BP人工神经网络在光谱定量预测中的应用

人工神经网络是模仿大脑神经元网络结构和功能而建立的一种信息处理系统,广泛的应用于各种波谱数据处理。误差反向传播多层前馈式网络(back-propagation network,简称BP网络)应用最广,发展最为迅速。将BP神经网络用于紫外-可见吸收光谱和拉曼光谱数据的定量分析和预测,与原文的一元线性回归模型数据处理方法相比,获得了比较满意的预测结果,预测精度有显著提高。这为相关的光谱分析和数据处理提供了一种更有效、更精确的方法。     

Prediction of Apple Fruit Quality by Soil Nutrient Content and Artificial Neural Network

The effect of soil nutrient content on fruit yield and fruit quality is very important. To explore the effect of soil nutrients on apple quality we investigated 200 fruit samples from 40 orchards in Feng County, Jiangsu Province. Soil mineral elements and fruit quality were measured. The effect of soil nutrient content on fruit quality was analyzed by artificial neural network (ANN) model. The results showed that the prediction accuracy was highest (R²= 0.851, 0.847, 0.885, 0.678 and 0.746) in mass per fruit (MPF), hardness (HB), soluble solids concentrations (SSC), titratable acid concentration (TA) and solid-acid ratio (SSC/TA), respectively. The sensitivity analysis of the prediction model showed that soil available P, K, Ca and Mg contents had the greatest impact on the quality of apple fruit. Response surface method (RSM) was performed to determine the optimum range of the available P, K, Ca, and Mg contents in orchards In Feng County, which were 10∼20 mg⋅kg⁻¹, 170∼200 mg⋅kg⁻¹, 1000∼1500 mg⋅kg⁻¹, and 80∼200 mg⋅kg⁻¹, respectively. The research also concluded that improving the content of available P and available Ca in orchard soil was crucial to improve apple fruit quality in Feng County, Jiangsu Province.     

Prediction of Disease Causing Non-Synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP

We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: http://www.cbs.dtu.dk/services/NetDiseaseSNP     

Quantitative Design of Regulatory Elements Based on High-Precision Strength Prediction Using Artificial Neural Network

Accurate and controllable regulatory elements such as promoters and ribosome binding sites (RBSs) are indispensable tools to quantitatively regulate gene expression for rational pathway engineering. Therefore, de novo designing regulatory elements is brought back to the forefront of synthetic biology research. Here we developed a quantitative design method for regulatory elements based on strength prediction using artificial neural network (ANN). One hundred mutated Trc promoter & RBS sequences, which were finely characterized with a strength distribution from 0 to 3.559 (relative to the strength of the original sequence which was defined as 1), were used for model training and test. A precise strength prediction model, NET90_19_576, was finally constructed with high regression correlation coefficients of 0.98 for both model training and test. Sixteen artificial elements were in silico designed using this model. All of them were proved to have good consistency between the measured strength and our desired strength. The functional reliability of the designed elements was validated in two different genetic contexts. The designed parts were successfully utilized to improve the expression of BmK1 peptide toxin and fine-tune deoxy-xylulose phosphate pathway in Escherichia coli. Our results demonstrate that the methodology based on ANN model can de novo and quantitatively design regulatory elements with desired strengths, which are of great importance for synthetic biology applications.     

Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.     

Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

Background

This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches.

Methods and Materials

We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared.

Results

Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses.

Conclusion

The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset.     

相空间重构与神经网络融合预测模型及其在害虫测报中的应用

针对混沌时间序列,建立了相空间重构和BP神经网络融合预测模型,经实例验证,该方法能有效地提高预测精度,避免了一般混沌预测要求较长时间序列的缺陷,为害虫的可预测性提供了一种新的依据。     

Maximum Likelihood Neural Network Prediction Models

Neural networks have received much attention in recent years mostly by non-statisticians. The purpose of this paper is to incorporate neural networks in a non-linear regression model and obtain maximum likelihood estimates of the network parameters using a standard Newton-Raphson algorithm. We use maximum likelihood estimators instead of the usual back-propagation technique and compare the neural network predictions with predictions of quadratic regression models and with non-parametric nearest neighbor predictions. These comparisons are made using data generated from a variety of functions. Because of the number of parameters involved, neural network models can easily over-fit the data, hence validation of results is crucial.     

Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study

Background

Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since the treatments for advanced radiographic knee OA are limited, clinicians face a significant challenge of identifying patients who are at high risk of OA in a timely and appropriate way. Therefore, we developed a simple self-assessment scoring system and an improved artificial neural network (ANN) model for knee OA.

Methods

The Fifth Korea National Health and Nutrition Examination Surveys (KNHANES V-1) data were used to develop a scoring system and ANN for radiographic knee OA. A logistic regression analysis was used to determine the predictors of the scoring system. The ANN was constructed using 1777 participants and validated internally on 888 participants in the KNHANES V-1. The predictors of the scoring system were selected as the inputs of the ANN. External validation was performed using 4731 participants in the Osteoarthritis Initiative (OAI). Area under the curve (AUC) of the receiver operating characteristic was calculated to compare the prediction models.

Results

The scoring system and ANN were built using the independent predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain. In the internal validation, both scoring system and ANN predicted radiographic knee OA (AUC 0.73 versus 0.81, p<0.001) and symptomatic knee OA (AUC 0.88 versus 0.94, p<0.001) with good discriminative ability. In the external validation, both scoring system and ANN showed lower discriminative ability in predicting radiographic knee OA (AUC 0.62 versus 0.67, p<0.001) and symptomatic knee OA (AUC 0.70 versus 0.76, p<0.001).

Conclusions

The self-assessment scoring system may be useful for identifying the adults at high risk for knee OA. The performance of the scoring system is improved significantly by the ANN. We provided an ANN calculator to simply predict the knee OA risk.     

利用神经网络对蛋白质家族进行预测

提出了一种利用神经网络为蛋白质家族建立模型的方法,这一方法的理论出发点是利用神经网络从一组同家族蛋白质序列中识别出共同的特征模式,建好的模型可用于预测蛋白质家族,使用这一方法。所能识别的模式在长度、位点等方面都不受限制。而且建模及预测过程中输入神经网络的蛋白质序列不需要作预对齐。对Pfam蛋白质库中的二十个家族运用此方法,预测的平均正确率达到了95．5％。     

基于人工神经网络的昆虫鸣声识别

以常见的7种飞虱雄虫求偶鸣声信号的频率峰值作为输入向量,用人工神经网络来识别它们的鸣声,平均识别率达90．6％。人工神经网络可以用于昆虫鸣声识别。     

FT-IR Spectroscopy and Artificial Neural Network Identification of Fusarium Species

A rapid spectroscopic approach for whole‐organism fingerprinting of Fourier transform infrared (FT‐IR) spectroscopy was used to analyse 16 isolates from five closely related species of Fusarium: F. graminearum, F. moniliforme, F. nivale, F. semitectum and F. oxysporum. Principal components analysis and hierarchical cluster analysis were used to study the clusters in the data. On visual inspection of the clusters from both methods, the spectra were not differentiated into five separate clusters corresponding to species and these unsupervised methods failed to identify these fungal strains. When the data were trained by back propagation algorithm of artificial neural networks (ANNs) with principal components scores of spectra used as input modes, the strains were accurately predicted and recognized. The results in this study show that FT‐IR spectroscopy in combination with principal component artificial neural networks (PC‐ANNs) is well suited for identifying Fusarium spp. It would be advantageous to establish a comprehensive database of taxonomically well‐defined Fusarium species to aid the identification of unknown strains.     

Artificial Neural Network Method for Predicting the Specificity of GalNAc-transferase

The specificity of GalNAc-transferase is consistent with the existence of an extended site composed of nine subsites, denoted by R₄, R₃, R₂, R₁, R₀, R₁, R₂, R₃, and R₄, where the acceptor at R₀ is either Ser or Thr to which the reducing monosaccharide is anchored. To predict whether a peptide will react with the enzyme to form a Ser- or Thr-conjugated glycopeptide, a neural network method—Kohonen's self-organization model is proposed in this paper. Three hundred five oligopeptides are chosen for the training site, with another 30 oligopeptides for the test set. Because of its high correct prediction rate (26/30=86.7%) and stronger fault-tolerant ability, it is expected that the neural network method can be used as a technique for predicting O-glycosylation and designing effective inhibitors of GalNAc-transferase. It might also be useful for targeting drugs to specific sites in the body and for enzyme replacement therapy for the treatment of genetic disorders.     

基于BP神经网络的SARS传播预测

政府的控制措施作为影响SARS传播的因素,利用BP网络,对SARS的传播规律进行预测.以北京市的SARS数据来进行验证,结果显示,该方法准确率非常高.     

Improved Glomerular Filtration Rate Estimation by an Artificial Neural Network

Background

Accurate evaluation of glomerular filtration rates (GFRs) is of critical importance in clinical practice. A previous study showed that models based on artificial neural networks (ANNs) could achieve a better performance than traditional equations. However, large-sample cross-sectional surveys have not resolved questions about ANN performance.

Methods

A total of 1,180 patients that had chronic kidney disease (CKD) were enrolled in the development data set, the internal validation data set and the external validation data set. Additional 222 patients that were admitted to two independent institutions were externally validated. Several ANNs were constructed and finally a Back Propagation network optimized by a genetic algorithm (GABP network) was chosen as a superior model, which included six input variables; i.e., serum creatinine, serum urea nitrogen, age, height, weight and gender, and estimated GFR as the one output variable. Performance was then compared with the Cockcroft-Gault equation, the MDRD equations and the CKD-EPI equation.

Results

In the external validation data set, Bland-Altman analysis demonstrated that the precision of the six-variable GABP network was the highest among all of the estimation models; i.e., 46.7 ml/min/1.73 m² vs. a range from 71.3 to 101.7 ml/min/1.73 m², allowing improvement in accuracy (15% accuracy, 49.0%; 30% accuracy, 75.1%; 50% accuracy, 90.5% [P<0.001 for all]) and CKD stage classification (misclassification rate of CKD stage, 32.4% vs. a range from 47.3% to 53.3% [P<0.001 for all]). Furthermore, in the additional external validation data set, precision and accuracy were improved by the six-variable GABP network.

Conclusions

A new ANN model (the six-variable GABP network) for CKD patients was developed that could provide a simple, more accurate and reliable means for the estimation of GFR and stage of CKD than traditional equations. Further validations are needed to assess the ability of the ANN model in diverse populations.     

Identification of Cryptosporidium parvum Oocysts by an Artificial Neural Network Approach

Microscopic detection of Cryptosporidium parvum oocysts is time-consuming, requires trained analysts, and is frequently subject to significant human errors. Artificial neural networks (ANN) were developed to help identify immunofluorescently labeled C. parvum oocysts. A total of 525 digitized images of immunofluorescently labeled oocysts, fluorescent microspheres, and other miscellaneous nonoocyst images were employed in the training of the ANN. The images were cropped to a 36- by 36-pixel image, and the cropped images were placed into two categories, oocyst and nonoocyst images. The images were converted to grayscale and processed into a histogram of gray color pixel intensity. Commercially available software was used to develop and train the ANN. The networks were optimized by varying the number of training images, number of hidden neurons, and a combination of these two parameters. The network performance was then evaluated using a set of 362 unique testing images which the network had never “seen” before. Under optimized conditions, the correct identification of authentic oocyst images ranged from 81 to 97%, and the correct identification of nonoocyst images ranged from 78 to 82%, depending on the type of fluorescent antibody that was employed. The results indicate that the ANN developed were able to generalize the training images and subsequently discern previously unseen oocyst images efficiently and reproducibly. Thus, ANN can be used to reduce human errors associated with the microscopic detection of Cryptosporidium oocysts.     

基于点云卷积神经网络的蛋白质柔性预测

目的 蛋白质的柔性运动对生物体各种反应有着重要意义,基于蛋白质的空间结构预测其柔性运动是蛋白质结构-功能关系领域的重要问题.卷积神经网络(convolutional neural network,CNN)在蛋白质结构-功能关系研究中已有成功应用.方法 本研究借鉴计算机视觉研究中PointNet方法的思想,提出了一种蛋白...     

Sea water chlorophyll-a estimation using hyperspectral images and supervised Artificial Neural Network

The use of satellite hyperspectral images has improved the extraction of information compared to multispectral images. Although designed as a technical demonstration for land applications, Hyperion satellite hyperspectral images are used to estimate sea water parameters in the coastal area. A combination of turbid river inputs, as well as the open sea flushing, determines the quality of the sea water in the coastal area and the status of its environment. In addition, the existence of different source of pollution adds to the complexity of the coastal sea water analysis. The field campaigns to retrieve sea water parameters provided by the past completed projects were coincident with acquisition of the Hyperion image covering the pilot area. A robust method based on a supervised Feed-Forward Back-Propagation Artificial Neural Network (ANN-BP) algorithm is applied to retrieve the concentration of chlorophyll-a from hyperspectral image. In addition, Hyperion images are used to show the variation of chlorophyll-a during two different periods of time. The variation is due to many manmade environmental disasters such as oil spill and continuous discharge of chemical and solid wastes. The research proves that the new method based on ANN has improved the mathematical regression methods to a coefficient of determination almost equal 1 compared to about 0.4 for the methods not based on ANN-BP.