Displaying publications 1 - 20 of 414 in total

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  1. Azizul Isha, Nor Azah Yusof, Musa Ahmad, Dedy Suhendra, Wan Md. Zin Wan Yunus, Zulkarnain Zainal
    MyJurnal
    An artificial neural network (ANN) was applied for the determination of V(V) based on immobilized fatty hydroxamic acid (FHA) in poly(methyl methacrylate) (PMMA). Spectra obtained from the V(V)-FHA complex at single wavelengths was used as the input data for the ANN. The V(V)-FHA complex shows a limited linear dynamic range of V(V) concentration of 10 - 100 mg/ L. After training with ANN, the linear dynamic range was extended with low calibration error. A three layer feed forward neural network using backpropagation (BP) algorithm was employed in this study. The input layer consisted of single neurons, 30 neurons in hidden a layer and one output neuron was found appropriate for the multivariate calibration used. The network were trained up to 10000 epochs with 0.003 % learning rate. This reagent also provided a good analytical pedormance with reproducibility characters of the method yielding relative standard deviation (RSD) of 9.29% and 7.09% for V(V) at concentrations of 50 mg/ L and 200 mg/ L, respectively. The limit of detection of the method was 8.4 mg/ L.
  2. Majeed MA, Shafri HZM, Wayayok A, Zulkafli Z
    Geospat Health, 2023 May 25;18(1).
    PMID: 37246539 DOI: 10.4081/gh.2023.1176
    This research proposes a 'temporal attention' addition for long-short term memory (LSTM) models for dengue prediction. The number of monthly dengue cases was collected for each of five Malaysian states i.e. Selangor, Kelantan, Johor, Pulau Pinang, and Melaka from 2011 to 2016. Climatic, demographic, geographic and temporal attributes were used as covariates. The proposed LSTM models with temporal attention was compared with several benchmark models including a linear support vector machine (LSVM), a radial basis function support vector machine (RBFSVM), a decision tree (DT), a shallow neural network (SANN) and a deep neural network (D-ANN). In addition, experiments were conducted to analyze the impact of look-back settings on each model performance. The results showed that the attention LSTM (A-LSTM) model performed best, with the stacked, attention LSTM (SA-LSTM) one in second place. The LSTM and stacked LSTM (S-LSTM) models performed almost identically but with the accuracy improved by the attention mechanism was added. Indeed, they were both found to be superior to the benchmark models mentioned above. The best results were obtained when all attributes were included in the model. The four models (LSTM, S-LSTM, A-LSTM and SA-LSTM) were able to accurately predict dengue presence 1-6 months ahead. Our findings provide a more accurate dengue prediction model than previously used, with the prospect of also applying this approach in other geographic areas.
  3. Basri KN, Yazid F, Mohd Zain MN, Md Yusof Z, Abdul Rani R, Zoolfakar AS
    PMID: 38394882 DOI: 10.1016/j.saa.2024.124063
    Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries. Artificial neural network with hyperparameter tuning was employed to train spectral data for the classification based on the International Caries Detection and Assesment System (ICDAS). Spectra preprocessing namely mean center (MC), autoscale (AS) and Savitzky Golay smoothing (SG) were applied on the data for spectra correction. The best performance of ANN model obtained has accuracy of 0.85 with precision of 1.00. Convolutional neural network (CNN) combined with Savitzky Golay smoothing performed on the spectral data has accuracy, precision, sensitivity and specificity for validation data of 1.00 respectively. The result obtained shows that the application of ANN and CNN capable to produce robust model to be used as an early screening of dental caries.
  4. Altharan YM, Shamsudin S, Lajis MA, Al-Alimi S, Yusuf NK, Alduais NAM, et al.
    PLoS One, 2024;19(3):e0300504.
    PMID: 38484005 DOI: 10.1371/journal.pone.0300504
    Direct recycling of aluminum waste is crucial in sustainable manufacturing to mitigate environmental impact and conserve resources. This work was carried out to study the application of hot press forging (HPF) in recycling AA6061 aluminum chip waste, aiming to optimize operating factors using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Genetic algorithm (GA) strategy to maximize the strength of recycled parts. The experimental runs were designed using Full factorial and RSM via Minitab 21 software. RSM-ANN models were employed to examine the effect of factors and their interactions on response and to predict output, while GA-RSM and GA-ANN were used for optimization. The chips of different morphology were cold compressed into billet form and then hot forged. The effect of varying forging temperature (Tp, 450-550°C), holding time (HT, 60-120 minutes), and chip surface area to volume ratio (AS:V, 15.4-52.6 mm2/mm3) on ultimate tensile strength (UTS) was examined. Maximum UTS (237.4 MPa) was achieved at 550°C, 120 minutes and 15.4 mm2/mm3 of chip's AS: V. The Tp had the largest contributing effect ratio on the UTS, followed by HT and AS:V according to ANOVA analysis. The proposed optimization process suggested 550°C, 60 minutes, and 15.4 mm2 as the optimal condition yielding the maximum UTS. The developed models' evaluation results showed that ANN (with MSE = 1.48%) outperformed RSM model. Overall, the study promotes sustainable production by demonstrating the potential of integrating RSM and ML to optimize complex manufacturing processes and improve product quality.
    Matched MeSH terms: Neural Networks (Computer)*
  5. Wang X, Song T, Gong F, Zheng P
    Sci Rep, 2016 06 10;6:27624.
    PMID: 27283843 DOI: 10.1038/srep27624
    Neural-like computing models are versatile computing mechanisms in the field of artificial intelligence. Spiking neural P systems (SN P systems for short) are one of the recently developed spiking neural network models inspired by the way neurons communicate. The communications among neurons are essentially achieved by spikes, i. e. short electrical pulses. In terms of motivation, SN P systems fall into the third generation of neural network models. In this study, a novel variant of SN P systems, namely SN P systems with self-organization, is introduced, and the computational power of the system is investigated and evaluated. It is proved that SN P systems with self-organization are capable of computing and accept the family of sets of Turing computable natural numbers. Moreover, with 87 neurons the system can compute any Turing computable recursive function, thus achieves Turing universality. These results demonstrate promising initiatives to solve an open problem arisen by Gh Păun.
    Matched MeSH terms: Neural Networks (Computer)*
  6. Tan M, Aghaei F, Wang Y, Zheng B
    Phys Med Biol, 2017 01 21;62(2):358-376.
    PMID: 27997380 DOI: 10.1088/1361-6560/aa5081
    The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a 'scoring fusion' artificial neural network classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC  =  0.793  ±  0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions.
  7. Karaman C, Karaman O, Show PL, Karimi-Maleh H, Zare N
    Chemosphere, 2022 Mar;290:133346.
    PMID: 34929270 DOI: 10.1016/j.chemosphere.2021.133346
    Herein, it was aimed to optimize, model, and forecast the biosorption of Congo Red onto biomass-derived biosorbent. Therefore, the waste-orange-peels were processed to fabricate biomass-derived carbon, which was activated by ZnCl2 and modified with cetyltrimethylammonium bromide. The physicochemical properties of the biosorbents were explored by scanning electron microscopy and N2 adsorption/desorption isotherms. The effects of pH, initial dye concentration, temperature, and contact duration on the biosorption capacity were investigated and optimized by batch experimental process, followed by the kinetics, equilibrium, and thermodynamics of biosorption were modeled. Furthermore, various artificial neural network (ANN) architectures were applied to experimental data to optimize the ANN model. The kinetic modeling of the biosorption offered that biosorption was in accordance both with the pseudo-second-order and saturation-type kinetic model, and the monolayer biosorption capacity was calculated as 666.67 mg g-1 at 25 °C according to Langmuir isotherm model. According to equilibrium modeling, the Freundlich isotherm model was better fitted to the experimental data than the Langmuir isotherm model. Moreover, the thermodynamic modeling revealed biosorption took place spontaneously as an exothermic process. The findings revealed that the best ANN architecture trained with trainlm as the backpropagation algorithm, with tansig-purelin transfer functions, and 14 neurons in the single hidden layer with the highest coefficient of determination (R2 = 0.9996) and the lowest mean-squared-error (MSE = 0.0002). The well-agreement between the experimental and ANN-forecasted data demonstrated that the optimized ANN model can predict the behavior of the anionic dye biosorption onto biomass-derived modified carbon materials under various operation conditions.
  8. Sohail A, Khan A, Nisar H, Tabassum S, Zameer A
    Med Image Anal, 2021 08;72:102121.
    PMID: 34139665 DOI: 10.1016/j.media.2021.102121
    Mitotic nuclei estimation in breast tumour samples has a prognostic significance in analysing tumour aggressiveness and grading system. The automated assessment of mitotic nuclei is challenging because of their high similarity with non-mitotic nuclei and heteromorphic appearance. In this work, we have proposed a new Deep Convolutional Neural Network (CNN) based Heterogeneous Ensemble technique "DHE-Mit-Classifier" for analysis of mitotic nuclei in breast histopathology images. The proposed technique in the first step detects candidate mitotic patches from the histopathological biopsy regions, whereas, in the second step, these patches are classified into mitotic and non-mitotic nuclei using the proposed DHE-Mit-Classifier. For the development of a heterogeneous ensemble, five different deep CNNs are designed and used as base-classifiers. These deep CNNs have varying architectural designs to capture the structural, textural, and morphological properties of the mitotic nuclei. The developed base-classifiers exploit different ideas, including (i) region homogeneity and feature invariance, (ii) asymmetric split-transform-merge, (iii) dilated convolution based multi-scale transformation, (iv) spatial and channel attention, and (v) residual learning. Multi-layer-perceptron is used as a meta-classifier to develop a robust and accurate classifier for providing the final decision. The performance of the proposed ensemble "DHE-Mit-Classifier" is evaluated against state-of-the-art CNNs. The performance evaluation on the test set suggests the superiority of the proposed ensemble with an F-score (0.77), recall (0.71), precision (0.83), and area under the precision-recall curve (0.80). The good generalisation of the proposed ensemble with a considerably high F-score and precision suggests its potential use in the development of an assistance tool for pathologists.
  9. Ruslan SA, Muharam FM, Zulkafli Z, Omar D, Zambri MP
    PLoS One, 2019;14(10):e0223968.
    PMID: 31626637 DOI: 10.1371/journal.pone.0223968
    Metisa plana (Walker) is a leaf defoliating pest that is able to cause staggering economical losses to oil palm cultivation. Considering the economic devastation that the pest could bring, an early warning system to predict its outbreak is crucial. The state of art of satellite technologies are now able to derive environmental factors such as relative humidity (RH) that may influence pest population's fluctuations in rapid, harmless, and cost-effective manners. This study examined the relationship between the presence of Metisa plana at different time lags and remote sensing (RS) derived RH by using statistical and machine learning approaches. Metisa plana census data of cumulated larvae instar 1, 2, 3, and 4 were collected biweekly in 2014 and 2015 in an oil palm plantation in Muadzam Shah, Pahang, Malaysia. Relative humidity values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were apportioned to 6 time lags; 1 week (T1), 2 weeks (T2), 3 week (T3), 4 weeks (T4), 5 week (T5) and 6 weeks (T6) and paired with the respective census data. Pearson's correlation was carried out to analyse the relationship between Metisa plana and RH at different time lags. Regression analyses and artificial neural network (ANN) were also conducted to develop the best prediction model of Metisa plana's outbreak. The results showed relatively high correlations, positively or negatively, between the presences of Metisa plana with RH ranging from 0.46 to 0.99. ANN was found to be superior to regression models with the adjusted coefficient of determination (R2) between the actual and predicted Metisa plana values ranging from 0.06 to 0.57 versus 0.00 to 0.05. The analysis on the best time lags illustrated that the multiple time lags were more influential on the Metisa plana population than the individual time lags. The best Metisa plana prediction model was derived from T1, T2 and T3 multiple time lags modelled using the ANN algorithm with R2 value of 0.57, errors below 1.14 and accuracies above 93%. Based on the result of this study, the elucidation of Metisa plana's landscape ecology was possible with the utilization of RH as the predictor variable in consideration of the time lag effects of RH on the pest's population.
    Matched MeSH terms: Neural Networks (Computer)*
  10. Ali A, Almaiah MA, Hajjej F, Pasha MF, Fang OH, Khan R, et al.
    Sensors (Basel), 2022 Jan 12;22(2).
    PMID: 35062530 DOI: 10.3390/s22020572
    The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchain-enabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system.
  11. Chai LE, Loh SK, Low ST, Mohamad MS, Deris S, Zakaria Z
    Comput Biol Med, 2014 May;48:55-65.
    PMID: 24637147 DOI: 10.1016/j.compbiomed.2014.02.011
    Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described.
  12. Goudarzi S, Haslina Hassan W, Abdalla Hashim AH, Soleymani SA, Anisi MH, Zakaria OM
    PLoS One, 2016;11(7):e0151355.
    PMID: 27438600 DOI: 10.1371/journal.pone.0151355
    This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF-FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model's performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF-FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF-FFA model can be applied as an efficient technique for the accurate prediction of vertical handover.
    Matched MeSH terms: Neural Networks (Computer)*
  13. Azamathulla HM, Zakaria NA
    Water Sci Technol, 2011;63(10):2225-30.
    PMID: 21977642
    The process involved in the local scour below pipelines is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This paper describes the use of artificial neural networks (ANN) to estimate the pipeline scour depth. The data sets of laboratory measurements were collected from published works and used to train the network or evolve the program. The developed networks were validated by using the observations that were not involved in training. The performance of ANN was found to be more effective when compared with the results of regression equations in predicting the scour depth around pipelines.
    Matched MeSH terms: Neural Networks (Computer)*
  14. Yang Q, Hayat N, Al Mamun A, Makhbul ZKM, Zainol NR
    PLoS One, 2022;17(3):e0264899.
    PMID: 35245323 DOI: 10.1371/journal.pone.0264899
    Social media has changed the marketing phenomenon, as firms use social media to inform, impress, and retain the existing consumers. Social media marketing empowers business firms to generate perceived brand equity activities and build the notion among consumers to continue using the firms' products and services. The current exploratory study aimed to examine the effects of social media marketing activities on brand equity (brand awareness and brand image) and repurchase intention of high-tech products among Chinese consumers. The study used a cross-sectional design, and the final analysis was performed on 477 valid responses that were collected through an online survey. Partial least squares structural equation modelling (PLS-SEM) and artificial neural network (ANN) analysis were performed. The obtained results revealed positive and significant effects of trendiness, interaction, and word of mouth on brand awareness. Customisation, trendiness, interaction, and word of mouth were found to positively affect brand image. Brand awareness and brand image were found to affect repurchase intention. The results of multilayer ANN analysis suggested trendiness as the most notable factor in developing brand awareness and brand image. Brand awareness was found to be an influential factor that nurtures repurchase intention. The study's results confirmed the relevance of social media marketing activities in predicting brand equity and brand loyalty by repurchase intention. Marketing professionals need to concentrate on entertainment and customisation aspects of social media marketing that can help to achieve brand awareness and image. The limitations of study and future research opportunities are presented at the end of this article.
  15. Chang SW, Abdul-Kareem S, Merican AF, Zain RB
    BMC Bioinformatics, 2013;14:170.
    PMID: 23725313 DOI: 10.1186/1471-2105-14-170
    Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers.
  16. Mohd Tahir Ismail, Zaidi Isa
    Sains Malaysiana, 2006;35:55-62.
    The behaviour of many financial time series cannot be modeled solely by linear time series model. Phenomena such as mean reversion, volatility of stock markets and structural breaks cannot be modelled implicitly using simple linear time series model. Thus, to overcome this problem, nonlinear time series models are typically designed to accommodate these nonlinear features in the data. In this paper, we use portmanteau test and structural change test to detect nonlinear feature in three ASEAN countries exchange rates (Malaysia, Singapore and Thailand). It is found that the null hypothesis of linearity is rejected and there is evidence of structural breaks in the exchange rates series. Therefore, the decision of using regime switching model in this study is justified. Using model selection criteria (AIC, SBC, HQC), we compare the in-sample fitting between two types of regime switching model. The two regime switching models we considered were the Self-Exciting Threshold Autoregressive (SETAR) model and the Markov switching Autoregressive (MS-AR) model where these models can explain the abrupt changes in a time series but differ as how they model the movement between regimes. From the AIC, SBC and HQC values, it is found that the MS -AR model is the best fitted model for all the return series. In addition, the regime switching model also found to perform better than simple autoregressive model in in-sample fitting. This result justified that nonlinear model give better in-sample fitting than linear model.
  17. Asrami MR, Pirouzi A, Nosrati M, Hajipour A, Zahmatkesh S
    Chemosphere, 2024 Jan;347:140652.
    PMID: 37967679 DOI: 10.1016/j.chemosphere.2023.140652
    Although algal-based membrane bioreactors (AMBRs) have been demonstrated to be effective in treating wastewater (landfill leachate), there needs to be more research into the effectiveness of these systems. This study aims to determine whether AMBR is effective in treating landfill leachate with hydraulic retention times (HRTs) of 8, 12, 14, 16, 21, and 24 h to maximize AMBR's energy efficiency, microalgal biomass production, and removal efficiency using artificial neural network (ANN) models. Experimental results and simulations indicate that biomass production in bioreactors depends heavily on HRT. A decrease in HRT increases algal (Chlorella vulgaris) biomass productivity. Results also showed that 80% of chemical oxygen demand (COD) was removed from algal biomass by bioreactors. To determine the most efficient way to process the features as mentioned above, nondominated sorting genetic algorithm II (NSGA-II) techniques were applied. A mesophilic, suspended-thermophilic, and attached-thermophilic organic loading rate (OLR) of 1.28, 1.06, and 2 kg/m3/day was obtained for each method. Compared to suspended-thermophilic growth (3.43 kg/m3.day) and mesophilic growth (1.28 kg/m3.day), attached-thermophilic growth has a critical loading rate of 10.5 kg/m3.day. An energy audit and an assessment of the system's auto-thermality were performed at the end of the calculation using the Monod equation for biomass production rate (Y) and bacteria death constant (Kd). According to the results, a high removal level of COD (at least 4000 mg COD/liter) leads to auto-thermality.
  18. Han F, Hessen AS, Amari A, Elboughdiri N, Zahmatkesh S
    Environ Res, 2024 Mar 15;245:117972.
    PMID: 38141913 DOI: 10.1016/j.envres.2023.117972
    Metal-organic framework (MOF)--based composites have received significant attention in a variety of applications, including pollutant adsorption processes. The current investigation was designed to model, forecast, and optimize heavy metal (Cu2+) removal from wastewater using a MOF nanocomposite. This work has been modeled by response surface methodology (RSM) and artificial neural network (ANN) algorithms. In addition, the optimization of the mentioned factors has been performed through the RSM method to find the optimal conditions. The findings show that RSM and ANN can accurately forecast the adsorption process's the Cu2+ removal efficiency (RE). The maximum values of RE are achieved at the highest value of time (150 min), the highest value of adsorbent dosage (0.008 g), and the highest value of pH (=6). The R2 values obtained were 0.9995, 0.9992, and 0.9996 for ANN modeling of adsorption capacity based on different adsorbent dosages, Cu2+ solution pHs, and different ion concentrations, respectively. The ANN demonstrated a high level of accuracy in predicting the local minima of the graph. In addition, the RSM optimization results showed that the optimum mode for RE occurred at an adsorbent dosage value of 0.007 g and a time value of 144.229 min.
  19. Agatonovic-Kustrin S, Beresford R, Yusof AP
    J Pharm Biomed Anal, 2001 Sep;26(2):241-54.
    PMID: 11470201
    A quantitative structure-permeability relationship was developed using Artificial Neural Network (ANN) modeling to study penetration across a polydimethylsiloxane membrane. A set of 254 compounds and their experimentally derived maximum steady state flux values used in this study was gathered from the literature. A total of 42 molecular descriptors were calculated for each compound. A genetic algorithm was used to select important molecular descriptors and supervised ANN was used to correlate selected descriptors with the experimentally derived maximum steady-state flux through the polydimethylsiloxane membrane (log J). Calculated molecular descriptors were used as the ANN's inputs and log J as the output. Developed model indicates that molecular shape and size, inter-molecular interactions, hydrogen-bonding capacity of drugs, and conformational stability could be used to predict drug absorption through skin. A 12-descriptor nonlinear computational neural network model has been developed for the estimation of log J values for a data set of 254 drugs. Described model does not require experimental parameters and could potentially provide useful prediction of membrane penetration of new drugs and reduce the need for actual compound synthesis and flux measurements.
    Matched MeSH terms: Neural Networks (Computer)*
  20. Agatonovic-Kustrin S, Beresford R, Yusof AP
    J Pharm Biomed Anal, 2001 May;25(2):227-37.
    PMID: 11275432
    A quantitative structure-human intestinal absorption relationship was developed using artificial neural network (ANN) modeling. A set of 86 drug compounds and their experimentally-derived intestinal absorption values used in this study was gathered from the literature and a total of 57 global molecular descriptors, including constitutional, topological, chemical, geometrical and quantum chemical descriptors, calculated for each compound. A supervised network with radial basis transfer function was used to correlate calculated molecular descriptors with experimentally-derived measures of human intestinal absorption. A genetic algorithm was then used to select important molecular descriptors. Intestinal absorption values (IA%) were used as the ANN's output and calculated molecular descriptors as the inputs. The best genetic neural network (GNN) model with 15 input descriptors was chosen, and the significance of the selected descriptors for intestinal absorption examined. Results obtained with the model that was developed indicate that lipophilicity, conformational stability and inter-molecular interactions (polarity, and hydrogen bonding) have the largest impact on intestinal absorption.
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