Displaying publications 1 - 20 of 252 in total

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  1. de Vries B, Narayan R, McGeechan K, Santiagu S, Vairavan R, Burke M, et al.
    Acta Obstet Gynecol Scand, 2018 Jun;97(6):668-676.
    PMID: 29450884 DOI: 10.1111/aogs.13310
    INTRODUCTION: Cesarean section rates continue to increase globally. Prediction of intrapartum cesarean section could lead to preventive measures. Our aim was to assess the association between sonographically measured cervical length at 37 weeks of gestation and cesarean section among women planning a vaginal birth. The population was women with a low-risk pregnancy or with gestational diabetes.

    MATERIAL AND METHODS: This was a prospective cohort study conducted in a tertiary referral hospital in Sydney, Australia. In all, 212 women with a low-risk pregnancy or with gestational diabetes were recruited including 158 nulliparous and 54 parous women. Maternal demographic, clinical and ultrasound characteristics were collected at 37 weeks of gestation. Semi-Bayesian logistic regression and Markov chain Monte Carlo simulation were used to assess the relation between cervical length and cesarean section in labor.

    RESULTS: Rates of cesarean section were 5% (2/55) for cervical length ≤20 mm, 17% (17/101) for cervical length 20-32 mm, and 27% (13/56) for cervical length >32 mm. These rates were 4, 22 and 33%, respectively, in nulliparous women. In the semi-Bayesian analysis, the odds ratio for cesarean section was 6.2 (95% confidence interval 2.2-43) for cervical length 20-32 mm and 10 (95% confidence interval 4.8-74) for cervical length >32 mm compared with the lowest quartile of cervical length, after adjusting for maternal age, parity, height, prepregnancy body mass index, gestational diabetes, induction of labor, neonatal sex and birthweight centile.

    CONCLUSIONS: Cervical length at 37 weeks of gestation is associated with intrapartum cesarean section.

    Matched MeSH terms: Bayes Theorem
  2. Zulkifley MA, Mustafa MM, Hussain A, Mustapha A, Ramli S
    PLoS One, 2014;9(12):e114518.
    PMID: 25485630 DOI: 10.1371/journal.pone.0114518
    Recycling is one of the most efficient methods for environmental friendly waste management. Among municipal wastes, plastics are the most common material that can be easily recycled and polyethylene terephthalate (PET) is one of its major types. PET material is used in consumer goods packaging such as drinking bottles, toiletry containers, food packaging and many more. Usually, a recycling process is tailored to a specific material for optimal purification and decontamination to obtain high grade recyclable material. The quantity and quality of the sorting process are limited by the capacity of human workers that suffer from fatigue and boredom. Several automated sorting systems have been proposed in the literature that include using chemical, proximity and vision sensors. The main advantages of vision based sensors are its environmentally friendly approach, non-intrusive detection and capability of high throughput. However, the existing methods rely heavily on deterministic approaches that make them less accurate as the variations in PET plastic waste appearance are too high. We proposed a probabilistic approach of modeling the PET material by analyzing the reflection region and its surrounding. Three parameters are modeled by Gaussian and exponential distributions: color, size and distance of the reflection region. The final classification is made through a supervised training method of likelihood ratio test. The main novelty of the proposed method is the probabilistic approach in integrating various PET material signatures that are contaminated by stains under constant lighting changes. The system is evaluated by using four performance metrics: precision, recall, accuracy and error. Our system performed the best in all evaluation metrics compared to the benchmark methods. The system can be further improved by fusing all neighborhood information in decision making and by implementing the system in a graphics processing unit for faster processing speed.
    Matched MeSH terms: Bayes Theorem*
  3. Zulkifley MA, Rawlinson D, Moran B
    Sensors (Basel), 2012;12(11):15638-70.
    PMID: 23202226 DOI: 10.3390/s121115638
    In video analytics, robust observation detection is very important as the content of the videos varies a lot, especially for tracking implementation. Contrary to the image processing field, the problems of blurring, moderate deformation, low illumination surroundings, illumination change and homogenous texture are normally encountered in video analytics. Patch-Based Observation Detection (PBOD) is developed to improve detection robustness to complex scenes by fusing both feature- and template-based recognition methods. While we believe that feature-based detectors are more distinctive,however, for finding the matching between the frames are best achieved by a collection of points as in template-based detectors. Two methods of PBOD-the deterministic and probabilistic approaches-have been tested to find the best mode of detection. Both algorithms start by building comparison vectors at each detected points of interest. The vectors are matched to build candidate patches based on their respective coordination. For the deterministic method, patch matching is done in 2-level test where threshold-based position and size smoothing are applied to the patch with the highest correlation value. Forthe second approach, patch matching is done probabilistically by modelling the histograms of the patches by Poisson distributions for both RGB and HSV colour models. Then,maximum likelihood is applied for position smoothing while a Bayesian approach is appliedfor size smoothing. The result showed that probabilistic PBOD outperforms the deterministic approach with average distance error of 10.03% compared with 21.03%. This algorithm is best implemented as a complement to other simpler detection methods due to heavy processing requirement.
    Matched MeSH terms: Bayes Theorem
  4. Zhu M, Shen J, Zeng Q, Tan JW, Kleepbua J, Chew I, et al.
    Front Public Health, 2021 07 30;9:685315.
    PMID: 34395364 DOI: 10.3389/fpubh.2021.685315
    Background: The ongoing coronavirus disease 2019 (COVID-19) pandemic has posed an unprecedented challenge to public health in Southeast Asia, a tropical region with limited resources. This study aimed to investigate the evolutionary dynamics and spatiotemporal patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the region. Materials and Methods: A total of 1491 complete SARS-CoV-2 genome sequences from 10 Southeast Asian countries were downloaded from the Global Initiative on Sharing Avian Influenza Data (GISAID) database on November 17, 2020. The evolutionary relationships were assessed using maximum likelihood (ML) and time-scaled Bayesian phylogenetic analyses, and the phylogenetic clustering was tested using principal component analysis (PCA). The spatial patterns of SARS-CoV-2 spread within Southeast Asia were inferred using the Bayesian stochastic search variable selection (BSSVS) model. The effective population size (Ne) trajectory was inferred using the Bayesian Skygrid model. Results: Four major clades (including one potentially endemic) were identified based on the maximum clade credibility (MCC) tree. Similar clustering was yielded by PCA; the first three PCs explained 46.9% of the total genomic variations among the samples. The time to the most recent common ancestor (tMRCA) and the evolutionary rate of SARS-CoV-2 circulating in Southeast Asia were estimated to be November 28, 2019 (September 7, 2019 to January 4, 2020) and 1.446 × 10-3 (1.292 × 10-3 to 1.613 × 10-3) substitutions per site per year, respectively. Singapore and Thailand were the two most probable root positions, with posterior probabilities of 0.549 and 0.413, respectively. There were high-support transmission links (Bayes factors exceeding 1,000) in Singapore, Malaysia, and Indonesia; Malaysia involved the highest number (7) of inferred transmission links within the region. A twice-accelerated viral population expansion, followed by a temporary setback, was inferred during the early stages of the pandemic in Southeast Asia. Conclusions: With available genomic data, we illustrate the phylogeography and phylodynamics of SARS-CoV-2 circulating in Southeast Asia. Continuous genomic surveillance and enhanced strategic collaboration should be listed as priorities to curb the pandemic, especially for regional communities dominated by developing countries.
    Matched MeSH terms: Bayes Theorem
  5. Zheng Y, Fu J, Li S
    Mol Phylogenet Evol, 2009 Jul;52(1):70-83.
    PMID: 19348953 DOI: 10.1016/j.ympev.2009.03.026
    Several anuran groups of Laurasian origin are each co-distributed in four isolated regions of the Northern Hemisphere: central/southern Europe and adjacent areas, Korean Peninsula and adjacent areas, Indo-Malaya, and southern North America. Similar distribution patterns have been observed in diverse animal and plant groups. Savage [Savage, J.M., 1973. The geographic distribution of frogs: patterns and predictions. In: Vial, J.L. (Ed.), Evolutionary Biology of the Anurans. University of Missouri Press, Columbia, pp. 351-445] hypothesized that the Miocene global cooling and increasing aridities in interiors of Eurasia and North America caused a southward displacement and range contraction of Laurasian frogs (and other groups). We use the frog genus Bombina to test Savage's biogeographical hypothesis. A phylogeny of Bombina is reconstructed based on three mitochondrial and two nuclear gene fragments. The genus is divided into three major clades: an Indo-Malaya clade includes B. fortinuptialis, B. lichuanensis, B. maxima, and B. microdeladigitora; a European clade includes B. bombina, B. pachypus, and B. variegata; and a Korean clade contains B. orientalis. The European and Korean clades form sister-group relationship. Molecular dating of the phylogenetic tree using the penalized likelihood and Bayesian analyses suggests that the divergence between the Indo-Malaya clade and other Bombina species occurred 5.9-28.6 million years ago. The split time between the European clade and the Korean clade is estimated at 5.1-20.9 million years ago. The divergence times of these clades are not significantly later than the timing of Miocene cooling and drying, and therefore can not reject Savage's hypothesis. Some other aspects of biogeography of Bombina also are discussed. The Korean Peninsula and the Shandong Peninsula might have supplied distinct southern refugia for B. orientalis during the Pleistocene glacial maxima. In the Indo-Malaya clade, the uplift of the Tibetan Plateau might have promoted the split between B. maxima and the other species.
    Matched MeSH terms: Bayes Theorem
  6. Zakaria MN, Nik Othman NA, Musa Z
    Acta Otolaryngol, 2021 Nov;141(11):984-988.
    PMID: 34669557 DOI: 10.1080/00016489.2021.1990996
    BACKGROUND: The non-invasive tympanic electrocochleography (TM-ECochG) is useful for clinical diagnoses. Nevertheless, the influence of the electrode location on tympanic membrane (TM) on ECochG results needs to be studied.

    OBJECTIVE: The aim of the present study was to compare the TM-ECochG results obtained when the electrode was placed on the superior region versus the inferior region of TM.

    MATERIALS AND METHODS: Forty healthy adults (aged 29 to 50 years) participated in this comparative study. The TM-ECochG testing was conducted with the electrode placed on the superior and inferior regions of TM.

    RESULTS: SP and AP amplitudes were statistically higher for the inferior region of TM (p < .05). In contrast, SP/AP ratios were comparable between the two regions of TM (p = .417).

    CONCLUSIONS AND SIGNIFICANCE: In TM-ECochG recording, when the electrode was placed on the inferior region of TM, SP and AP amplitudes were greater than when the electrode was placed on the superior region of TM. On the other hand, SP/AP amplitude ratio was not affected by the location of electrode on TM. The findings from the present study could be useful to guide clinicians in optimizing TM-ECochG recording when testing their respective patients.

    Matched MeSH terms: Bayes Theorem
  7. Yusoff, A.N., Mohamad, M., Hamid, K.A., Hamid, A.I.A., Manan, H.A., Hashim, M.H.
    ASM Science Journal, 2010;4(2):158-172.
    MyJurnal
    In this multiple-subject study, intrinsic couplings between the primary motor (M1) and supplementary motor areas (SMA) were investigated. Unilateral (UNIright and UNIleft) self-paced tapping of hand fingers were performed to activate M1 and SMA. The intrinsic couplings were analysed using statistical parametric mapping, dynamic causal modeling (DCM) and Bayesian model analysis. Brain activation observed for UNIright and UNIleft showed contralateral and ipsilateral involvement of M1 and SMA. Ten full connectivity models were constructed with right and left M1 and SMA as processing centres. DCM indicated that all subjects prefer M1 as the intrinsic input for UNIright and UNIleft as indicated by a large group Bayes factor (GBF). Positive evidence ratio (PER) that showed strong evidence of Model 3 and Model 6 against other models in at least 12 out of 16 subjects, supported GBF results. The GBF and PER results were later found to be consistent with that of BMS for group studies with high expected posterior probability and exceedance probability. It was concluded that during unilateral finger tapping, the contralateral M1 would act as the input centre which in turn triggered the propagation of signals to SMA in the same hemisphere and to M1 and SMA in the opposite hemisphere.
    Matched MeSH terms: Bayes Theorem
  8. Yu D, Zhang J, Li P, Zheng R, Shao C
    PLoS One, 2015;10(4):e0124825.
    PMID: 25875761 DOI: 10.1371/journal.pone.0124825
    he Chinese tiger frog Hoplobatrachus rugulosus is widely distributed in southern China, Malaysia, Myanmar, Thailand, and Vietnam. It is listed in Appendix II of CITES as the only Class II nationally-protected frog in China. The bred tiger frog known as the Thailand tiger frog, is also identified as H. rugulosus. Our analysis of the Cyt b gene showed high genetic divergence (13.8%) between wild and bred samples of tiger frog. Unexpected genetic divergence of the complete mt genome (14.0%) was also observed between wild and bred samples of tiger frog. Yet, the nuclear genes (NCX1, Rag1, Rhod, Tyr) showed little divergence between them. Despite this and their very similar morphology, the features of the mitochondrial genome including genetic divergence of other genes, different three-dimensional structures of ND5 proteins, and gene rearrangements indicate that H. rugulosus may be a cryptic species complex. Using Bayesian inference, maximum likelihood, and maximum parsimony analyses, Hoplobatrachus was resolved as a sister clade to Euphlyctis, and H. rugulosus (BT) as a sister clade to H. rugulosus (WT). We suggest that we should prevent Thailand tiger frogs (bred type) from escaping into wild environments lest they produce hybrids with Chinese tiger frogs (wild type).
    Matched MeSH terms: Bayes Theorem
  9. Yavari Nejad F, Varathan KD
    BMC Med Inform Decis Mak, 2021 04 30;21(1):141.
    PMID: 33931058 DOI: 10.1186/s12911-021-01493-y
    BACKGROUND: Dengue fever is a widespread viral disease and one of the world's major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50-100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction.

    METHODS: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of 4-year data (January 2010 to December 2013) collected in Malaysia.

    RESULTS: This research has two major contributions. A new risk factor, called the TempeRain factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% for predicting dengue outbreaks.

    CONCLUSIONS: This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.

    Matched MeSH terms: Bayes Theorem
  10. Wu WH, Kuo TC, Lin YT, Huang SW, Liu HF, Wang J, et al.
    PLoS One, 2013;8(12):e83711.
    PMID: 24391812 DOI: 10.1371/journal.pone.0083711
    Enterovirus 71 (EV71), a causative agent of hand, foot, and mouth disease can be classified into three genotypes and many subtypes. The objectives of this study were to conduct a molecular epidemiological study of EV71 in the central region of Taiwan from 2002-2012 and to test the hypothesis that whether the alternative appearance of different EV71 subtypes in Taiwan is due to transmission from neighboring countries or from re-emergence of pre-existing local strains. We selected 174 EV71 isolates and used reverse transcription-polymerase chain reaction to amplify their VP1 region for DNA sequencing. Phylogenetic analyses were conducted using Neighbor-Joining, Maximum Likelihood and Bayesian methods. We found that the major subtypes of EV71 in Taiwan were B4 for 2002 epidemic, C4 for 2004-2005 epidemic, B5 for 2008-2009 epidemic, C4 for 2010 epidemic and B5 for 2011-2012 epidemic. Phylogenetic analysis demonstrated that the 2002 and 2008 epidemics were associated with EV71 from Malaysia and Singapore; while both 2010 and 2011-2012 epidemics originated from different regions of mainland China including Shanghai, Henan, Xiamen and Gong-Dong. Furthermore, minor strains have been identified in each epidemic and some of them were correlated with the subsequent outbreaks. Therefore, the EV71 infection in Taiwan may originate from pre-existing minor strains or from other regions in Asia including mainland China. In addition, 101 EV71 isolates were selected for the detection of new recombinant strains using the nucleotide sequences spanning the VP1-2A-2B region. No new recombinant strain was found. Analysis of clinical manifestations showed that patients infected with C4 had significantly higher rates of pharyngeal vesicles or ulcers than patients infected with B5. This is the first study demonstrating that different EV 71 genotypes may have different clinical manifestations and the association of EV71 infections between Taiwan and mainland China.
    Matched MeSH terms: Bayes Theorem
  11. Wong RS, Ismail NA
    PLoS One, 2016;11(3):e0151949.
    PMID: 27007413 DOI: 10.1371/journal.pone.0151949
    There are not many studies that attempt to model intensive care unit (ICU) risk of death in developing countries, especially in South East Asia. The aim of this study was to propose and describe application of a Bayesian approach in modeling in-ICU deaths in a Malaysian ICU.
    Matched MeSH terms: Bayes Theorem
  12. Wilcox JS, Kerschner A, Hollocher H
    Infect Genet Evol, 2019 11;75:103994.
    PMID: 31421245 DOI: 10.1016/j.meegid.2019.103994
    Plasmodium knowlesi is an important causative agent of malaria in humans of Southeast Asia. Macaques are natural hosts for this parasite, but little is conclusively known about its patterns of transmission within and between these hosts. Here, we apply a comprehensive phylogenetic approach to test for patterns of cryptic population genetic structure between P. knowlesi isolated from humans and long-tailed macaques from the state of Sarawak in Malaysian Borneo. Our approach differs from previous investigations through our exhaustive use of archival 18S Small Subunit rRNA (18S) gene sequences from Plasmodium and Hepatocystis species, our inclusion of insertion and deletion information during phylogenetic inference, and our application of Bayesian phylogenetic inference to this problem. We report distinct clades of P. knowlesi that predominantly contained sequences from either human or macaque hosts for paralogous A-type and S-type 18S gene loci. We report significant partitioning of sequence distances between host species across both types of loci, and confirmed that sequences of the same locus type showed significantly biased assortment into different clades depending on their host species. Our results support the zoonotic potential of Plasmodium knowlesi, but also suggest that humans may be preferentially infected with certain strains of this parasite. Broadly, such patterns could arise through preferential zoonotic transmission of some parasite lineages or a disposition of parasites to transmit within, rather than between, human and macaque hosts. Available data are insufficient to address these hypotheses. Our results suggest that the epidemiology of P. knowlesi may be more complicated than previously assumed, and highlight the need for renewed and more vigorous explorations of transmission patterns in the fifth human malarial parasite.
    Matched MeSH terms: Bayes Theorem
  13. Waqas S, Harun NY, Arshad U, Laziz AM, Sow Mun SL, Bilad MR, et al.
    Chemosphere, 2024 Feb;349:140830.
    PMID: 38056711 DOI: 10.1016/j.chemosphere.2023.140830
    Membrane fouling is a critical bottleneck to the widespread adoption of membrane separation processes. It diminishes the membrane permeability and results in high operational energy costs. The current study presents optimizing the operating parameters of a novel rotating biological contactor (RBC) integrated with an external membrane (RBC + ME) that combines membrane technology with an RBC. In the RBC + ME, the membrane panel is placed external to the bioreactor. Response surface methodology (RSM) is applied to optimize the membrane permeability through three operating parameters (hydraulic retention time (HRT), rotational disk speed, and sludge retention time (SRT)). The artificial neural networks (ANN) and support vector machine (SVM) are implemented to depict the statistical modelling approach using experimental data sets. The results showed that all three operating parameters contribute significantly to the performance of the bioreactor. RSM revealed an optimum value of 40.7 rpm disk rotational speed, 18 h HRT and 12.4 d SRT, respectively. An ANN model with ten hidden layers provides the highest R2 value, while the SVM model with the Bayesian optimizer provides the highest R2. RSM, ANN, and SVM models reveal the highest R-square values of 0.97, 0.99, and 0.99, respectively. Machine learning techniques help predict the model based on the experimental results and training data sets.
    Matched MeSH terms: Bayes Theorem
  14. Wang L, Meng Z, Liu X, Zhang Y, Lin H
    Int J Mol Sci, 2011;12(7):4378-94.
    PMID: 21845084 DOI: 10.3390/ijms12074378
    In the present study, we employed microsatellite DNA markers to analyze the genetic diversity and differentiation between and within cultured stocks and wild populations of the orange-spotted grouper originating from the South China Sea and Southeast Asia. Compared to wild populations, genetic changes including reduced genetic diversity and significant differentiation have taken place in cultured grouper stocks, as shown by allele richness and heterozygosity studies, pairwise F(st), structure, molecular variance analysis, as well as multidimensional scaling analysis. Although two geographically adjacent orange-spotted grouper populations in China showed negligible genetic divergence, significant population differentiation was observed in wild grouper populations distributed in a wide geographical area from China, through Malaysia to Indonesia. However, the Mantel test rejected the isolation-by-distance model of genetic structure, which indicated the genetic differentiation among the populations could result from the co-effects of various factors, such as historical dispersal, local environment, ocean currents, river flows and island blocks. Our results demonstrated that microsatellite markers could be suitable not only for genetic monitoring cultured stocks but also for revealing the population structuring of wild orange-spotted grouper populations. Meanwhile, our study provided important information for breeding programs, management of cultured stocks and conservation of wild populations of the orange-spotted grouper.
    Matched MeSH terms: Bayes Theorem
  15. Walters K, Cox A, Yaacob H
    Genet Epidemiol, 2021 Jun;45(4):386-401.
    PMID: 33410201 DOI: 10.1002/gepi.22375
    The Gaussian distribution is usually the default causal single-nucleotide polymorphism (SNP) effect size prior in Bayesian population-based fine-mapping association studies, but a recent study showed that the heavier-tailed Laplace prior distribution provided a better fit to breast cancer top hits identified in genome-wide association studies. We investigate the utility of the Laplace prior as an effect size prior in univariate fine-mapping studies. We consider ranking SNPs using Bayes factors and other summaries of the effect size posterior distribution, the effect of prior choice on credible set size based on the posterior probability of causality, and on the noteworthiness of SNPs in univariate analyses. Across a wide range of fine-mapping scenarios the Laplace prior generally leads to larger 90% credible sets than the Gaussian prior. These larger credible sets for the Laplace prior are due to relatively high prior mass around zero which can yield many noncausal SNPs with relatively large Bayes factors. If using conventional credible sets, the Gaussian prior generally yields a better trade off between including the causal SNP with high probability and keeping the set size reasonable. Interestingly when using the less well utilised measure of noteworthiness, the Laplace prior performs well, leading to causal SNPs being declared noteworthy with high probability, whilst generally declaring fewer than 5% of noncausal SNPs as being noteworthy. In contrast, the Gaussian prior leads to the causal SNP being declared noteworthy with very low probability.
    Matched MeSH terms: Bayes Theorem
  16. Walters K, Cox A, Yaacob H
    Genet Epidemiol, 2019 Sep;43(6):675-689.
    PMID: 31286571 DOI: 10.1002/gepi.22212
    The default causal single-nucleotide polymorphism (SNP) effect size prior in Bayesian fine-mapping studies is usually the Normal distribution. This choice is often based on computational convenience, rather than evidence that it is the most suitable prior distribution. The choice of prior is important because previous studies have shown considerable sensitivity of causal SNP Bayes factors to the form of the prior. In some well-studied diseases there are now considerable numbers of genome-wide association study (GWAS) top hits along with estimates of the number of yet-to-be-discovered causal SNPs. We show how the effect sizes of the top hits and estimates of the number of yet-to-be-discovered causal SNPs can be used to choose between the Laplace and Normal priors, to estimate the prior parameters and to quantify the uncertainty in this estimation. The methodology can readily be applied to other priors. We show that the top hits available from breast cancer GWAS provide overwhelming support for the Laplace over the Normal prior, which has important consequences for variant prioritisation. This work in this paper enables practitioners to derive more objective priors than are currently being used and could lead to prioritisation of different variants.
    Matched MeSH terms: Bayes Theorem
  17. Walters K, Yaacob H
    Genet Epidemiol, 2023 Apr;47(3):249-260.
    PMID: 36739616 DOI: 10.1002/gepi.22517
    Currently, the only effect size prior that is routinely implemented in a Bayesian fine-mapping multi-single-nucleotide polymorphism (SNP) analysis is the Gaussian prior. Here, we show how the Laplace prior can be deployed in Bayesian multi-SNP fine mapping studies. We compare the ranking performance of the posterior inclusion probability (PIP) using a Laplace prior with the ranking performance of the corresponding Gaussian prior and FINEMAP. Our results indicate that, for the simulation scenarios we consider here, the Laplace prior can lead to higher PIPs than either the Gaussian prior or FINEMAP, particularly for moderately sized fine-mapping studies. The Laplace prior also appears to have better worst-case scenario properties. We reanalyse the iCOGS case-control data from the CASP8 region on Chromosome 2. Even though this study has a total sample size of nearly 90,000 individuals, there are still some differences in the top few ranked SNPs if the Laplace prior is used rather than the Gaussian prior. R code to implement the Laplace (and Gaussian) prior is available at https://github.com/Kevin-walters/lapmapr.
    Matched MeSH terms: Bayes Theorem
  18. Wah, Yap Bee, Nurain Ibrahim, Hamzah Abdul Hamid, Shuzlina Abdul-Rahman, Fong, Simon
    MyJurnal
    Feature selection has been widely applied in many areas such as classification of spam emails, cancer cells, fraudulent claims, credit risk, text categorisation and DNA microarray analysis. Classification involves building predictive models to predict the target variable based on several input variables (features). This study compares filter and wrapper feature selection methods to maximise the classifier accuracy. The logistic regression was used as a classifier while the performance of the feature selection methods was based on the classification accuracy, Akaike information criteria (AIC), Bayesian information criteria (BIC), Area Under Receiver operator curve (AUC), as well as sensitivity and specificity of the classifier. The simulation study involves generating data for continuous features and one binary dependent variable for different sample sizes. The filter methods used are correlation based feature selection and information gain, while the wrapper methods are sequential forward and sequential backward elimination. The simulation was carried out using R, an open-source programming language. Simulation results showed that the wrapper method (sequential forward selection and sequential backward elimination) methods were better than the filter method in selecting the correct features.
    Matched MeSH terms: Bayes Theorem
  19. Vijayasarveswari V, Andrew AM, Jusoh M, Sabapathy T, Raof RAA, Yasin MNM, et al.
    PLoS One, 2020;15(8):e0229367.
    PMID: 32790672 DOI: 10.1371/journal.pone.0229367
    Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multi- stage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi- stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.
    Matched MeSH terms: Bayes Theorem
  20. Verma N, Dhiman RK, Singh V, Duseja A, Taneja S, Choudhury A, et al.
    Hepatol Int, 2021 Jun;15(3):753-765.
    PMID: 34173167 DOI: 10.1007/s12072-021-10175-w
    BACKGROUND: Multiple predictive models of mortality exist for acute-on-chronic liver failure (ACLF) patients that often create confusion during decision-making. We studied the natural history and evaluated the performance of prognostic models in ACLF patients.

    METHODS: Prospectively collected data of ACLF patients from APASL-ACLF Research Consortium (AARC) was analyzed for 30-day outcomes. The models evaluated at days 0, 4, and 7 of presentation for 30-day mortality were: AARC (model and score), CLIF-C (ACLF score, and OF score), NACSELD-ACLF (model and binary), SOFA, APACHE-II, MELD, MELD-Lactate, and CTP. Evaluation parameters were discrimination (c-indices), calibration [accuracy, sensitivity, specificity, and positive/negative predictive values (PPV/NPV)], Akaike/Bayesian Information Criteria (AIC/BIC), Nagelkerke-R2, relative prediction errors, and odds ratios.

    RESULTS: Thirty-day survival of the cohort (n = 2864) was 64.9% and was lowest for final-AARC-grade-III (32.8%) ACLF. Performance parameters of all models were best at day 7 than at day 4 or day 0 (p  12 had the lowest 30-day survival (5.7%).

    CONCLUSIONS: APASL-ACLF is often a progressive disease, and models assessed up to day 7 of presentation reliably predict 30-day mortality. Day-7 AARC model is a statistically robust tool for classifying risk of death and accurately predicting 30-day outcomes with relatively lower prediction errors. Day-7 AARC score > 12 may be used as a futility criterion in APASL-ACLF patients.

    Matched MeSH terms: Bayes Theorem
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