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machine learning-based ensemble approach in prediction of lung cancer predisposition using XRCC1 gene polymorphism

JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS 2024年第15期42卷 7828-7837页

作者： Choudhary, Abhishek Anand, Adarsh Singh, Amrita Roy, Pratima Singh, Navneet Kumar, Vinay Sharma, Siddharth Baranwal, Manoj Thapar Inst Engn & Technol Dept Comp Sci Patiala India Thapar Inst Engn & Technol Dept Elect & Commun Engn Patiala India Thapar Inst Engn & Technol Dept Biotechnol Patiala 147004 Punjab India Post Grad Inst Educ & Med Res PGIMER Dept Pulm Med Chandigarh India

The employment of machine learning approaches has shown promising results in predicting cancer. In the current study, polymorphisms data of five single nucleotide polymorphisms (SNPs) of DNA repair gene XRCC1 (XRCC1 399, XRCC1 194, XRCC1 206, XRCC1 632, XRCC1 280) of the north Indian population along with four smoking status data is considered as an input to the proposed ensemble model to predict the risk of individual susceptibility to the lung cancer. The prediction accuracy of the proposed ensemble model for cancer predisposition was found to be 85%. The model performance is also evaluated using sensitivity, specificity, precision and the Gini index, which is found in the range of 0.83-0.87. The proposed model also outperformed in all evaluation parameters when compared with the individual Model (LM, SVM, RF, KNN and baseline neural net). Collectively, current results suggest the potential of the proposed ensemble model in predicting the risk of cancer based on XRCC1 SNPs *** by Ramaswamy H. Sarma

关键词： Lung cancer single nucleotide polymorphism XRCC1 machine learning algorithms

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Integration of machine learning and remote sensing for assessing the change detection of mangrove forests along the Mumbai coast

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JOURNAL OF EARTH SYSTEM SCIENCE 2024年第4期133卷 186页

作者： Sawant, Suraj Bonala, Praneetha Joshi, Amit Shindikar, Mahesh Patil, Abhilasha Vyas, Swapnil Deobagkar, Deepti Pune Technol Univ Coll Engn Dept Comp Sci & Engn Wellesley Rd Pune 411005 India Coll Engn Pune Technol Univ Dept Appl Sci & Humanity Wellesley Rd Pune 411005 India Savitribai Phule Pune Univ Dept Geog Geoinformat Pune 411007 India Savitribai Phule Pune Univ Dept Geog Geoinformat Pune 411007 India

Mangrove forests, being high-yielding ecosystems, often dominate the intertidal sites along equatorial and subtropical coasts. Despite the known significance of mangroves to the coastal ecology, especially fisheries, deforestation remains a severe danger due to coercion for forest products, ground transformation for aquaculture, and seaside urban growth. Remote sensing is integral in mapping and analysing changes in mangrove forests' areal extent and spatial patterns due to natural disasters and anthropogenic causes over the last three decades. This work depicts remote sensing analysis for change detection in mangrove forest land use land cover from 2014 to 2019. Indian Remote-Sensing Satellite Resourcesat-2 LISS-IV datasets have been used for analysis. A comparison with the Sentinel-2A dataset and two machine learning models: Random Forest and Classification and Regression Tree, has been performed with 2019 data. This work identifies CART as a suitable choice for supervised landform classification utilising remotely sensed geophysical data that is used to decipher spatial changes concurred over time. An overall growth in the mangrove cover was observed from 2014 to 2019, from 86.26 to 89.63 km2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\hbox {km}<^>{2}}$$\end{document}, along the Mumbai coastline. Spatial comparison over the years shows the growth and loss of land-use cover areas. The performance metrics such as overall accuracy, producer accuracy, Kappa coefficient, and Matthews correlation coefficient are computed. The experiments were conducted using the Google Earth Engine, a powerful cloud computing platform.

关键词： Mangrove forest machine learning algorithms land use land cover assessment change detection Google Earth Engine

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Interpretable machine learning-based clinical prediction model for predicting lymph node metastasis in patients with intrahepatic cholangiocarcinoma

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BMC GASTROENTEROLOGY 2024年第1期24卷 1-10页

作者： Xie, Hui Hong, Tao Liu, Wencai Jia, Xiaodong Wang, Le Zhang, Huan Xu, Chan Zhang, Xiaoke Li, Wen-Le Wang, Quan Yin, Chengliang Lv, Xu Yanan Peoples Hosp Dept Gen Surg Yanan Peoples R China Chinese Acad Med Sci Fuwai Hosp Dept Cardiac Surg Shenzhen Peoples R China Nanchang Univ Affiliated Hosp 1 Dept Orthopaed Surg Nanchang Peoples R China Peoples Liberat Army Gen Hosp Med Ctr 5 Dept Oncol Beijing Peoples R China Dalian Med Univ Affiliated Hosp 1 Dept Thorac Surg Dalian Peoples R China Grad Sch Shaanxi Univ Chinese Med Grad Sch Xianyang 712046 Peoples R China Xiamen Univ Sch Publ Hlth State Key Lab MolecularVaccinol & Mol Diagnost Xiamen 361102 Peoples R China Xiamen Univ Ctr Mol Imaging & Translat Med Sch Publ Hlth Xiamen 361102 Peoples R China Peoples Liberat Army Gen Hosp Med Ctr 5 Radiat Oncol Dept Beijing Peoples R China Macau Univ Sci & Technol Fac Med Taipa Macao Peoples R China Yixing Canc Hosp Dept Gen Surg Yixing 214200 Jiangsu Peoples R China

Objective Prediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with *** A total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing *** In multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of *** The proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans.

关键词： Intrahepatic cholangiocarcinoma machine learning algorithms Lymph node metastasis Web calculator

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Rapid runtime learning by curating small datasets of high-quality items obtained from memory

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PLOS COMPUTATIONAL BIOLOGY 2023年第10期19卷 e1011445-e1011445页

作者： German, Joseph Scott Cui, Guofeng Xu, Chenliang Jacobs, Robert A. Tech Univ Darmstadt Inst Psychol Darmstadt Germany Tech Univ Darmstadt Ctr Cognit Sci Darmstadt Germany Rutgers State Univ Dept Comp Sci Piscataway NJ USA Univ Rochester Dept Comp Sci Rochester NY 14627 USA Univ Rochester Dept Brain & Cognit Sci Rochester NY 14627 USA

We propose the "runtime learning" hypothesis which states that people quickly learn to perform unfamiliar tasks as the tasks arise by using task-relevant instances of concepts stored in memory during mental training. To make learning rapid, the hypothesis claims that only a few class instances are used, but these instances are especially valuable for training. The paper motivates the hypothesis by describing related ideas from the cognitive science and machine learning literatures. Using computer simulation, we show that deep neural networks (DNNs) can learn effectively from small, curated training sets, and that valuable training items tend to lie toward the centers of data item clusters in an abstract feature space. In a series of three behavioral experiments, we show that people can also learn effectively from small, curated training sets. Critically, we find that participant reaction times and fitted drift rates are best accounted for by the confidences of DNNs trained on small datasets of highly valuable items. We conclude that the runtime learning hypothesis is a novel conjecture about the relationship between learning and memory with the potential for explaining a wide variety of cognitive phenomena. Human cognition is remarkably flexible, with the ability to reliably perform a wide variety of tasks, including idiosyncratic and unfamiliar ones, in a wide variety of contexts. However, despite impressive advances in machine learning, leading to artificial intelligences capable of outperforming humans on select individual tasks, this flexibility has yet to be replicated by computational models. Doing so is essential to both creating general artificial intelligence and to achieving a deeper understanding of the human brain and mind. To account for this vital property of human cognition, we introduce the "runtime learning hypothesis", in which the brain rapidly constructs task-specific models in response to the needs of the moment using an internal training proces

关键词： machine learning Human learning Reaction time learning Neural networks machine learning algorithms Memory Cognitive science

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Tribo-informative Analysis of Spark Plasma Sintered Al-SiC-HAp Hybrid Composite Through machine learning Techniques

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年 1-16页

作者： Venkatesh, V. S. S. Rao, R. Narasimha Manohar, Guttikonda Singh, Sapam Ningthemba Indian Inst Technol Bhubaneswar Sch Mech Sci Argul 752050 Odisha India Natl Inst Technol Warangal Warangal 506004 Telangana India Natl Inst Technol Silchar Silchar 788010 Assam India CVR Coll Engn Dept Mech Engn Rangareddy 501510 Telangana India

This research epitomizes the tribological analysis of the SiC and hydroxyapatite (HAp) reinforced aluminium composite. Hydroxyapatite was extracted from the mrigal fish and used as a secondary reinforcement to enhance the tribological characteristics of the aluminium matrix. A DUCOM TR20LE pin-on-disc tribometer and ECONEMET VH1MD hardness were used to assess the wear and hardness of the synthesized spark plasma sintered Al- 10%SiC- x%HAp (x = 0, 2.5, 5, 7.5, 10 and 12.5 wt%) hybrid composite respectively. X-ray diffraction analysis confirmed the presence of HAp and beta-tricalcium phosphate in the mrigal fish bone extracted HAp powder. The OH-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${OH}<^>{-}$$\end{document}, CO32-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${CO}_{3}<^>{2-}$$\end{document} and PO43-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${PO}_{4}<^>{3-}$$\end{document} bands were identified by conducting Fourier transform infrared spectroscopy which confirms the HAp in the calcinated powder. Hardness results reveal that the composite corresponds to the 7.5 wt% HAp achieved the maximum hardness of 129 VHN. The composite with 2.5 wt% reinforced HAp in the Al- 10wt%SiC- HAp composite exhibits the minimum wear and COF of 0.37 mg and 0.3 respectively for 5 N applied load. To investigate the wear behaviour of the synthesized composite, different machine learning techniques such as KNN, SVM, and ANN have been utilized

关键词： Aluminium matrix Hydroxyapatite (HAp) Spark plasma sintering machine learning algorithms Tribological properties

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machine learning Reveals Serum Glycopatterns as Potential Biomarkers for the Diagnosis of Nonalcoholic Fatty Liver Disease (NAFLD)

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JOURNAL OF PROTEOME RESEARCH 2024年第6期23卷 2253-2264页

作者： Li, Xiaocheng Xiao, Yaqing Chen, Xinhuan Zhu, Yayun Du, Haoqi Shu, Jian Yu, Hanjie Ren, Xiameng Zhang, Fan Dang, Jing Zhang, Chen Su, Shi Li, Zheng Northwest Univ Coll Life Sci Lab Funct Glyc Xian 710069 Peoples R China Xi An Jiao Tong Univ Affiliated Hosp 1 Dept Hlth Sci Ctr Xian 710061 Peoples R China Northwest Univ Fac Life Sci & Med Sch Med Xian 710069 Peoples R China

Nonalcoholic fatty liver disease (NAFLD) has emerged as the predominant chronic liver condition globally, and underdiagnosis is common, particularly in mild cases, attributed to the asymptomatic nature and traditional ultrasonography's limited sensitivity to detect early-stage steatosis. Consequently, patients may experience progressive liver pathology. The objective of this research is to ascertain the efficacy of serum glycan glycopatterns as a potential diagnostic biomarker, with a particular focus on the disease's early stages. We collected a total of 170 serum samples from volunteers with mild-NAFLD (Mild), severe-NAFLD (Severe), and non-NAFLD (None). Examination via lectin microarrays has uncovered pronounced disparities in serum glycopatterns identified by 19 distinct lectins. Following this, we employed four distinct machine learning algorithms to categorize the None, Mild, and Severe groups, drawing on the alterations observed in serum glycopatterns. The gradient boosting decision tree (GBDT) algorithm outperformed other models in diagnostic accuracy within the validation set, achieving an accuracy rate of 95% in differentiating the None group from the Mild group. Our research indicates that employing lectin microarrays to identify alterations in serum glycopatterns, when integrated with advanced machine learning algorithms, could constitute a promising approach for the diagnosis of NAFLD, with a special emphasis on its early detection.

关键词： nonalcoholic fatty liver disease serum glycopatterns machine learning algorithms

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What could be the role of genetic tests and machine learning of AXIN2 variant dominance in non-syndromic hypodontia? A case-control study in orthodontically treated patients

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PROGRESS IN ORTHODONTICS 2024年第1期25卷 31页

作者： Alhazmi, Nora Alaqla, Ali Almuzzaini, Bader Aldrees, Mohammed Alnaqa, Ghaida Almasoud, Farah Aldibasi, Omar Alshamlan, Hala King Saud bin Abdulaziz Univ Hlth Sci Coll DentMinist Natl Guard Hlth Affairs King Abdullah Int Med Res Ctr Dept Prevent Dent Sci Riyadh Saudi Arabia King Saud bin Abdulaziz Univ Hlth Sci Coll DentMinist Natl Guard Hlth Affairs King Abdullah Int Med Res Ctr Dept Restorat & Prosthet Dent Sci Riyadh Saudi Arabia King Saud bin Abdulaziz Univ Hlth Sci King Abdullah Int Med Res Ctr Dept Med Genom Res Minist Natl Guard Hlth Affairs Riyadh Saudi Arabia King Saud bin Abdulaziz Univ Hlth Sci Coll Dent King Abdullah Int Med Res Ctr Minist Natl Guard Hlth Affairs Riyadh Saudi Arabia King Saud bin Abdulaziz Univ Hlth Sci King Abdullah Int Med Res Ctr Biostat Sect Minist Natl Guard Hlth Affairs Riyadh Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh Saudi Arabia

Background Hypodontia is the most prevalent dental anomaly in humans, and is primarily attributed to genetic factors. Although genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNP) associated with hypodontia, genetic risk assessment remains challenging due to population-specific SNP variants. Therefore, we aimed to conducted a genetic analysis and developed a machine-learning-based predictive model to examine the association between previously reported SNPs and hypodontia in the Saudi Arabian population. Our case-control study included 106 participants (aged 8-50 years;64 females and 42 males), comprising 54 hypodontia cases and 52 controls. We utilized TaqMan (TM) Real-Time Polymerase Chain Reaction and allelic genotyping to analyze three selected SNPs (AXIN2: rs2240308, PAX9: rs61754301, and MSX1: rs12532) in unstimulated whole saliva samples. The chi-square test, multinomial logistic regression, and machine-learning techniques were used to assess genetic risk by using odds ratios (ORs) for multiple target variables. Results Multivariate logistic regression indicated a significant association between homozygous AXIN2 rs2240308 and the hypodontia phenotype (ORs [95% confidence interval] 2.893 [1.28-6.53]). machine-learning algorithms revealed that the AXIN2 homozygous (A/A) genotype is a genetic risk factor for hypodontia of teeth #12, #22, and #35, whereas the AXIN2 homozygous (G/G) genotype increases the risk for hypodontia of teeth #22, #35, and #45. The PAX9 homozygous (C/C) genotype is associated with an increased risk for hypodontia of teeth #22 and #35. Conclusions Our study confirms a link between AXIN2 and hypodontia in Saudi orthodontic patients and suggests that combining machine-learning models with SNP analysis of saliva samples can effectively identify individuals with non-syndromic hypodontia.

关键词： machine learning algorithms Non-syndromic hypodontia Single nucleotide polymorphism AXIN2 PAX9 MSX1 Genetics Orthodontics

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Applying machine learning to Estimate the Effort and Duration of Individual Tasks in Software Projects

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IEEE ACCESS 2023年 11卷 89933-89946页

作者： Sousa, Andre Veloso, Daniel Goncalves, Henrique Faria, Joao Pascoal Mendes-Moreira, Joao Graca, Ricardo Gomes, Duarte Castro, Rui Nuno Henriques, Pedro Castro Univ Porto Fac Engn P-4200465 Porto Portugal INESC TEC P-4200465 Porto Portugal Fraunhofer Portugal AICOS P-4200135 Porto Portugal Strongstep P-4100429 Porto Portugal

Software estimation is a vital yet challenging project management activity. Various methods, from empirical to algorithmic, have been developed to fit different development contexts, from plan-driven to agile. Recently, machine learning techniques have shown potential in this realm but are still underexplored, especially for individual task estimation. We investigate the use of machine learning techniques in predicting task effort and duration in software projects to assess their applicability and effectiveness in production environments, identify the best-performing algorithms, and pinpoint key input variables (features) for predictions. We conducted experiments with datasets of various sizes and structures exported from three project management tools used by partner companies. For each dataset, we trained regression models for predicting the effort and duration of individual tasks using eight machine learning algorithms. The models were validated using k-fold cross-validation and evaluated with several metrics. Ensemble algorithms like Random Forest, Extra Trees Regressor, and XGBoost consistently outperformed non-ensemble ones across the three datasets. However, the estimation accuracy and feature importance varied significantly across datasets, with a Mean Magnitude of Relative Error (MMRE) ranging from 0.11 to 9.45 across the datasets and target variables. Nevertheless, even in the worst-performing dataset, effort estimates aggregated to the project level showed good accuracy, with MMRE = 0.23. machine learning algorithms, especially ensemble ones, seem to be a viable option for estimating the effort and duration of individual tasks in software projects. However, the quality of the estimates and the relevant features may depend largely on the characteristics of the available datasets and underlying projects. Nevertheless, even when the accuracy of individual estimates is poor, the aggregated estimates at the project level may present a good accuracy due to erro

关键词： Task analysis Software Software algorithms Maximum likelihood estimation Prediction algorithms Predictive models machine learning algorithms Software development management Project management Effort estimation duration estimation machine learning task estimation software projects

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machine learning prediction models for ground motion parameters and seismic damage assessment of buildings at a regional scale

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Resilient Cities and Structures 2024年第1期3卷 84-102页

作者： Sanjeev Bhatta Xiandong Kang Ji Dang Graduate School of Civil and Environmental Engineering Saitama UniversitySaitamaJapan Beijing Earthquake Agency BeijingChina

This study examines the feasibility of using a machine learning approach for rapid damage assessment of rein-forced concrete(RC)buildings after the *** the real-world damaged datasets are lacking,have limited access,or are imbalanced,a simulation dataset is prepared by conducting a nonlinear time history *** machine learning(ML)models are trained considering the structural parameters and ground motion characteristics to predict the RC building damage into five categories:null,slight,moderate,heavy,and *** random forest classifier(RFC)has achieved a higher prediction accuracy on testing and real-world damaged *** structural parameters can be extracted using different means such as Google Earth,Open Street Map,unmanned aerial vehicles,***,recording the ground motion at a closer distance requires the installation of a dense array of sensors which requires a higher *** places with no earthquake recording station/device,it is difficult to have ground motion *** that different ML-based regressor models are developed utilizing past-earthquake information to predict ground motion parameters such as peak ground acceleration and peak ground *** random forest regressor(RFR)achieved better results than other regression models on testing and validation ***,compared with the results of similar research works,a better result is obtained using RFC and RFR on validation *** the end,these models are uti-lized to predict the damage categories of RC buildings at Saitama University and Okubo Danchi,Saitama,Japan after an *** damage information is crucial for government agencies or decision-makers to respond systematically in post-disaster situations.

关键词： Seismic damage prediction Ground motion parameter machine learning algorithms Nonlinear time history analysis RC buildings

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Analysis of learning algorithms for Predicting Carbon Emissions of Light-Duty Vehicles

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第7期15卷 584-589页

作者： Kale, Rashmi B. Shaikh, Nuzhat Faiz Smt Kashibai Navale Coll Engn SPPU Dept Comp Engn Pune Pune India Wadia Coll Engn Dept Comp Engn Pune Pune India

research presents a comparative analysis of different learning methods developed for the prediction of carbon emissions from light-duty vehicles. With the growing concern over environmental sustainability, accurate prediction of carbon emissions is vital for developing effective mitigation strategies. The work assesses the performance of various algorithms trained on vehicle-specific data attributes to predict the emission patterns of a fuel type of different light duty models. This work uses two realtime petrol and diesel datasets collected by CariQ app and device. Canada government dataset is also used from the online repository for prediction of the vehicle emission. The evaluation is based on their predictive accuracy. The findings reveal insights into the effectiveness of different learning techniques in accurately estimating carbon emissions from vehicles, providing valuable guidance for policymakers and researchers in the field of environmental sustainability and transportation planning.

关键词： Carbon - Carbon emission machine learning algorithms CariQ carbon emission dataset An Air Quality Index (AQI)

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