Automatizace a robotizace i v medicíně je vítaným pokrokem pro diagnostiku a léčbu. Aby však tyto metody byly opravdu účinné, nesmí zakrývat diagnostické tápání, alibistickou výkonnost a bezcílné hledání, musí být efektivně indikované. Paradoxně se zaváděním těchto metod např. v molekulární genetice došlo k ocenění významu -ruční a mentální- práce lékaře, který může zajistit, aby indikace těchto moderních metod byly cílené a tedy rychlé a nenákladné. Docenění tak dosáhla spolu s genealogií i genetická dysmorfologie, jako účinná diagnostická metoda genetických syndromů.
Automation and robotics are welcomed progress also in the medicine for the diagnostics and treatment. But, with a view to make these methods really effective, they should not mask a diagnostic fumble, alibistic productivity and aimless searching, they should be effectively indicated. Paradox is, that with an implementation of such methods, e.g. in molecular genetics, an importance of -manual and mental- work of doctor, who can provide aimed, that is fast and inexpensive indications of these modern methods, has been valued. Together with genealogy a genetic dysmorphology has been valued, as an efficient diagnostic method of genetic syndromes.
- MeSH
- Algorithms MeSH
- Molecular Diagnostic Techniques MeSH
- Child MeSH
- Research Support as Topic MeSH
- Genetic Diseases, Inborn diagnosis etiology MeSH
- Genetics, Medical MeSH
- Humans MeSH
- Mutation MeSH
- Signs and Symptoms diagnosis MeSH
- Syndrome diagnosis genetics MeSH
- Check Tag
- Child MeSH
- Humans MeSH
- Publication type
- Review MeSH
- Comparative Study MeSH
... Contents -- Preface xv -- 1 Introduction 1 -- 2 Algorithms and Complexity 7 -- 2.1 What Is an Algorithm ... ... 7 -- 2.2 Biological Algorithms versus Computer Algorithms 14 -- 2.3 The Change Problem 17 -- 2.4 Correct ... ... versus Incorrect Algorithms 20 -- 2.5 Recursive Algorithms 24 -- 2.6 Iterative versus Recursive Algorithms ... ... 28 -- 2.7 Fast versus Slow Algorithms 33 -- 2.8 Big-O Notation 37 -- 2.9 Algorithm Design Techniques ... ... 40 -- 2.9.1 Exhaustive Search 41 -- 2.9.2 Branch-and-Bound Algorithms 42 -- 2.9.3 Greedy Algorithms ...
Computational molecular biology series
[1st ed.] xviii, 435 s. : il.
- MeSH
- Algorithms MeSH
- Informatics MeSH
- Conspectus
- Lékařské vědy. Lékařství
- NML Fields
- lékařská informatika
Úvod: Při léčbě warfarinem je zapotřebí užívat široké rozpětí dávky k udržení terapeutického INR s nutností častých kontrol a následných korekcí. Na individuální senzitivitě se mimo negenetických faktorů podílí individuální genetická výbava – polymorfizmy genů CYP2C9 a VKORC1. Na základě farmakogenetiky je možné před zahájením terapie odhadnout výpočtem individuální dávku warfarinu. Cíl: Ověřit rozložení polymorfizmů genů CYP2C9 a VKORC1 v české populaci a srovnat skutečnou denní dávku warfarinu s dávkou vypočtenou pomocí tří publikovaných farmakogenetických algoritmů. Soubor a metodika: Genotypizace CYP2C9 a VKORC1 byla provedena u 1 972 pacientů. Přesnost výpočtu dávky byla ověřena na souboru 280 pacientů se známou stabilizovanou dávkou warfarinu. Byla provedena genotypizace polymorfizmů ovlivňujících dávku warfarinu (alely *1, *2 a *3 CYP2C9, haplotypy A a B genu VKORC1). Od pacientů byly získány údaje relevantní pro výpočet dávky warfarinu podle porovnávaných algoritmů a skutečně užívaná dávka warfarinu. Výsledky: Variantní genotyp CYP2C9 spojený se sníženou metabolizací warfarinu byl v populační kohortě přítomen následovně: heterozygoti 11,6 %, homozygoti 1,1 %. Haplotyp A/A VKORC1 spojený s vyšší citlivostí na warfarin byl zastoupen ve 14 %. Standardní očekávatelnou dávku warfarinu mají pacienti s žádnou (29,2 %) nebo jednou variantní alelou (41,5 %). Hodnoty koeficientu determinace (R2) jednotlivých algoritmů byly: podle Andersona 21,9 %, Gage 23,8 % a Sconceové 58,4 %. Závěr: Největší přesnosti dosahuje na naší kohortě algoritmus Sconceové, kdy má pacient 4krát větší šanci, že vypočtená dávka bude ? 20 % skutečné dávky proti ostatním algoritmům. Použití farmakogenetických algoritmů je přesné u pacientů všech váhových kategorií, pacientů nad 80 let, není však výhodné u pacientů do 40 let věku.
Introduction: Warfarin is used in a wide range of doses and requires frequent INR monitoring with respective dose adjustment. Intra-individual variability of warfarin dose is determined by the individual genotype and CYP2C9 and VKORC1 gene polymorphisms. Pharmacogenetic algorithms could be used to predict the daily dose of warfarin even before the initiation of warfarin treatment. Aim of study: To assess frequency of CYP2C9 and VKORC1 polymorphism in the Czech population and to compare empirical daily dose of warfarin with the dose predicted by three previously published pharmacogenetic algorithms. Methods: CYP2C9 (alleles *1, *2 and *3) and VKORC1 (haplotypes A and B) genotyping was performed in 1,972 patients. Accuracy of warfarin daily dose prediction was assessed in a cohort of 280 patients with complete relevant clinical data and on a stable dose of warfarin. Results: The heterozygous form of the variant genotype of CYP2C9 (reduced warfarin metabolism) was present in 11.6% of patients in our cohort, the homozygous form was found in 1.1%. VKORC1 haplotype A/A (lower sensitivity for warfarin) was present in 14% of the cohort. Standard expected mean dose of warfarin was used by patients with no (29.2%) or 1 variant allele (41.5%). Coefficients of determination (R2) of the respective assessed algorithms were: Anderson 21.9%, Gage 23.8% and Sconce 58.4%. Conclusion: The algorithm by Sconce et al provided the highest agreement between the predicted and empirical daily dose, with 4-fold higher probability that the predicted dose will be ?20% of the empirical dose compared to other assessed algorithms. Pharmacogenetic algorithms were found to be useful in patients of all body weight categories and in patients older than 80 years but not in patients younger than 40 years. Key words: warfarin – pharmacogenetics – CYP2C9 protein – VKORC1 protein The authors declare they have no potential conflicts of interest concerning drugs, products, or services used in the study. The Editorial Board declares that the manuscript met the ICMJE “uniform requirements” for biomedical papers.
- MeSH
- Algorithms MeSH
- Pharmacogenetics * MeSH
- Genetic Testing * MeSH
- Genotype MeSH
- Platelet Aggregation Inhibitors MeSH
- Comorbidity MeSH
- Drug Interactions MeSH
- Middle Aged MeSH
- Humans MeSH
- Polymorphism, Genetic MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Statistics as Topic MeSH
- Cytochrome P-450 Enzyme System * genetics MeSH
- Body Weights and Measures MeSH
- Dose-Response Relationship, Drug * MeSH
- Warfarin * administration & dosage pharmacology MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
elektronický časopis
- MeSH
- Molecular Biology MeSH
- Conspectus
- Biologické vědy
- NML Fields
- biologie
- NML Publication type
- elektronické časopisy
The analysis and segmentation of articular cartilage magnetic resonance (MR) images belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal system of the knee area. Conventional regional segmentation methods, which are based either on the histogram partitioning (e.g., Otsu method) or clustering methods (e.g., K-means), have been frequently used for the task of regional segmentation. Such methods are well known as fast and well working in the environment, where cartilage image features are reliably recognizable. The well-known fact is that the performance of these methods is prone to the image noise and artefacts. In this context, regional segmentation strategies, driven by either genetic algorithms or selected evolutionary computing strategies, have the potential to overcome these traditional methods such as Otsu thresholding or K-means in the context of their performance. These optimization strategies consecutively generate a pyramid of a possible set of histogram thresholds, of which the quality is evaluated by using the fitness function based on Kapur's entropy maximization to find the most optimal combination of thresholds for articular cartilage segmentation. On the other hand, such optimization strategies are often computationally demanding, which is a limitation of using such methods for a stack of MR images. In this study, we publish a comprehensive analysis of the optimization methods based on fuzzy soft segmentation, driven by artificial bee colony (ABC), particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO), and a genetic algorithm for an optimal thresholding selection against the routine segmentations Otsu and K-means for analysis and the features extraction of articular cartilage from MR images. This study objectively analyzes the performance of the segmentation strategies upon variable noise with dynamic intensities to report a segmentation's robustness in various image conditions for a various number of segmentation classes (4, 7, and 10), cartilage features (area, perimeter, and skeleton) extraction preciseness against the routine segmentation strategies, and lastly the computing time, which represents an important factor of segmentation performance. We use the same settings on individual optimization strategies: 100 iterations and 50 population. This study suggests that the combination of fuzzy thresholding with an ABC algorithm gives the best performance in the comparison with other methods as from the view of the segmentation influence of additive dynamic noise influence, also for cartilage features extraction. On the other hand, using genetic algorithms for cartilage segmentation in some cases does not give a good performance. In most cases, the analyzed optimization strategies significantly overcome the routine segmentation methods except for the computing time, which is normally lower for the routine algorithms. We also publish statistical tests of significance, showing differences in the performance of individual optimization strategies against Otsu and K-means method. Lastly, as a part of this study, we publish a software environment, integrating all the methods from this study.
The open-pollinated (OP) family testing combines the simplest known progeny evaluation and quantitative genetics analyses as candidates' offspring are assumed to represent independent half-sib families. The accuracy of genetic parameter estimates is often questioned as the assumption of "half-sibling" in OP families may often be violated. We compared the pedigree- vs. marker-based genetic models by analysing 22-yr height and 30-yr wood density for 214 white spruce [Picea glauca (Moench) Voss] OP families represented by 1694 individuals growing on one site in Quebec, Canada. Assuming half-sibling, the pedigree-based model was limited to estimating the additive genetic variances which, in turn, were grossly overestimated as they were confounded by very minor dominance and major additive-by-additive epistatic genetic variances. In contrast, the implemented genomic pairwise realized relationship models allowed the disentanglement of additive from all nonadditive factors through genetic variance decomposition. The marker-based models produced more realistic narrow-sense heritability estimates and, for the first time, allowed estimating the dominance and epistatic genetic variances from OP testing. In addition, the genomic models showed better prediction accuracies compared to pedigree models and were able to predict individual breeding values for new individuals from untested families, which was not possible using the pedigree-based model. Clearly, the use of marker-based relationship approach is effective in estimating the quantitative genetic parameters of complex traits even under simple and shallow pedigree structure.
- MeSH
- Algorithms MeSH
- Phenotype MeSH
- Genetic Variation MeSH
- Genome, Plant * MeSH
- Genomics * methods MeSH
- Genotype MeSH
- Genotyping Techniques MeSH
- Quantitative Trait, Heritable MeSH
- Models, Genetic MeSH
- Pollination genetics MeSH
- Picea classification genetics MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
