Displaying publications 1 - 20 of 253 in total

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  1. Modelling Asthma Cases by Count Analysis Approach: Poisson INGARCH and Negative Binomial INGARCH
    'Aaishah Radziah Jamaludin, Fadhilah Yusof, Suhartono
    MATEMATIKA, 2020;36(1):15-30.
    MyJurnal
    Johor Bahru with its rapid development where pollution is an issue that needs to be considered because it has contributed to the number of asthma cases in this area. Therefore, the goal of this study is to investigate the behaviour of asthma disease in Johor Bahru by count analysis approach namely; Poisson Integer Generalized Autoregressive Conditional Heteroscedasticity (Poisson-INGARCH) and Negative Binomial INGARCH (NB-INGARCH) with identity and log link function. Intervention analysis was conducted since the outbreak in the asthma data for the period of July 2012 to July 2013. This occurs perhaps due to the extremely bad haze in Johor Bahru from Indonesian fires. The estimation of the parameter will be done by quasi-maximum likelihood estimation. Model assessment was evaluated from the Pearson residuals, cumulative periodogram, the probability integral transform (PIT) histogram, log-likelihood value, Akaike’s Information Criterion (AIC) and Bayesian information criterion (BIC). Our result shows that NB-INGARCH with identity and log link function is adequate in representing the asthma data with uncorrelated Pearson residuals, higher in log likelihood, the PIT exhibits normality yet the lowest AIC and BIC. However, in terms of forecasting accuracy, NB-INGARCH with identity link function performed better with the smaller RMSE (8.54) for the sample data. Therefore, NB-INGARCH with identity link function can be applied as the prediction model for asthma disease in Johor Bahru. Ideally, this outcome can assist the Department of Health in executing counteractive action and early planning to curb asthma diseases in Johor Bahru.
    Matched MeSH terms: Bayes Theorem
  2. Fulltext COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings
    Abbasian Ardakani A, Acharya UR, Habibollahi S, Mohammadi A
    Eur Radiol, 2021 Jan;31(1):121-130.
    PMID: 32740817 DOI: 10.1007/s00330-020-07087-y
    OBJECTIVES: CT findings of COVID-19 look similar to other atypical and viral (non-COVID-19) pneumonia diseases. This study proposes a clinical computer-aided diagnosis (CAD) system using CT features to automatically discriminate COVID-19 from non-COVID-19 pneumonia patients.

    METHODS: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases.

    RESULTS: Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier.

    CONCLUSIONS: This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis.

    KEY POINTS: • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively.

    Matched MeSH terms: Bayes Theorem
  3. Implementing relevance feedback in ligand-based virtual screening using Bayesian inference network
    Abdo A, Salim N, Ahmed A
    J Biomol Screen, 2011 Oct;16(9):1081-8.
    PMID: 21862688 DOI: 10.1177/1087057111416658
    Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets.
    Matched MeSH terms: Bayes Theorem
  4. Ligand expansion in ligand-based virtual screening using relevance feedback
    Abdo A, Saeed F, Hamza H, Ahmed A, Salim N
    J Comput Aided Mol Des, 2012 Mar;26(3):279-87.
    PMID: 22249773 DOI: 10.1007/s10822-012-9543-4
    Query expansion is the process of reformulating an original query to improve retrieval performance in information retrieval systems. Relevance feedback is one of the most useful query modification techniques in information retrieval systems. In this paper, we introduce query expansion into ligand-based virtual screening (LBVS) using the relevance feedback technique. In this approach, a few high-ranking molecules of unknown activity are filtered from the outputs of a Bayesian inference network based on a single ligand molecule to form a set of ligand molecules. This set of ligand molecules is used to form a new ligand molecule. Simulated virtual screening experiments with the MDL Drug Data Report and maximum unbiased validation data sets show that the use of ligand expansion provides a very simple way of improving the LBVS, especially when the active molecules being sought have a high degree of structural heterogeneity. However, the effectiveness of the ligand expansion is slightly less when structurally-homogeneous sets of actives are being sought.
    Matched MeSH terms: Bayes Theorem
  5. New fragment weighting scheme for the Bayesian inference network in ligand-based virtual screening
    Abdo A, Salim N
    J Chem Inf Model, 2011 Jan 24;51(1):25-32.
    PMID: 21155550 DOI: 10.1021/ci100232h
    Many of the conventional similarity methods assume that molecular fragments that do not relate to biological activity carry the same weight as the important ones. One possible approach to this problem is to use the Bayesian inference network (BIN), which models molecules and reference structures as probabilistic inference networks. The relationships between molecules and reference structures in the Bayesian network are encoded using a set of conditional probability distributions, which can be estimated by the fragment weighting function, a function of the frequencies of the fragments in the molecule or the reference structure as well as throughout the collection. The weighting function combines one or more fragment weighting schemes. In this paper, we have investigated five different weighting functions and present a new fragment weighting scheme. Later on, these functions were modified to combine the new weighting scheme. Simulated virtual screening experiments with the MDL Drug Data Report (23) and maximum unbiased validation data sets show that the use of new weighting scheme can provide significantly more effective screening when compared with the use of current weighting schemes.
    Matched MeSH terms: Bayes Theorem
  6. Similarity-based virtual screening with a bayesian inference network
    Abdo A, Salim N
    ChemMedChem, 2009 Feb;4(2):210-8.
    PMID: 19072820 DOI: 10.1002/cmdc.200800290
    Many methods have been developed to capture the biological similarity between two compounds for use in drug discovery. A variety of similarity metrics have been introduced, the Tanimoto coefficient being the most prominent. Many of the approaches assume that molecular features or descriptors that do not relate to the biological activity carry the same weight as the important aspects in terms of biological similarity. Herein, a novel similarity searching approach using a Bayesian inference network is discussed. Similarity searching is regarded as an inference or evidential reasoning process in which the probability that a given compound has biological similarity with the query is estimated and used as evidence. Our experiments demonstrate that the similarity approach based on Bayesian inference networks is likely to outperform the Tanimoto similarity search and offer a promising alternative to existing similarity search approaches.
    Matched MeSH terms: Bayes Theorem*
  7. Fulltext Ligand-based virtual screening using Bayesian networks
    Abdo A, Chen B, Mueller C, Salim N, Willett P
    J Chem Inf Model, 2010 Jun 28;50(6):1012-20.
    PMID: 20504032 DOI: 10.1021/ci100090p
    A Bayesian inference network (BIN) provides an interesting alternative to existing tools for similarity-based virtual screening. The BIN is particularly effective when the active molecules being sought have a high degree of structural homogeneity but has been found to perform less well with structurally heterogeneous sets of actives. In this paper, we introduce an alternative network model, called a Bayesian belief network (BBN), that seeks to overcome this limitation of the BIN approach. Simulated virtual screening experiments with the MDDR, WOMBAT and MUV data sets show that the BIN and BBN methods allow effective screening searches to be carried out. However, the results obtained are not obviously superior to those obtained using a much simpler approach that is based on the use of the Tanimoto coefficient and of the square roots of fragment occurrence frequencies.
    Matched MeSH terms: Bayes Theorem
  8. Fulltext On Bayesian approach to composite Pareto models
    Abdul Majid MH, Ibrahim K
    PLoS One, 2021;16(9):e0257762.
    PMID: 34555115 DOI: 10.1371/journal.pone.0257762
    In data modelling using the composite Pareto distribution, any observations above a particular threshold value are assumed to follow Pareto type distribution, whereas the rest of the observations are assumed to follow a different distribution. This paper proposes on the use of Bayesian approach to the composite Pareto models involving specification of the prior distribution on the proportion of data coming from the Pareto distribution, instead of assuming the prior distribution on the threshold, as often done in the literature. Based on a simulation study, it is found that the parameter estimates determined when using uniform prior on the proportion is less biased as compared to the point estimates determined when using uniform prior on the threshold. Applications on income data and finance are included for illustrative examples.
    Matched MeSH terms: Bayes Theorem
  9. Phylogeography of foot-and-mouth disease virus types O and A in Malaysia and surrounding countries
    Abdul-Hamid NF, Hussein NM, Wadsworth J, Radford AD, Knowles NJ, King DP
    Infect Genet Evol, 2011 Mar;11(2):320-8.
    PMID: 21093614 DOI: 10.1016/j.meegid.2010.11.003
    Foot-and-mouth disease (FMD) is endemic in the countries of mainland Southeast Asia where it represents a major obstacle to the development of productive animal industries. The aim of this study was to use genetic data to determine the distribution of FMD virus (FMDV) lineages in the Southeast Asia region, and in particular identify possible sources of FMDV causing outbreaks in Malaysia. Complete VP1 sequences, obtained from 214 samples collected between 2000 and 2009, from FMD outbreaks in six Southeast Asian countries, were compared with sequences previously reported. Phylogenetic analysis of these sequences showed that there were two patterns of FMDV distribution in Malaysia. Firstly, for some lineages (O/SEA/Mya98 and serotype A), outbreaks occurred every year in the country and did not appear to persist, suggesting that these incursions were quickly eradicated. Furthermore, for these lineages FMD viruses in Malaysia were closely related to those from neighbouring countries, demonstrating the close epidemiological links between countries in the region. In contrast, for O/ME-SA/PanAsia lineage, viruses were introduced and remained to cause outbreaks in subsequent years. In particular, the recent incursion and maintenance of the PanAsia-2 sublineage into Malaysia appears to be unique and independent from other outbreaks in the region. This study is the first characterisation of FMDV in Malaysia and provides evidence for different epidemiological sources of virus introduction into the country.
    Matched MeSH terms: Bayes Theorem
  10. Fulltext Continental monophyly and molecular divergence of Peninsular Malaysia's Macaca fascicularis fascicularis
    Abdul-Latiff MA, Ruslin F, Faiq H, Hairul MS, Rovie-Ryan JJ, Abdul-Patah P, et al.
    Biomed Res Int, 2014;2014:897682.
    PMID: 25143948 DOI: 10.1155/2014/897682
    The phylogenetic relationships of long-tailed macaque (Macaca fascicularis fascicularis) populations distributed in Peninsular Malaysia in relation to other regions remain unknown. The aim of this study was to reveal the phylogeography and population genetics of Peninsular Malaysia's M. f. fascicularis based on the D-loop region of mitochondrial DNA. Sixty-five haplotypes were detected in all populations, with only Vietnam and Cambodia sharing four haplotypes. The minimum-spanning network projected a distant relationship between Peninsular Malaysian and insular populations. Genetic differentiation (F(ST), Nst) results suggested that the gene flow among Peninsular Malaysian and the other populations is very low. Phylogenetic tree reconstructions indicated a monophyletic clade of Malaysia's population with continental populations (NJ = 97%, MP = 76%, and Bayesian = 1.00 posterior probabilities). The results demonstrate that Peninsular Malaysia's M. f. fascicularis belonged to Indochinese populations as opposed to the previously claimed Sundaic populations. M. f. fascicularis groups are estimated to have colonized Peninsular Malaysia ~0.47 million years ago (MYA) directly from Indochina through seaways, by means of natural sea rafting, or through terrestrial radiation during continental shelf emersion. Here, the Isthmus of Kra played a central part as biogeographical barriers that then separated it from the remaining continental populations.
    Matched MeSH terms: Bayes Theorem
  11. Fulltext Phylogenetic relationships of Malaysia's long-tailed macaques, Macaca fascicularis, based on cytochrome b sequences
    Abdul-Latiff MA, Ruslin F, Fui VV, Abu MH, Rovie-Ryan JJ, Abdul-Patah P, et al.
    Zookeys, 2014.
    PMID: 24899832 DOI: 10.3897/zookeys.407.6982
    Phylogenetic relationships among Malaysia's long-tailed macaques have yet to be established, despite abundant genetic studies of the species worldwide. The aims of this study are to examine the phylogenetic relationships of Macaca fascicularis in Malaysia and to test its classification as a morphological subspecies. A total of 25 genetic samples of M. fascicularis yielding 383 bp of Cytochrome b (Cyt b) sequences were used in phylogenetic analysis along with one sample each of M. nemestrina and M. arctoides used as outgroups. Sequence character analysis reveals that Cyt b locus is a highly conserved region with only 23% parsimony informative character detected among ingroups. Further analysis indicates a clear separation between populations originating from different regions; the Malay Peninsula versus Borneo Insular, the East Coast versus West Coast of the Malay Peninsula, and the island versus mainland Malay Peninsula populations. Phylogenetic trees (NJ, MP and Bayesian) portray a consistent clustering paradigm as Borneo's population was distinguished from Peninsula's population (99% and 100% bootstrap value in NJ and MP respectively and 1.00 posterior probability in Bayesian trees). The East coast population was separated from other Peninsula populations (64% in NJ, 66% in MP and 0.53 posterior probability in Bayesian). West coast populations were divided into 2 clades: the North-South (47%/54% in NJ, 26/26% in MP and 1.00/0.80 posterior probability in Bayesian) and Island-Mainland (93% in NJ, 90% in MP and 1.00 posterior probability in Bayesian). The results confirm the previous morphological assignment of 2 subspecies, M. f. fascicularis and M. f. argentimembris, in the Malay Peninsula. These populations should be treated as separate genetic entities in order to conserve the genetic diversity of Malaysia's M. fascicularis. These findings are crucial in aiding the conservation management and translocation process of M. fascicularis populations in Malaysia.
    Matched MeSH terms: Bayes Theorem
  12. Fulltext Population genetic structure of Aedes aegypti subspecies in selected geographical locations in Sudan
    Abuelmaali SA, Mashlawi AM, Ishak IH, Wajidi MFF, Jaal Z, Avicor SW, et al.
    Sci Rep, 2024 Feb 05;14(1):2978.
    PMID: 38316804 DOI: 10.1038/s41598-024-52591-6
    Although knowledge of the composition and genetic diversity of disease vectors is important for their management, this is limiting in many instances. In this study, the population structure and phylogenetic relationship of the two Aedes aegypti subspecies namely Aedes aegypti aegypti (Aaa) and Aedes aegypti formosus (Aaf) in eight geographical areas in Sudan were analyzed using seven microsatellite markers. Hardy-Weinberg Equilibrium (HWE) for the two subspecies revealed that Aaa deviated from HWE among the seven microsatellite loci, while Aaf exhibited departure in five loci and no departure in two loci (A10 and M201). The Factorial Correspondence Analysis (FCA) plots revealed that the Aaa populations from Port Sudan, Tokar, and Kassala clustered together (which is consistent with the unrooted phylogenetic tree), Aaf from Fasher and Nyala populations clustered together, and Gezira, Kadugli, and Junaynah populations also clustered together. The Bayesian cluster analysis structured the populations into two groups suggesting two genetically distinct groups (subspecies). Isolation by distance test revealed a moderate to strong significant correlation between geographical distance and genetic variations (p = 0.003, r = 0.391). The migration network created using divMigrate demonstrated that migration and gene exchange between subspecies populations appear to occur based on their geographical proximity. The genetic structure of the Ae. aegypti subspecies population and the gene flow among them, which may be interpreted as the mosquito vector's capacity for dispersal, were revealed in this study. These findings will help in the improvement of dengue epidemiology research including information on the identity of the target vector/subspecies and the arboviruses vector surveillance program.
    Matched MeSH terms: Bayes Theorem
  13. Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals
    Adam M, Oh SL, Sudarshan VK, Koh JE, Hagiwara Y, Tan JH, et al.
    Comput Methods Programs Biomed, 2018 Jul;161:133-143.
    PMID: 29852956 DOI: 10.1016/j.cmpb.2018.04.018
    Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs.
    Matched MeSH terms: Bayes Theorem
  14. Genetic Polymorphism of Aedes albopictus Population Inferred From ND5 Gene Variabilities In Subang Jaya, Malaysia
    Adilah-Amrannudin N, Hamsidi M, Ismail NA, Ismail R, Dom NC, Ahmad AH, et al.
    J Am Mosq Control Assoc, 2016 Dec;32(4):265-272.
    PMID: 28206858 DOI: 10.2987/16-6579.1
    This study was performed to establish the genetic variability of Aedes albopictus within Subang Jaya, Selangor, Malaysia, by using the nicotinamide adenine dinucleotide dehydrogenase 5 subunit (ND5) mitochondrial DNA (mtDNA) marker. A total of 90 samples were collected from 9 localities within an area of the Subang Jaya Municipality. Genetic variability was determined through the amplification and sequencing of a fragment of the ND5 gene. Eight distinct mtDNA haplotypes were identified. The evolutionary relationship of the local haplotypes alongside 28 reference strains was used to construct a phylogram, the analysis of which revealed low genetic differentiation in terms of both nucleotide and haplotype diversity. Bayesian method was used to infer the phylogenetic tree, revealing a unique relationship between local isolates. The study corroborates the reliability of ND5 to identify distinct lineages for polymorphism-based studies and supplements the existing body of knowledge regarding its genetic diversity. This in turn could potentially aid existing vector control strategies to help mitigate the risk and spread of the dengue virus.
    Matched MeSH terms: Bayes Theorem
  15. Fulltext The inference of breast cancer metastasis through gene regulatory networks
    Ahmad FK, Deris S, Othman NH
    J Biomed Inform, 2012 Apr;45(2):350-62.
    PMID: 22179053 DOI: 10.1016/j.jbi.2011.11.015
    Understanding the mechanisms of gene regulation during breast cancer is one of the most difficult problems among oncologists because this regulation is likely comprised of complex genetic interactions. Given this complexity, a computational study using the Bayesian network technique has been employed to construct a gene regulatory network from microarray data. Although the Bayesian network has been notified as a prominent method to infer gene regulatory processes, learning the Bayesian network structure is NP hard and computationally intricate. Therefore, we propose a novel inference method based on low-order conditional independence that extends to the case of the Bayesian network to deal with a large number of genes and an insufficient sample size. This method has been evaluated and compared with full-order conditional independence and different prognostic indices on a publicly available breast cancer data set. Our results suggest that the low-order conditional independence method will be able to handle a large number of genes in a small sample size with the least mean square error. In addition, this proposed method performs significantly better than other methods, including the full-order conditional independence and the St. Gallen consensus criteria. The proposed method achieved an area under the ROC curve of 0.79203, whereas the full-order conditional independence and the St. Gallen consensus criteria obtained 0.76438 and 0.73810, respectively. Furthermore, our empirical evaluation using the low-order conditional independence method has demonstrated a promising relationship between six gene regulators and two regulated genes and will be further investigated as potential breast cancer metastasis prognostic markers.
    Matched MeSH terms: Bayes Theorem
  16. Analysis of foveal avascular zone in colour fundus images for grading of diabetic retinopathy severity
    Ahmad Fadzil M, Ngah NF, George TM, Izhar LI, Nugroho H, Adi Nugroho H
    Annu Int Conf IEEE Eng Med Biol Soc, 2010;2010:5632-5.
    PMID: 21097305 DOI: 10.1109/IEMBS.2010.5628041
    Diabetic retinopathy (DR) is a sight threatening complication due to diabetes mellitus that affects the retina. At present, the classification of DR is based on the International Clinical Diabetic Retinopathy Disease Severity. In this paper, FAZ enlargement with DR progression is investigated to enable a new and an effective grading protocol DR severity in an observational clinical study. The performance of a computerised DR monitoring and grading system that digitally analyses colour fundus image to measure the enlargement of FAZ and grade DR is evaluated. The range of FAZ area is optimised to accurately determine DR severity stage and progression stages using a Gaussian Bayes classifier. The system achieves high accuracies of above 96%, sensitivities higher than 88% and specificities higher than 96%, in grading of DR severity. In particular, high sensitivity (100%), specificity (>98%) and accuracy (99%) values are obtained for No DR (normal) and Severe NPDR/PDR stages. The system performance indicates that the DR system is suitable for early detection of DR and for effective treatment of severe cases.
    Matched MeSH terms: Bayes Theorem
  17. Fulltext Functional specialisation and effective connectivity in cerebral motor cortices: an fMRI study on seven right handed female subjects
    Ahmad Nazlim Yusoff, Mazlyfarina Mohamad, Aini Ismafairus Abd Hamid, Wan Ahmad Kamil Wan Abdullah, Mohd Harith Hashim, Nurul Zafirah Zulkifli
    Malaysian Journal of Medicine and Health Sciences, 2010;6(2):71-92.
    MyJurnal
    Objective: This study investigates functional specialisation in, and effective connectivity between the
    precentral gyrus (PCG) and supplementary motor area (SMA) in seven right handed female subjects.
    Methods: Unimanual (UNIright and UNIleft) and bimanual (BIM) self-paced tapping of hand fingers were
    performed by the subjects to activate PCG and SMA. Brain activations and effective connectivity were
    analysed using statistical parametric mapping (SPM), dynamic causal modeling (DCM) and Bayesian
    model selection (BMS) and were reported based on group fixed (FFX) and random (RFX) effects
    analyses. Results: Group results showed that the observed brain activation for UNIright and UNIleft fulfill contralateral behavior of motor coordination with a larger activation area for UNIright. The activation for BIM occurs in both hemispheres with BIMright showing higher extent of activation as compared to BIMleft. Region of interest (ROI) analyses reveal that the number of activated voxel (NOV) and percentage of signal change (PSC) on average is higher in PCG than SMA for all tapping conditions. However, comparing between hemispheres for both UNI and BIM, higher PSC is observed in the right PCG and the left SMA. DCM and BMS results indicate that most subjects prefer PCG as the intrinsic input for UNIright and UNIleft. The input was later found to be bi-directionally connected to SMA for UNIright. The bi-directional model was then used for BIM in the left and right hemispheres. The model was in favour of six out of seven subjects. DCM results for BIM indicate the existence of interhemispheric connectivity between the right and left hemisphere PCG. Conclusion: The findings strongly support the existence of functional specialisation and integration i.e. effective connectivity in human brain during finger tapping and can be used as baselines in determining the probable motor coordination pathways and their connection strength in a population of subjects.
    Matched MeSH terms: Bayes Theorem
  18. Fulltext Ligand-based virtual screening using Bayesian inference network and reweighted fragments
    Ahmed A, Abdo A, Salim N
    ScientificWorldJournal, 2012;2012:410914.
    PMID: 22623895 DOI: 10.1100/2012/410914
    Many of the similarity-based virtual screening approaches assume that molecular fragments that are not related to the biological activity carry the same weight as the important ones. This was the reason that led to the use of Bayesian networks as an alternative to existing tools for similarity-based virtual screening. In our recent work, the retrieval performance of the Bayesian inference network (BIN) was observed to improve significantly when molecular fragments were reweighted using the relevance feedback information. In this paper, a set of active reference structures were used to reweight the fragments in the reference structure. In this approach, higher weights were assigned to those fragments that occur more frequently in the set of active reference structures while others were penalized. Simulated virtual screening experiments with MDL Drug Data Report datasets showed that the proposed approach significantly improved the retrieval effectiveness of ligand-based virtual screening, especially when the active molecules being sought had a high degree of structural heterogeneity.
    Matched MeSH terms: Bayes Theorem
  19. Fulltext Evidence of weak genetic structure and recent gene flow between Bactrocera dorsalis s.s. and B. papayae, across Southern Thailand and West Malaysia, supporting a single target pest for SIT applications
    Aketarawong N, Isasawin S, Thanaphum S
    BMC Genet, 2014;15:70.
    PMID: 24929425 DOI: 10.1186/1471-2156-15-70
    Bactrocera dorsalis s.s. (Hendel) and B. papayae Drew & Hancock, are invasive pests belonging to the B. dorsalis complex. Their species status, based on morphology, is sometimes arguable. Consequently, the existence of cryptic species and/or population isolation may decrease the effectiveness of the sterile insect technique (SIT) due to an unknown degree of sexual isolation between released sterile flies and wild counterparts. To evaluate the genetic relationship and current demography in wild populations for guiding the application of area-wide integrated pest management using SIT, seven microsatellite-derived markers from B. dorsalis s.s. and another five from B. papayae were used for surveying intra- and inter-specific variation, population structure, and recent migration among sympatric and allopatric populations of the two morphological forms across Southern Thailand and West Malaysia.
    Matched MeSH terms: Bayes Theorem
  20. A penalized quantitative structure-property relationship study on melting point of energetic carbocyclic nitroaromatic compounds using adaptive bridge penalty
    Al-Fakih AM, Algamal ZY, Lee MH, Aziz M
    SAR QSAR Environ Res, 2018 May;29(5):339-353.
    PMID: 29493376 DOI: 10.1080/1062936X.2018.1439531
    A penalized quantitative structure-property relationship (QSPR) model with adaptive bridge penalty for predicting the melting points of 92 energetic carbocyclic nitroaromatic compounds is proposed. To ensure the consistency of the descriptor selection of the proposed penalized adaptive bridge (PBridge), we proposed a ridge estimator ([Formula: see text]) as an initial weight in the adaptive bridge penalty. The Bayesian information criterion was applied to ensure the accurate selection of the tuning parameter ([Formula: see text]). The PBridge based model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], the Y-randomization test, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of PBridge for the training dataset outperforms the other methods used. PBridge shows the highest [Formula: see text] of 0.959, [Formula: see text] of 0.953, [Formula: see text] of 0.949 and [Formula: see text] of 0.959, and the lowest [Formula: see text] and [Formula: see text]. For the test dataset, PBridge shows a higher [Formula: see text] of 0.945 and [Formula: see text] of 0.948, and a lower [Formula: see text] and [Formula: see text], indicating its better prediction performance. The results clearly reveal that the proposed PBridge is useful for constructing reliable and robust QSPRs for predicting melting points prior to synthesizing new organic compounds.
    Matched MeSH terms: Bayes Theorem
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