Application of an expert system based on Genetic Algorithm–Adaptive Neuro-Fuzzy Inference System (GA–ANFIS) in QSAR of cathepsin K inhibitors

Shahlaei, Mohsen; Madadkar-Sobhani, Armin; Saghaie, Lotfollah; Fassihi, Afshin

doi:10.1016/j.eswa.2011.11.106

Cited by 40 publications

(26 citation statements)

References 39 publications

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“…More theoretical details about ANFIS could be find in the corresponding literature [450,453]. ANFIS has been shown good ability as a modeling tool in different QSARs/QSPRs [184,[454][455][456][457][458][459].…”

Section: Accepted Manuscriptmentioning

confidence: 98%

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Chemometrics tools in QSAR/QSPR studies: A historical perspective

Yousefinejad

Hemmateenejad

2015

Chemometrics and Intelligent Laboratory Systems

117

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Section: Accepted Manuscriptmentioning

confidence: 98%

“…Application of the specific combined versions of ANN with GA can be found in QSAR/QSPR studies. Some examples are, GA-ANFIS (Adaptive Neuro-Fuzzy Inference System) [184] and BRGNN (Bayesian-regularized genetic neural networks) [185].…”

mentioning

confidence: 99%

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Yousefinejad

Hemmateenejad

2015

Chemometrics and Intelligent Laboratory Systems

117

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“…The next data object is chosen by identifying the one farthest away from the previously selected data candidates. This process repeats until the desired number of candidates has been identified [28], [29].…”

Section: Class Imbalance Data Problemmentioning

confidence: 99%

The Effect of Sample Age and Prediction Resolution on Myocardial Infarction Risk Prediction

Tay

Poh

Reeth

et al. 2015

IEEE J. Biomed. Health Inform.

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Myocardial infarction (MI) is one of the leading causes of death in many developed countries. Hence, early detection of MI events is critical for effective preventative therapies, potentially reducing avoidable mortality. One approach for early disease prediction is the use of risk prediction models developed using machine learning techniques. One important component of these models is to provide clinicians with the flexibility to customize (e.g., the prediction range) and use the risk prediction model that they deemed most beneficial for their patients. Therefore, in this paper, we develop MI prediction models and investigate the effect of sample age and prediction resolution on the performance of MI risk prediction models. The cardiovascular health study dataset was used in this study. Results indicate that the prediction model developed using SVM algorithm is capable of achieving high sensitivity, specificity, and balanced accuracy of 95.3%, 84.8%, and 90.1%, respectively, over a time span of 6 years. Both sample age and prediction resolution were found not to have a significant impact on the performance of MI risk prediction models developed using subjects aged 65 and above. This implies that risk prediction models developed using different sample age and prediction resolution is a feasible approach. These models can be integrated into a computer aided screening tool which clinicians can use to interpret and predict the MI risk status of the individual patients after performing the necessary clinical assessments (e.g., cognitive function, physical function, electrocardiography, general changes to health/lifestyle, and medications) required by the models. This could offer a means for clinicians to screen the patients at risk of having MI in the near future and prescribe early medical intervention to reduce the risk.

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“…Such techniques have extensive potential for time and cost savings in product development, reducing the need for lengthy and expensive experiments, and promoting green chemistry. [14] Several novel nature-inspired stochastic search and optimization algorithms, including particle swarm optimization, [15,16] ant colony optimization, [17] bee algorithm, [18] and genetic algorithm (GA), [19] have shown good performance in QSAR/QSPR studies. GA is a stochastic global search method, whose basic concept is mimicking the evolution of a species, according to the Darwinian theory of the "survival of the fittest."…”

Section: Introductionmentioning

confidence: 99%

Prediction of photolysis half-lives of dihydroindolizines by genetic algorithm-multiple linear regression (GA-MLR)

Beiknejad

Chaichi

Fatemi

2016

J. Phys. Org. Chem.

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Quantitative structure-property relationship study was carried out for the prediction of photolysis half-lives of dihydroindolizines. Genetic algorithm variable selection was used in the multiple linear regression modeling. Four descriptors including topological (AAC), 2D-Autocorrelations (GATS7m), 3D-MoRSE (Mor19p), and GETAWAY (HATS3p) descriptors were selected by the algorithm, revealing that a hybrid of topologic, geometric, and electronic features of the molecule affects photolysis half-life of studied photochromic compounds. The results showed that the developed model provides statistically significant predictions of photolysis half-lives of dihydroindolizines.

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Application of an expert system based on Genetic Algorithm–Adaptive Neuro-Fuzzy Inference System (GA–ANFIS) in QSAR of cathepsin K inhibitors

Cited by 40 publications

References 39 publications

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Chemometrics tools in QSAR/QSPR studies: A historical perspective

The Effect of Sample Age and Prediction Resolution on Myocardial Infarction Risk Prediction

Prediction of photolysis half-lives of dihydroindolizines by genetic algorithm-multiple linear regression (GA-MLR)

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