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CRISPR/Cas9 cleavage efficiency regression through boosting algorithms and Markov sequence profiling

BIOINFORMATICS 2018年第18期34卷 3069-3077页

作者： Peng, Hui Zheng, Yi Blumenstein, Michael Tao, Dacheng Li, Jinyan Univ Technol Sydney Fac Engn & Informat Technol Adv Analyt Inst Broadway NSW 2007 Australia Univ Sydney Fac Engn & Informat Technol Sch Informat Technol Darlington NSW 2008 Australia

Motivation: CRISPR/Cas9 system is a widely used genome editing tool. A prediction problem of great interests for this system is: how to select optimal single-guide RNAs (sgRNAs), such that its cleavage efficiency is high meanwhile the off-target effect is low. Results: This work proposed a two-step averaging method (TSAM) for the regression of cleavage efficiencies of a set of sgRNAs by averaging the predicted efficiency scores of a boosting algorithm and those by a support vector machine (SVM). We also proposed to use profiled Markov properties as novel features to capture the global characteristics of sgRNAs. These new features are combined with the outstanding features ranked by the boosting algorithm for the training of the SVM regressor. TSAM improved the mean Spearman correlation coefficiencies comparing with the state-of-the-art performance on benchmark datasets containing thousands of human, mouse and zebrafish sgRNAs. Our method can be also converted to make binary distinctions between efficient and inefficient sgRNAs with superior performance to the existing methods. The analysis reveals that highly efficient sgRNAs have lower melting temperature at the middle of the spacer, cut at 5'-end closer parts of the genome and contain more 'A' but less 'G' comparing with inefficient ones. Comprehensive further analysis also demonstrates that our tool can predict an sgRNA's cutting efficiency with consistently good performance no matter it is expressed from an U6 promoter in cells or from a T7 promoter in vitro.

关键词： CRISPRS (Genetics) boosting algorithms MARKOV processes SUPPORT vector machines RANK correlation (Statistics)

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Using boosting algorithms to predict bank failure: An untold story

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INTERNATIONAL REVIEW OF ECONOMICS & FINANCE 2021年 76卷 40-54页

作者： Pham, Xuan T. T. Ho, Tin H. Univ Econ & Law Ctr Econ & Financial Res Ho Chi Minh City Vietnam Univ Econ & Law Inst Dev & Res Banking Technol Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam

From a modeling point of view, our work provides a novel approach to better use XGBoost for bank failure prediction, determining the essential technical aspects that can improve the predictive accuracy. Of these technical aspects, the two crucial factors are assigning correct values to target variables and careful predictor selection (through ANOVA, correlation, information value tests, and weight of evidence). We also highlight that bank failure could be predicted four to five quarters earlier when all predictive signals simultaneously appear. Hence, we strongly suggest using quarterly data instead of yearly data. In addition to practical implications, our present work also contributed to the existing literature. We confirm the results of existing studies that emphasized that XGBoost has strong predictive power (Carmona, Climent, and Momparler (2018)). Moreover, we provide evidence that XGBoost outperforms other models in the same boosting family, including gradient boosting and AdaBoost, through an intensive comparison of predictive power. These contributions might facilitate future work on bank failure prediction.

关键词： U S banks Bank failure prediction boosting algorithms XGBoost Variable selection techniques Target variables

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Effects of Dimension Reduction Methods on boosting algorithms for Better Prediction Accuracies on Classifications of Stress EEGs 6

Effects of Dimension Reduction Methods on Boosting Algorithm...

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6th International Conference on Electronics and Electrical Engineering Technology (EEET)

作者： Sim, Doreen Y. Y. Chong, C. K. Univ Nottingham Fac Sci & Engn Sch Comp Sci Malaysia Campus Semenyih Selangor Malaysia

ISBN: (纸本)9798350395600;9798350395594

This research aims to investigate the effects of various dimension reduction methods, namely Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA) on the prediction accuracies of the stressful state in the EEG signaling when performing different mental tasks. The dataset used for this research is the SAM-40 dataset. It consists of 40 subjects performing three different mental tasks, i.e. Arithmetic Problem Solving Task, Stroop Color Word Test and Mirror Image Recognition Task. Each task was carried out with 3 trials. The results after applying the different dimension reduction methods of PCA, ICA and LDA to different boosting algorithms were analyzed and compared meticulously. These boosting algorithms are mainly the ensemble techniques of AdaBoostM1 and RUSBoost algorithms. Among all the experimented results shown, the LDA induced boosted classification methods showed the best prediction accuracy result, i.e. around 30% of prediction accuracy improvement.

关键词： dimension reduction methods stressful state in EEG signaling boosting algorithms AdaBoostM1 RUSBoost LDA induced boosted classification methods

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Sentiment Analysis for Hindi Cinema Using boosting algorithms 8th

Sentiment Analysis for Hindi Cinema Using Boosting Algorithm...

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Mann, Parul Jha, Anmol Rani, Ritu Sharma, Arun Dev, Amita Indira Gandhi Delhi Tech Univ Women New Delhi 110006 India

ISBN: (纸本)9789819713288;9789819713295

In today's rapidly evolving world, with ubiquitous access to technology, there are massive amounts of data being generated. This data contains key insights that shape better decision-making. Hence, tools that help us extract such insights from this data are of the utmost importance. Sentiment analysis is one such tool. It helps us determine the emotions behind a piece of text. Although there are many resources for sentiment analysis in English, resources for Hindi are limited. We aim to remedy this issue with our work where we scrape, annotate, and pre-process our own Hindi review corpus from the field of cinema. We propose a novel methodology to perform Hindi sentiment analysis using various boosting algorithms and create a foundation to aid better model and framework selection for vernacular natural language processing tasks.

关键词： Hindi sentiment analysis Indic languages Natural language processing boosting algorithms Machine learning

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An overview of cardiovascular disease infection: A comparative analysis of boosting algorithms and some single based classifiers

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Statistical Journal of the IAOS 2020年第4期36卷 1189-1198页

作者： Adeboye, Nureni Olawale Abimbola, Olawale Victor Department of Mathematics and Statistics Federal Polythechnic Ilaro Nigeria

Machine learning is a branch of artificial intelligence that helps machines learn from observational data without being explicitly programmed and its methods have been found to be very useful in the modern age for medical diagnosis and for early detection of diseases. According to the World Health Organization, 12 million deaths occur annually due to heart-related diseases. Thus, its early detection and treatment are of interest. This research introduces a better way of improving the timely prediction of cardiovascular diseases in suspected patients by comparing the efficiency of two boosting algorithms with four (4) other single based classifiers on cardiovascular official data. The best model was selected based on performances of 5 different evaluation metrics. From the results, Adaptive boosting is seen to outperform all other algorithms with a classification accuracy of 74.2%, closely followed by gradient boosting. However, gradient boosting was chosen as an acceptable technique because it trains faster than Adaboost with a better precision of 74.9% compared to 74.7% exhibited by Adaboost. Thus boosting algorithms are better predictors compared to single based classifiers with factors of age, systolic blood pressure, weight, cholesterol, height, and diastolic blood pressure as the major contributors to the model building. © 2020 - IOS Press and the authors. All rights reserved.

关键词： AdaBoost boosting algorithms Cardiovascular diseases ensemble gradient boosting

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Diabetes classification model based on boosting algorithms

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BMC BIOINFORMATICS 2018年第20180327期19卷 109-109页

作者： Chen, Peihua Pan, Chuandi Wenzhou Med Univ Inst Biopharmaceut Informat & Technol Wenzhou Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Dept Comp Technol & Informat Management Wenzhou City Peoples R China

Background: Diabetes mellitus is a common and complicated chronic lifelong disease. Hence, it is of high clinical significance to find the most relevant clinical indexes and to perform efficient computer-aided pre-diagnoses and diagnoses. Results: Non-parametric statistical testing is performed on hundreds of medical measurement index results between diabetic and non-diabetic populations. Two common boosting algorithms, Adaboost.M1 and LogitBoost, are selected to establish a machine model for diabetes diagnosis based on these clinical test data, involving a total of 35,669 individuals. The machine classification models built by these two algorithms have very good classification ability. Here, the LogitBoost classification model is slightly better than the Adaboost. M1 classification model. The overall accuracy of the LogitBoost classification model reached 95.30% when using 10-fold cross validation. The true positive, true negative, false positive, and false negative rates of the binary classification model were 0.921, 0.969, 0.031, and 0.079, respectively, and the area under the receiver operating characteristic curve reached 0.99. Conclusions: The boosting algorithms show excellent performance for the diabetes classification models based on clinical medical data. The coefficient matrix of the original data is a sparse matrix, because some of the test results were missing, including some that were directly related to disease diagnosis. Therefore, the model is robust and has a degree of pre-diagnosis function. In the process of selecting the preferred test items, the most statistically significant discriminating factors between the diabetic and general populations were obtained and can be used as reference risk factors for diabetes mellitus.

关键词： Diabetes boosting algorithms Computer-aided diagnoses

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A comprehensive intrusion detection framework using boosting algorithms

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 100卷 107869-107869页

作者： Kilincer, Ilhan Firat Ertam, Fatih Sengur, Abdulkadir Firat Univ Technol Fac Dept Elect Elect Engn Elazig Turkey Firat Univ Technol Fac Dept Digital Forens Engn Elazig Turkey

Intrusion Detection Systems are one of the most effective technologies that protect systems against cyber-attacks. In this study, a new Comprehensive Cyber Security Intrusion Detection Dataset (CCiDD) was created. The CCiDD_A and CCiDD_B datasets are derived from the created dataset. Two datasets were compared with the NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets. In the study, the most optimal features for all datasets were determined by the Extra Tree algorithm and the new sub-datasets were classified by machine learning methods with default parameters. As a result of the classification, LGBM and XGBoost algorithms were selected as the most successful algorithms. Hyper parameter optimization was applied to LGBM and XGBoost algorithms to increase classification performance. LGBM classifier surpassed XGBoost classifier in terms of performance and processing time. LGBM algorithm achieved performance values of 99.84%, 98.02%, 99.94%, 95.68% and 99.98% for NSL-KDD, UNSW-NB15, CSE-CICIDS2018, CCiDD_A and CCiDD_B datasets, respectively. Since detection time of attacks is a critical issue, the LGBM classifier is recommended for attack detection in terms of time and performance.

关键词： IDS boosting algorithms Extra tree algorithm Cyber security Machine learning

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boosting Algorithm Techniques for Detecting Credit Card Fraud in Heterogeneous Datasets 24th

Boosting Algorithm Techniques for Detecting Credit Card Frau...

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24th International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication, RelStat 2024

作者： Viduss, Justs Spiridovska, Nadezda Transport and Telecommunication Institute Lauvas iela 2 Riga1019 Latvia

ISBN: (纸本)9783031875311

Manual reviews and rule-based systems, along with data mining techniques such as clustering and classification algorithms, are crucial for identifying credit card fraud as they help identify fraudulent transactions. Despite challenges in gathering training data, more data has recently become available;however, a comprehensive comparison of current machine learning approaches has yet to be conducted. algorithms like XGBoost, AdaBoost, and Gradient boosting Machine frequently outperform older methods. This study compares boosting algorithms to traditional approaches using three different credit card transaction datasets: synthetic, balanced with 50% fraudulent transactions, and highly unbalanced with only 0.17% fraudulent transactions. The genuine transaction datasets contained 28 anonymized parameters, including time and location. Each method was evaluated using the F1 score, accuracy, precision, and recall. This study offers recommendations on which algorithms to use in real-world scenarios, providing valuable insights for future research and practical applications in credit card fraud detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： boosting algorithms Fraud Detection Machine Learning

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Short-term forecasting of emergency medical services demand exploring machine learning☆

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Shahidian, Nika Abreu, Paulo Santos, Daniel Barbosa-Povoa, Ana Univ Lisbon CEGIST Inst Super Tecn Lisbon Portugal

This study addresses the challenge of forecasting short-term demand in Emergency Medical Services (EMS) using machine learning techniques, which is essential for improving resource allocation, optimizing response times, and enhancing overall system efficiency. In emergency situations, the swift allocation of medical personnel and vehicles is crucial for ensuring faster response times and increasing survival rates. To tackle this, the study explores nine machine learning algorithms, including Gaussian Mixture Models, Naive Bayes, KNearest-Neighbors, Support Vector Machine, Random Forest, Extremely Randomized Trees, Gradient boosting, Adaptive boosting, and Bagging, to provide accurate forecasts. Model performance is analyzed using two variable selection methods: filter and wrapper methods. The best-performing algorithms, Gradient boosting and AdaBoost, undergo further analysis after hyperparameter fine-tuning. Principal results show that Gradient boosting and AdaBoost outperformed other algorithms, with Random Search and Genetic Algorithm methods enhancing model performance. The study also demonstrates how the combination of different methods influences both performance and computational complexity. In this context, the developed models not only achieve high levels of accuracy in predicting short-term EMS demand but also serve as versatile tools. They distinguish priority calls and vehicle types when predicting call volume per hour from different districts and the expected number of dispatches at each base per shift, respectively. These models can be used as standalone tools or integrated with other optimization approaches, providing valuable inputs for optimizing staff scheduling, location, and relocation of emergency vehicles, thus contributing to the efficiency and performance improvement of EMS systems.

关键词： Machine learning Short-term forecasting Genetic algorithms Computational complexity analysis boosting algorithms

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XGBoost-enabled low-complexity approach for signal integrity assessment in coherent communication systems

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PHYSICA SCRIPTA 2025年第1期100卷

作者： Jha, Dhirendra Kumar Mishra, Jitendra K. Indian Inst Informat Technol Ranchi Dept Elect & Commun Engn Ranchi 835217 Jharkhand India

Error vector magnitude (EVM) provides critical information for assessing signal integrity and system performance in optical communication systems. In this research, a regression approach using boosting algorithms is developed to retrieve EVM information from complex signal constellation structures applicable to various modulation formats. Amplitude histograms are created and collected at different OSNR levels, launch powers, and transmission distances using an offline preprocessing approach. The impact of various evaluation techniques, including mean absolute error, coefficient of determination, root mean square error (RMSE), and mean absolute percentage error (MAPE) is discussed in detail. The results show that the proposed extreme gradient boosting framework considerably increases estimation precision compared to categorical boosting and light gradient boosting machine, especially under diverse transmission conditions, with MAPE staying below 1.7% and RMSE below 0.5, thereby enhancing overall performance monitoring in optical communication systems. This research provides a comprehensive and accurate representation of the proposed EVM estimation technique, making it a valuable resource for the development of advanced optical performance monitoring systems in the future.

关键词： error vector magnitude optical performance monitoring deep learning boosting algorithms extreme gradient boosting

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