scholarly journals An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zheng-Yang Zhao ◽  
Wen-Zhun Huang ◽  
Xin-Ke Zhan ◽  
Jie Pan ◽  
Yu-An Huang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Biological Evolution ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Sets ◽  
Standard Data ◽  
Light Gradient ◽  
Golden Standard ◽  
Drug Reposition

Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.

scholarly journals Ensemble Learning Prediction of Drug-Target Interactions Using GIST Descriptor Extracted from PSSM-Based Evolutionary Information

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xinke Zhan ◽  
Zhuhong You ◽  
Changqing Yu ◽  
Liping Li ◽  
Jie Pan
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Target Pair ◽  
Evolutionary Information ◽  
Support Vector ◽  
Svm Classifier ◽  
New Drug ◽  
Golden Standard ◽  
Scoring Matrix ◽  
G Protein Coupled

Identifying the drug-target interactions (DTIs) plays an essential role in new drug development. However, there still has the limited knowledge of DTIs and a significant number of unknown DTI pairs. Moreover, the traditional experimental methods have inevitable disadvantages such as high cost and time-consuming. Therefore, developing computational methods for predicting DTIs is attracting more and more attention. In this study, we report a novel computational approach for predicting DTI using GIST feature, position-specific scoring matrix (PSSM), and rotation forest (RF). Specifically, each target protein is first converted into a PSSM for retaining evolutionary information. Then, the GIST feature is extracted from PSSM and substructure fingerprint information is adopted to extract the feature of the drug. Finally, combining each protein and drug features to form a new drug-target pair, which is employed as input feature for RF classifier. In the experiment, the proposed method achieves high average accuracies of 89.25%, 85.93%, 82.36%, and 73.89% on enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively. For further evaluating the prediction performance of the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the same golden standard dataset. These promising results illustrate that the proposed method is more effective and stable than other methods. We expect the proposed method to be a useful tool for predicting large-scale DTIs.

scholarly journals Establishing a Credit Risk Evaluation System for SMEs Using the Soft Voting Fusion Model

Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 202
Author(s):  
Ge Gao ◽  
Hongxin Wang ◽  
Pengbin Gao
Keyword(s):  
Credit Risk ◽  
Evaluation System ◽  
Predictive Accuracy ◽  
Assessment System ◽  
Gradient Boosting ◽  
Support Vector ◽  
Fusion Model ◽  
Light Gradient ◽  
Extreme Gradient Boosting ◽  
The Government

In China, SMEs are facing financing difficulties, and commercial banks and financial institutions are the main financing channels for SMEs. Thus, a reasonable and efficient credit risk assessment system is important for credit markets. Based on traditional statistical methods and AI technology, a soft voting fusion model, which incorporates logistic regression, support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), is constructed to improve the predictive accuracy of SMEs’ credit risk. To verify the feasibility and effectiveness of the proposed model, we use data from 123 SMEs nationwide that worked with a Chinese bank from 2016 to 2020, including financial information and default records. The results show that the accuracy of the soft voting fusion model is higher than that of a single machine learning (ML) algorithm, which provides a theoretical basis for the government to control credit risk in the future and offers important references for banks to make credit decisions.

scholarly journals Interpretable Machine Learning for Early Neurological Deterioration Prediction in Atrial Fibrillation-Related Stroke

2021 ◽  
Author(s):  
Seong Hwan Kim ◽  
Eun-Tae Jeon ◽  
Sungwook Yu ◽  
Kyungmi O ◽  
Chi Kyung Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Neurological Deterioration ◽  
Gradient Boosting ◽  
Support Vector ◽  
Light Gradient ◽  
Interpretable Machine Learning ◽  
Extreme Gradient Boosting ◽  
Early Neurological Deterioration ◽  
Feature Importance

Abstract We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multi-center prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanations (SHAP) method to evaluate feature importance. Of the 3,623 stroke patients, the 2,363 who had arrived at the hospital within 24 hours of symptom onset and had available information regarding END were included. Of these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.778, 95% CI, 0.726 - 0.830). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the SHAP method can be adjusted to individualize the features’ effects on the predictive power of the model.

Identification of DNA-binding proteins via Hypergraph based Laplacian Support Vector Machine

2021 ◽  
Vol 16 ◽  
Author(s):  
Yuqing Qian ◽  
Hao Meng ◽  
Weizhong Lu ◽  
Zhijun Liao ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Binding ◽  
Large Scale ◽  
Binding Proteins ◽  
Predictive Accuracy ◽  
Dna Binding Proteins ◽  
Research Field ◽  
Support Vector ◽  
Data Sets ◽  
Independent Test

Background: The identification of DNA binding proteins (DBP) is an important research field. Experiment-based methods are time-consuming and labor-intensive for detecting DBP. Objective: To solve the problem of large-scale DBP identification, some machine learning methods are proposed. However, these methods have insufficient predictive accuracy. Our aim is to develop a sequence-based machine learning model to predict DBP. Methods: In our study, we extract six types of features (including NMBAC, GE, MCD, PSSM-AB, PSSM-DWT, and PsePSSM) from protein sequences. We use Multiple Kernel Learning based on Hilbert-Schmidt Independence Criterion (MKL-HSIC) to estimate the optimal kernel. Then, we construct a hypergraph model to describe the relationship between labeled and unlabeled samples. Finally, Laplacian Support Vector Machines (LapSVM) is employed to train the predictive model. Our method is tested on PDB186, PDB1075, PDB2272 and PDB14189 data sets. Result: Compared with other methods, our model achieves best results on benchmark data sets. Conclusion: The accuracy of 87.1% and 74.2% are achieved on PDB186 (Independent test of PDB1075) and PDB2272 (Independent test of PDB14189), respectively.

Large-Scale Data Classification Based on Ball Vector Machine

2013 ◽  
Vol 312 ◽  
pp. 771-776
Author(s):  
Min Juan Zheng ◽  
Guo Jian Cheng ◽  
Fei Zhao
Keyword(s):  
Quadratic Programming ◽  
Programming Problem ◽  
Time Complexity ◽  
Large Scale ◽  
Space Complexity ◽  
Quadratic Programming Problem ◽  
Support Vector ◽  
Data Sets ◽  
Standard Support Vector Machine ◽  
Large Scale Data

The quadratic programming problem in the standard support vector machine (SVM) algorithm has high time complexity and space complexity in solving the large-scale problems which becomes a bottleneck in the SVM applications. Ball Vector Machine (BVM) converts the quadratic programming problem of the traditional SVM into the minimum enclosed ball problem (MEB). It can indirectly get the solution of quadratic programming through solving the MEB problem which significantly reduces the time complexity and space complexity. The experiments show that when handling five large-scale and high-dimensional data sets, the BVM and standard SVM have a considerable accuracy, but the BVM has higher speed and less requirement space than standard SVM.

scholarly journals Evaluation of Machine Learning Algorithms for Classification of Primary Biological Aerosol using a new UV-LIF spectrometer

2016 ◽  
Author(s):  
Simon Ruske ◽  
David O. Topping ◽  
Virginia E. Foot ◽  
Paul H. Kaye ◽  
Warren R. Stanley ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Fungal Spores ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Sets ◽  
Agglomerative Clustering ◽  
Real World Data ◽  
Linear Discriminant ◽  
Accuracy Of Measurements

Abstract. Characterisation of bio-aerosols has important implications within Environment and Public Health sectors. Recent developments in Ultra-Violet Light Induced Fluorescence (UV-LIF) detectors such as the Wideband Integrated bio-aerosol Spectrometer (WIBS) and the newly introduced Multiparameter bio-aerosol Spectrometer (MBS) has allowed for the real time collection of fluorescence, size and morphology measurements for the purpose of discriminating between bacteria, fungal Spores and pollen. This new generation of instruments has enabled ever larger data sets to be compiled with the aim of studying more complex environments. In real world data sets, particularly those from an urban environment, the population may be dominated by non-biological fluorescent interferents bringing into question the accuracy of measurements of quantities such as concentrations. It is therefore imperative that we validate the performance of different algorithms which can be used for the task of classification. For unsupervised learning we test Hierarchical Agglomerative Clustering with various different linkages. For supervised learning, ten methods were tested; including decision trees, ensemble methods: Random Forests, Gradient Boosting and AdaBoost; two implementations for support vector machines: libsvm and liblinear; Gaussian methods: Gaussian naïve Bayesian, quadratic and linear discriminant analysis and finally the k-nearest neighbours algorithm. The methods were applied to two different data sets measured using a new Multiparameter bio-aerosol Spectrometer which provides multichannel UV-LIF fluorescence signatures for single airborne biological particles. Clustering, in general performs slightly worse than the supervised learning methods correctly classifying, at best, only 72.7 and 91.1 percent for the two data sets respectively. For supervised learning the gradient boosting algorithm was found to be the most effective, on average correctly classifying 88.1 and 97.8 percent of the testing data respectively across the two data sets.

scholarly journals Influencing Factors of e-Commerce Enterprise Development Based on Mobile Computing Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yixue Zhu ◽  
Boyue Chai
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Big Data Analysis ◽  
Support Vector ◽  
Data Sets ◽  
Large Scale Data ◽  
Vector Machines ◽  
Physical Information ◽  
Scale Data

With the development of increasingly advanced information technology and electronic technology, especially with regard to physical information systems, cloud computing systems, and social services, big data will be widely visible, creating benefits for people and at the same time facing huge challenges. In addition, with the advent of the era of big data, the scale of data sets is getting larger and larger. Traditional data analysis methods can no longer solve the problem of large-scale data sets, and the hidden information behind big data is digging out, especially in the field of e-commerce. We have become a key factor in competition among enterprises. We use a support vector machine method based on parallel computing to analyze the data. First, the training samples are divided into several working subsets through the SOM self-organizing neural network classification method. Compared with the ever-increasing progress of information technology and electronic equipment, especially the related physical information system finally merges the training results of each working set, so as to quickly deal with the problem of massive data prediction and analysis. This paper proposes that big data has the flexibility of expansion and quality assessment system, so it is meaningful to replace the double-sidedness of quality assessment with big data. Finally, considering the excellent performance of parallel support vector machines in data mining and analysis, we apply this method to the big data analysis of e-commerce. The research results show that parallel support vector machines can solve the problem of processing large-scale data sets. The emergence of data dirty problems has increased the effective rate by at least 70%.

scholarly journals HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

2021 ◽  
Author(s):  
Leila Zahedi ◽  
Farid Ghareh Mohammadi ◽  
M. Hadi Amini
Keyword(s):  
Real World ◽  
Large Scale ◽  
Convergence Rates ◽  
Parameter Tuning ◽  
Population Based ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Wide Range ◽  
Extreme Gradient Boosting

<p>Machine learning techniques lend themselves as promising decision-making and analytic tools in a wide range of applications. Different ML algorithms have various hyper-parameters. In order to tailor an ML model towards a specific application working at its best, its hyper-parameters should be tuned. Tuning the hyper-parameters directly affects the performance. However, for large-scale search spaces, efficiently exploring the ample number of combinations of hyper-parameters is computationally expensive. Many of the automated hyper-parameter tuning techniques suffer from low convergence rates and high experimental time complexities. In this paper, we propose HyP-ABC, an automatic innovative hybrid hyper-parameter optimization algorithm using the modified artificial bee colony approach, to measure the classification accuracy of three ML algorithms: random forest, extreme gradient boosting, and support vector machine. In order to ensure the robustness of the proposed method, the algorithm takes a wide range of feasible hyper-parameter values and is tested using a real-world educational dataset. Experimental results show that HyP-ABC is competitive with state-of-the-art techniques. Also, it has fewer hyper-parameters to be tuned than other population-based algorithms, making it worthwhile for real-world HPO problems.</p>

scholarly journals Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

10.2196/23938 ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. e23938
Author(s):  
Ruairi O'Driscoll ◽  
Jake Turicchi ◽  
Mark Hopkins ◽  
Cristiana Duarte ◽  
Graham W Horgan ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Neural Networks ◽  
Random Forest ◽  
Energy Expenditure ◽  
Superior Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Sets ◽  
Out Of Sample

Background Accurate solutions for the estimation of physical activity and energy expenditure at scale are needed for a range of medical and health research fields. Machine learning techniques show promise in research-grade accelerometers, and some evidence indicates that these techniques can be applied to more scalable commercial devices. Objective This study aims to test the validity and out-of-sample generalizability of algorithms for the prediction of energy expenditure in several wearables (ie, Fitbit Charge 2, ActiGraph GT3-x, SenseWear Armband Mini, and Polar H7) using two laboratory data sets comprising different activities. Methods Two laboratory studies (study 1: n=59, age 44.4 years, weight 75.7 kg; study 2: n=30, age=31.9 years, weight=70.6 kg), in which adult participants performed a sequential lab-based activity protocol consisting of resting, household, ambulatory, and nonambulatory tasks, were combined in this study. In both studies, accelerometer and physiological data were collected from the wearables alongside energy expenditure using indirect calorimetry. Three regression algorithms were used to predict metabolic equivalents (METs; ie, random forest, gradient boosting, and neural networks), and five classification algorithms (ie, k-nearest neighbor, support vector machine, random forest, gradient boosting, and neural networks) were used for physical activity intensity classification as sedentary, light, or moderate to vigorous. Algorithms were evaluated using leave-one-subject-out cross-validations and out-of-sample validations. Results The root mean square error (RMSE) was lowest for gradient boosting applied to SenseWear and Polar H7 data (0.91 METs), and in the classification task, gradient boost applied to SenseWear and Polar H7 was the most accurate (85.5%). Fitbit models achieved an RMSE of 1.36 METs and 78.2% accuracy for classification. Errors tended to increase in out-of-sample validations with the SenseWear neural network achieving RMSE values of 1.22 METs in the regression tasks and the SenseWear gradient boost and random forest achieving an accuracy of 80% in classification tasks. Conclusions Algorithms trained on combined data sets demonstrated high predictive accuracy, with a tendency for superior performance of random forests and gradient boosting for most but not all wearable devices. Predictions were poorer in the between-study validations, which creates uncertainty regarding the generalizability of the tested algorithms.

scholarly journals Reliable photometric membership (RPM) of galaxies in clusters – I. A machine learning method and its performance in the local universe

2020 ◽  
Vol 493 (3) ◽  
pp. 3429-3441
Author(s):  
Paulo A A Lopes ◽  
André L B Ribeiro
Keyword(s):  
Machine Learning ◽  
Galaxy Evolution ◽  
Large Scale ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Validation Data ◽  
Membership Probability ◽  
Cluster Membership ◽  
Stochastic Gradient Boosting

ABSTRACT We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive a membership classification. After testing several machine learning techniques (such as stochastic gradient boosting, model averaged neural network and k-nearest neighbours), we found the support vector machine algorithm to perform better when applied to our data. Our training and validation data are from the Sloan Digital Sky Survey main sample. Hence, to be complete to $M_r^* + 3$, we limit our work to 30 clusters with $z$phot-cl ≤ 0.045. Masses (M200) are larger than $\sim 0.6\times 10^{14} \, \mathrm{M}_{\odot }$ (most above $3\times 10^{14} \, \mathrm{M}_{\odot }$). Our results are derived taking in account all galaxies in the line of sight of each cluster, with no photometric redshift cuts or background corrections. Our method is non-parametric, making no assumptions on the number density or luminosity profiles of galaxies in clusters. Our approach delivers extremely accurate results (completeness, C $\sim 92{\rm{ per\ cent}}$ and purity, P $\sim 87{\rm{ per\ cent}}$) within R200, so that we named our code reliable photometric membership. We discuss possible dependencies on magnitude, colour, and cluster mass. Finally, we present some applications of our method, stressing its impact to galaxy evolution and cosmological studies based on future large-scale surveys, such as eROSITA, EUCLID, and LSST.

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