• MeSH
• experimentální radiační poranění mortalita   MeSH
• experimenty na zvířatech MeSH
• fyziologická adaptace MeSH
• teoretické modely MeSH

Explainable artificial intelligence holds a great promise for neuroscience and plays an important role in the hypothesis generation process. We follow-up a recent machine learning-oriented study that constructed a deep convolutional neural network to automatically identify biological sex from EEG recordings in healthy individuals and highlighted the discriminative role of beta-band power. If generalizing, this finding would be relevant not only theoretically by pointing to some specific neurobiological sexual dimorphisms, but potentially also as a relevant confound in quantitative EEG diagnostic practice. To put this finding to test, we assess whether the automatic identification of biological sex generalizes to another dataset, particularly in the presence of a psychiatric disease, by testing the hypothesis of higher beta power in women compared to men on 134 patients suffering from Major Depressive Disorder. Moreover, we construct ROC curves and compare the performance of the classifiers in determining sex both before and after the antidepressant treatment. We replicate the observation of a significant difference in beta-band power between men and women, providing classification accuracy of nearly 77%. The difference was consistent across the majority of electrodes, however multivariate classification models did not generally improve the performance. Similar results were observed also after the antidepressant treatment (classification accuracy above 70%), further supporting the robustness of the initial finding.

• Publikační typ
• časopisecké články MeSH

Current classifications (World Health Organization-HAEM5/ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by transcriptome sequencing (RNA-Seq) is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning-based classifier using RNA-Seq gene expression data to allocate BCP-ALL samples to all 21 gene expression-defined molecular subtypes. Trained on n = 1869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n = 1018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). High-confidence predictions were achieved in 83.7% of samples with 98.9% accuracy. Only 1.2% of samples remained unclassified. ALLCatchR outperformed existing tools and identified novel driver candidates in previously unassigned samples. Additional modules provided predictions of samples blast counts, patient's sex, and immunophenotype, allowing the imputation in cases where these information are missing. We established a novel RNA-Seq reference of human B-lymphopoiesis using 7 FACS-sorted progenitor stages from healthy bone marrow donors. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory. This identified shared proximity patterns of BCP-ALL subtypes to normal lymphopoiesis stages, extending immunophenotypic classifications with a novel framework for developmental comparisons of BCP-ALL. ALLCatchR enables RNA-Seq routine application for BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocation and novel insights into underlying developmental trajectories.

• Publikační typ
• časopisecké články MeSH

Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

• MeSH
• algoritmy MeSH
• databáze proteinů MeSH
• fylogeneze MeSH
• genetická variace MeSH
• genetické nemoci vrozené genetika   MeSH
• genom lidský MeSH
• internet MeSH
• jednonukleotidový polymorfismus * MeSH
• lidé MeSH
• mutace * MeSH
• počítačová simulace MeSH
• software MeSH
• výpočetní biologie metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Cílem práce bylo porovnání úspěšnosti texturního klasifikátoru a vyšetřujícího lékaře (radiologa) při diagnóze autoimunitní thyroiditidy ze sonografického obrazu snímaného v B-módu. Určení inter- a intrapersonální variability lékařů. Datový soubor obsahující 161 vyšetřovaných subjektů byl rozdělen do tří skupin dle celkového klinického vyšetření: normální – H (healthy); hraniční stav – BS (border state); autoimunitní thyroiditida – AT. Následně byl soubor čtyřmi vyšetřujícími lékaři a Bayesovským klasifikátorem, založeným na texturních příznacích, hodnocen do těchto skupin. Dva lékaři dosáhli vyšší úspěšnosti při hodnocení subjektů z normální skupiny (74,4 % a 83,3 %) a dva lékaři hodnotili lépe subjekty s autoimunitní thyroiditidou (59,0% a 77,4 %). Klasifikátor dosáhl relativně vysoké a vyrovnané úspěšnosti pro obě tyto skupiny (100,0 % pro normální a 87,5 % pro thyroiditidu). Rozdílný úspěch jednotlivých lékařů při hodnocení subjektů vyústil ve vyšší interpersonální variabilitu, tedy nízkou shodu mezi nimi. V intrapersonální variabilitě jednotlivých lékařů nebyl nalezen významný rozdíl. Vzhledem ke slabé shodě mezi vyšetřujícími lékaři při diagnostice autoimunitní thyroiditidy ze sonografických obrazů a vysoké a vyrovnané úspěšnosti klasifikátoru se zdá jako nejvýhodnější pro stanovení konečné diagnózy kombinace automatické klasifikace obrazů a klinických zkušeností lékařů.

The objective has been to compare success of the texture classifier and a human observer in diagnosis of the autoimmune thyroiditis from B-mode ultrasound images and to determine inter- and intra-observer variability. The data set of 161 subjects was classified by four human observers and by the Bayes classifier based on the texture features to three classes (healthy, border state, autoimmune thyroiditis). Two observers had a higher success rate when classifying the healthy class (74.4 % and 83.3 %), the other two observers classified better cases with autoimmune thyroiditis (59.0 % and 77.4 %). The classifier gave the relatively high and balanced success rate for both classes (100.0 % for healthy and 875 % for thyroiditis). The different observers’ success rates resulted in the high inter-observer variability, showing only a fair agreement among the human observers. There was no significant difference among human observers in the intra-observer variability. Due to the fair agreement among observers in the diagnosis of autoimmune thyroiditis from ultrasound images and good results of the classifier, the best way in establishing diagnosis is computer-aided diagnosis combined with observers’ clinical experience.

• Klíčová slova
• sonografický obraz, B-mode sonografie, texturní analýza, počítačem podporovaná diagnóza, interpersonální variabilita, koeficient Kappa, vážený koeficient Kappa,
• MeSH
• autoimunitní tyreoiditida diagnóza   ultrasonografie   MeSH
• financování organizované MeSH
• interpretace obrazu počítačem přístrojové vybavení   využití   MeSH
• interpretace statistických dat MeSH
• lidé MeSH
• odchylka pozorovatele MeSH
• reprodukovatelnost výsledků MeSH
• senzitivita a specificita MeSH
• štítná žláza MeSH
• ultrasonografie metody   statistika a číselné údaje   MeSH
• Check Tag
• lidé MeSH

Currently, methods of combined classification are the focus of intense research. A properly designed group of combined classifiers exploiting knowledge gathered in a pool of elementary classifiers can successfully outperform a single classifier. There are two essential issues to consider when creating combined classifiers: how to establish the most comprehensive pool and how to design a fusion model that allows for taking full advantage of the collected knowledge. In this work, we address the issues and propose an AdaSS+, training algorithm dedicated for the compound classifier system that effectively exploits local specialization of the elementary classifiers. An effective training procedure consists of two phases. The first phase detects the classifier competencies and adjusts the respective fusion parameters. The second phase boosts classification accuracy by elevating the degree of local specialization. The quality of the proposed algorithms are evaluated on the basis of a wide range of computer experiments that show that AdaSS+ can outperform the original method and several reference classifiers.

• MeSH
• algoritmy MeSH
• lidé MeSH
• počítačová simulace MeSH
• rozpoznávání automatizované * MeSH
• teoretické modely * MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

BACKGROUND: Diabetic nephropathy (DN) is one of the major late complications of diabetes. Treatment aimed at slowing down the progression of DN is available but methods for early and definitive detection of DN progression are currently lacking. The 'Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In TYpe 2 diabetic patients with normoalbuminuria trial' (PRIORITY) aims to evaluate the early detection of DN in patients with type 2 diabetes (T2D) using a urinary proteome-based classifier (CKD273). METHODS: In this ancillary study of the recently initiated PRIORITY trial we aimed to validate for the first time the CKD273 classifier in a multicentre (9 different institutions providing samples from 165 T2D patients) prospective setting. In addition we also investigated the influence of sample containers, age and gender on the CKD273 classifier. RESULTS: We observed a high consistency of the CKD273 classification scores across the different centres with areas under the curves ranging from 0.95 to 1.00. The classifier was independent of age (range tested 16-89 years) and gender. Furthermore, the use of different urine storage containers did not affect the classification scores. Analysis of the distribution of the individual peptides of the classifier over the nine different centres showed that fragments of blood-derived and extracellular matrix proteins were the most consistently found. CONCLUSION: We provide for the first time validation of this urinary proteome-based classifier in a multicentre prospective setting and show the suitability of the CKD273 classifier to be used in the PRIORITY trial.

• MeSH
• diabetes mellitus 2. typu komplikace   diagnóza   moč   MeSH
• diabetické nefropatie diagnóza   etiologie   moč   MeSH
• diferenciální diagnóza MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• následné studie MeSH
• peptidomimetika moč   MeSH
• progrese nemoci MeSH
• prospektivní studie MeSH
• proteomika metody   MeSH
• senioři MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• práce podpořená grantem MeSH
• randomizované kontrolované studie MeSH
• validační studie MeSH

Genetic abnormalities provide vital diagnostic and prognostic information in pediatric acute lymphoblastic leukemia (ALL) and are increasingly used to assign patients to risk groups. We recently proposed a novel classifier based on the copy-number alteration (CNA) profile of the 8 most commonly deleted genes in B-cell precursor ALL. This classifier defined 3 CNA subgroups in consecutive UK trials and was able to discriminate patients with intermediate-risk cytogenetics. In this study, we sought to validate the United Kingdom ALL (UKALL)-CNA classifier and reevaluate the interaction with cytogenetic risk groups using individual patient data from 3239 cases collected from 12 groups within the International BFM Study Group. The classifier was validated and defined 3 risk groups with distinct event-free survival (EFS) rates: good (88%), intermediate (76%), and poor (68%) (P < .001). There was no evidence of heterogeneity, even within trials that used minimal residual disease to guide therapy. By integrating CNA and cytogenetic data, we replicated our original key observation that patients with intermediate-risk cytogenetics can be stratified into 2 prognostic subgroups. Group A had an EFS rate of 86% (similar to patients with good-risk cytogenetics), while group B patients had a significantly inferior rate (73%, P < .001). Finally, we revised the overall genetic classification by defining 4 risk groups with distinct EFS rates: very good (91%), good (81%), intermediate (73%), and poor (54%), P < .001. In conclusion, the UKALL-CNA classifier is a robust prognostic tool that can be deployed in different trial settings and used to refine established cytogenetic risk groups.

• MeSH
• cytogenetické vyšetření MeSH
• dítě MeSH
• genetická predispozice k nemoci MeSH
• genetické asociační studie MeSH
• hodnocení výsledků pacienta MeSH
• kojenec MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• nádorové biomarkery * MeSH
• následné studie MeSH
• pre-B-buněčná leukemie diagnóza   epidemiologie   genetika   MeSH
• předškolní dítě MeSH
• prognóza MeSH
• proporcionální rizikové modely MeSH
• surveillance populace MeSH
• variabilita počtu kopií segmentů DNA * MeSH
• Check Tag
• dítě MeSH
• kojenec MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Spojené království MeSH

Glioblastoma multiforme (GBM) is the most malignant primary brain tumor. The prognosis of GBM patients varies considerably and the histopathological examination is not sufficient for individual risk estimation. MicroRNAs (miRNAs) are small, non-coding RNAs that function as post-transcriptional regulators of gene expression and were repeatedly proved to play important roles in pathogenesis of GBM. In our study, we performed global miRNA expression profiling of 58 glioblastoma tissue samples obtained during surgical resections and 10 non-tumor brain tissues. The subsequent analysis revealed 28 significantly deregulated miRNAs in GBM tissue, which were able to precisely classify all examined samples. Correlation with clinical data led to identification of six-miRNA signature significantly associated with progression free survival [hazard ratio (HR) 1.98, 95% confidence interval (CI) 1.33-2.94, P < 0.001] and overa+ll survival (HR 2.86, 95% CI 1.91-4.29, P < 0.001). O(6)-methylguanine-DNA methyltransferase methylation status was evaluated as reference method and Risk Score based on six-miRNA signature indicated significant superiority in prediction of clinical outcome in GBM patients. Multivariate Cox analysis indicated that the Risk Score based on six-miRNA signature is an independent prognostic classifier of GBM patients. We suggest that the Risk Score presents promising prognostic algorithm with potential for individualized treatment decisions in clinical management of GBM patients.

• MeSH
• algoritmy MeSH
• DNA modifikační methylasy genetika   MeSH
• enzymy opravy DNA genetika   MeSH
• glioblastom genetika   mortalita   patologie   MeSH
• lidé MeSH
• metylace DNA MeSH
• mikro RNA genetika   MeSH
• míra přežití MeSH
• mozek metabolismus   MeSH
• nádorové biomarkery genetika   MeSH
• nádorové supresorové proteiny genetika   MeSH
• nádory mozku genetika   mortalita   patologie   MeSH
• následné studie MeSH
• prognóza MeSH
• proliferace buněk * MeSH
• promotorové oblasti (genetika) genetika   MeSH
• retrospektivní studie MeSH
• staging nádorů MeSH
• stanovení celkové genové exprese * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH