Genetic Programming Neural Networks as a Bioinformatics Tool for Human Genetics

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

A western North Pacific tropical cyclone (TC) intensity prediction scheme has been developed based on climatology and persistence (CLIPER) factors as potential predictors and using genetic neural network (GNN) model. TC samples during June–October spanning 2001–2010 are used for model development. The GNN model input is constructed from potential predictors by employing both a stepwise regression method and an Isometric Mapping (Isomap) algorithm. The Isomap algorithm is capable of finding meaningful low-dimensional architectures hidden in their nonlinear high-dimensional data space and separating the underlying factors. In this scheme, the new developed model, which is termed the GNN-Isomap model, is used for monthly TC intensity prediction at 24- and 48-h lead times. Using identical modeling samples and independent samples, predictions of the GNN-Isomap model are compared with the widely used CLIPER method. By adopting different numbers of nearest neighbors, results of sensitivity experiments show that the mean absolute prediction errors of the independent samples using GNN-Isomap model at 24- and 48-h forecasts are smaller than those using CLIPER method. Positive skills are obtained as compared to the CLIPER method with being above 12 % at 24 h and above 14 % at 48 h. Analyses of the new scheme suggest that the useful linear and nonlinear prediction information of the full pool of potential predictors is excavated in terms of the stepwise regression method and the Isomap algorithm. Moreover, the GNN is built by integrating multiple individual neural networks with the same expected output and network architecture is optimized by an evolutionary genetic algorithm, so the generalization capacity of the GNN-Isomap model is significantly enhanced, indicating a potentially better operational weather prediction.

Based on the structural feature of high speed railway bridge , one space finite element model is established for the construction of the influence factors of the bridge deformation sensitivity analysis .Using adaptive genetic algorithm and neural network technology , taking the deformation of the bridge sample error minimizing as the objective function to get the new BP neural network after the neural network model weights and thresholds optimized.And compile eformation intelligent forecasting system based on MATLAB platform.Taking the cantilever construction of Shanghai-Kunming high-speed rail a continuous beam of measured data compared with the predicted results of experimental data, the results shows that the method is of high accuracy , good feasibility, provided reference for high precision linear intelligent control to similar Bridges.

NOVEL STAGING TOOL FOR LOCALIZED PROSTATE CANCER: A PILOT STUDY USING GENETIC ADAPTIVE NEURAL NETWORKS

The large number of markers considered in a genome-wide association study (GWAS) has resulted in a simplification of analyses conducted. Most studies are analyzed one marker at a time using simple tests like the trend test. Methods that account for the special features of genetic association studies, yet remain computationally feasible for genome-wide analysis, are desirable as they may lead to increased power to detect associations. Haplotype sharing attempts to translate between population genetics and genetic epidemiology. Near a recent mutation that increases disease risk, haplotypes of case participants should be more similar to each other than haplotypes of control participants; conversely, the opposite pattern may be found near a recent mutation that lowers disease risk. We give computationally simple association tests based on haplotype sharing that can be easily applied to GWASs while allowing use of fast (but not likelihood-based) haplotyping algorithms and properly accounting for the uncertainty introduced by using inferred haplotypes. We also give haplotype-sharing analyses that adjust for population stratification. Applying our methods to a GWAS of Parkinson's disease, we find a genome-wide significant signal in the CAST gene that is not found by single-SNP methods. Further, a missing-data artifact that causes a spurious single-SNP association on chromosome 9 does not impact our test.

Looking beneath the Surface: The Cell Death Pathway of Fas/APO-1 (CD95)

A new recognition method of vehicle license plates based on neural network is presented in this paper. For the Back Propagation (BP) neural network often trap into the local minimum in the training process, a Genetic Neural Network (GNN), GABP was constructed by combining the Genetic Algorithm(GA)with BP neural network. The training of the GABP neural network was finished in two steps. The GA was firstly used to make a thorough searching in the global space for the weights and thresholds of the neural network, which can ensure they fall into the neighborhood of global optimal solution. Then, in order to improve the convergence precision, the gradient method was used to finely train the network and find the global optimum or second-best solution with good performance. On the other side, feature extraction is also important for improving the recognition rate of the network. So both the structure features and the statistic features are used in this paper, which include mesh feature, direction line element feature and Zernike moments feature. Experimental results show that the proposed method can save the time of training network and achieve a highly recognition rate.

Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target data set. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here, we employ domain adaptation techniques – Maximum Mean Discrepancy as an additional transfer loss and Domain Adversarial Neural Networks – and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated data sets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ${\sim }20{{\ \rm per\ cent}}$. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys.

Combination of the evolutionary optimization algorithm and traditional neural network, one new kind of neural network which is called evolutionary neural network can be generated. To overcome the demerits of previously proposed evolutionary neural networks, combining the immune continuous ant colony algorithm proposed by author and BP neural network, a new evolutionary neural network is proposed. In this new evolutionary neural network, the architecture and connection weights of BP neural network are all optimized simultaneously by immune continuous ant colony algorithm. At last, to verify this new evolutionary neural network, the typical XOR problem is used. And also, the new evolutionary neural network is compared and analyzed with BP neural network, traditional evolutionary neural network based on genetic algorithm and evolutionary neural network based on evolutionary programming. The computing results show that the precision and efficiency of the new evolutionary neural network are all the best.

Reducer failure was analyzed and by use of BP neural network in the paper. Model of failure diagnosis was established. By using genetic algorithms, the value of neural networks, the threshold, and the network structure were optimized. Genetic neural network model was applied to the system design of remote reducer fault diagnosis. To compare training error curve of BP neural network with genetic neural network, it was shown that genetic neural network in the training of speed and accuracy higher than the neural network training model.

Background Urinary tract infections (UTIs) are a common cause of healthcare visits and hospitalizations. A recent study attributed about 8.7 million ER visits to UTIs from 2016 to 20231. Asymptomatic bacteriuria (ASB) is often mistaken for a UTI, leading to unnecessary treatment and the development of resistant bacteria. This study aims to develop a deep learning model to differentiate ASB from a UTI based on urinalysis parameters and to compare its performance to traditional statistical methods, specifically logistic regression.Figure 1.Feedforward neural network model with 3 hidden layers and 2, 128, and 8 nodes in their respective layers.Figure 2.Proportion of ASB (Blue) vs. a UTI (Red) of urinalysis parameters. Methods This retrospective case-control study included adult patients with ASB or UTIs from January 1st, 2010 to December 31st, 2022. Urinalysis parameters and urine culture results were collected. A feedforward neural network with 30 input features and 2 hidden layers was trained and tuned to predict either ASB or a UTI (Figure 1). The model's performance was compared to a stepwise bidirectional logistic regression model. Results 272 patient encounters were included (85 UTI, 187 ASB). The neural network model was found to be the optimal hyperparameter for the model based on the highest validation AUC of 0.69 (Figure 1). The tuned model achieved a binary accuracy of 0.83, precision of 0, and recall of 0 on the validation set with batch size of 4 and learning rate of 0.0003. The testing set showed an AUC of 0.35, binary accuracy of 0.842, precision of 0, and recall of 0 (Figure 2). The stepwise bidirectional logistic regression model, with an AIC of 270.65, demonstrated an accuracy of 0.745, but a precision of 0, a recall of 0, and an AUC of 0.525. Statistically significant parameters (p< 0.01) included red color (estimate: 0.6956), straw color (estimate: -0.292), and densely turbid clarity (estimate: 1.696). Conclusion While the neural network model showed some predictive capabilities, the logistic regression model had higher accuracy. However, it also lacked precision and recall, indicating limitations in its ability to correctly identify UTI cases. Future tests with larger datasets may improve the models' accuracy in differentiating ASB from a UTI, if such patterns exist. Our results highlight the complexity of developing a robust predictive model for differentiating ASB vs. a UTI and the need for continued research on the topic. Disclosures All Authors: No reported disclosures

BackgroundGenetic factors strongly influence the risk of coronary heart disease (CHD), but their contribution to variation in coronary atherosclerosis beyond that measured by traditional CHD risk factors is uncertain.MethodsWe recruited...

A Risk Score for Chronic Kidney Disease in the General Population

920 Conditional Analysis Identifies Two Major Histocompatibility Complex Loci Associated With Primary Biliary Cirrhosis