• MeSH
• Data Mining MeSH
• Medicine MeSH

• Conspectus
• Lékařské vědy. Lékařství
• NML Fields
• lékařství
• lékařská informatika
• NML Publication type
• elektronické časopisy

V souvislosti s narůstajícím objemem dostupných klinických dat dochází ke stále častějším aplikacím metod tzv. dolování dat („data-mining“) v klinickém výzkumu a praxi. Celý proces data-miningu lze rozdělit na řadu samostatných a poměrně snadno uchopitelných kroků od uložení dat a jejich přípravy, přes pochopení datové struktury souboru až po modelování a extrakci využitelných poznatků. Ve vytvořeném e-kurzu přinášíme kromě teoretického popisu metod i řadu řešených případových studií, např. při mapování genové exprese nebo při modelování strukturovaných dat z klinické praxe.

Data mining has become a standard approach in many fields of clinical research. The whole data-mining process can be divided into sets of simple logical steps from the data preparation and validation, through definition of data structure and statistical description, up to data modelling and mining. The newly developed e-learning course addresses all the main steps of the data mining together with case studies of microarrays data analysis.

• Keywords
• CRISP-DM, microarrays,
• MeSH
• Data Mining * MeSH
• Multimedia utilization   MeSH
• Computer-Assisted Instruction * MeSH
• Education, Professional methods   MeSH
• Geographicals
• Czech Republic MeSH

• MeSH
• Data Mining MeSH
• Data Collection methods   MeSH
• Information Storage and Retrieval * MeSH
• Publication type
• Dataset MeSH
• Periodical MeSH

• Conspectus
• Věda. Všeobecnosti. Základy vědy a kultury. Vědecká práce
• NML Fields
• věda a výzkum

Data mining (DM) is a widely adopted methodology for the analysis of large datasets which is on the other hand often overestimated or incorrectly considered as a universal solution. This statement is also valid for clinical research, in which large and heterogeneous datasets are often processed. DM in general uses standard methods available in common statistical software and combines them into a complex workflow methodology covering all the steps of data analysis from data acquisition through pre-processing and data analysis to interpretation of the results. The whole workflow is aimed at one final goal – to find any interesting, non-trivially hidden and potentially useful information. This innovative concept of data mining was adopted in our educational course of the Faculty of Medicine at the Masaryk University accessible from its e-learning portal http://portal. med.muni.cz/clanek-318-zavedeni-technologie-data-miningu-a-analyzy-dat--genovych-expresnich-map-do-vyuky.html.

• MeSH
• Biostatistics methods   MeSH
• Data Mining * methods   trends   MeSH
• Humans MeSH
• Multifactor Dimensionality Reduction methods   MeSH
• Computer-Assisted Instruction * methods   trends   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH

• Keywords
• Bioinformatika, Multimedia, Data,

• Conspectus
• Lékařské vědy. Lékařství
• NML Fields
• lékařská informatika

• Keywords
• statistika, vícerozměrná analýza, velké datové soubory,
• MeSH
• Databases, Genetic trends   utilization   MeSH
• Education, Distance methods   trends   MeSH
• Financing, Organized MeSH
• Genetic Techniques trends   utilization   MeSH
• Medical Informatics MeSH
• Humans MeSH
• Computer-Assisted Instruction instrumentation   utilization   MeSH
• Data Collection methods   trends   MeSH
• Statistics as Topic MeSH
• Models, Theoretical MeSH
• Data Display trends   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Database MeSH

A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big "omic" data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges to maximize model performance. In this context we also describe a novel in silico screening process, based on Association Rule Mining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large sample-spaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine.

• MeSH
• Data Mining * MeSH
• Humans MeSH
• Neoplasms drug therapy   MeSH
• Computer Simulation MeSH
• Machine Learning * MeSH
• Treatment Outcome MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH
• Research Support, N.I.H., Extramural MeSH

Monografie se zaměřuje na otázky právní povahy informace a dat, práva duševního vlastnictví k datům, osobnostní práva a práva jednotlivců na ochranu osobních údajů. Nakladatelská anotace. Kráceno; Právní rámec sběru, zpracování a dalšího šíření výzkumných dat na základě stavu k 31. 12. 2017.

• MeSH
• Data Mining legislation & jurisprudence   MeSH
• Databases as Topic legislation & jurisprudence   MeSH
• Human Rights legislation & jurisprudence   MeSH
• Civil Rights legislation & jurisprudence   MeSH
• Research legislation & jurisprudence   MeSH

• Conspectus
• Obchodní právo. Finanční právo. Právo průmyslového vlastnictví. Patentové právo. Autorské právo
• NML Fields
• právo, zákonodárství
• věda a výzkum
• NML Publication type
• kolektivní monografie

As the amount of genome information increases rapidly, there is a correspondingly greater need for methods that provide accurate and automated annotation of gene function. For example, many high-throughput technologies--e.g., next-generation sequencing--are being used today to generate lists of genes associated with specific conditions. However, their functional interpretation remains a challenge and many tools exist trying to characterize the function of gene-lists. Such systems rely typically in enrichment analysis and aim to give a quick insight into the underlying biology by presenting it in a form of a summary-report. While the load of annotation may be alleviated by such computational approaches, the main challenge in modern annotation remains to develop a systems form of analysis in which a pipeline can effectively analyze gene-lists quickly and identify aggregated annotations through computerized resources. In this article we survey some of the many such tools and methods that have been developed to automatically interpret the biological functions underlying gene-lists. We overview current functional annotation aspects from the perspective of their epistemology (i.e., the underlying theories used to organize information about gene function into a body of verified and documented knowledge) and find that most of the currently used functional annotation methods fall broadly into one of two categories: they are based either on 'known' formally-structured ontology annotations created by 'experts' (e.g., the GO terms used to describe the function of Entrez Gene entries), or--perhaps more adventurously--on annotations inferred from literature (e.g., many text-mining methods use computer-aided reasoning to acquire knowledge represented in natural languages). Overall however, deriving detailed and accurate insight from such gene lists remains a challenging task, and improved methods are called for. In particular, future methods need to (1) provide more holistic insight into the underlying molecular systems; (2) provide better follow-up experimental testing and treatment options, and (3) better manage gene lists derived from organisms that are not well-studied. We discuss some promising approaches that may help achieve these advances, especially the use of extended dictionaries of biomedical concepts and molecular mechanisms, as well as greater use of annotation benchmarks.

• MeSH
• Data Mining methods   trends   MeSH
• Databases, Genetic * trends   MeSH
• Gene Ontology * trends   MeSH
• Humans MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

Lipidomics and metabolomics communities comprise various informatics tools; however, software programs handling multimodal mass spectrometry (MS) data with structural annotations guided by the Lipidomics Standards Initiative are limited. Here, we provide MS-DIAL 5 for in-depth lipidome structural elucidation through electron-activated dissociation (EAD)-based tandem MS and determining their molecular localization through MS imaging (MSI) data using a species/tissue-specific lipidome database containing the predicted collision-cross section values. With the optimized EAD settings using 14 eV kinetic energy, the program correctly delineated lipid structures for 96.4% of authentic standards, among which 78.0% had the sn-, OH-, and/or C = C positions correctly assigned at concentrations exceeding 1 μM. We showcased our workflow by annotating the sn- and double-bond positions of eye-specific phosphatidylcholines containing very-long-chain polyunsaturated fatty acids (VLC-PUFAs), characterized as PC n-3-VLC-PUFA/FA. Using MSI data from the eye and n-3-VLC-PUFA-supplemented HeLa cells, we identified glycerol 3-phosphate acyltransferase as an enzyme candidate responsible for incorporating n-3 VLC-PUFAs into the sn1 position of phospholipids in mammalian cells, which was confirmed using EAD-MS/MS and recombinant proteins in a cell-free system. Therefore, the MS-DIAL 5 environment, combined with optimized MS data acquisition methods, facilitates a better understanding of lipid structures and their localization, offering insights into lipid biology.

• MeSH
• Data Mining * methods   MeSH
• Phosphatidylcholines metabolism   chemistry   MeSH
• HeLa Cells MeSH
• Mass Spectrometry methods   MeSH
• Humans MeSH
• Lipidomics * methods   MeSH
• Lipids chemistry   analysis   MeSH
• Metabolomics methods   MeSH
• Fatty Acids, Unsaturated metabolism   chemistry   MeSH
• Software MeSH
• Tandem Mass Spectrometry methods   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH