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Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis

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JOURNAL OF BIG DATA 2021年第1期8卷 1-21页

作者： Ataei, Atousa Majidi, Niloufar Seyed Zahiri, Javad Rostami, Mehrdad Arab, S. Shahriar Rizvanov, Albert A. Kazan Fed Univ Inst Fundamental Med & Biol Kazan Russia Tarbiat Modares Univ Fac Biol Sci Dept Biophys Tehran Iran Tarbiat Modares Univ Fac Biol Sci Dept Biophys Bioinformat & Computat Omics Lab BioCOOL Tehran Iran Univ Kurdistan Dept Comp Engn Sanandaj Iran

Most of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This study aims to predict the resistance of several cancer cell-lines to a drug known as Cisplatin. In this papers the NCBI GEO database was used to obtain data and then the harvested data was normalized and its batch effects were corrected by the Combat software. In order to select the appropriate features for machine learning, the feature selection/reduction was performed based on the Fisher Score method. Six different algorithms were then used as machine learning algorithms to detect Cisplatin resistant and sensitive samples in cancer cell lines. Moreover, Differentially Expressed Genes (DEGs) between all the sensitive and resistance samples were harvested. The selected genes were enriched in biological pathways by the enrichr database. Topological analysis was then performed on the constructed networks using Cytoscape software. Finally, the biological description of the output genes from the performed analyses was investigated through literature review. Among the six classifiers which were trained to distinguish between cisplatin resistance samples and the sensitive ones, the KNN and the Naive Bayes algorithms were proposed as the most convenient machines according to some calculated measures. Furthermore, the results of the systems biology analysis determined several potential chemoresistance genes among which PTGER3, YWHAH, CTNNB1, ANKRD50, EDNRB, ACSL6, IFNG and, CTNNB1 are topologically more important than others. These predictions pave the way for further experimental researches.

关键词： machine learning algorithms Network topology Cancer Drug resistance Classification Feature selection

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Influence of elevation on plastic-covered greenhouse mapping accuracy using high-resolution satellite imagery and machine learning algorithms

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REMOTE SENSING LETTERS 2023年第4期14卷 413-422页

作者： Veettil, Bijeesh Kozhikkodan Acharki, Siham Van Lang Univ Sci & Technol Adv Inst Lab Ecol & Environm Management Ho Chi Minh City Vietnam Van Lang Univ Fac Appl Technol Sch Technol Ho Chi Minh City Vietnam Abdelmalek Essaadi Univ Fac Sci & Tech Tangier Dept Earth Sci Tetouan Morocco

Proliferation of plastic-covered greenhouse (PCG) farming areas has been unparalleled in recent decades around the globe. Therefore, PCG's proper mapping and continuous monitoring are required to maintain protected farming sustainability and to reduce plastic pollution. Topography is one of the key factors affecting PCG mapping accuracy from satellite imagery. In this research, in order to understand the influence of elevation on PCG mapping accuracy in a Moroccan (with altitude below 200 m) and Vietnamese (an average altitude of 1,500 m) area, five supervised algorithms were applied to high-resolution PlanetScope and RapidEye imagery: maximum likelihood classification (MLC), k-nearest neighbours (k-NN), random forest (RF), support vector machine with linear kernel (SVML) and radial basis kernel (SVMRB). The findings indicated that k-NN (SVMRB) produced the greatest overall accuracy, followed by SVMRB (RF) for Dalat City (Loukkos). The present study also found that RapidEye outperforms PlanetScope in both areas and independently of the machine learning algorithm. Elevation data inclusion significantly improves (1.6% to 6.8%) various satellites' performance, particularly in Dalat City. Thus, this study demonstrated that topography has an impact on PCG mapping, especially in hilly areas.

关键词： High-resolution imagery topography plastic-covered greenhouse protected horticulture machine learning algorithms

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A review and guide on selecting and optimizing machine learning algorithms for daylight prediction

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BUILDING AND ENVIRONMENT 2023年 244卷

作者： Liu, Qiuping Chen, Yaodong Liu, Yang Lei, Yuanfang Wang, Yibo Hu, Pantin Southwest Jiaotong Univ Sch Architecture Chengdu Peoples R China SWJTU Southwest Jiaotong Univ Chengdu Sichuan Peoples R China

Daylight confers extensive benefits for building occupants and improves energy efficiency;thus, its prediction and performance are significant for design decision-making on building and lighting. However, real-time prediction with high precision is difficult since daylight is spatiotemporal correlated and highly dynamic (e.g., periodic, irregularity). In recent years, numerous studies have been focused on machine learning algorithms due to their high generalization ability, robustness, and accuracy in dealing with complex problems. In contrast, selecting the optimal algorithms for a specific research problem is time-consuming and challenging, especially for new learners. This review attempts to develop a systematic guide for algorithms selection and optimization by: (i) discussing the main principles, model structure, and characteristics of 36 of the most representative algorithms;(ii) reviewing and conducting a whole process statistical analysis on the machine learning-based daylight-prediction studies, while abstracting the statistical issues (regression and classification) and prediction tasks (time series, spatial, and spatiotemporal predictions) of different research topics and purposes;and (iii) integrating the findings, developing the selection and optimization methodologies for algorithms. In conclusion, the order of specific machine learning algorithms is highly recommended for different problem types: (i) for regression, Back Propagation Neural Network (recommendation level = 0.82) > Convolutional Neural Network (0.78) > Kernel Support Vector Regression (0.72);(ii) for classification, Kernel Support Vector machine (0.8) > Radial Basis Function Neural Network (0.56) > Random Forest (0.53);(iii) for time series prediction, Back Propagation Neural Network (0.66) > Random Forest (0.61) > Gated Recurrent Unit Network (0.52) = EXtreme Gradient Boosting (0.52);(iv) for spatial prediction, Back Propagation Neural Network (0.82) > Convolutional Neural Network (0.78

关键词： Daylight prediction machine learning algorithms Time-series prediction Spatial prediction Artificial neural network

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Comprehensive evaluation of machine learning algorithms for flood susceptibility mapping in Wardha River sub-basin, India

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ACTA GEOPHYSICA 2024年第3期73卷 2725-2748页

作者： Sharma, Asheesh Nerkar, Sudhanshu Banyal, Rishit Poonia, Mandeep Kadaverugu, Rakesh Damahe, Lalit Tuegel, Franziska Holzbecher, Ekkehard Hinkelmann, Reinhard CSIR Natl Environm Engn Res Inst CSIR NEERI Nehru Marg Nagpur 440020 Maharashtra India Tech Univ Berlin TU Berlin Fac Water Resources Management & Modeling Hydrosys Gustav Meyer Alee 25Sec TIB1-B14 D-13355 Berlin Germany CSIR NEERI Nagpur India Yashwantrao Chavan Coll Engn Head Dept Comp Sci & Engn Nagpur India Univ Twente Fac Geoinformat Sci & Earth Observat ITC Dept Water Resources Drienerlolaan 5 NL-7522 NB Enschede Netherlands Tech Univ Berlin TU Berlin Fac Engn Technol Dept Water Engn & Management Gustav Meyer Allee 25Sec TIB1 B14 D-13355 Berlin Germany German Univ Technol Oman GUtech Dept Appl Geosci POB 1816 Muscat 130 Oman Tech Univ Berlin TU Berlin Chair Water Resources Management & Modeling Hydros Gustav Meyer Allee 25Sec TIB1 B14 D-13355 Berlin Germany

machine learning offers a powerful and versatile approach to flood susceptibility mapping, enabling us to leverage complex data and improve prediction accuracy. Given the plethora of available techniques and the challenges in selecting the optimal approach, this study investigates prominent ML algorithms for flood susceptibility mapping (FSM) in the Wardha River sub-basin, India. Seven machine learning algorithms, viz. support vector machine (SVM), extreme gradient boosting (XGB), artificial neural network (ANN), generalized linear model (GLM), gradient boosting machine (GBM), random forest (RF), and linear discriminant analysis (LDA), were evaluated at varying spatial resolutions (30 m, 50 m, 100 m, and 200 m). Seven flood-inducing factors (elevation, flow accumulation, topographic wetness index, slope, rainfall, land use, and drain density) were considered. Model performance was assessed using sensitivity, specificity, area under the curve (AUC), overall correlation, overall standard deviation ratio, and overall root mean square difference (RMSD). The impact of spatial resolution on models' accuracy was analysed. SVM, GBM, and RF were significantly affected, while ANN, GLM, and XGB were less sensitive. LDA excelled in execution time and spatial resolution resilience. The overall ranking of models was executed based on their accuracy, AUC, and execution time. XGB outperformed GBM and RF, securing first place, while SVM ranked last. GLM, ANN, and LDA ranked third to fifth. The results highlighted the importance of algorithm selection in accurately mapping flood susceptibility, particularly when working with varying spatial resolution data. The study findings can inform the decision-making process for implementing FSM using these machine learning algorithms.

关键词： Flood susceptibility mapping machine learning algorithms Flood-contributing factor Spatial resolution Execution time

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Use of machine learning algorithms to assess the state of rockburst hazard in underground coal mine openings

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第3期14卷 703-713页

作者： Lukasz Wojtecki Sebastian Iwaszenko Derek B.Apel Mirosawa Bukowska Janusz Makówka Central Mining Institute Katowice40-166Poland School of Mining and Petroleum Engineering University of AlbertaEdmontonT6G 2R3Canada

The risk of rockbursts is one of the main threats in hard coal mines. Compared to other underground mines, the number of factors contributing to the rockburst at underground coal mines is much *** such as the coal seam tendency to rockbursts, the thickness of the coal seam, and the stress level in the seam have to be considered, but also the entire coal seam-surrounding rock system has to be evaluated when trying to predict the rockbursts. However, in hard coal mines, there are stroke or stress-stroke rockbursts in which the fracture of a thick layer of sandstone plays an essential role in predicting rockbursts. The occurrence of rockbursts in coal mines is complex, and their prediction is even more difficult than in other mines. In recent years, the interest in machine learning algorithms for solving complex nonlinear problems has increased, which also applies to geosciences. This study attempts to use machine learning algorithms, i.e. neural network, decision tree, random forest, gradient boosting, and extreme gradient boosting(XGB), to assess the rockburst hazard of an active hard coal mine in the Upper Silesian Coal Basin. The rock mass bursting tendency index WTGthat describes the tendency of the seam-surrounding rock system to rockbursts and the anomaly of the vertical stress component were applied for this purpose. Especially, the decision tree and neural network models were proved to be effective in correctly distinguishing rockbursts from tremors, after which the excavation was not damaged. On average, these models correctly classified about 80% of the rockbursts in the testing datasets.

关键词： Hard coal mining Rockburst hazard machine learning algorithms

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CaPBug-A Framework for Automatic Bug Categorization and Prioritization Using NLP and machine learning algorithms

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IEEE ACCESS 2021年 9卷 50496-50512页

作者： Ahmed, Hafiza Anisa Bawany, Narmeen Zakaria Shamsi, Jawwad Ahmed Jinnah Univ Women Dept Comp Sci & Software Engn Karachi 74600 Pakistan Natl Univ Comp & Emerging Sci NUCES Dept Comp Sci Karachi 75400 Pakistan

Bug reports facilitate software development teams in improving the quality of software. These reports include significant information related to problems encountered within a software, possible enhancement suggestions, and other potential issues. Bug reports are typically complex and are too detailed;hence a lot of resources are required to analyze and process them manually. Moreover, it leads to delays in the resolution of high priority bugs. Accurate and timely processing of bug reports based on their category and priority plays a significant role in improving the quality of software maintenance. Therefore, an automated process of categorization and prioritization of bug reports is needed to address the aforementioned issues. Automated categorization and prioritization of bug reports have been explored recently by many researchers;however, limited progress has been made in this regard. In this research, we present a novel framework, titled CaPBug, for automated categorization and prioritization of bug reports. The framework is implemented using Natural Language Processing (NLP) and supervised machine learning algorithms. A baseline corpus is built with six categories and five prioritization levels by analyzing more than 2000 bug reports of Mozilla and Eclipse repository. Four classification algorithms i.e., Naive Bayes, Random Forest, Decision Tree, and Logistic Regression have been used to categorize and prioritize bug reports. We demonstrate that the CaPBug framework achieved an accuracy of 88.78% by using a Random Forest classifier with a textual feature for predicting the category. Similarly, using the CaPBug framework, an accuracy of 90.43% was achieved in predicting the priority of bug reports. Synthetic Minority Over-Sampling Technique (SMOTE) has been applied to address the class imbalance issue in priority classes.

关键词： Computer bugs Software machine learning algorithms Feature extraction Classification algorithms Prediction algorithms Location awareness Bug reports natural language processing machine learning bug report categorization bug report prioritization

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Hospital Length-of-Stay Prediction Using machine learning algorithms-A Literature Review

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10523页

作者： Almeida, Guilherme Correia, Fernanda Brito Borges, Ana Rosa Bernardino, Jorge Polytech Univ Coimbra Coimbra Inst Engn ISEC Rua Misericordia P-3045093 Coimbra Portugal Univ Coimbra Ctr Informat & Syst Polo 2 P-3030290 Coimbra Portugal

Predicting hospital length of stay is critical for efficient hospital management, enabling proactive resource allocation, the optimization of bed availability, and optimal patient care. This paper explores the potential of machine learning algorithms to revolutionize hospital length-of-stay predictions, contributing to healthcare efficiency and patient care. The main objective is to identify the most effective machine learning algorithm for building a predictive model capable of predicting hospital length of stay. The bibliographic search of the existing literature on machine learning algorithms applied to hospital length of stay predictions highlighted the most relevant papers within this area of research. The papers were analyzed in terms of model types and metrics that contributed to the considerable impact on healthcare decision making. We also discuss the challenges and limitations of machine learning algorithms for predicting length of stay, and the importance of data quality and ethical considerations.

关键词： hospital length of stay prediction models machine learning algorithms

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Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: Some comparisons

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EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES 2022年第1期25卷 147-156页

作者： Hudait, Manas Patel, Priyank Pravin Presidency Univ Dept Geog Kolkata India

Crop acreage analysis and yield estimation are of prime importance in field-level agricultural monitoring and management. This enables prudent decision making during any crop failure event and for ensuing crop insurance. The free availability of the high resolution Sentinel-2 satellite datasets has created new possibilities for mapping and monitoring agricultural lands in this regard. In the present study conducted on the Tamluk Subdivision of the Purba Medinipur District of West Bengal, the heterogeneous crop area was mapped according to the respective crop type, using Sentinel-2 multi-spectral images and two machine learning algorithms-K Nearest Neighbour (KNN) and Random Forest (RF). Plot-level field information was collected from different cropland types to frame the training and validation datasets (comprising 70% and 30% of the total dataset, respectively) for cropland classification and accuracy assessment. Through this, the major summer crop acreage was identified (Boro rice, vegetables and betel vine-the three main crops in the study area). The extracted maps had an overall accuracy of 97.16% and 97.22%, respectively, in the KNN and RF classifications, with respective Kappa index values of 95.99% and 96.08%, and the RF method proved to be more accurate. This study was particularly useful in mapping the betel leaf acreage herein since scant information exists for this crop and it is cultivated by many smallholder farmers in the region. The methods used in this paper can be readily applied elsewhere for accurately enumerating the respective crop acreages.(c) 2022 National Authority of Remote Sensing & Space Science. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： machine learning algorithms Smallholder agriculture Crop acreage Betel vine cultivation

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A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using machine learning algorithms

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MOBILE INFORMATION SYSTEMS 2018年第Pt.3期2018卷 3860146.1-3860146.21页

作者： Ul Haq, Amin Li, Jian Ping Memon, Muhammad Hammad Nazir, Shah Sun, Ruinan Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China Univ Swabi Dept Comp Sci Khyber Pakhtunkhwa Pakistan

Heart disease is one of the most critical human diseases in the world and affects human life very badly. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart disease is important for heart failure prevention and treatment. The diagnosis of heart disease through traditional medical history has been considered as not reliable in many aspects. To classify the healthy people and people with heart disease, noninvasive-based methods such as machine learning are reliable and efficient. In the proposed study, we developed a machine-learning-based diagnosis system for heart disease prediction by using heart disease dataset. We used seven popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and seven classifiers performance evaluation metrics such as classification accuracy, specificity, sensitivity, Matthews' correlation coefficient, and execution time. The proposed system can easily identify and classify people with heart disease from healthy people. Additionally, receiver optimistic curves and area under the curves for each classifier was computed. We have discussed all of the classifiers, feature selection algorithms, preprocessing methods, validation method, and classifiers performance evaluation metrics used in this paper. The performance of the proposed system has been validated on full features and on a reduced set of features. The features reduction has an impact on classifiers performance in terms of accuracy and execution time of classifiers. The proposed machine-learning-based decision support system will assist the doctors to diagnosis heart patients efficiently.

关键词： Heart Diseases hybrid intelligent system machine learning algorithms Persons selection algorithm Performance Evaluation execution time diagnostic system Classifiers

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Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia

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Geoscience Frontiers 2021年第2期12卷 639-655页

作者： Ahmed Mohamed Youssef Hamid Reza Pourghasemi Geology Department Faculty of ScienceSohag UniversityEgypt Geological Hazards Department Applied Geology SectorSaudi Geological SurveyP.O.Box 54141Jeddah21514Saudi Arabia Department of Natural Resources and Environmental Engineering College of AgricultureShiraz UniversityShirazIran

The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their *** machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important *** study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi ***,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data *** the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating *** landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and *** area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs *** results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for *** findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other *** outcome of this study and the landslide susceptibility maps would be useful for environmental protection.

关键词： Landslide susceptibility machine learning algorithms Variables importance Saudi Arabia

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