Genetic programming neural networks: A powerful bioinformatics tool for human genetics

The identification of genes that influence the risk of common, complex diseases primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions. We have previously introduced a genetic programming neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. Previous empirical studies suggest GPNN has excellent power for identifying gene-gene interactions. The goal of this study was to compare the power of GPNN and stepwise logistic regression (SLR) for identifying gene-gene interactions. Using simulated data, we show that GPNN has higher power to identify gene-gene interactions than SLR. These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene interactions.KeywordsStepwise Logistic RegressionBack Propagation Neural NetworkMultifactor Dimensionality ReductionGenetic EpidemiologyPattern Recognition ApproachThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Genetic variants in PD-1 and its ligands, gene-gene and gene-environment interactions in allergic rhinitis.

BackgroundThere is growing evidence that gene-gene interactions are ubiquitous in determining the susceptibility to common human diseases. The investigation of such gene-gene interactions presents new statistical challenges for studies with relatively small sample sizes as the number of potential interactions in the genome can be large. Breast cancer provides a useful paradigm to study genetically complex diseases because commonly occurring single nucleotide polymorphisms (SNPs) may additively or synergistically disturb the system-wide communication of the cellular processes leading to cancer development.MethodsIn this study, we systematically studied SNP-SNP interactions among 19 SNPs from 18 key genes involved in major cancer pathways in a sample of 398 breast cancer cases and 372 controls from Ontario. We discuss the methodological issues associated with the detection of SNP-SNP interactions in this dataset by applying and comparing three commonly used methods: the logistic regression model, classification and regression trees (CART), and the multifactor dimensionality reduction (MDR) method.ResultsOur analyses show evidence for several simple (two-way) and complex (multi-way) SNP-SNP interactions associated with breast cancer. For example, all three methods identified XPD-[Lys751Gln]*IL10-[G(-1082)A] as the most significant two-way interaction. CART and MDR identified the same critical SNPs participating in complex interactions. Our results suggest that the use of multiple statistical approaches (or an integrated approach) rather than a single methodology could be the best strategy to elucidate complex gene interactions that have generally very different patterns.ConclusionThe strategy used here has the potential to identify complex biological relationships among breast cancer genes and processes. This will lead to the discovery of novel biological information, which will improve breast cancer risk management.

ABSTRAKKabupaten Tangerang mengalami pertumbuhan penduduk pesat yang mendorong perubahan tutupan lahan. Rencana Tata Ruang Wilayah (RTRW) 2020 digunakan sebagai panduan pemanfaatan lahan, dengan klasifikasi utama: lahan terbangun, badan air, vegetasi, dan lahan terbuka. Penelitian ini menganalisis perubahan tutupan lahan menggunakan algoritma Classification and Regression Trees (CART) pada citra Sentinel-2A tahun 2019 dan 2023 di Google Earth Engine. Hasil menunjukkan peningkatan luas bangunan dan lahan terbuka masing-masing 19,866 km² dan 17,877 km², sementara vegetasi dan badan air menurun 33,446 km² dan 4,297 km². Akurasi klasifikasi mendapatkan 89,36% (2019) dan 90,29% (2023). Selain itu, kesesuaian tutupan lahan dengan RTRW meningkat 36,71 km² atau 4%. Hasil ini menunjukkan efektivitas metode CART dalam memantau perubahan tutupan lahan serta relevansinya dengan kebijakan tata ruang di Kabupaten Tangerang.Kata Kunci : Tutupan lahan; Rencana Tata Ruang Wilayah (RTRW), Google Earth Engine (GEE),Classification and Regression Trees (CART), Penginderaan jauh.ABSTRACTTangerang Regency is experiencing rapid population growth that is driving land cover change. The 2020 Regional Spatial Plan (RTRW) is used as a guide for land use, with the main classifications: built-up land, water bodies, vegetation, and open land. This study analyzed land cover change using the Classification and Regression Trees (CART) algorithm on 2019 and 2023 Sentinel-2A images in Google Earth Engine. The results showed an increase in building area and open land of 19.866 km² and 17.877 km² respectively, while vegetation and water bodies decreased by 33.446 km² and 4.297 km². The classification accuracy was 89.36% (2019) and 90.29% (2023). In addition, land cover conformity with the RTRW increased by 36.71 km² or 4%. These results demonstrate the effectiveness of the CART method in monitoring land cover change and its relevance to spatial policy in Tangerang District.Keywords: Land cover, Regional Spatial Plan (RTRW), Supervised Classification, Google Earth Engine (GEE), Classification and Regression Trees (CART), Remote sensing

False Financial Statements (FFS) have long been a serious problem in China and other Asian countries, which significantly dampen the confidence of the investors. Regardless of listed companies or non-listed companies, the percentage of financial statements that contained false information is quite high, which is one of the major reasons why China stock markets moved in the opposite direction towards its wonderful economic growth over the past few years. The objective of this research is to introduce one statistical technique — Classification and Regression Tree (CART), to identify and predict the impacts of FFS. We survey financial statements manipulation tricks, FFS indicators and FFS detection techniques from both China and international perspective, and further look into ten listed companies with known FFS history in China; combining these findings, we propose key indicators to work with CART. Our analysis includes 24 false financial reports, and 124 non-false financial reports. We use CART to develop two FFS detecting models: CART without industry benchmark and CART with industry benchmark. For supporting comparison, we also build a Logit regression which is a commonly used technique in FFS detecting. We find that CART is effective in distinguishing FFS from non-FFS. Both CART models achieve better accuracy in identifying fraud cases and making predictions than Logit regression does, and CART with industry benchmark is slightly better than CART without benchmark, but it does not always have superior performance. Our CART model also tries to capture the indicators and their combinations that could reflect firms with high possibility of FFS in China.

Ordinal Logic Regression: A classifier for discovering combinations of binary markers for ordinal outcomes

Introduction: No accurate diagnostic tests exist for exocrine pancreatic insufficiency (EPI). As a result, EPI is misdiagnosed, including in chronic pancreatitis (CP), and there is an unmet need for clinical tools to help clinicians evaluate patients with CP at risk for EPI. The aim of this study was to generate a simple clinical tool to predict EPI in patients with CP. Methods: 49 variables from medical records of CP patients with or without EPI were entered into a Classification and Regression Tree (CART) model using the unimputed full analysis set and 4 other models (Logistic Regression with Least Absolute Shrinkage and Selection Operator regularization, Support Vector Machine, Random Forest, and Gradient Boosting Machine) using imputed full analysis set. EPI misclassification rate (mRate) served as primary metric to train the 5 prediction models. Each model’s generalizability was assessed using 5-fold cross-validation and the model that was best suited for clinician use based on mRate and other characteristics was selected. Results: Records of 274 patients with CP from 6 pancreatitis centers across the US were included. Among the 160 (58%)/114 (42%) patients with EPI/without EPI, 48%/55% were female, mean age was 55/52 years, 76%/69% were white, 29%/52% were never smokers, and 39%/53% were never alcohol users. All 5 prediction models demonstrated similar EPI mRates. The CART model was selected. The final CART decision tree includes 10 variables (in order of importance): smoking, pancreatic atrophy, steatorrhea, alcohol use, hemoglobin, serum vitamin D, pancreatic duct contour change, serum albumin, pancreatic parenchymal calcifications, and use of opioid analgesics. The mRate without and with 5-fold cross-validation of the CART was 0.153 (training error) and 0.314 (prediction error), and the area under the receiver operating characteristic curve was 0.889 and 0.682, respectively (Figure 1). Sensitivity/specificity without/with 5-fold cross validation was 0.888/0.789 and 0.794/0.535, respectively. Conclusion: A parsimonious set of 10 variables associated with EPI in CP was identified. The CART decision tree demonstrated good prediction performance for EPI and was selected as the most practical screening tool to aid clinicians in the assessment of EPI in patients with CP. Further external validation of the EPI screening tool is planned.Figure 1.: AUC-ROC of the CART model trained on the full analysis set and after 5-fold cross-validation. AUC, area under the curve; CART, classification and regression tree; CV, cross-validation; ROC, receiver operating characteristic.

Developing and validating a questionnaire to screen commercial driver’s license applicants for sleep-disordered breathing

ObjectiveTo assess biomarkers between symptomatic and asymptomatic patients, and to construct a classification and regression tree (CART) algorithm for their discrimination.Patients and Methods136 patients were enrolled. They were symptomatic (high risk) (N = 82, stenosis degree ≥ 50%, proven to be responsible for ischemic stroke the last six months) and asymptomatic (low risk) (N = 54, stenosis degree ≤ 50%). Levels of fibrinogen, matrix metalloproteinase-1 (MMP-1), tissue inhibitor of metalloproteinase-1 (TIMP-1), soluble intercellular adhesion molecule (SiCAM), soluble vascular cell adhesion molecule (SvCAM), adiponectin and insulin were measured on a Luminex 3D platform and their differences were evaluated; subsequently, a CART model was created and evaluated.ResultsAll measured biomarkers, except adiponectin, had significantly higher levels in symptomatic patients. The constructed CART prognostic model had 97.6% discrimination accuracy on symptomatic patients and 79.6% on asymptomatic, while the overall accuracy was 90.4%. Moreover, the population was split into training and test sets for CART validation.ConclusionSignificant differences were found in the biomarkers between symptomatic and asymptomatic patients. The CART model proved to be a simple decision-making algorithm linked with risk probabilities and provided evidence to identify and, therefore, treat patients being at high risk for cardiovascular disease.

In this paper, a hybrid model consisting of the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) is formulated. FAM is useful for tackling the stability---plasticity dilemma pertaining to data-based learning systems, while CART is useful for depicting its learned knowledge explicitly in a tree structure. By combining the benefits of both models, FAM---CART is capable of learning data samples stably and, at the same time, explaining its predictions with a set of decision rules. In other words, FAM---CART possesses two important properties of an intelligent system, i.e., learning in a stable manner (by overcoming the stability---plasticity dilemma) and extracting useful explanatory rules (by overcoming the opaqueness issue). To evaluate the usefulness of FAM---CART, six benchmark medical data sets from the UCI repository of machine learning and a real-world medical data classification problem are used for evaluation. For performance comparison, a number of performance metrics which include accuracy, specificity, sensitivity, and the area under the receiver operation characteristic curve are computed. The results are quantified with statistical indicators and compared with those reported in the literature. The outcomes positively indicate that FAM---CART is effective for undertaking data classification tasks. In addition to producing good results, it provides justifications of the predictions in the form of a decision tree so that domain users can easily understand the predictions, therefore making it a useful decision support tool.

ABSTRACTIn order to achieve the accurate estimation of state of charge (SOC) of the battery in a hybrid electric vehicle (HEV), this paper proposed a new estimation model based on the classification and regression tree (CART) which belongs to a kind of decision tree. The basic principle and modelling process of the CART decision tree were introduced in detail in this paper, and we used the voltage, current, and temperature of the battery in an HEV to estimate the value of SOC under the driving cycle. Meanwhile, we took the energy feedback of the HEV under the regenerative braking into consideration. The simulation data and experimental data were used to test the effectiveness of the estimation model of CART, and the results indicate that the proposed estimation model has high accuracy, the relative error of simulation is within 0.035, while the relative error of experiment is less than 0.05.

Groundwater is considered one of the most valuable fresh water resources. The main objective of this study was to produce groundwater spring potential maps in the Koohrang Watershed, Chaharmahal-e-Bakhtiari Province, Iran, using three machine learning models: boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). Thirteen hydrological-geological-physiographical (HGP) factors that influence locations of springs were considered in this research. These factors include slope degree, slope aspect, altitude, topographic wetness index (TWI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Subsequently, groundwater spring potential was modeled and mapped using CART, RF, and BRT algorithms. The predicted results from the three models were validated using the receiver operating characteristics curve (ROC). From 864 springs identified, 605 (≈70 %) locations were used for the spring potential mapping, while the remaining 259 (≈30 %) springs were used for the model validation. The area under the curve (AUC) for the BRT model was calculated as 0.8103 and for CART and RF the AUC were 0.7870 and 0.7119, respectively. Therefore, it was concluded that the BRT model produced the best prediction results while predicting locations of springs followed by CART and RF models, respectively. Geospatially integrated BRT, CART, and RF methods proved to be useful in generating the spring potential map (SPM) with reasonable accuracy.

The comparative analysis of three methods for objective grid-based bias removal in mesoscale numerical weather prediction models is considered. The first technique is the local observation-based (LOB) method that extends further the approaches of several recent studies and is focused on utilizing the information obtained from meteorological stations or neighbor grid points in the proximity of a site of interest. The bias at a site of interest might then be considered as a spatiotemporal function of the weighted information on the past biases observed in the cluster of neighbors during a certain time window. The second method is an extension of model output statistics (MOS), combining several modern multiple regression techniques such as the classification and regression trees (CARTs) and the alternative conditional expectation (ACE) and, therefore, is named the CART–ACE method. The CART–ACE method allows representing possible nonlinear aspects of the bias in a parsimonious linearized statistical model. Finally, the third considered method is a natural combination of the LOB and CART–ACE methods in which the information provided by the LOB method is interpreted as an extra predictor in the regression model of the CART–ACE method. The proposed methods are illustrated by a case study of an observation-based verification and bias correction of fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) 48-h surface temperature, that is, 2-m temperature, forecasts over the Pacific Northwest.

To develop a predictive system for the occurrence of nosocomial pneumonia in patients who had cardiac surgery performed. Retrospective cohort study.Setting. Two cardiologic tertiary care hospitals in Rio de Janeiro, Brazil. Between June 2000 and August 2002, there were 1,158 consecutive patients who had complex heart surgery performed. Patients older than 18 years who survived the first 48 postoperative hours were included in the study. The occurrence of pneumonia was diagnosed through active surveillance by an infectious diseases specialist according to the following criteria: the presence of new infiltrate on a radiograph in association with purulent sputum and either fever or leukocytosis until day 10 after cardiac surgery. Predictive models were built on the basis of logistic regression analysis and classification and regression tree (CART) analysis. The original data set was divided randomly into 2 parts, one used to construct the models (ie, "test sample") and the other used for validation (ie, "validation sample"). The area under the receiver-operating characteristic (ROC) curve was 69% for the logistic regression model and 76% for the CART model. Considering a probability greater than 7% to be predictive of pneumonia for both models, sensitivity was higher for the logistic regression models, compared with the CART models (64% vs 56%). However, the CART models had a higher specificity (92% vs 70%) and global accuracy (90% vs 70%) than the logistic regression models. Both models showed good performance, based on the 2-graph ROC, considering that 84.6% and 84.3% of the predictions obtained by regression and CART analyses were regarded as valid. Although our findings are preliminary, the predictive models we created showed fairly good specificity and fair sensitivity.

This study focuses on enhancing classification tree (CT) methodologies, commonly used in diverse planning sectors, by integrating a genetic algorithm (GA) to overcome inherent biases in traditional approaches. Classification and Regression Tree (CART), a widely used tree-based method, often shows a selection bias toward variables with more split options. This occurs due to the simultaneous selection of both split variables and points within the conventional CART approach. To address this issue, the study introduces an alternative approach that combines CART with GA optimization. This hybrid method aims to reduce the selection bias in variable splitting. The effectiveness of this new GA-based CART technique was tested using an agricultural ergonomics dataset from the ICAR-Indian Agricultural Research Institute in New Delhi. A comparative analysis was conducted between the new GA-based CART approach, the standard CART method, and an existing GA-based CART variant. The results demonstrated that the proposed GA-integrated CART approach outperformed the other methods. This indicates its potential as a more accurate and unbiased tool in classification methodologies, especially when dealing with complex, real-world datasets.