namely classifier chains (CCs) and ensembles of classifier chains (ECCs), we were able to improve overall prediction accuracy for all drugs compared to hitherto applied binary classification models.
The development of fast and precise models to predict drug resistance in HIV-1 is highly important to enable a highly effective personalized therapy. Cross-resistance information can be exploited to improve prediction accuracy of computational drug resistance models.
DNA methylation affects transcriptional regulation and constitutes a drug target in cancer biology. In cardiac hypertrophy, DNA methylation may control the fetal gene program. We therefore ...investigated DNA methylation signatures and their dynamics in an in vitro model of cardiac hypertrophy based on engineered heart tissue (EHT). We exposed EHTs from neonatal rat cardiomyocytes to a 12-fold increased afterload (AE) or to phenylephrine (PE 20 µM) and compared DNA methylation signatures to control EHT by pull-down assay and DNA methylation microarray. A 7-day intervention sufficed to induce contractile dysfunction and significantly decrease promoter methylation of hypertrophy-associated upregulated genes such as Nppa (encoding ANP) and Acta1 (α-skeletal actin) in both intervention groups. To evaluate whether pathological consequences of AE are affected by inhibiting de novo DNA methylation we applied AE in the absence and presence of DNA methyltransferase (DNMT) inhibitors: 5-aza-2′-deoxycytidine (aza, 100 µM, nucleosidic inhibitor), RG108 (60 µM, non-nucleosidic) or methylene disalicylic acid (MDSA, 25 µM, non-nucleosidic). Aza had no effect on EHT function, but RG108 and MDSA partially prevented the detrimental consequences of AE on force, contraction and relaxation velocity. RG108 reduced AE-induced Atp2a2 (SERCA2a) promoter methylation. The results provide evidence for dynamic DNA methylation in cardiac hypertrophy and warrant further investigation of the potential of DNA methylation in the treatment of cardiac hypertrophy.
Data from GWAS suggest that SNPs associated with complex diseases or traits tend to co-segregate in regions of low recombination, harbouring functionally linked gene clusters. This phenomenon allows ...for selecting a limited number of SNPs from GWAS repositories for large-scale studies investigating shared mechanisms between diseases. For example, we were interested in shared mechanisms between adult-attained height and post-menopausal breast cancer (BC) and colorectal cancer (CRC) risk, because height is a risk factor for these cancers, though likely not a causal factor. Using SNPs from public GWAS repositories at p-values < 1 × 10
and a genomic sliding window of 1 mega base pair, we identified SNP clusters including at least one SNP associated with height and one SNP associated with either post-menopausal BC or CRC risk (or both). SNPs were annotated to genes using HapMap and GRAIL and analysed for significantly overrepresented pathways using ConsensuspathDB. Twelve clusters including 56 SNPs annotated to 26 genes were prioritised because these included at least one height- and one BC risk- or CRC risk-associated SNP annotated to the same gene. Annotated genes were involved in Indian hedgehog signalling (p-value = 7.78 × 10
) and several cancer site-specific pathways. This systematic approach identified a limited number of clustered SNPs, which pinpoint potential shared mechanisms linking together the complex phenotypes height, post-menopausal BC and CRC.
The development of computational approaches for predictive modeling of diseases, for example, in cancer diagnostics or prognostics, or drug resistance predictions in infectious diseases, has opened a ...new era in precision medicine. Clinical decision-support-systems have been designed for assistance in molecular diagnostics (MDx) or companion diagnostics (CDx) to enhance therapeutic success. These systems are typically based on statistical or machine learning models, built upon clinical or corporate patient data. However, these software systems are not ready to use in MDx or CDx settings—due to regulatory requirements, such as specific ISO standards, which are needed for regulatory approval. As these standards are generally not fulfilled in academic software, there is currently only a limited use of such systems in clinical routine. Owing to the fact that academia cannot solve this problem on its own, we propose a scheme where the three key players, that is, academia, clinics, and industry, work more closely together in software development processes for medical devices.
Conventional univariate statistics are common and widespread in neuroimaging research. However, functional and structural MRI data reveal a multivariate nature, since neighboring voxels are highly ...correlated and different localized brain regions activate interdependently. Multivariate pattern classification techniques are capable of overcoming shortcomings of univariate statistics. A rising interest in such approaches on neuroimaging data leads to an increasing demand of appropriate software and tools in this field. Here, we introduce and release MANIA—
Machine learning Application for NeuroImaging Analyses
. MANIA is a Matlab based software toolbox enabling easy pattern classification of neuroimaging data and offering a broad assortment of machine learning algorithms and feature selection methods. Between groups classifications are the main scope of this software, for instance the differentiation between patients and controls. A special emphasis was placed on an intuitive and easy to use graphical user interface allowing quick implementation and guidance also for clinically oriented researchers. MANIA is free and open source, published under GPL3 license. This work will give an overview regarding the functionality and the modular software architecture as well as a comparison between other software packages.
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