Lung cancer prediction from microarray data by gene expression programming

Lung cancer is a leading cause of cancer‐related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)‐based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP‐based prediction models. Prediction performance evaluations and comparisons between the authors’ GEP models and three representative machine learning methods, support vector machine, multi‐layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross‐data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.Inspec keywords: lung, cancer, medical diagnostic computing, patient diagnosis, genetic algorithms, feature selection, learning (artificial intelligence), support vector machines, multilayer perceptrons, radial basis function networks, reliability, sensitivity analysisOther keywords: lung cancer prediction, cancer‐related death, cancer diagnosis, gene profiles, gene expression programming‐based model, gene selection, GEP‐based prediction models, prediction performance evaluations, representative machine learning methods, support vector machine, multilayer perceptron, radial basis function neural network, real microarray lung cancer datasets, cross‐data set validation, reliability, receiver operating characteristic curve     

Optical, real-time monitoring of the glomerular filtration rate

An easy and accurate assessment of the renal function is a critical requirement for detecting the initial functional decline of the kidney induced by acute or chronic renal disease. A method for measuring the glomerular filtration rate is developed with the accuracy of clearance techniques and the convenience of plasma creatinine. The renal function is measured in rats as the rate of clearance determined from time-resolved transcutaneous fluorescence measurements of a new fluorescent glomerular filtration agent. The agent has a large dose-safety coefficient and the same space distribution and clearance characteristics as iothalamate. This new approach is a convenient and accurate way to perform real-time measurements of the glomerular filtration rate to detect early kidney disease before the renal function becomes severely and irreversibly compromised.     

Estimation of magnetohydrodynamic radiative nanofluid flow over a porous non‐linear stretching surface: application in biomedical research

The present study investigates the effects of thermal radiation and chemical reaction on magnetohydrodynamic flow, heat, and mass transfer characteristics of nanofluids such as Cu–water and Ag–water over a non‐linear porous stretching surface in the presence of viscous dissipation and heat generation. Using similarity transformation, the governing boundary layer equations of the problem are transformed into non‐linear ordinary differential equations and solved numerically by the shooting method along with the Runge–Kutta–Fehlberg fourth–fifth‐order integration scheme. The influences of various parameters on velocity, temperature, and concentration profiles of the flow field are analysed and the results are plotted graphically. A backpropagation neural network is applied to predict the skin friction coefficient, Nusselt number, and Sherwood number and these results are presented through graphs. The present numerical results are compared with the existing results and are found to be in good agreement. The results of artificial neural network and the obtained numerical values agree well with an error <5%.Inspec keywords: silver, copper, transforms, nanofluidics, friction, backpropagation, heat radiation, water, external flows, partial differential equations, nonlinear differential equations, boundary layers, Runge‐Kutta methods, mass transfer, flow through porous media, magnetohydrodynamicsOther keywords: magnetohydrodynamic radiative nanofluid flow, nonlinear stretching surface, biomedical research, thermal radiation, chemical reaction, magnetohydrodynamic flow, nonlinear porous stretching surface, viscous dissipation, similarity transformation, governing boundary layer equations, nonlinear ordinary differential equations, shooting method, Runge–Kutta–Fehlberg fourth–fifth‐order integration scheme, flow field, backpropagation neural network, Cu–water nanofluid, Ag–water nanofluid, skin friction coefficient, Nusselt number, Sherwood number, artificial neural network, Ag‐H₂ O, Cu‐H₂ O     

Efficient prediction of drug–drug interaction using deep learning models

A drug–drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug–drug interaction is defined as an ill‐posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug–drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug–drug interaction score efficiently. However, these models suffer from the over‐fitting issue. Therefore, these models are not so‐effective for predicting the drug–drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.Inspec keywords: cancer, learning (artificial intelligence), drugs, recurrent neural nets, convolutional neural nets, drug delivery systemsOther keywords: drug synergy, drug–drug interaction score, drug–drug interaction prediction, deep learning, cancer treatment, machine learning, convolutional mixture density recurrent neural network     

Efficient heart disease prediction‐based on optimal feature selection using DFCSS and classification by improved Elman‐SFO

Prediction of cardiovascular disease (CVD) is a critical challenge in the area of clinical data analysis. In this study, an efficient heart disease prediction is developed based on optimal feature selection. Initially, the data pre‐processing process is performed using data cleaning, data transformation, missing values imputation, and data normalisation. Then the decision function‐based chaotic salp swarm (DFCSS) algorithm is used to select the optimal features in the feature selection process. Then the chosen attributes are given to the improved Elman neural network (IENN) for data classification. Here, the sailfish optimisation (SFO) algorithm is used to compute the optimal weight value of IENN. The combination of DFCSS–IENN‐based SFO (IESFO) algorithm effectively predicts heart disease. The proposed (DFCSS–IESFO) approach is implemented in the Python environment using two different datasets such as the University of California Irvine (UCI) Cleveland heart disease dataset and CVD dataset. The simulation results proved that the proposed scheme achieved a high‐classification accuracy of 98.7% for the CVD dataset and 98% for the UCI dataset compared to other classifiers, such as support vector machine, K‐nearest neighbour, Elman neural network, Gaussian Naive Bayes, logistic regression, random forest, and decision tree.Inspec keywords: cardiovascular system, medical diagnostic computing, feature extraction, regression analysis, data mining, learning (artificial intelligence), Bayes methods, neural nets, support vector machines, diseases, pattern classification, data handling, decision trees, cardiology, data analysis, feature selectionOther keywords: efficient heart disease prediction‐based, optimal feature selection, improved Elman‐SFO, cardiovascular disease, clinical data analysis, data pre‐processing process, data cleaning, data transformation, values imputation, data normalisation, decision function‐based chaotic salp swarm algorithm, optimal features, feature selection process, improved Elman neural network, data classification, sailfish optimisation algorithm, optimal weight value, DFCSS–IENN‐based SFO algorithm, DFCSS–IESFO, California Irvine Cleveland heart disease dataset, CVD dataset, high‐classification accuracy     

Cancer adjuvant chemotherapy prediction model for non‐small cell lung cancer

Non‐small cell lung cancer (NSCLC) is the most popular and dangerous type of lung cancer. Adjuvant chemotherapy (ACT) is the main treatment after surgery resection to prevent the patient from cancer recurrence. However, ACT could be toxic and unhelpful in some cases. Therefore, it is highly desired in clinical applications to predict the treatment outcomes of chemotherapy. Conventional methods of predicting cancer treatment rely solely on histopathology and the results are not reliable in some cases. This study aims at building a predictive model to identify who needs ACT treatment and who should avoid it. To this end, the authors propose an innovative method to identify NSCLC‐related prognostic genes from microarray gene‐expression datasets. They also propose a new model using gene‐expression programming algorithm for ACT classification. The proposed model was evaluated on integrated microarray datasets from four institutes and compared with four representative methods: general regression neural network, decision tree, support vector machine and naive Bayes. Evaluation results demonstrated the effectiveness of the proposed model with accuracy 89.8% which is higher than other representative models. They obtained four probes (four genes) that can get good prediction results. These genes are 204891_s_at (LCK), 208893_s_at (DUSP6), 202454_s_at (ERBB3) and 201076_at (MMD).Inspec keywords: neural nets, regression analysis, decision trees, surgery, medical computing, cancer, cellular biophysics, lung, genetics, support vector machines, Bayes methods, biochemistryOther keywords: cancer ACT prediction model, nonsmall cell lung cancer, adjuvant chemotherapy, surgery resection, cancer recurrence, conventional methods, cancer treatment, microarray gene‐expression technology, NSCLC treatment, ACT treatment, NSCLC‐related prognostic genes, microarray gene‐expression datasets, gene‐expression programming algorithm, ACT classification, ACT information, integrated microarray datasets, representative models, survival time, general regression neural network, decision tree, support vector machine, naive Bayes     

Protein interaction network constructing based on text mining and reinforcement learning with application to prostate cancer

Constructing interaction network from biomedical texts is a very important and interesting work. The authors take advantage of text mining and reinforcement learning approaches to establish protein interaction network. Considering the high computational efficiency of co‐occurrence‐based interaction extraction approaches and high precision of linguistic patterns approaches, the authors propose an interaction extracting algorithm where they utilise frequently used linguistic patterns to extract the interactions from texts and then find out interactions from extended unprocessed texts under the basic idea of co‐occurrence approach, meanwhile they discount the interaction extracted from extended texts. They put forward a reinforcement learning‐based algorithm to establish a protein interaction network, where nodes represent proteins and edges denote interactions. During the evolutionary process, a node selects another node and the attained reward determines which predicted interaction should be reinforced. The topology of the network is updated by the agent until an optimal network is formed. They used texts downloaded from PubMed to construct a prostate cancer protein interaction network by the proposed methods. The results show that their method brought out pretty good matching rate. Network topology analysis results also demonstrate that the curves of node degree distribution, node degree probability and probability distribution of constructed network accord with those of the scale‐free network well.Inspec keywords: cancer, proteins, molecular biophysics, learning (artificial intelligence), data mining, text analysis, medical computing, topology, statistical distributionsOther keywords: text mining, reinforcement learning, cooccurrence‐based interaction extraction approach, reinforcement learning‐based algorithm, prostate cancer protein interaction network, matching rate, scale‐free network, probability distribution, node degree probability, node degree distribution, network topology     

Evolutionary neural network modeling for software cumulative failure time prediction

An evolutionary neural network modeling approach for software cumulative failure time prediction based on multiple-delayed-input single-output architecture is proposed. Genetic algorithm is used to globally optimize the number of the delayed input neurons and the number of neurons in the hidden layer of the neural network architecture. Modification of Levenberg–Marquardt algorithm with Bayesian regularization is used to improve the ability to predict software cumulative failure time. The performance of our proposed approach has been compared using real-time control and flight dynamic application data sets. Numerical results show that both the goodness-of-fit and the next-step-predictability of our proposed approach have greater accuracy in predicting software cumulative failure time compared to existing approaches.     

The relationship of insulin resistance and body fat in chronic kidney disease patients.

Background:  Insulin resistance has been associated with type 2 diabetes, hypertension, central obesity, and dyslipidemia, all of which are important risk factors for progression of chronic kidney disease (CKD). A greater degree of insulin resistance may predispose to renal injury by worsening renal hemodynamics through the elevation of glomerular filtration fraction. However, there are sparse data on the relationship between insulin resistance, glomerular filtration rate (GFR), and total body fat or phase angle in CKD without diabetes. Methods:  We examined 84 non‐diabetes CKD patients according to the K/DOQI definitions; only 79 patients were enrolled into the study (GFR between 15 and 90 ml/min/1.73 m²). The value of insulin resistance was obtained by homeostasis model assessment (HOMA). Bioelectrical impedance analysis was performed to determine the percentage of total body fat or phase angle. GFR was calculated by the average of creatinine and urea clearances. Results:  The correlation analysis showed that HOMA‐insulin resistance was positively correlated with phase angle (r = 0.35, P < 0.01), percentage of total body fat (r = 0.27, P < 0.01), body mass index (r = 0.48, P < 0.01) and serum triglyceride levels (r = 0.32, P < 0.01), but not significantly correlated with gender (r = −0.07, P > 0.05), age (r = 0.05, P > 0.05), GFR (r = −0.006, P > 0.05), and mean arterial blood pressure (r = 0.11, P > 0.05). Conclusion:  In non‐diabetic chronic kidney disease patients, the major risk factor for insulin resistance is the amount of total body fat. The insulin level is not dependent on the GFR in these patients.     

Parallel implementation of D‐Phylo algorithm for maximum likelihood clusters

This study explains a newly developed parallel algorithm for phylogenetic analysis of DNA sequences. The newly designed D‐Phylo is a more advanced algorithm for phylogenetic analysis using maximum likelihood approach. The D‐Phylo while misusing the seeking capacity of k ‐means keeps away from its real constraint of getting stuck at privately conserved motifs. The authors have tested the behaviour of D‐Phylo on Amazon Linux Amazon Machine Image(Hardware Virtual Machine)i2.4xlarge, six central processing unit, 122 GiB memory, 8 ×  800 Solid‐state drive Elastic Block Store volume, high network performance up to 15 processors for several real‐life datasets. Distributing the clusters evenly on all the processors provides us the capacity to accomplish a near direct speed if there should arise an occurrence of huge number of processors.Inspec keywords: parallel algorithms, Linux, pattern clustering, DNA, molecular biophysics, genetics, biology computingOther keywords: D‐Phylo algorithm parallel implementation, maximum likelihood clusters, DNA sequence phylogenetic analysis, Amazon Linux AMI, HVM, central processing unit, SSD, real‐life datasets, processors, high‐network performance     

BP神经网络在测试系统动态补偿中的应用

测试系统的非线性动态补偿是仪器技术的一个重要方面．采用BP神经网络对测试系统进行动态补偿．BP神经网络的结果决定于网络输入、隐层和输出节点．由于其非线性映射特性,BP神经网络完全能够反映测试系统的动态响应特性．采用了收敛速度较快的递推预报误差算法训练神经网络．试验结果表明,BP神经网络的特性完全能够满足测试系统的动态补偿要求．表明本文的方法是有效的．     

Effects of noise and time delay on E2F's expression level in a bistable Rb‐E2F gene’s regulatory network

The bistable Rb‐E2F gene regulatory network plays a central role in regulating cellular proliferation‐quiescence transition. Based on Gillespie''s chemical Langevin method, the stochastic bistable Rb‐E2F gene’s regulatory network with time delays is proposed. It is found that under the moderate intensity of internal noise, delay in the Cyclin E synthesis rate can greatly increase the average concentration value of E2F. When the delay is considered in both E2F‐related positive feedback loops, within a specific range of delay (3‐13)hr, the average expression of E2F is significantly increased. Also, this range is in the scope with that experimentally given by Dong et al. [65]. By analysing the quasi‐potential curves at different delay times, simulation results show that delay regulates the dynamic behaviour of the system in the following way: small delay stabilises the bistable system; the medium delay is conducive to a high steady‐state, making the system fluctuate near the high steady‐state; large delay induces approximately periodic transitions between high and low steady‐state. Therefore, by regulating noise and time delay, the cell itself can control the expression level of E2F to respond to different situations. These findings may provide an explanation of some experimental result intricacies related to the cell cycle.     

Degree‐adjusted algorithm for prioritisation of candidate disease genes from gene expression and protein interactome

Computational methods play an important role in the disease genes prioritisation by integrating many kinds of data sources such as gene expression, functional annotations and protein–protein interactions. However, the existing methods usually perform well in predicting highly linked genes, whereas they work quite poorly for loosely linked genes. Motivated by this observation, a degree‐adjusted strategy is applied to improve the algorithm that was proposed earlier for the prediction of disease genes from gene expression and protein interactions. The authors also showed that the modified method is good at identifying loosely linked disease genes and the overall performance gets enhanced accordingly. This study suggests the importance of statistically adjusting the degree distribution bias in the background network for network‐based modelling of complex diseases.Inspec keywords: biochemistry, bioinformatics, diseases, genetics, genomics, medical computing, physiological models, proteins, statistical analysis, proteomicsOther keywords: degree‐adjusted algorithm, candidate disease genes prioritisation, gene expression, protein interactome, computational method, functional annotation, protein–protein interaction, highly linked genes prediction, disease genes prediction, loosely linked disease genes identification, degree distribution bias statistical adjustment, complex disease network‐based modelling     

Predictable capability control scheme for oxygen‐exchange blood flow regulation in an extracorporeal membrane oxygenation system

Extracorporeal membrane oxygenation system is used for rescue treatment strategies for temporary cardiopulmonary function support to facilitate adequately oxygenated blood to return into the systemic and pulmonary circulation systems. Therefore, a servo flow regulator is used to adjust the roller motor speed, while support blood flow can match the sweep gas flow (GF) in a membrane oxygenator. A generalised regression neural network is designed as an estimator to automatically estimate the desired roller pump speed and control parameters. Then, the proportional–integral–derivative controller with tuning control parameters showed good performance to achieve speed regulation and speed tracking in the desired operating point. Given the pressure of carbon dioxide, drainage blood flow, and cannula size, the proposed predictable capability control scheme can be validated to meet the intended uses in clinical applications.Inspec keywords: haemodynamics, three‐term control, medical control systems, patient treatment, neural netsOther keywords: oxygen‐exchange blood flow regulation, extracorporeal membrane oxygenation system, rescue treatment strategies, temporary cardiopulmonary function support, roller motor speed, sweep gas flow, generalised regression neural network, proportional–integral–derivative controller, tuning control parameters, speed regulation, speed tracking     

基于BP神经网络的压电陶瓷蠕变预测

压电陶瓷驱动器的蠕变误差随时间呈现非线性变化,难以实时修正。提出基于BP神经网络的压电陶瓷蠕变预测方法,使用压电陶瓷驱动系统采集数据,对数据进行归一化处理,通过实验设计BP神经网络的隐含层数、隐含层节点数、节点转移函数和训练函数,构建BP神经网络预测模型,建立压电陶瓷蠕变与时间的关系。用BP神经网络模型对压电陶瓷蠕变进行了预测仿真,并将结果与实测数据进行了对比。结果表明,蠕变预测结果与实验数据的最大绝对误差均小于0.1 μm,最大蠕变误差均不超过0.6%,最大均方误差仅为0.0021,可见,BP预测模型具有较高的预测精度,可作为预测压电陶瓷蠕变误差的一种有效手段。     

Engineering molecular communications integrated with carbon nanotubes in neural sensor nanonetworks

There have been recent advances in the engineering of molecular communication (MC)‐based networks for nanomedical applications. However, the integration of MC with biomaterials such as carbon nanotubes (CNTs) presents various critical research challenges. In this study, the authors envisaged integrating MC‐based nanonetwork with CNTs to optimise nanonetwork performance. In neural networks, a chronic reduction in the concentration of the neurotransmitter acetylcholine (ACh) eventually leads to the development of neurodegenerative diseases; therefore, they used CNTs as a molecular switch to optimise ACh conductivity supported by artificial MC. Furthermore, MC enables communication between transmitter neurons and receiver neurons for fine‐tuning the ACh release rate according to the feedback concentration of ACh. Subsequently, they proposed a min/max feedback scheme to fine‐tune the expected throughput and ACh transmission efficiency. For demonstration purposes, they deduced analytical forms for the proposed schemes in terms of throughput, incurred traffic rates, and average packet delay.Inspec keywords: carbon nanotubes, cellular biophysics, diseases, feedback, nanomedicine, nanosensors, neural nets, neurophysiologyOther keywords: carbon nanotubes, neural sensor nanonetworks, nanomedical applications, biomaterials, molecular communication‐based nanonetwork, neural networks, neurotransmitter acetylcholine, neurodegenerative diseases, transmitter neurons, receiver neurons     

基于递归神经网络的传感器非线性动态建模

根据动态校准实验结果建立传感器的动态数学模型,以研究传感器的动态性能,是动态测试的一个重要内容。讨论了递归神经网络模型在传感器动态建模中的应用,给出了递归神经网络模型的结构及相应的训练算法。由于其反馈特征,使得递归神经网络模型能获取系统的动态响应。该方法特别适用于传感器非线性动态建模,而且避免了传感器模型阶次的选择的困难。试验结果表明,应用递归神经网络对传感器进行动态建模是一种行之有效的方法。     

Identifying latent dynamic components in biological systems

In computational systems biology, the general aim is to derive regulatory models from multivariate readouts, thereby generating predictions for novel experiments. In the past, many such models have been formulated for different biological applications. The authors consider the scenario where a given model fails to predict a set of observations with acceptable accuracy and ask the question whether this is because of the model lacking important external regulations. Real‐world examples for such entities range from microRNAs to metabolic fluxes. To improve the prediction, they propose an algorithm to systematically extend the network by an additional latent dynamic variable which has an exogenous effect on the considered network. This variable''s time course and influence on the other species is estimated in a two‐step procedure involving spline approximation, maximum‐likelihood estimation and model selection. Simulation studies show that such a hidden influence can successfully be inferred. The method is also applied to a signalling pathway model where they analyse real data and obtain promising results. Furthermore, the technique can be employed to detect incomplete network structures.Inspec keywords: biology computing, RNA, splines (mathematics), maximum likelihood estimation, approximation theory, biochemistryOther keywords: latent dynamic components, biological systems, computational system biology, regulatory models, multivariate readouts, biological applications, external regulations, real‐world examples, microRNA, metabolic fluxes, latent dynamic variables, variable time course, two‐step procedure, spline approximation, maximum‐likelihood estimation, model selection, signalling pathway model, real data, incomplete network structures