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Recognizing protein-metal ion ligands binding residues by random forest algorithm with adding orthogonal properties

COMPUTATIONAL BIOLOGY AND CHEMISTRY 2022年第0期98卷 107693页

作者： You, Xiaoxiao Hu, Xiuzhen Feng, Zhenxing Wang, Ziyang Hao, Sixi Yang, Caiyun Inner Mongolia Univ Technol Coll Sci Hohhot 010051 Peoples R China Neural Network Modeling Inner Mongolia Key Lab Stat Anal Theory Life Data Hohhot Peoples R China

Accurately identifying protein-metal ion ligand binding residues is the key to study protein functions. Because the number of binding residues and non-binding residues is significantly imbalanced, false positives is hard to be eliminated from the binding residues prediction result. Therefore, identification of protein-metal ion ligand binding residues remains challenging. In this paper, the binding site of 7 metal ions (Ca2+, Mg2+, Zn2+, Fe3+, Mn2+, Cu2+ and Co2+) were used as the objects of the study. Besides generally adopted parameters: amino acids and predicted secondary structure information, we creatively introduced ten orthogonal properties as a parameter. These orthogonal properties are clustering of 188 physical and chemical characteristics that can be used to describe three-dimension structural information. With the optimized parameters, we used the random forest algorithm to predict ion ligand binding residues. The proposed method obtained good prediction results with the MCC values of Mg2+, Ca2+ and Zn2+ reaching 0.255, 0.254, 0.540, respectively. Comparing to the IonSeq method, the method developed in this paper has advantages on the binding residues prediction of some ions.

关键词： Metal ion ligands Binding residues Ten orthogonal properties random forest algorithm

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Process optimization of biomass gasification with a Monte Carlo approach and random forest algorithm

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ENERGY CONVERSION AND MANAGEMENT 2022年 264卷 1页

作者： Fang, Yi Ma, Li Yao, Zhiyi Li, Wangliang You, Siming Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Lanark Scotland Nanjing Univ Aeronaut & Astronaut Natl Key Lab Rotorcraft Aeromech Nanjing 210016 Peoples R China CBE Ecosolut Pte Ltd Singapore 117602 Singapore Chinese Acad Sci Inst Proc Engn CAS Key Lab Green Proc & Engn Beijing 100190 Peoples R China

Gasification technologies have been extensively studied for their potential to convert biomass feedstocks into syngas (a mixture of CH4, H-2, and CO mainly) that can be further turned into heat or electricity upon combustion. It is crucial to understand optimal gasification process parameters for practical design and operation for maximizing the potential. This study combined the Monte Carlo simulation approach, gasification kinetic modeling, and the random forest algorithm to predict the optimal gasification process parameters (i.e. water content, particle size, porosity, thermal conductivity, emissivity, shape, and reaction temperature) towards a maximum syngas yield. The Monte Carlo approach randomly generated a data pool of the process parameters following either a normal or uniform distribution, which was then fed into a validated kinetic model to create 2,000 datasets (process parameters and syngas yields). For the random forest model, the mean decrease accuracy and mean decrease Gini were used to assess the importance of the process parameters on syngas yields. The accuracy of the optimization method was evaluated using the coefficient of determination (R-2), the root means square error (RMSE), and the mean absolute error (MAE). Generally, the predictions for the normal distribution case were closer to the experimental data obtained from existing literature than that for the uniform distribution case. The model was used to predict the optimal syngas yield and process parameters of wood gasification and it was shown that the predictions were generally in good agreement (<12% difference for the case of normal distribution) with existing experimental results. The method serves as a useful tool for determining optimal gasification process parameters for process and operation design.

关键词： Biomass gasification Kinetic model Monte Carlo simulation Machine-learning random forest algorithm

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A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm

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PEERJ COMPUTER SCIENCE 2022年 8卷 e1110页

作者： Yuan, Ke Yu, Daoming Feng, Jingkai Yang, Longwei Jia, Chunfu Huang, Yiwang Henan Univ Sch Comp & Informat Engn Kaifeng Henan Peoples R China Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng Henan Peoples R China Henan Univ Int Educ Coll Zhengzhou Henan Peoples R China Nankai Univ Coll Cybersecur Tianjin Peoples R China Tongren Univ Sch Data Sci Tongren Guizhou Peoples R China

Cryptographic algorithm identification, which refers to analyzing and identifying the encryption algorithm used in cryptographic system, is of great significance to cryptanalysis. In order to improve the accuracy of identification work, this article proposes a new ensemble learning-based model named hybrid k-nearest neighbor and random forest (HKNNRF), and constructs a block cipher algorithm identification scheme. In the ciphertext-only scenario, we use NIST randomness test methods to extract ciphertext features, and carry out binary-classification and five-classification experiments on the block cipher algorithms using proposed scheme. Experiments show that when the ciphertext size and other experimental conditions are the same, compared with the baselines, the HKNNRF model has higher classification accuracy. Specifically, the average binary-classification identification accuracy of HKNNRF is 69.5%, which is 13%, 12.5%, and 10% higher than the single-layer support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) respectively. The five-classification identification accuracy can reach 34%, which is higher than the 21% accuracy of KNN, the 22% accuracy of RF and the 23% accuracy of SVM respectively under the same experimental conditions.

关键词： Cryptographic algorithm identification Machine learning randomness test random forest algorithm K-nearest neighbor algorithm

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Feature Engineering of Geohazard Susceptibility Analysis Based on the random forest algorithm: Taking Tianshui City, Gansu Province, as an Example

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REMOTE SENSING 2022年第22期14卷 5658-5658页

作者： Ling, Xiao Zhu, Yueqin Ming, Dongping Chen, Yangyang Zhang, Liang Du, Tongyao China Univ Geosci Beijing Sch Informat Engn 29 Xueyuan Rd Beijing 100083 Peoples R China Minist Emergency Management Natl Inst Nat Hazards Beijing 100085 Peoples R China China Aero Geophys Survey & Remote Sensing Ctr Na Beijing 100083 Peoples R China

In this paper, Feature Engineering (FE) was applied to Landslide Susceptibility Mapping (LSM), while the most suitable conditioning feature dataset and analysis method were tested and analyzed. Tianshui city was taken as the study area, three types of geohazard (collapse, landslide, and unstable slopes) were used, while a total of twenty-three conditioning features were generated;two dimensionless methods (normalization and standardization) were tested afterward. Four random-forest-based (RF-based) feature selection methods using different indicators (Gini Impurity, GI;Out of Bag Accuracy, OOBA) were proposed and tested separately. The LSMs of four models were carried out under the guidance results of FE, namely Classification and Regression Tree (CART), random forest (RF), Logistic Regression (LR), and Support Vector Machine for Classification (SVC). For feature enhancement, standardization had significant advantages over normalization. All RF-based methods were proven effective, lifting the AUC by 0.01 similar to 0.02. The RF model achieved the highest LSM accuracies, respectively, 0.949 (landslide), 0.957, and 0.949 (unstable slopes), improved by 0.008 (landslide), 0.005 (collapse), and 0.013 (unstable slopes). This proved that the FE helped to improve LSM and can help to decide the dominant conditioning factors for regional geohazards.

关键词： landslide feature engineering landslide susceptibility mapping random forest algorithm

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Automatic Correction of Parameters of Rice Phenology Prediction Model Based on random forest algorithm

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Procedia Computer Science 2022年 208卷 435-441页

作者： Yingying Yang Xiaodong Li

With the rapid development of various Internet of Things and cloud computing, a large amount of data is rapidly accumulated in various fields, and the resulting data dimension is getting higher and higher. Evolutionary algorithm has been widely used in the field of crop model parameter calibration. Because the structure of crop model includes many sets of ordinary differential equations, which are nonlinear, discontinuous and multi peak, it is particularly important to choose an appropriate evolutionary algorithm according to the characteristics of crop model. At the same time, the parameters of evolutionary algorithm have a great impact on the performance of the algorithm. The improved differential evolution algorithm with adaptive control parameters shows good convergence speed and stability in the parameter calibration of rice phenology model. Variable selection is particularly important in high-dimensional data processing, and the importance rating of variables is the key problem. Aiming at the problem of low classification accuracy and generalization ability of the model in the single variable selection algorithm, this paper proposes a hybrid variable selection algorithm. The algorithm is divided into two stages: the filtering stage, which uses mutual information to quickly eliminate some irrelevant variables and reduce the dimension of the sample space; In the encapsulation stage, under the framework of replacement theory, the residual variables are selected by random forest, which shows that the algorithm has higher classification accuracy and generalization ability than the comparison algorithm.

关键词： random forest algorithm phenological phase of rice model parameter evolutionary algorithms

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Using random forest algorithm to predict super-secondary structure in proteins

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JOURNAL OF SUPERCOMPUTING 2020年第5期76卷 3199-3210页

作者： Hu, Xiu-zhen Long, Hai-xia Ding, Chang-jiang Gao, Su-juan Hou, Rui Inner Mongolia Univ Technol Coll Sci Hohhot 010051 Peoples R China Hulunbuir Univ Coll Phys & Elect Informat Hulunbuir 021008 Peoples R China

A new super-secondary structure dataset with sequence identity < 25%, resolution better than 2.0 angstrom, which contained 2805 non-redundant protein chains and could be classified into four motifs, is built for prediction. The matrix scoring values and hydropathy distribution are extracted from the protein sequences, which are then input to the random forest algorithm to predict the super-secondary structure in proteins. The predictive overall accuracy is 72.71% and 68.54% by fivefold cross-validation and independent dataset test, respectively. The proposed method is also tested on a previous independent test dataset and the predictive overall accuracy 84.89%, which is better than the performance of the previous predictions.

关键词： random forest algorithm Matrix scoring value Super-secondary structure Hydropathy distribution

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Evaluation of patient safety culture using a random forest algorithm

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2020年 204卷 107186-107186页

作者： Simsekler, Mecit Can Emre Qazi, Abroon Alalami, Mohammad Amjad Ellahham, Samer Al Ozonoff Khalifa Univ Sci & Technol Dept Ind & Syst Engn POB 127788 Abu Dhabi 127788 U Arab Emirates Boston Childrens Hosp Dept Med Div Infect Dis 300 Longwood Ave Boston MA 02115 USA Amer Univ Sharjah Sch Business Adm Sharjah 26666 U Arab Emirates Cleveland Clin Abu Dhabi Heart & Vasc Inst Abu Dhabi 112412 U Arab Emirates Harvard Med Sch Dept Pediat 25 Shattuck St Boston MA 02115 USA

Safety culture is a multidimensional concept that may be associated with medical errors and patient safety events in healthcare delivery systems. However, limited evidence is available regarding which safety culture dimensions drive overall patient safety. Moreover, the use of advanced statistical analysis has been limited in past studies of safety culture data. To address these issues, we use hospital-level aggregate survey data from U.S. hospitals to analyze the relationship between the defined safety culture dimensions and the patient safety grade. We use a tree-based machine learning algorithm, random forests, to estimate accurate and stable associations. The results of our analysis show that safety perception, management support, and supervisor/manager expectations are the leading drivers of patient safety grade. More specifically, safety problems in the work unit and work climate provided by hospital management are specific drivers of patient safety outcomes. The random forest model sheds new light on the most important cultural features relevant to patient safety.

关键词： Patient safety Safety culture Healthcare operations random forest algorithm Machine learning

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Mapping the Distribution of Coffee Plantations from Multi-Resolution, Multi-Temporal, and Multi-Sensor Data Using a random forest algorithm

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REMOTE SENSING 2020年第23期12卷 3933-3933页

作者： Tridawati, Anggun Wikantika, Ketut Susantoro, Tri Muji Harto, Agung Budi Darmawan, Soni Yayusman, Lissa Fajri Ghazali, Mochamad Firman Inst Teknol Bandung Fac Earth Sci & Technol Remote Sensing & Geog Informat Sci Res Grp Dept Geodesy & Geomat Bandung 40132 Indonesia Inst Teknol Bandung Ctr Remote Sensing Bandung 40132 Indonesia Univ Lampung Fac Engn Dept Geodesy & Geomat Engn Sumantri Brojonegoro 1 Bandar Lampung 35141 Indonesia Minist Energy & Mineral Resources Res & Dev Ctr Oil & Gas Technol LEMIGAS Jakarta 12230 Indonesia Inst Teknol Nas Bandung PHH Mustofa 23 Bandung 40124 Indonesia

Indonesia is the world's fourth largest coffee producer. Coffee plantations cover 1.2 million ha of the country with a production of 500 kg/ha. However, information regarding the distribution of coffee plantations in Indonesia is limited. This study aimed to assess the accuracy of classification model and determine its important variables for mapping coffee plantations. The model obtained 29 variables which derived from the integration of multi-resolution, multi-temporal, and multi-sensor remote sensing data, namely, pan-sharpened GeoEye-1, multi-temporal Sentinel 2, and DEMNAS. Applying a random forest algorithm (tree = 1000, mtry = all variables, minimum node size: 6), this model achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy of 79.333%, 0.774, 92.000%, and 90.790%, respectively. In addition, 12 most important variables achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy 79.333%, 0.774, 91.333%, and 84.570%, respectively. Our results indicate that random forest algorithm is efficient in mapping coffee plantations in an agroforestry system.

关键词： random forest algorithm tree multi-temporal multi-resolution

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Optimization of parallel random forest algorithm based on distance weight

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第2期39卷 1951-1963页

作者： Wang, Qinge Chen, Huihua Cent South Univ Sch Civil Engn Changsha Peoples R China

In order to overcome the problems of long execution time and low parallelism of existing parallel random forest algorithms, an optimization method for parallel random forest algorithm based on distance weights is proposed. The concept of distance weights is introduced to optimize the algorithm. Firstly, the training sample data are extracted from the original data set by random selection. Based on the extracted results, a single decision tree is constructed. The single decision tree is grouped together according to different grouping methods to form a random forest. The distance weights of the training sample data set are calculated, and then the weighted optimization of the random forest model is realized. The experimental results show that the execution time of the parallel random forest algorithm after optimization is 110 000 ms less than that before optimization, and the operation efficiency of the algorithm is greatly improved, which effectively solves the problems existing in the traditional random forest algorithm.

关键词： Distance weights parallel algorithm random forest algorithm algorithm optimization

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Application of stable isotopic and elemental composition combined with random forest algorithm for the botanical classification of Chinese honey

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2022年第0期110卷

作者： Liu, Zhaolong Xu, Tianyang Zhou, Jinhui Chen, Lanzhen Chinese Acad Agr Sci Inst Apicultural Res Beijing 100093 Peoples R China Minist Agr & Rural Affairs Key Lab Bee Prod Qual & Safety Control Beijing 100093 Peoples R China

To ensure that honey belongs to a very appreciated botanical class, the classical methodology is melissopalynology analysis to identify and count pollen grains. However, this method is time-consuming and laborious. In this work, four stable isotopes (delta(13)Ch, delta Cp-13, delta O-18 and delta H-2) and twelve elemental contents (Na, Mg, Ca, K, Fe, Cr, Mn, Co, Cu, Sr, Se, Mo) were used to build the dataset, and the random forest (RF) algorithm, Support Vector Machines (SVM), Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA) were investigated to classify six varieties of Chinese honey (linden, sunflower, vetch, rape, acacia, and jujube). The results showed the RF algorithm exhibits the highest training accuracy (99.4%) and testing accuracy (96.5%) of the four algorithms. Hence, the RF algorithm was selected to rank the 16 attributes according to their contribution, and delta H-2, Sr, delta O-18, Mn, CA, and K were comsidered the most important factors for identifying six varieties of honey. Furthermore, the results of the RF algorithm were verified by the parallel coordinates plot. This suggests that the RF algorithm provides an effective and accurate approach for classifying Chinese honey according to stable isotopic and elemental composition, which theoretically can be used to classify more types of honey.

关键词： random forest algorithm Chinese honey Botanical classification Stable isotopic Elemental composition Food analysis Food composition

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