Displaying publications 1 - 20 of 311 in total

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  1. The use of BLRP model for disaggregating daily rainfall affected by monsoon in Peninsular Malaysia
    Zulkifli Yusop, Harisaweni, Fadhilah Yusof
    Sains Malaysiana, 2016;45:87-97.
    Rainfall intensity is the main input variable in various hydrological analysis and modeling. Unfortunately, the quality of rainfall data is often poor and reliable data records are available at coarse intervals such as yearly, monthly and daily. Short interval rainfall records are scarce because of high cost and low reliability of the measurement and the monitoring systems. One way to solve this problem is by disaggregating the coarse intervals to generate the short one using the stochastic method. This paper describes the use of the Bartlett Lewis Rectangular Pulse (BLRP) model. The method was used to disaggregate 10 years of daily data for generating hourly data from 5 rainfall stations in Kelantan as representative area affected by monsoon period and 5 rainfall stations in Damansara affected by inter-monsoon period. The models were evaluated on their ability to reproduce standard and extreme rainfall model statistics derived from the historical record over disaggregation simulation results. The disaggregation of daily to hourly rainfall produced monthly and daily means and variances that closely match the historical records. However, for the disaggregation of daily to hourly rainfall, the standard deviation values are lower than the historical ones. Despite the marked differences in the standard deviation, both data series exhibit similar patterns and the model adequately preserve the trends of all the properties used in evaluating its performances.
    Matched MeSH terms: Models, Statistical
  2. Fulltext Robust foreground detection: a fusion of masked grey world, probabilistic gradient information and extended conditional random field approach
    Zulkifley MA, Moran B, Rawlinson D
    Sensors (Basel), 2012;12(5):5623-49.
    PMID: 22778605 DOI: 10.3390/s120505623
    Foreground detection has been used extensively in many applications such as people counting, traffic monitoring and face recognition. However, most of the existing detectors can only work under limited conditions. This happens because of the inability of the detector to distinguish foreground and background pixels, especially in complex situations. Our aim is to improve the robustness of foreground detection under sudden and gradual illumination change, colour similarity issue, moving background and shadow noise. Since it is hard to achieve robustness using a single model, we have combined several methods into an integrated system. The masked grey world algorithm is introduced to handle sudden illumination change. Colour co-occurrence modelling is then fused with the probabilistic edge-based background modelling. Colour co-occurrence modelling is good in filtering moving background and robust to gradual illumination change, while an edge-based modelling is used for solving a colour similarity problem. Finally, an extended conditional random field approach is used to filter out shadow and afterimage noise. Simulation results show that our algorithm performs better compared to the existing methods, which makes it suitable for higher-level applications.
    Matched MeSH terms: Models, Statistical
  3. Fulltext Hepatitis C virus treatment as prevention in an extended network of people who inject drugs in the USA: a modelling study
    Zelenev A, Li J, Mazhnaya A, Basu S, Altice FL
    Lancet Infect Dis, 2018 02;18(2):215-224.
    PMID: 29153265 DOI: 10.1016/S1473-3099(17)30676-X
    BACKGROUND: Chronic infections with hepatitis C virus (HCV) and HIV are highly prevalent in the USA and concentrated in people who inject drugs. Treatment as prevention with highly effective new direct-acting antivirals is a prospective HCV elimination strategy. We used network-based modelling to analyse the effect of this strategy in HCV-infected people who inject drugs in a US city.

    METHODS: Five graph models were fit using data from 1574 people who inject drugs in Hartford, CT, USA. We used a degree-corrected stochastic block model, based on goodness-of-fit, to model networks of injection drug users. We simulated transmission of HCV and HIV through this network with varying levels of HCV treatment coverage (0%, 3%, 6%, 12%, or 24%) and varying baseline HCV prevalence in people who inject drugs (30%, 60%, 75%, or 85%). We compared the effectiveness of seven treatment-as-prevention strategies on reducing HCV prevalence over 10 years and 20 years versus no treatment. The strategies consisted of treatment assigned to either a randomly chosen individual who injects drugs or to an individual with the highest number of injection partners. Additional strategies explored the effects of treating either none, half, or all of the injection partners of the selected individual, as well as a strategy based on respondent-driven recruitment into treatment.

    FINDINGS: Our model estimates show that at the highest baseline HCV prevalence in people who inject drugs (85%), expansion of treatment coverage does not substantially reduce HCV prevalence for any treatment-as-prevention strategy. However, when baseline HCV prevalence is 60% or lower, treating more than 120 (12%) individuals per 1000 people who inject drugs per year would probably eliminate HCV within 10 years. On average, assigning treatment randomly to individuals who inject drugs is better than targeting individuals with the most injection partners. Treatment-as-prevention strategies that treat additional network members are among the best performing strategies and can enhance less effective strategies that target the degree (ie, the highest number of injection partners) within the network.

    INTERPRETATION: Successful HCV treatment as prevention should incorporate the baseline HCV prevalence and will achieve the greatest benefit when coverage is sufficiently expanded.

    FUNDING: National Institute on Drug Abuse.

    Matched MeSH terms: Models, Statistical
  4. A hybrid data envelopment analysis and game theory model for performance measurement in healthcare
    Zare H, Tavana M, Mardani A, Masoudian S, Kamali Saraji M
    Health Care Manag Sci, 2019 Sep;22(3):475-488.
    PMID: 30225622 DOI: 10.1007/s10729-018-9456-4
    Performance measurement plays an important role in the successful design and reform of regional healthcare management systems. In this study, we propose a hybrid data envelopment analysis (DEA) and game theory model for measuring the performance and productivity in the healthcare centers. The input and output variables associated with the efficiency of the healthcare centers are identified by reviewing the relevant literature, and then used in conjunction with the internal organizational data. The selected indicators and collected data are then weighted and prioritized with the help of experts in the field. A case study is presented to demonstrate the applicability and efficacy of the proposed model. The results reveal useful information and insights on the efficiency levels of the regional healthcare centers in the case study.
    Matched MeSH terms: Models, Statistical*
  5. Selecting a probability distribution for extreme rainfall series in Malaysia
    Zalina MD, Desa MN, Nguyen VT, Kassim AH
    Water Sci Technol, 2002;45(2):63-8.
    PMID: 11890166
    This paper discusses the comparative assessment of eight candidate distributions in providing accurate and reliable maximum rainfall estimates for Malaysia. The models considered were the Gamma, Generalised Normal, Generalised Pareto, Generalised Extreme Value, Gumbel, Log Pearson Type III, Pearson Type III and Wakeby. Annual maximum rainfall series for one-hour resolution from a network of seventeen automatic gauging stations located throughout Peninsular Malaysia were selected for this study. The length of rainfall records varies from twenty-three to twenty-eight years. Model parameters were estimated using the L-moment method. The quantitative assessment of the descriptive ability of each model was based on the Probability Plot Correlation Coefficient test combined with root mean squared error, relative root mean squared error and maximum absolute deviation. Bootstrap resampling was employed to investigate the extrapolative ability of each distribution. On the basis of these comparisons, it can be concluded that the GEV distribution is the most appropriate distribution for describing the annual maximum rainfall series in Malaysia.
    Matched MeSH terms: Models, Statistical*
  6. Fulltext Statistical methods used to test for agreement of medical instruments measuring continuous variables in method comparison studies: a systematic review
    Zaki R, Bulgiba A, Ismail R, Ismail NA
    PLoS One, 2012;7(5):e37908.
    PMID: 22662248 DOI: 10.1371/journal.pone.0037908
    Accurate values are a must in medicine. An important parameter in determining the quality of a medical instrument is agreement with a gold standard. Various statistical methods have been used to test for agreement. Some of these methods have been shown to be inappropriate. This can result in misleading conclusions about the validity of an instrument. The Bland-Altman method is the most popular method judging by the many citations of the article proposing this method. However, the number of citations does not necessarily mean that this method has been applied in agreement research. No previous study has been conducted to look into this. This is the first systematic review to identify statistical methods used to test for agreement of medical instruments. The proportion of various statistical methods found in this review will also reflect the proportion of medical instruments that have been validated using those particular methods in current clinical practice.
    Matched MeSH terms: Models, Statistical*
  7. Fulltext Flexible model-based clustering of mixed binary and continuous data: application to genetic regulation and cancer
    Zainul Abidin FN, Westhead DR
    Nucleic Acids Res, 2017 04 20;45(7):e53.
    PMID: 27994031 DOI: 10.1093/nar/gkw1270
    Clustering is used widely in 'omics' studies and is often tackled with standard methods, e.g. hierarchical clustering. However, the increasing need for integration of multiple data sets leads to a requirement for clustering methods applicable to mixed data types, where the straightforward application of standard methods is not necessarily the best approach. A particularly common problem involves clustering entities characterized by a mixture of binary data (e.g. presence/absence of mutations, binding, motifs and epigenetic marks) and continuous data (e.g. gene expression, protein abundance, metabolite levels). Here, we present a generic method based on a probabilistic model for clustering this type of data, and illustrate its application to genetic regulation and the clustering of cancer samples. We show that the resulting clusters lead to useful hypotheses: in the case of genetic regulation these concern regulation of groups of genes by specific sets of transcription factors and in the case of cancer samples combinations of gene mutations are related to patterns of gene expression. The clusters have potential mechanistic significance and in the latter case are significantly linked to survival. The method is available as a stand-alone software package (GNU General Public Licence) from http://github.com/BioToolsLeeds/FlexiCoClusteringPackage.git.
    Matched MeSH terms: Models, Statistical*
  8. Wavelet neural networks applied to pulping of oil palm fronds
    Zainuddin Z, Wan Daud WR, Pauline O, Shafie A
    Bioresour Technol, 2011 Dec;102(23):10978-86.
    PMID: 21996481 DOI: 10.1016/j.biortech.2011.09.080
    In the organosolv pulping of the oil palm fronds, the influence of the operational variables of the pulping reactor (viz. cooking temperature and time, ethanol and NaOH concentration) on the properties of the resulting pulp (yield and kappa number) and paper sheets (tensile index and tear index) was investigated using a wavelet neural network model. The experimental results with error less than 0.0965 (in terms of MSE) were produced, and were then compared with those obtained from the response surface methodology. Performance assessment indicated that the neural network model possessed superior predictive ability than the polynomial model, since a very close agreement between the experimental and the predicted values was obtained.
    Matched MeSH terms: Models, Statistical
  9. Kinetic behaviour of free lipase and mica-based immobilized lipase catalyzing the synthesis of sugar esters
    Zaidan UH, Abdul Rahman MB, Othman SS, Basri M, Abdulmalek E, Rahman RN, et al.
    Biosci Biotechnol Biochem, 2011;75(8):1446-50.
    PMID: 21821960
    The utilization of natural mica as a biocatalyst support in kinetic investigations is first described in this study. The formation of lactose caprate from lactose sugar and capric acid, using free lipase (free-CRL) and lipase immobilized on nanoporous mica (NER-CRL) as a biocatalyst, was evaluated through a kinetic study. The apparent kinetic parameters, K(m) and V(max), were determined by means of the Michaelis-Menten kinetic model. The Ping-Pong Bi-Bi mechanism with single substrate inhibition was adopted as it best explains the experimental findings. The kinetic results show lower K(m) values with NER-CRL than with free-CRL, indicating the higher affinity of NER-CRL towards both substrates at the maximum reaction velocity (V(max,app)>V(max)). The kinetic parameters deduced from this model were used to simulate reaction rate data which were in close agreement with the experimental values.
    Matched MeSH terms: Models, Statistical
  10. Application of statistical experimental methodology to optimize bioremediation of n-alkanes in aquatic environment
    Zahed MA, Aziz HA, Mohajeri L, Mohajeri S, Kutty SR, Isa MH
    J Hazard Mater, 2010 Dec 15;184(1-3):350-6.
    PMID: 20837377 DOI: 10.1016/j.jhazmat.2010.08.043
    Response surface methodology (RSM) was employed to optimize nitrogen and phosphorus concentrations for removal of n-alkanes from crude oil contaminated seawater samples in batch reactors. Erlenmeyer flasks were used as bioreactors; each containing 250 mL dispersed crude oil contaminated seawater, indigenous acclimatized microorganism and different amounts of nitrogen and phosphorus based on central composite design (CCD). Samples were extracted and analyzed according to US-EPA protocols using a gas chromatograph. During 28 days of bioremediation, a maximum of 95% total aliphatic hydrocarbons removal was observed. The obtained Model F-value of 267.73 and probability F<0.0001 implied the model was significant. Numerical condition optimization via a quadratic model, predicted 98% n-alkanes removal for a 20-day laboratory bioremediation trial using nitrogen and phosphorus concentrations of 13.62 and 1.39 mg/L, respectively. In actual experiments, 95% removal was observed under these conditions.
    Matched MeSH terms: Models, Statistical
  11. Fulltext Statins and risk of treated incident diabetes in a primary care population
    Zaharan NL, Williams D, Bennett K
    Br J Clin Pharmacol, 2013 Apr;75(4):1118-24.
    PMID: 22845189 DOI: 10.1111/j.1365-2125.2012.04403.x
    (i) To examine the incidence of new onset treated diabetes in patients treated with different types of statins and (ii) the relationship between the duration and dose of statins and the subsequent development of new onset treated diabetes.
    Matched MeSH terms: Models, Statistical*
  12. Development of statistical models for predicting muscle and mental activities during repetitive precision tasks
    Zadry HR, Dawal SZ, Taha Z
    Int J Occup Saf Ergon, 2016 Sep;22(3):374-83.
    PMID: 27053140 DOI: 10.1080/10803548.2016.1150094
    This study was conducted to develop muscle and mental activities on repetitive precision tasks. A laboratory experiment was used to address the objectives. Surface electromyography was used to measure muscle activities from eight upper limb muscles, while electroencephalography recorded mental activities from six channels. Fourteen university students participated in the study. The results show that muscle and mental activities increase for all tasks, indicating the occurrence of muscle and mental fatigue. A linear relationship between muscle activity, mental activity and time was found while subjects were performing the task. Thus, models were developed using those variables. The models were found valid after validation using other students' and workers' data. Findings from this study can contribute as a reference for future studies investigating muscle and mental activity and can be applied in industry as guidelines to manage muscle and mental fatigue, especially to manage job schedules and rotation.
    Matched MeSH terms: Models, Statistical*
  13. Human evaluation in association to the mathematical analysis of arch forms: Two-dimensional study
    Zabidin N, Mohamed AM, Zaharim A, Marizan Nor M, Rosli TI
    Int Orthod, 2018 03;16(1):133-143.
    PMID: 29478934 DOI: 10.1016/j.ortho.2018.01.009
    OBJECTIVES: To evaluate the relationship between human evaluation of the dental-arch form, to complete a mathematical analysis via two different methods in quantifying the arch form, and to establish agreement with the fourth-order polynomial equation.

    MATERIALS AND METHODS: This study included 64 sets of digitised maxilla and mandible dental casts obtained from a sample of dental arch with normal occlusion. For human evaluation, a convenient sample of orthodontic practitioners ranked the photo images of dental cast from the most tapered to the less tapered (square). In the mathematical analysis, dental arches were interpolated using the fourth-order polynomial equation with millimetric acetate paper and AutoCAD software. Finally, the relations between human evaluation and mathematical objective analyses were evaluated.

    RESULTS: Human evaluations were found to be generally in agreement, but only at the extremes of tapered and square arch forms; this indicated general human error and observer bias. The two methods used to plot the arch form were comparable.

    CONCLUSION: The use of fourth-order polynomial equation may be facilitative in obtaining a smooth curve, which can produce a template for individual arch that represents all potential tooth positions for the dental arch.

    Matched MeSH terms: Models, Statistical*
  14. The effects of drainage basin geomorphometry on minimum low flow discharge: the study of small watershed in Kelang River Valley in Peninsular Malaysia
    Yunus AJ, Nakagoshi N, Salleh KO
    J Environ Sci (China), 2003 Mar;15(2):249-62.
    PMID: 12765268
    This study investigate the relationships between geomorphometric properties and the minimum low flow discharge of undisturbed drainage basins in the Taman Bukit Cahaya Seri Alam Forest Reserve, Peninsular Malaysia. The drainage basins selected were third-order basins so as to facilitate a common base for sampling and performing an unbiased statistical analyses. Three levels of relationships were observed in the study. Significant relationships existed between the geomorphometric properties as shown by the correlation network analysis; secondly, individual geomorphometric properties were observed to influence minimum flow discharge; and finally, the multiple regression model set up showed that minimum flow discharge (Q min) was dependent of basin area (AU), stream length (LS), maximum relief (Hmax), average relief (HAV) and stream frequency (SF). These findings further enforced other studies of this nature that drainage basins were dynamic and functional entities whose operations were governed by complex interrelationships occurring within the basins. Changes to any of the geomorphometric properties would influence their role as basin regulators thus influencing a change in basin response. In the case of the basin's minimum low flow, a change in any of the properties considered in the regression model influenced the "time to peak" of flow. A shorter time period would mean higher discharge, which is generally considered the prerequisite to flooding. This research also conclude that the role of geomorphometric properties to control the water supply within the stream through out the year even though during the drought and less precipitations months. Drainage basins are sensitive entities and any deteriorations involve will generate reciprocals and response to the water supply as well as the habitat within the areas.
    Matched MeSH terms: Models, Statistical
  15. Random walker with improved weighting function for interactive medical image segmentation
    Yin LK, Rajeswari M
    Biomed Mater Eng, 2014;24(6):3333-41.
    PMID: 25227043 DOI: 10.3233/BME-141156
    To segment an image using the random walks algorithm; users are often required to initialize the approximate locations of the objects and background in the image. Due to its segmenting model that is mainly reflected by the relationship among the neighborhood pixels and its boundary conditions, random walks algorithm has made itself sensitive to the inputs of the seeds. Instead of considering the relationship between the neighborhood pixels solely, an attempt has been made to modify the weighting function that accounts for the intensity changes between the neighborhood nodes. Local affiliation within the defined neighborhood region of the two nodes is taken into consideration by incorporating an extra penalty term into the weighting function. Besides that, to better segment images, particularly medical images with texture features, GLCM variance is incorporated into the weighting function through kernel density estimation (KDE). The probability density of each pixel belonging to the initialized seeds is estimated and integrated into the weighting function. To test the performance of the proposed weighting model, several medical images that mainly made up of 174-brain tumor images are experimented. These experiments establish that the proposed method produces better segmentation results than the original random walks.
    Matched MeSH terms: Models, Statistical
  16. Fulltext Optimization of extraction time and temperature on antioxidant activity of Schizophyllum commune aqueous extract using response surface methodology
    Yim HS, Chye FY, Rao V, Low JY, Matanjun P, How SE, et al.
    J Food Sci Technol, 2013 Apr;50(2):275-83.
    PMID: 24425917 DOI: 10.1007/s13197-011-0349-5
    Central composite design of response surface methodology (RSM) was employed to optimize the extraction time (X 1 : 99.5-290.5 min) and temperature (X 2 : 30.1-54.9 °C) of Schizophyllum commune aqueous extract with high antioxidant activities and total phenolic content (TPC). Results indicated that the data were adequately fitted into four second-order polynomial models. The extraction time and temperature were found to have significant linear, quadratic and interaction effects on antioxidant activities and TPC. The optimal extraction time and temperature were: 290.5 min and 35.7 °C (DPPH(•) scavenging ability); 180.7 min and 41.7 °C (ABTS(•+) inhibition ability); 185.2 min and 42.4 °C (ferric reducing antioxidant power, FRAP); 290.5 min and 40.3 °C (TPC). These optimum conditions yielded 85.10%; 94.31%; 0.74 mM Fe(2+) equivalent/100 g; 635.76 mg gallic acid equivalent/100 g, respectively. The yields of antioxidant activities and TPC obtained experimentally were close to its predicted values. The establishment of such model provides a good experimental basis employing RSM for optimizing the extraction time and temperature on antioxidants from S. commune aqueous extract.
    Matched MeSH terms: Models, Statistical
  17. Fulltext Synthetic-type control charts for time-between-events monitoring
    Yen FY, Chong KM, Ha LM
    PLoS One, 2013;8(6):e65440.
    PMID: 23755231 DOI: 10.1371/journal.pone.0065440
    This paper proposes three synthetic-type control charts to monitor the mean time-between-events of a homogenous Poisson process. The first proposed chart combines an Erlang (cumulative time between events, Tr ) chart and a conforming run length (CRL) chart, denoted as Synth-Tr chart. The second proposed chart combines an exponential (or T) chart and a group conforming run length (GCRL) chart, denoted as GR-T chart. The third proposed chart combines an Erlang chart and a GCRL chart, denoted as GR-Tr chart. By using a Markov chain approach, the zero- and steady-state average number of observations to signal (ANOS) of the proposed charts are obtained, in order to evaluate the performance of the three charts. The optimal design of the proposed charts is shown in this paper. The proposed charts are superior to the existing T chart, Tr chart, and Synth-T chart. As compared to the EWMA-T chart, the GR-T chart performs better in detecting large shifts, in terms of the zero- and steady-state performances. The zero-state Synth-T4 and GR-Tr (r = 3 or 4) charts outperform the EWMA-T chart for all shifts, whereas the Synth-Tr (r = 2 or 3) and GR-T 2 charts perform better for moderate to large shifts. For the steady-state process, the Synth-Tr and GR-Tr charts are more efficient than the EWMA-T chart in detecting small to moderate shifts.
    Matched MeSH terms: Models, Statistical*
  18. Fulltext Magnetic resonance image tissue classification using an automatic method
    Yazdani S, Yusof R, Riazi A, Karimian A
    Diagn Pathol, 2014;9:207.
    PMID: 25540017 DOI: 10.1186/s13000-014-0207-7
    Brain segmentation in magnetic resonance images (MRI) is an important stage in clinical studies for different issues such as diagnosis, analysis, 3-D visualizations for treatment and surgical planning. MR Image segmentation remains a challenging problem in spite of different existing artifacts such as noise, bias field, partial volume effects and complexity of the images. Some of the automatic brain segmentation techniques are complex and some of them are not sufficiently accurate for certain applications. The goal of this paper is proposing an algorithm that is more accurate and less complex).
    Matched MeSH terms: Models, Statistical
  19. Fulltext A Unified Framework for Brain Segmentation in MR Images
    Yazdani S, Yusof R, Karimian A, Riazi AH, Bennamoun M
    Comput Math Methods Med, 2015;2015:829893.
    PMID: 26089978 DOI: 10.1155/2015/829893
    Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets.
    Matched MeSH terms: Models, Statistical
  20. Ancestry-informative marker (AIM) SNP panel for the Malay population
    Yahya P, Sulong S, Harun A, Wangkumhang P, Wilantho A, Ngamphiw C, et al.
    Int J Legal Med, 2020 Jan;134(1):123-134.
    PMID: 31760471 DOI: 10.1007/s00414-019-02184-0
    Ancestry-informative markers (AIMs) can be used to infer the ancestry of an individual to minimize the inaccuracy of self-reported ethnicity in biomedical research. In this study, we describe three methods for selecting AIM SNPs for the Malay population (Malay AIM panel) using different approaches based on pairwise FST, informativeness for assignment (In), and PCA-correlated SNPs (PCAIMs). These Malay AIM panels were extracted from genotype data stored in SNP arrays hosted by the Malaysian node of the Human Variome Project (MyHVP) and the Singapore Genome Variation Project (SGVP). In particular, genotype data from a total of 165 Malay individuals were analyzed, comprising data on 117 individual genotypes from the Affymetrix SNP-6 SNP array platform and data on 48 individual genotypes from the OMNI 2.5 Illumina SNP array platform. The HapMap phase 3 database (1397 individuals from 11 populations) was used as a reference for comparison with the Malay genotype data. The accuracy of each resulting Malay AIM panel was evaluated using a machine learning "ancestry-predictive model" constructed by using WEKA, a comprehensive machine learning platform written in Java. A total of 1250 SNPs were finally selected, which successfully identified Malay individuals from other world populations with an accuracy of 90%, but the accuracy decreased to 80% using 157 SNPs according to the pairwise FST method, while a panel of 200 SNPs selected using In and PCAIMs could be used to identify Malay individuals with an accuracy of approximately 80%.
    Matched MeSH terms: Models, Statistical
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