Robust Prediction of B-Factor Profile from Sequence Using Two-Stage SVR Based on Random Forest Feature Selection

This paper devoted to the optimization of surface electromyography (sEMG) based hand gesture recognition classifier model. Twenty-two features in time domain, frequency domain and time-frequency domain were extracted from sEMG of 6 muscles to classify 13 hand gestures. Correlation analysis was used to screen the features. Random forest (RF) and principal component analysis (PCA) feature selection were used to further reduce the feature dimensions. Finally, mean absolute value (MAV), root mean square (RMS), slope sign change (SSC), mean frequency (MNF), Willison amplitude (WAMP) and autoregressive coefficients (AR1), totally 6 features were selected. LDA (Linear Discriminant Analysis), SVM (Support Vector Machine), KNN (k-Nearest Neighbor), DT (Decision Tree) and NN (Neural Network), in total of five classification methods were applied. The accuracy of the classifier had no significant reduction, but the dimension of the feature set was reduced from 132 to 36, and the training time was greatly reduced. The result indicated that the combination of correlation analysis and random forest feature selection can optimize the classifier model and greatly reduce the training time of the classifier.

Cancer remains the most lethal condition in the world, accounting for 10 million deaths worldwide i.e. 1/6 of all deaths. Currently, cancer detection is primar-ily through symptomatic routes wherein patients present with serious symptoms and undergo imaging and subsequently biopsy of suspected cancer growths for histopathological confirmation of cancer. A diagnostic tool through serum anal-ysis could revolutionise current cancer pathways. Raman spectroscopy offers the ability to measure a complex biochemical fingerprint of a sample through vibra-tional energy shifts. Numerous studies with Raman of biological samples (serum, plasma, tissue, cellular) exist showing promising results for the detection of can-cer. However, these studies are typically limited to proof-of-concept and halt at larger scale studies or forward looking to how these methods might inter-rupt current clinical pathways. Presented in this thesis are a methodology for the optimisation of cancer detection from Raman spectroscopy of human blood serum. The first results chapters show tools in R for the pre-processing of Raman spectra using a variety of techniques. This is followed by an application for qual-ity control of Raman spectra with a view of the necessary safety nets required for tools to integrate into a clinical setting. These tools are then utilised for the task of optimising pre-processing specifically for colorectal cancer detection with human blood serum spectra using high-performance computing (HPC). 2.4 million different pre-processing permutations are trialled in total. This method-ology saw an improvement in diagnostic abilities, with sensitivity increasing by 14.6%, specificity increasing by 6.9%, positive predictive value (PPV) increasing by 3.4%, and negative predictive value increasing by 2.4% when compared to a standard pre-processing optimisation. A similar methodology using HPC is then utilised in chapter 7 to optimise machine learning algorithm selection and feature reduction for colorectal cancer detection of serum spectra. Feature reduction methods principle component analysis (PCA), factor analysis (FA), ElasticNet (EN), and random forest feature selection (RFFS) combined with model types k nearest-neighbours (KNN), logistic regression (LR), support vector machines (SVM), and random forest (RF) are trialled. Traditional feature reduction meth-ods such as PCA and FA were found to perform poorly compared to techniques EN and RFFS. In addition, model types SVM and RF outperform methods LR and KNN. This chapter also shows results from applying artificial neural net-work architectures for colorectal cancer detection and finding that linear machine learning methods (SVM/RF) outperform neural networks. Finally, the last re-sults chapter presents the culmination of these optimised methods applied to building machine learning models for the detection of other cancer types; breast, pancreatic, lung, colorectal cancer. The models achieved 90.9% sensitivity and 77.3% specificity for pancreatic cancer vs. controls, 90.3% sensitivity and 64.5%specificity for lung cancer vs. controls, 92.1% sensitivity and 65.8% specificity for breast cancer and controls, and finally 91.3% sensitivity and 44.0% specificity for colorectal cancer vs. controls where models are thresholded to achieve a minimum of 90% sensitivity. This chapter also focuses on differences in metabolite profile from the Raman spectra between different cancer types and aims to elucidate biochemical linkages.

Potential of radial basis function based support vector regression for global solar radiation prediction

A major challenge in structural bioinformatics is the prediction of protein structure and function from primary amino acid sequences. The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. In this paper, we developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression with parametric insensitive model.KeywordsRoot Mean Square ErrorSupport Vector RegressionPrimary Amino Acid SequenceSequence SeparationProtein Secondary Structure PredictionThese 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.

Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission

In order to manage the grid resources more effectively, the prediction information of grid resources is necessary in the grid system. This study developed a new model, ISGA-SVR, for parameters optimization in support vector regression (SVR), which is then applied to grid resources prediction. In order to build an effective SVR model, SVR’s parameters must be selected carefully. Therefore, we develop genetic algorithms with improved simulated binary crossover (ISBX) that can automatically determine the optimal parameters of SVR with higher predictive accuracy. In ISBX, we proposed a new method to deal with the bounded search space. This method can improve the search ability of original simulated binary crossover (SBX) .The proposed model was tested with grid resources benchmark data set. Experimental results demonstrated that ISGA-SVR worked better than SVR optimized by genetic algorithm with SBX(SGA-SVR) and back-propagation neural network (BPNN).KeywordsGrid resources predictionSupport vector regressionGenetic algorithmsImproved Simulated Binary Crossover

The absorption of capillary water is one of the most crucial factors in the flow of groundwater in rocks (CWA). Although meticulous experimental studies are needed to determine a rock’s CWA, predictive techniques might cut down on the expense and effort. There are various data mining methods for this purpose, but the considered algorithms in this study were not proposed so far for predicting the CWA. Different rock samples were taken for this purpose from various locations, yielding diverse rocks. For the prediction procedures, four support vector regression (SVR) models were created: a traditional SVR, two ensembled models, and a hybrid SVR model using the whale optimization technique (WOA - SVR). Results show that all models have acceptable performance in predicting the CWA with R2 larger than 0.797 and 0.806 for the training and testing data, respectively, representing the acceptable correlation between observed and predicted values. Regarding developed models, the conventional SVR model has the worst performance of all models. All statistical evaluation criteria were improved by assembling models, which present the ability of additive regression and bagging predictions in improving prediction processes. The hybrid WOA - SVR model has the best performance considering all indices. This hybrid model could also gain the lowest values of error indices between all SVR models, which leads to outperforming the WOA - SVR model compared to other methods.

This paper investigates the applicability of time and time-frequency features\nbased classifiers to distinguish internal faults and other transients -\nmagnetizing inrush, sympathetic inrush, external faults with current\ntransformer saturation, and overexcitation - for Indirect Symmetrical Phase\nAngle Regulating Transformers (ISPAR). Then the faulty transformer unit\n(series/exciting) of the ISPAR is located, or else the transient disturbance is\nidentified. An event detector detects variation in differential currents and\nregisters one-cycle of 3-phase post transient samples which are used to extract\nthe time and time-frequency features for training seven classifiers. Three\ndifferent sets of features - wavelet coefficients, time-domain features, and\ncombination of time and wavelet energy - obtained from exhaustive search using\nDecision Tree, random forest feature selection, and maximum Relevance Minimum\nRedundancy are used. The internal fault is detected with a balanced accuracy of\n99.9%, the faulty unit is localized with balanced accuracy of 98.7% and the\nno-fault transients are classified with balanced accuracy of 99.5%. The results\nshow potential for accurate internal fault detection and localization, and\ntransient identification. The proposed scheme can supervise the operation of\nexisting microprocessor-based differential relays resulting in higher stability\nand dependability. The ISPAR is modeled and the transients are simulated in\nPSCAD/EMTDC by varying several parameters.\n

Predictive modeling for the development of diabetes mellitus using key factors in various machine learning approaches

8572 Background: Small Cell Lung Cancer (SCLC) is an aggressive malignancy with a rapid growth, and Chemotherapy remains mainstay of treatment. Identifying therapeutic targets in SCLC presents a challenge, partially due to a lack of accurate and consistently predictive biomarkers. In this study we sought to evaluate the utility of a combination of computer-extracted radiographic and pathology features from pretreatment baseline CT and H&E biopsy images to predict sensitivity to platinum-based chemotherapy and overall survival (OS) in SCLC. Methods: Seventy-eight patients with extensive and limited-stage SCLC who received platinum-doublet chemotherapy were selected. Objective response to chemotherapy (RECIST criteria) and overall survival (OS) as clinical endpoints were available for 51 and 78 patients respectively. The patients were divided randomly into two sets (Training (Sd), Validation (Sv)) with a constraint (equal number of responders and nonresponders in Sd)—Sd comprised twenty-one patients with SCLC. Sv included thirty patients. CT scans and digitized Hematoxylin Eosin-stained (H&E) biopsy images were acquired for each patient. A set of CT derived (46%) and tissue derived (53%) image features were captured. These included shape and textural patterns of the tumoral and peritumoral regions from CT scans and of tumor regions on H&E images. A random forest feature selection and linear regression model were used to identify the most predictive CT and H&E derived image features associated with chemotherapy response from Sd. A Cox proportional hazard regression model was used with these features to compute a risk score for each patients in Sd. Patients in Sv were stratified into high and low-risk groups based on the median risk score. Kaplan-Meier survival analysis was used to assess the prognostic ability of the risk score on Sv. Results: The risk score comprised nine CT (intra and peri-tumoral texture) and six H&E derived (cancer cell texture and shape) features. A linear regression model in conjunction with these 15 features was significantly associated with chemo-sensitivity in Sv (AUC = 0.76, PRC = 0.81). A multivariable model with these 15 features was significantly associated with OS in Sv (HR = 2.5, 95% CI: 1.3-4.9, P = 0.0043). Kaplan-Meier survival analysis revealed a significantly reduced OS in the high-risk group compared to the low-risk group. Conclusions: A combined CT and H&E tissue derived image signature model predicted response to chemotherapy and improved OS in SCLC patients. Image features from baseline CT scans and H&E tissue slide images may help in better risk stratification of SCLC patients. Additional independent validation of these quantitative image-based biomarkers is warranted.

IntroductionDespite technological advancements in recent years, glenoid component loosening remains a common complication after anatomical total shoulder arthroplasty (ATSA) and is one of the main causes of revision surgery. Increasing...

Two-phase multi-kernel LP-SVR for feature sparsification and forecasting

BackgroundAs genome sequencing is becoming routine in biomedical research, the total number of protein sequences is increasing exponentially, recently reaching over 108 million. However, only a tiny portion of these proteins (i.e. ~75,000 or < 0.07%) have solved tertiary structures determined by experimental techniques. The gap between protein sequence and structure continues to enlarge rapidly as the throughput of genome sequencing techniques is much higher than that of protein structure determination techniques. Computational software tools for predicting protein structure and structural features from protein sequences are crucial to make use of this vast repository of protein resources.ResultsTo meet the need, we have developed a comprehensive MULTICOM toolbox consisting of a set of protein structure and structural feature prediction tools. These tools include secondary structure prediction, solvent accessibility prediction, disorder region prediction, domain boundary prediction, contact map prediction, disulfide bond prediction, beta-sheet topology prediction, fold recognition, multiple template combination and alignment, template-based tertiary structure modeling, protein model quality assessment, and mutation stability prediction.ConclusionsThese tools have been rigorously tested by many users in the last several years and/or during the last three rounds of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7-9) from 2006 to 2010, achieving state-of-the-art or near performance. In order to facilitate bioinformatics research and technological development in the field, we have made the MULTICOM toolbox freely available as web services and/or software packages for academic use and scientific research. It is available at http://sysbio.rnet.missouri.edu/multicom_toolbox/.

Time series analysis and prediction is an important means of dynamic system modeling. A new method of time series prediction based on support vector regression (SVR) is introduced to resolve the problem of non-linear system modeling. For the purpose of reducing calculation complexity, smooth method is presented to improve standard SVR arithmetic, and is utilized to build the combustion state model of flame in the furnace of utility boilers according to the feature parameters of flame image, in order to predict the combustion state of flame. The flame images are gained from the flame image gathering system on-line. The feature parameters of the flame image are extracted, and are used to determined combustion indices which can denote different combustion states of flame. The time series of combustion indices are used for constructing the smooth support vector regression (SSVR) model and predicting the combustion state of flame. The results of experimentation indicate that SSVR has excellent performance on time series prediction. Compared with traditional time series prediction method such as artificial neural network, SSVR has faster convergence speed and higher fitting precision, which effectively extends the application of SVR.

The co-gasification of biomass and plastic waste offers a promising pathway for sustainable syngas production, necessitating precise prediction of its composition to optimize efficiency. This study compares the performance of Aspen Plus models, including the thermodynamic equilibrium model (TEM) and restricted thermodynamic equilibrium model (RTM), with machine learning (ML) techniques, focusing on the support vector regression (SVR) for syngas prediction during steam and air co-gasification. Aspen Plus simulations provided valuable mechanistic insights, while the ML model demonstrated superior predictive accuracy. The SVR, enhanced by principal component analysis (PCA), significantly improved performance, achieved R� values of 0.879 for H2, 0.856 for CO, 0.859 for CO2, and 0.744 for CH4 on the testing dataset. It also outperformed other models in terms of RMSE, achieving exceptional precision for CH4 (0.0087), CO (0.0193), and H2 (0.0194). In contrast, RTM exhibited moderate accuracy with minimal deviations, while TEM showed the highest RMSE values across all predictions, indicating its limited reliability for real-world applications. This study advances gasification technologies by demonstrating the advantages of ML models, particularly SVR, in predicting syngas composition and supporting the global transition toward sustainable energy systems.