Article
Automation & Control Systems
Pia Pfeiffer, Bettina Ronai, Georg Vorlaufer, Nicole Doerr, Peter Filzmoser
Summary: The aim of this study is to quantify the relationship between different methods of artificial oil alteration and engine oils collected from a passenger car using FTIR spectroscopic data and chemometric methods. The study proposes a comprehensive procedure for the analysis of FTIR spectra and validates its effectiveness on a real-world dataset.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Automation & Control Systems
Daniel R. Kowal
Summary: Subset selection is a valuable tool for interpretability, scientific discovery, and data compression. We propose a Bayesian approach to address the challenges in classical subset selection, and introduce a strategy that focuses on finding near-optimal subsets rather than a single best subset. We apply Bayesian decision analysis to derive the optimal linear coefficients for any subset of variables, and our approach outperforms competing methods in prediction, interval estimation, and variable selection. By analyzing a large education dataset, we gain unique insights into the factors that predict educational outcomes and identify over 200 distinct subsets of variables that offer near-optimal predictive accuracy.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Biochemical Research Methods
Ayyuce Begum Bektas, Cigdem Ak, Mehmet Gonen
Summary: With the increasing sizes of computational biology datasets, previous kernel-based machine learning algorithms have failed to provide satisfactory interpretability. To address this issue, we propose a fast and efficient multiple kernel learning algorithm that can extract significant information from genomic data. Our experiments demonstrate that the algorithm outperforms baseline methods while using only a small fraction of input features, and it has the potential to discover new biomarkers and therapeutic guidelines.
Article
Mathematics
Zhongzheng Wang, Guangming Deng, Jianqi Yu
Summary: The proposed group screening procedure based on the information gain ratio for a classification model is shown to have better screening performance and classification accuracy.
JOURNAL OF MATHEMATICS
(2022)
Article
Biochemical Research Methods
Akio Onogi, Aisaku Arakawa
Summary: An R package has been developed to implement multiple linear learners in a single model. It uses fast algorithms to obtain solutions and is useful for incorporating multimodal and high-dimensional explanatory variables in regression models.
Article
Computer Science, Artificial Intelligence
Zhe Liu, Xiangfeng Yang
Summary: Variable selection is important in regression analysis, but can be challenging when data is imprecise. This paper introduces a method using uncertain variables for variable selection and parameter estimation, along with a proposed approach for tuning parameter selection through cross-validation. Numerical examples demonstrate the effectiveness of the methods presented.
Article
Mathematics
Juan C. Laria, M. Carmen Aguilera-Morillo, Enrique Alvarez, Rosa E. Lillo, Sara Lopez-Taruella, Maria del Monte-Millan, Antonio C. Picornell, Miguel Martin, Juan Romo
Summary: This paper introduces a methodology to deal with variable selection and model estimation problems in a high-dimensional set-up, which can be particularly useful in the whole genome context.
Article
Computer Science, Interdisciplinary Applications
Jasleen Kaur Sethi, Mamta Mittal
Summary: This research investigates the effectiveness of a feature selection method based on LASSO for predicting air quality in Delhi and surrounding cities, identifying meteorological factors and pollutant concentrations as the most important influencing factors, and suggesting preventive measures to improve air quality.
EARTH SCIENCE INFORMATICS
(2021)
Article
Engineering, Chemical
Yohei Murase, Kozo Takayama, Takeaki Uchimoto, Hiromasa Uchiyama, Kazunori Kadota, Yuichi Tozuka
Summary: Die filling is a critical step in the pharmaceutical tableting process. This study applied sparse modeling to select critical flow properties and predict tablet weight variability.
Article
Automation & Control Systems
Chin Gi Soh, Ying Zhu
Summary: This paper proposes a sparse fused group lasso model for predicting the percentage purity of oil blends using Fourier-transform infrared spectroscopic data. The method improves the interpretability and prediction performance of the resultant models, while capturing group structure and coefficient structure smoothness.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Ali Mahzarnia, Jun Song
Summary: This paper proposes methods for functional predictor selection and the estimation of smooth functional coefficients simultaneously in a scalar-on-function regression problem under a high-dimensional multivariate functional data setting. Two methods for functional group-sparse regression under a generic Hilbert space of infinite dimension are developed. The convergence of algorithms and the consistency of the estimation and the selection (oracle property) under infinite-dimensional Hilbert spaces are shown. Simulation studies demonstrate the effectiveness of the methods in both the selection and the estimation of functional coefficients. The applications to functional magnetic resonance imaging (fMRI) reveal the regions of the human brain related to ADHD and IQ.
Article
Computer Science, Artificial Intelligence
Chao Yang, Qiang Liu, Yi Liu, Yiu-Ming Cheung
Summary: This article proposes a novel dynamic latent variable (DLV)-based transfer learning approach, called transfer DLV regression (TDLVR), for quality prediction of multimode processes with dynamics. It can extract the dynamics between process variables and quality variables in the principal operating mode (POM) and also the co-dynamic variations among process variables between the POM and the new mode. An error compensation mechanism is incorporated to adapt to the conditional distribution discrepancy and make full use of the available labeled samples from the new mode.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Biotechnology & Applied Microbiology
Nalini Chintalapudi, Ulrico Angeloni, Gopi Battineni, Marzio di Canio, Claudia Marotta, Giovanni Rezza, Getu Gamo Sagaro, Andrea Silenzi, Francesco Amenta
Summary: Seafarers face a higher risk of illnesses and accidents compared to land workers. Lack of medical professionals on seagoing vessels makes disease diagnosis even more challenging. This study proposes a text mining approach combined with sentiment analysis and the LASSO regression algorithm to classify and establish an Epidemiological Observatory of Seafarers' Pathologies and Injuries. The proposed approach achieves a high accuracy in classifying text documents and provides potential for health assistance and disease classification.
BIOENGINEERING-BASEL
(2022)
Article
Mathematics
Adewale Folaranmi Lukman, Jeza Allohibi, Segun Light Jegede, Emmanuel Taiwo Adewuyi, Segun Oke, Abdulmajeed Atiah Alharbi
Summary: A new penalized estimator based on the Kibria-Lukman estimator with L1-norms is proposed in this study for regularization and variable selection. Through simulations and real-life applications, it is found that the new method performs well in both low- and high-dimensional data and achieves better prediction accuracy than existing methods.
Article
Multidisciplinary Sciences
Sunyun Qi, Yu Zhang, Hua Gu, Fei Zhu, Meiying Gao, Hongxiao Liang, Qifeng Zhang, Yanchao Gao
Summary: A surge of patent applications among public hospitals in China sparks research interest. This paper explores the relationship between the number of patents and ten independent variables, addressing multicollinearity and utilizing the Poisson model, negative binomial model, and negative binomial mixed model. Goodness of fit tests were conducted, revealing the superiority of the negative binomial mixed model. Three variables, including the number of health technicians per 10,000 people and financial expenditure on science and technology, show a significantly positive relationship with the number of patents in Chinese tertiary public hospitals.
SCIENTIFIC REPORTS
(2023)
Article
Environmental Sciences
Minji Lee, Sun Ju Chung, Youngjo Lee, Sera Park, Jun-Gun Kwon, Dai Jin Kim, Donghwan Lee, Jung-Seok Choi
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2020)
Article
Environmental Sciences
Jinhee Kim, Donghwan Lee, Kyung-Bok Son, SeungJin Bae
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2020)
Article
Environmental Sciences
Jiryoun Gong, Juhee Han, Donghwan Lee, Seungjin Bae
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2020)
Review
Statistics & Probability
Woojoo Lee, Il Do Ha, Maengseok Noh, Donghwan Lee, Youngjo Lee
Summary: This paper reviews the application of the h-likelihood method in various statistical areas since its introduction in 1994. It covers clustered survival data analysis, competing risk models, joint models, high-dimensional analysis, spatial analysis, and multiple testing.
JOURNAL OF THE KOREAN STATISTICAL SOCIETY
(2021)
Article
Environmental Sciences
Green Bae, SeungJin Bae, Donghwan Lee, Juhee Han, Dong-Hoe Koo, Do Yeun Kim, Hee-Jun Kim, Sung Young Oh, Hee Yeon Lee, Jong Hwan Lee, Hye Sook Han, Hyerim Ha, Jin Hyoung Kang
Summary: This study aimed to develop a reliable Korean oncology value framework, by translating and examining the frameworks of ASCO and ESMO, and collecting data using AHP and FGIs. The results showed good reliability for ASCO, with AHP indicating that clinical benefit has the highest priority, and FGIs suggesting that ESMO and ASCO should be used complementarily.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2021)
Article
Environmental Sciences
Yunjeong Jang, Donghyun Jee, Donghwan Lee, Nam-Kyong Choi, SeungJin Bae
Summary: This study aimed to analyze medication adherence and persistence among open-angle glaucoma patients in Korea. Results showed that older age, female gender, the use of prostaglandins as the index medication, and visits to secondary or tertiary hospitals were associated with higher rates of adherence and persistence during the study period.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2021)
Article
Environmental Sciences
Yeonsoo Kang, Boram Jeong, Doo-Hyeon Lim, Donghwan Lee, Kyung-Min Lim
Summary: This study aimed to construct an in silico model to predict the eye irritation category of liquid chemicals, and achieved high accuracy in ternary categorization of eye irritation potential with a two-stage random forest approach. The prediction model showed excellent performance in distinguishing Category 1 and Category 2 chemicals.
JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH-PART A-CURRENT ISSUES
(2021)
Article
Psychiatry
Su Mi Park, Boram Jeong, Da Young Oh, Chi-Hyun Choi, Hee Yeon Jung, Jun-Young Lee, Donghwan Lee, Jung-Seok Choi
Summary: The study successfully predicted major psychiatric disorders using machine learning techniques combined with EEG data, suggesting the potential value of electronic devices in identifying psychiatric patients.
FRONTIERS IN PSYCHIATRY
(2021)
Review
Statistics & Probability
Woojoo Lee, Jeonghwan Kim, Donghwan Lee
Summary: This study aims to clarify the relationships among various statistical methods for detecting and handling overdispersion in categorical data analysis, compare their performances, and propose a method for correcting finite sample bias. It also aims to reconsider the current practice for handling overdispersed categorical data and provide graphical tools for model selection. Furthermore, it investigates the assumptions behind the score statistics and their applicability to analyzing overdispersed data.
JOURNAL OF APPLIED STATISTICS
(2023)
Article
Pharmacology & Pharmacy
Sooyoung Lee, Moonsik Song, Jongdae Han, Donghwan Lee, Bo-Hyung Kim
Summary: In this study, a classifier using machine learning was developed to select a suitable vancomycin pharmacokinetic model for therapeutic drug monitoring in patients. Through training and validation, the classifier showed stable accuracy and may contribute to the improvement of therapeutic drug monitoring.
Article
Neurosciences
Boram Jeong, Jiyoon Lee, Heejung Kim, Seungyeon Gwak, Yu Kyeong Kim, So Young Yoo, Donghwan Lee, Jung-Seok Choi
Summary: This study used machine learning methods to analyze multimodal neuroimaging data and improve the prediction accuracy of Internet gaming disorder (IGD). The results showed that the multiple-kernel support vector machine method had higher accuracy in predicting IGD, and clinical variables contributed the most to the prediction model.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Mathematical & Computational Biology
Woojoo Lee, Donghwan Lee, Yudi Pawitan
Summary: This paper focuses on reproducibility assessment in high-throughput studies and proposes a selection-adjusted false-discovery rate (sFDR) as an overall assessment measure. By integrating information from both training and validation studies and considering the effects of non-random selection, sFDR provides a more accurate evaluation. Simulation studies and real metabolomic datasets are used to illustrate the application of sFDR in high-throughput data analysis.
STATISTICS IN MEDICINE
(2022)
Article
Mathematics, Interdisciplinary Applications
Jayoun Kim, Boram Jeong, Il Do Ha, Kook-Hwan Oh, Ji Yong Jung, Jong Cheol Jeong, Donghwan Lee
Summary: This study introduces a new method for handling semi-competing risk data. By incorporating penalized likelihood estimation and the gamma frailty model, the proposed method reduces bias caused by rare events in datasets with a small number of events.
LIFETIME DATA ANALYSIS
(2023)
Article
Pharmacology & Pharmacy
Jihyun Jung, Soyoung Lee, Jaeseong Oh, SeungHwan Lee, In-Jin Jang, Donghwan Lee, Kyung-Sang Yu
Summary: The new FDC of fimasartan/amlodipine/hydrochlorothiazide 60/10/25 mg demonstrated similar PK profiles to the corresponding loose combination, and both treatments were well tolerated.
TRANSLATIONAL AND CLINICAL PHARMACOLOGY
(2021)
Article
Statistics & Probability
Kwangju Choi, June-Yub Lee, Younjin Kim, Donghwan Lee
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS
(2020)
Article
Automation & Control Systems
Haifei Peng, Jian Long, Cheng Huang, Shibo Wei, Zhencheng Ye
Summary: This paper proposes a novel multi-modal hybrid modeling strategy (GMVAE-STA) that can effectively extract deep multi-modal representations and complex spatial and temporal relationships, and applies it to industrial process prediction.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2024)