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Increasing prediction performance of colorectal cancer disease status using random forests classification based on metagenomic shotgun sequencing data

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Synthetic and Systems Biotechnology 2022年第1期7卷 574-585页

作者： Yilin Gao Zifan Zhu Fengzhu Sun Department of Quantitative and Computational Biology University of Southern California1050 Childs WayRRI 201Los AngelesCAUnited States

Dysfunction of microbial communities in various human body sites has been shown to be associated with a variety of diseases raising the possibility of predicting diseases based on metagenomic *** many studies have investigated this problem,there are no consensus on the optimal approaches for predicting disease status based on metagenomic *** six human gut metagenomic datasets consisting of large numbers of colorectal cancer patients and healthy controls from different countries,we investigated different software packages for extracting relative abundances of known microbial genomes and for integrating mapping and as-sembly approaches to obtain the relative abundance profiles of both known and novel *** random forests(RF)classification algorithm was then used to predict colorectal cancer status based on the microbial relative abundance *** on within data cross-validation and cross-dataset prediction,we show that the RF prediction performance using the microbial relative abundance profiles estimated by Centrifuge is generally higher than that using the microbial relative abundance profiles estimated by MetaPhlAn2 and *** also develop a novel method to integrate the relative abundance profiles of both known and novel microbial or-ganisms to further increase the prediction performance for colorectal cancer from metagenomes.

关键词： Microbiome Colorectal cancer Metagenomic shotgun sequencing random forests

Variable importance-weighted random forests

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Frontiers of Electrical and Electronic Engineering in China 2017年第4期5卷 338-351页

作者： Yiyi Liu Hongyu Zhao Department of Biostatistics School of Public Health Yale University New Haven CT 06511 USA Program of Computational Biology and Bioinformatics Yale University New Haven CT 06511 USA

Background： random forests is a popular classification and regression method that has proven powerful for various prediction problems in biological studies. However, its performance often deteriorates when the number of features increases. To address this limitation, feature elimination random forests was proposed that only uses features with the largest variable importance scores. Yet the performance of this method is not satisfying, possibly due to its rigid feature selection, and increased correlations between trees of forest. Methods： We propose variable importance-weighted random forests, which instead of sampling features with equal probability at each node to build up trees, samples features according to their variable importance scores, and then select the best split from the randomly selected features. Results： We evaluate the performance of our method through comprehensive simulation and real data analyses, for both regression and classification. Compared to the standard random forests and the feature elimination random forests methods, our proposed method has improved performance in most cases. Conclusions： By incorporating the variable importance scores into the random feature selection step, our method can better utilize more informative features without completely ignoring less informative ones, hence has improved prediction accuracy in the presence of weak signals and large noises. We have implemented an R package ＂virandomforests＂ based on the original R package ＂randomForest＂ and it can be freely downloaded from http：// ***/software.

关键词： random forests variable importance score classification regression

random forests Algorithm Based Duplicate Detection in On-Site Programming Big Data Environment

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Journal of Information Hiding and Privacy Protection 2020年第4期2卷 199-205页

作者： Qianqian Li Meng Li Lei Guo Zhen Zhang University of Science and Technology Beijing Beijing100083China Beijing University of Posts and Telecommunications Beijing100876China Systems Engineering Institute AMS Beijing100071China Audio Analytic 2 QuaysideCambridgeCB58ABUK

On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time,complexity and high-difficulty for ***,data cleaning is essential for on-site programming big *** data detection is an important step in data cleaning,which can save storage resources and enhance data *** to the insufficiency in traditional Sorted Neighborhood Method(SNM)and the difficulty of high-dimensional data detection,an optimized algorithm based on random forests with the dynamic and adaptive window size is *** efficiency of the algorithm can be elevated by improving the method of the key-selection,reducing dimension of data set and using an adaptive variable size sliding *** results show that the improved SNM algorithm exhibits better performance and achieve higher accuracy.

关键词： On-site programming big data duplicate record detection random forests adaptive sliding window

random forests to predict survival of octogenarians with brain metastases from nonsmall-cell lung cancer

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Brain Science Advances 2024年第1期10卷 39-56页

作者： Lijun Song Yu Wang Xue Li Yi Liu Bingyi Yin Daorui Li Hongsheng Lin Yuqi Zhang Department of Neurosurgery Yuquan HospitalTsinghua UniversityBeijing 100040China Department of Neurosurgery Beijing Tiantan HospitalCapital Medical UniversityBeijing 100070China Department of Oncology Guang'anmen HospitalChina Academy of Chinese Medical SciencesBeijing 100053China

Background:To create and validate nomograms for the personalized prediction of survival in octogenarians with newly diagnosed nonsmall-cell lung cancer(NSCLC)with sole brain metastases(BMs).Methods:random forests(RF)were applied to identify independent prognostic factors for building nomogram *** predictive accuracy of the model was evaluated based on the receiver operating characteristic(ROC)curve,C-index,and calibration ***:The area under the curve(AUC)values for overall survival at 6,12,and 18 months in the validation cohort were 0.837,0.867,and 0.849,respectively;the AUC values for cancer-specific survival prediction were 0.819,0.835,and 0.818,*** calibration curves visualized the accuracy of the ***:The new nomograms have good predictive power for survival among octogenarians with sole BMs related to NSCLC.

关键词： octogenarian NSCLC brain metastases random forests nomogram

random forests for Object Detection

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Random Forests for Object Detection

Machine learning empowers efficient design of ternary organic solar cells with PM6 donor

2015年中国智能自动化学术会议

作者： Mingming Zhu Lang Ye Siyu Xia Hong Pan School of Automation Southeast University

We present a method for object detection based on random forests. It is accomplished through the generalized Hough transform paradigm, where object centers are voted from small and local parts in images. Some previous works such as the well-known implicit shape model(ISM) take unsupervised clustering method during the training stage and it often lead to false-positive detections due to random constellations of parts. Thus, we employ a random forest to learn a more discriminative model. We use the KAZE local features to construct a random forest classifier and all leaf nodes in each tree constitute a discriminative codebook *** codebook model is used to estimate object locations via probabilistic Hough voting. Furthermore, before the test stage, we also adopt a salient region detection step to reduce false-positive detections. Experiments show that our method provides good detection results in complicated environments.

关键词： Generalized hough transform random forests Salient region detection

来源： cnki会议评论

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Journal of Energy Chemistry 2025年第1期100卷 337-347页

作者： Kiran A.Nirmal Tukaram D.Dongale Santosh S.Sutar Atul C.Khot Tae Geun Kim School of Electrical Engineering Korea University Computational Electronics and Nanoscience Research Laboratory School of Nanoscience and Biotechnology Shivaji University Yashwantrao Chavan School of Rural Development Shivaji University

Organic solar cells(OSCs) hold great potential as a photovoltaic technology for practical ***, the traditional experimental trial-and-error method for designing and engineering OSCs can be complex, expensive, and time-consuming. Machine learning(ML) techniques enable the proficient extraction of information from datasets, allowing the development of realistic models that are capable of predicting the efficacy of materials with commendable accuracy. The PM6 donor has great potential for high-performance OSCs. However, it is crucial for the rational design of a ternary blend to accurately forecast the power conversion efficiency(PCE) of ternary OSCs(TOSCs) based on a PM6 ***, we collected the device parameters of PM6-based TOSCs and evaluated the feature importance of their molecule descriptors to develop predictive models. In this study, we used five different ML algorithms for analysis and prediction. For the analysis, the classification and regression tree provided different rules, heuristics, and patterns from the heterogeneous dataset. The random forest algorithm outperforms other prediction ML algorithms in predicting the output performance of PM6-based TOSCs. Finally, we validated the ML outcomes by fabricating PM6-based TOSCs. Our study presents a rapid strategy for assessing a high PCE while elucidating the substantial influence of diverse descriptors.

关键词： random forests

An improved deep forest model for forecast the outdoor atmospheric corrosion rate of low-alloy steels

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Journal of Materials Science & Technology 2020年第14期47卷 202-210页

作者： Yuanjie Zhi Tao Yang Dongmei Fu School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center of Industrial Spectrum Imaging University of Science and Technology BeijingBeijing 100083China

The paper proposes a new deep structure model,called Densely Connected Cascade Forest-Weighted K Nearest Neighbors(DCCF-WKNNs),to implement the corrosion data modelling and corrosion ***,we collect 409 outdoor atmospheric corrosion samples of low-alloy steels as experiment ***,we give the proposed methods process,including random forests-K nearest neighbors(RF-WKNNs)and ***,we use the collected datasets to verify the performance of the proposed *** results show that compared with commonly used and advanced machine-learning algorithms such as artificial neural network(ANN),support vector regression(SVR),random forests(RF),and cascade forests(cForest),the proposed method can obtain the best prediction *** addition,the method can predict the corrosion rates with variations of any one single environmental variable,like pH,temperature,relative humidity,SO2,rainfall or Cl-.By this way,the threshold of each variable,upon which the corrosion rate may have a large change,can be further obtained.

关键词： random forests Deep forest model Low-alloy steels Outdoor atmospheric corrosion Prediction and data-mining

Multi-modal Gesture Recognition using Integrated Model of Motion, Audio and Video

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Chinese Journal of Mechanical Engineering 2015年第4期28卷 657-665页

作者： GOUTSU Yusuke KOBAYASHI Takaki OBARA Junya KUSAJIMA Ikuo TAKEICHI Kazunari TAKANO Wataru NAKAMURA Yoshihiko Department of Mechano-Informatics School of Information Science and TechnologyUniversity of Tokyo7-3-1 HongoBunkyo-kuTokyoJapan

Gesture recognition is used in many practical applications such as human-robot interaction, medical rehabilitation and sign language. With increasing motion sensor development, multiple data sources have become available, which leads to the rise of multi-modal gesture recognition. Since our previous approach to gesture recognition depends on a unimodal system, it is difficult to classify similar motion patterns. In order to solve this problem, a novel approach which integrates motion, audio and video models is proposed by using dataset captured by Kinect. The proposed system can recognize observed gestures by using three models. Recognition results of three models are integrated by using the proposed framework and the output becomes the final result. The motion and audio models are learned by using Hidden Markov Model. random Forest which is the video classifier is used to learn the video model. In the experiments to test the performances of the proposed system, the motion and audio models most suitable for gesture recognition are chosen by varying feature vectors and learning methods. Additionally, the unimodal and multi-modal models are compared with respect to recognition accuracy. All the experiments are conducted on dataset provided by the competition organizer of MMGRC, which is a workshop for Multi-Modal Gesture Recognition Challenge. The comparison results show that the multi-modal model composed of three models scores the highest recognition rate. This improvement of recognition accuracy means that the complementary relationship among three models improves the accuracy of gesture recognition. The proposed system provides the application technology to understand human actions of daily life more precisely.

关键词： gesture recognition multi-modal integration hidden Markov model random forests

Estimating Daily Dew Point Temperature Based on Local and Cross-StationMeteorological Data Using CatBoost Algorithm

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Computer Modeling in Engineering & Sciences 2022年第2期130卷 671-700页

作者： Fuqi Yao Jinwei Sun Jianhua Dong School of Hydraulic Engineering Ludong UniversityYantai264010China State Key Laboratory ofWater Resources and Hydropower Engineering Science Wuhan UniversityWuhan430072China

Accurate estimation of dew point temperature(Tdew)plays a very important role in the fields of water resource management,agricultural engineering,climatology and energy ***,there are few studies on the applicability of local Tdew algorithms at regional *** study evaluated the performance of a new machine learning algorithm,i.e.,gradient boosting on decision trees with categorical features support(Cat Boost)to estimate daily Tdew using limited local and cross-station meteorological *** random forests(RF)algorithm was also assessed for *** meteorological data from 2016 to 2019,including maximum,minimum and average temperature(Tmax,Tmin and Tmean),maximum,minimum and average relative humidity(RHmax,RHmin and RHmean),maximum,minimum and average global solar radiation(Rsmax,Rsmin and Rsmean)from three weather stations in Hunan of China were used to evaluate the CatBoost and RF *** results showed that both algorithms achieved satisfactory estimation accuracy at the target stations(on average RMSE=1.020℃,R^(2)=0.969,MAE=0.718℃and NRMSE=0.087)in the absence of complete meteorological parameters(with only temperature data as input).The Cat Boost algorithm(on average RMSE=1.900℃and R^(2)=0.835)was better than the RF algorithm(on average RMSE=2.214℃andR^(2)=0.828).The accuracy and stability of the CatBoost and RF algorithms were positively correlated with the number of input parameters,and the three-parameter algorithms achieved higher estimation accuracy than the two-parameter *** developed methodology is helpful to predict Tdew at regional scale.

关键词： Dew point temperature categorical boosting random forests cross-station accuracy