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Journal articles on the topic 'Lasso (L1)'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Fearn, Tom. "The Lasso and L1 Shrinkage." NIR news 24, no. 5 (2013): 23–27. http://dx.doi.org/10.1255/nirn.1382.

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Wang, Jiasheng. "A L1 Regularized Logistic Regression Model for Highdimensional Questionnaire Data Analysis." Journal of Physics: Conference Series 2078, no. 1 (2021): 012052. http://dx.doi.org/10.1088/1742-6596/2078/1/012052.

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Abstract The LI regularization method, or Lasso, is a technique for feature selection in high-dimensional statistical analysis. This method compresses the coefficients of the model by using the absolute value of the coefficient function as a penalty term. By adding L1 regularization to log-likelihood function of Logistic model, variable screening method based on the logistic regression model can be realized. The process of variable selection via Lasso is illustrated in Figure 1. The purpose of the experiment is to figure out the important factors that influence interviewees' subjective well-be

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Yu, Haipeng, Guobin Chang, Shubi Zhang, Yuhua Zhu, and Yajie Yu. "Application of Sparse Regularization in Spherical Radial Basis Functions-Based Regional Geoid Modeling in Colorado." Remote Sensing 15, no. 19 (2023): 4870. http://dx.doi.org/10.3390/rs15194870.

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Spherical radial basis function (SRBF) is an effective method for calculating regional gravity field models. Calculating gravity field models with high accuracy and resolution requires dense basis functions, resulting in complex models. This study investigated the application of sparse regularization in SRBFs-based regional gravity field modeling. L1-norm regularization, also known as the least absolute shrinkage selection operator (LASSO), was employed in the parameter estimation procedure. LASSO differs from L2-norm regularization in that the solution obtained by LASSO is sparse, specificall

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Liu, Lu, Junheng Gao, Georgia Beasley, and Sin-Ho Jung. "LASSO and Elastic Net Tend to Over-Select Features." Mathematics 11, no. 17 (2023): 3738. http://dx.doi.org/10.3390/math11173738.

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Machine learning methods have been a standard approach to select features that are associated with an outcome and to build a prediction model when the number of candidate features is large. LASSO is one of the most popular approaches to this end. The LASSO approach selects features with large regression estimates, rather than based on statistical significance, that are associated with the outcome by imposing an L1-norm penalty to overcome the high dimensionality of the candidate features. As a result, LASSO may select insignificant features while possibly missing significant ones. Furthermore,

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Saperas-Riera, Jordi, Glòria Mateu-Figueras, and Josep Antoni Martín-Fernández. "Lp-Norm for Compositional Data: Exploring the CoDa L1-Norm in Penalised Regression." Mathematics 12, no. 9 (2024): 1388. http://dx.doi.org/10.3390/math12091388.

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The Least Absolute Shrinkage and Selection Operator (LASSO) regression technique has proven to be a valuable tool for fitting and reducing linear models. The trend of applying LASSO to compositional data is growing, thereby expanding its applicability to diverse scientific domains. This paper aims to contribute to this evolving landscape by undertaking a comprehensive exploration of the L1-norm for the penalty term of a LASSO regression in a compositional context. This implies first introducing a rigorous definition of the compositional Lp-norm, as the particular geometric structure of the com

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Boulesteix, Anne-Laure, Riccardo De Bin, Xiaoyu Jiang, and Mathias Fuchs. "IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data." Computational and Mathematical Methods in Medicine 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/7691937.

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As modern biotechnologies advance, it has become increasingly frequent that different modalities of high-dimensional molecular data (termed “omics” data in this paper), such as gene expression, methylation, and copy number, are collected from the same patient cohort to predict the clinical outcome. While prediction based on omics data has been widely studied in the last fifteen years, little has been done in the statistical literature on the integration of multiple omics modalities to select a subset of variables for prediction, which is a critical task in personalized medicine. In this paper,

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Zhang, Huiyuan, Xiangxi Meng, Zhe Wang, Xin Zhou, Yang Liu, and Nan Li. "Predicting PD-L1 in Lung Adenocarcinoma Using 18F-FDG PET/CT Radiomic Features." Diagnostics 15, no. 5 (2025): 543. https://doi.org/10.3390/diagnostics15050543.

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Background/Objectives: This study aims to retrospectively analyze the clinical and imaging data of 101 patients with lung adenocarcinoma who underwent [18F]FDG PET/CT examination and were pathologically confirmed in the Department of Nuclear Medicine at Peking University Cancer Hospital. This study explores the predictive value and important features of [18F]FDG PET/CT radiomics for PD-L1 expression levels in lung adenocarcinoma patients, assisting in screening patients who may benefit from immunotherapy. Methods: 101 patients with histologically confirmed lung adenocarcinoma who received pre-

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Liu, Lu, and Sin-Ho Jung. "Repeated Sieving for Prediction Model Building with High-Dimensional Data." Journal of Personalized Medicine 14, no. 7 (2024): 769. http://dx.doi.org/10.3390/jpm14070769.

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Background: The prediction of patients’ outcomes is a key component in personalized medicine. Oftentimes, a prediction model is developed using a large number of candidate predictors, called high-dimensional data, including genomic data, lab tests, electronic health records, etc. Variable selection, also called dimension reduction, is a critical step in developing a prediction model using high-dimensional data. Methods: In this paper, we compare the variable selection and prediction performance of popular machine learning (ML) methods with our proposed method. LASSO is a popular ML method that

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Li, Jessie. "The Proximal Bootstrap for Finite-Dimensional Regularized Estimators." AEA Papers and Proceedings 111 (May 1, 2021): 616–20. http://dx.doi.org/10.1257/pandp.20211036.

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We propose a proximal bootstrap that can consistently estimate the limiting distribution of sqrt(n)-consistent estimators with nonstandardasymptotic distributions in a computationally efficient manner by formulating the proximal bootstrap estimator as the solution to aconvex optimization problem, which can have a closed-form solution for certain designs. This paper considers the application to finite-dimensionalregularized estimators, such as the lasso, l1-norm regularized quantile regression, l1-norm support vector regression, and trace regression via nuclear norm regularization.

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Lin, Yingxue, Yinhui Yao, Ying Wang, Lingdi Wang, and Haipeng Cui. "PD-L1 and Immune Infiltration of m6A RNA Methylation Regulators and Its miRNA Regulators in Hepatocellular Carcinoma." BioMed Research International 2021 (May 15, 2021): 1–16. http://dx.doi.org/10.1155/2021/5516100.

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Background. The aim of this study was to systematically evaluate the relationship between the expression of m6A RNA methylation regulators and prognosis in HCC. Methods. We compared the expression of m6A methylation modulators and PD-L1 between HCC and normal in TCGA database. HCC samples were divided into two subtypes by consensus clustering of data from m6A RNA methylation regulators. The differences in PD-L1, immune infiltration, and prognosis between the two subtypes were further compared. The LASSO regression was used to build a risk score for m6A modulators. In addition, we identified mi

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Hayati, Ma'rufah, and Agus Muslim. "Generalized Linear Mixed Model and Lasso Regularization for Statistical Downscaling." Enthusiastic : International Journal of Applied Statistics and Data Science 1, no. 01 (2021): 36–52. http://dx.doi.org/10.20885/enthusiastic.vol1.iss1.art6.

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Rainfall is one of the climatic elements in the tropics which is very influential in agriculture, especially in determining the growing season. Thus, proper rainfall modeling is needed to help determine the best time to start cultivating the soil. Rainfall modeling can be done using the Statistical Downscaling (SDS) method. SDS is a statistical model in the field of climatology to analyze the relationship between large-scale and small-scale climate data. This study uses response variables as a small-scale climate data in the form of rainfall and explanatory variables as a large-scale climate d

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Zhang, Botao, Jingrong Zhou, and Wujian Rao. "Gaussian Process Prior And LASSO-Based Semiparametric Regression Model." Highlights in Business, Economics and Management 53 (March 17, 2025): 220–27. https://doi.org/10.54097/zjz7y433.

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The curse of dimensionality and autocorrelation effects are two common challenges encountered in regression models. In response to these issues, this paper proposes a semiparametric model based on L1 regularization and Gaussian process priors. On the one hand, the proposed method addresses the linear part using LASSO, which alleviates the curse of dimensionality and facilitates variable selection. On the other hand, it assigns a Gaussian process prior to the random effects, and by using different kernel functions, the model can handle autocorrelation effects in the response data while enhancin

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Lukman, Adewale Folaranmi, Jeza Allohibi, Segun Light Jegede, Emmanuel Taiwo Adewuyi, Segun Oke, and Abdulmajeed Atiah Alharbi. "Kibria–Lukman-Type Estimator for Regularization and Variable Selection with Application to Cancer Data." Mathematics 11, no. 23 (2023): 4795. http://dx.doi.org/10.3390/math11234795.

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Following the idea presented with regard to the elastic-net and Liu-LASSO estimators, we proposed a new penalized estimator based on the Kibria–Lukman estimator with L1-norms to perform both regularization and variable selection. We defined the coordinate descent algorithm for the new estimator and compared its performance with those of some existing machine learning techniques, such as the least absolute shrinkage and selection operator (LASSO), the elastic-net, Liu-LASSO, the GO estimator and the ridge estimator, through simulation studies and real-life applications in terms of test mean squ

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Nesterov, Yurii, and Arkadi Nemirovski. "On first-order algorithms for l1/nuclear norm minimization." Acta Numerica 22 (April 2, 2013): 509–75. http://dx.doi.org/10.1017/s096249291300007x.

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In the past decade, problems related to l1/nuclear norm minimization have attracted much attention in the signal processing, machine learning and optimization communities. In this paper, devoted to l1/nuclear norm minimization as ‘optimization beasts’, we give a detailed description of two attractive first-order optimization techniques for solving problems of this type. The first one, aimed primarily at lasso-type problems, comprises fast gradient methods applied to composite minimization formulations. The second approach, aimed at Dantzig-selector-type problems, utilizes saddle-point first-or

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Zhou, Zhi Hui, Gui Xia Liu, Ling Tao Su, Liang Han, and Lun Yan. "Detecting Epistasis by LASSO-Penalized-Model Search Algorithm in Human Genome-Wide Association Studies." Advanced Materials Research 989-994 (July 2014): 2426–30. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2426.

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Extensive studies have shown that many complex diseases are influenced by interaction of certain genes, while due to the limitations and drawbacks of adopting logistic regression (LR) to detect epistasis in human Genome-Wide Association Studies (GWAS), we propose a new method named LASSO-penalized-model search algorithm (LPMA) by restricting it to a tuning constant and combining it with a penalization of the L1-norm of the complexity parameter, and it is implemented utilizing the idea of multi-step strategy. LASSO penalized regression particularly shows advantageous properties when the number

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Dwinata, Alona, Khairil Anwar Notodiputro, and Bagus Sartono. "A Combination of Generalized Linear Mixed Model and LASSO Methods for Estimating Number of Patients Covid 19 in the Intensive Care Units." CAUCHY 7, no. 1 (2021): 13–21. http://dx.doi.org/10.18860/ca.v7i1.11575.

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Generalized linear mixed models (GLMM) combined with the L1 penalty (Least Absolute Shrinkage and Selection Operator/LASSO) is called LASSO GLMM. LASSO GLMM reduces overfitting and selects predictor variables in modeling. The aim of this study is to evaluate the model's performance for predicting Covid-19 patients with certain congenital disease that require ICU based on the results of blood tests laboratory and patient’s vital signs. This study used binary response variables, 1 if the patient was admitted to the ICU and 0 if the patient was not admitted to the ICU. The fixed effect predictor

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Lu, Gui-Fu, Jian Zou, and Yong Wang. "L1-norm and maximum margin criterion based discriminant locality preserving projections via trace Lasso." Pattern Recognition 55 (July 2016): 207–14. http://dx.doi.org/10.1016/j.patcog.2016.01.029.

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Sudjai, Narumol, Monthira Duangsaphon, and Chandhanarat Chandhanayingyong. "Relaxed Adaptive Lasso for Classification on High-Dimensional Sparse Data with Multicollinearity." International Journal of Statistics in Medical Research 12 (September 17, 2023): 97–108. http://dx.doi.org/10.6000/1929-6029.2023.12.13.

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High-dimensional sparse data with multicollinearity is frequently found in medical data. This problem can lead to poor predictive accuracy when applied to a new data set. The Least Absolute Shrinkage and Selection Operator (Lasso) is a popular machine-learning algorithm for variable selection and parameter estimation. Additionally, the adaptive Lasso method was developed using the adaptive weight on the l1-norm penalty. This adaptive weight is related to the power order of the estimators. Thus, we focus on 1) the power of adaptive weight on the penalty function, and 2) the two-stage variable s

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Gao, Han, Pei Shan Fam, Lea Tien Tay, and Heng Chin Low. "LOGISTIC REGRESSION TECHNIQUES BASED ON DIFFERENT SAMPLE SIZES IN LANDSLIDE SUSCEPTIBILITY ASSESSMENT: WHICH PERFORMS BETTER?" COMPUSOFT: An International Journal of Advanced Computer Technology 09, no. 04 (2020): 3624–28. https://doi.org/10.5281/zenodo.14912152.

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The main objective of this paper is to compare the landslide spatial prediction performance of logistic regression (LR) with different regularization methods, namely, Lasso LR and Ridge LR. Three types of training datasets with different sample sizes of 40,000, 4,000 and 400 are used to train and validate the models. ROC curves are used to evaluate the models’ performance. The results show that Lasso and Ridge LR models have comparative performance compared to the ordinary LR models based on the AUC values, which indicates that there are no redundant input features to remove from the mod

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Garassino, Marina Chiara, Claudia Proto, James M. Dolezal, et al. "PEOPLE (NTC03447678), a phase II trial to test pembrolizumab as first-line treatment in patients with advanced NSCLC with PD-L1 < 50%: A multiomics approach." Journal of Clinical Oncology 40, no. 16_suppl (2022): 9051. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.9051.

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9051 Background: Chemo-immunotherapy is the standard of care for patients with advanced NSCLC and PD-L1 < 50%. Efficacy has been reported in this setting with single agent pembrolizumab, but no reliable biomarkers yet exist for selecting patients likely to respond to single agent immunotherapy. The aim of this trial was to identify potential new immune biomarkers associated with PFS in NSCLC patients with PD-L1 < 50% treated with first line pembrolizumab. Methods: Advanced EGFR and ALK wild type treatment-naïve NSCLC patients with PD-L1 < 50% were enrolled. Gene expression profile was

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Berdnyk, M. I., A. B. Zakharov, and V. V. Ivanov. "Application Of L1- Regularization Approach In QSAR Problem. Linear Regression And Artificial Neural Networks." Methods and Objects of Chemical Analysis 14, no. 2 (2019): 79–90. http://dx.doi.org/10.17721/moca.2019.79-90.

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One of the primary tasks of analytical chemistry and QSAR/QSPR researches is building of prognostic regression equations based on descriptors sets. The one of the most important problems here is to decrease the number of descriptors in the initial descriptor set which is usually way too big. In current investigation the descriptor set is proposed to be reduced employing the least absolute shrinkage and selection operator (LASSO) approach. Decreased descriptor sets were used for calculations with application of the following QSAR/QSPR methods: ordinary least squares (OLS), the least absolute de

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Chen, Xi, Yan Liu, Han Liu, and Jaime Carbonell. "Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 425–30. http://dx.doi.org/10.1609/aaai.v24i1.7658.

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An important challenge in understanding climate change is to uncover the dependency relationships between various climate observations and forcing factors. Graphical lasso, a recently proposed L1 penalty based structure learning algorithm, has been proven successful for learning underlying dependency structures for the data drawn from a multivariate Gaussian distribution. However, climatological data often turn out to be non-Gaussian, e.g. cloud cover, precipitation, etc. In this paper, we examine nonparametric learning methods to address this challenge. In particular, we develop a methodology

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Wu, Qian (Vicky), Wei Sun, and Li Hsu. "Space-log: a novel approach to inferring gene-gene net-works using SPACE model with log penalty." F1000Research 9 (January 5, 2022): 1159. http://dx.doi.org/10.12688/f1000research.26128.2.

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Gene expression data have been used to infer gene-gene networks (GGN) where an edge between two genes implies the conditional dependence of these two genes given all the other genes. Such gene-gene networks are of-ten referred to as gene regulatory networks since it may reveal expression regulation. Most of existing methods for identifying GGN employ penalized regression with L1 (lasso), L2 (ridge), or elastic net penalty, which spans the range of L1 to L2 penalty. However, for high dimensional gene expression data, a penalty that spans the range of L0 and L1 penalty, such as the log penalty,

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Wu, Qian (Vicky), Wei Sun, and Li Hsu. "Space-log: a novel approach to inferring gene-gene net-works using SPACE model with log penalty." F1000Research 9 (September 21, 2020): 1159. http://dx.doi.org/10.12688/f1000research.26128.1.

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Gene expression data have been used to infer gene-gene networks (GGN) where an edge between two genes implies the conditional dependence of these two genes given all the other genes. Such gene-gene networks are of-ten referred to as gene regulatory networks since it may reveal expression regulation. Most of existing methods for identifying GGN employ penalized regression with L1 (lasso), L2 (ridge), or elastic net penalty, which spans the range of L1 to L2 penalty. However, for high dimensional gene expression data, a penalty that spans the range of L0 and L1 penalty, such as the log penalty,

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N, Susithra, Rajalakshmi K, and Ashwath P. "Performance analysis of compressive sensing and reconstruction by LASSO and OMP for audio signal processing applications." Scientific Temper 14, no. 01 (2023): 222–26. http://dx.doi.org/10.58414/scientifictemper.2023.14.1.28.

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Audio signal processing is used in acoustic IoT sensor nodes which have limitations in data storage, computation speed, hardware size and power. In most audio signal processing systems, the recovered data constitutes far less fraction of the sampled data providing scope for compressive sensing (CS) as an efficient way for sampling and signal recovery. Compressive sensing is a signal processing technique in which a sparse approximated signal is reconstructed at the receiving node by a signal recovery algorithm, using fewer samples compared to traditional sampling methods. It has two main stages

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Zheng, Runze. "Bayesian Optimization of Lasso and XGBoost Models for Comparative Analysis in Housing Price Prediction." ITM Web of Conferences 73 (2025): 03005. https://doi.org/10.1051/itmconf/20257303005.

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Fluctuations in housing prices have a profound impact on the broader economy and people's livelihoods. Accurate housing price predictions contribute to enhanced market transparency and the formulation of evidence-based policies. This paper focuses on optimizing two machine learning models, Lasso Regression and XGBoost, using Bayesian optimization for predicting housing prices. By leveraging economic features such as Average Earnings, Gross Domestic Product (GDP), Mortgage rates, Population, and Unemployment Rate, the models aim to improve prediction accuracy in the housing market. The Lasso mo

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Abu Afouna, Nour, and Majid Khan Majahar Ali. "The Impact of Heterogeneity in High-Ranking Variables Using Precision Farming." Malaysian Journal of Fundamental and Applied Sciences 20, no. 6 (2024): 1344–62. https://doi.org/10.11113/mjfas.v20n6.3564.

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Smart precision farming combines IoT, cloud computing, and big data to optimize agricultural productivity, reduce costs, and advance sustainability through digitalization and intelligent approaches. However, precision farming grapples with challenges like managing complex variables, addressing multicollinearity, handling outliers, ensuring model robustness, and improving accuracy, particularly with smaller or medium-sized datasets. Reducing retraining time and solving the calamity of complexity are necessary to overcome these obstacles and improve machine learning algorithms' performance, scal

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de Campos Souza, Paulo Vitor, Luiz Carlos Bambirra Torres, Gustavo Rodrigues Lacerda Silva, Antonio de Padua Braga, and Edwin Lughofer. "An Advanced Pruning Method in the Architecture of Extreme Learning Machines Using L1-Regularization and Bootstrapping." Electronics 9, no. 5 (2020): 811. http://dx.doi.org/10.3390/electronics9050811.

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Extreme learning machines (ELMs) are efficient for classification, regression, and time series prediction, as well as being a clear solution to backpropagation structures to determine values in intermediate layers of the learning model. One of the problems that an ELM may face is due to a large number of neurons in the hidden layer, making the expert model a specific data set. With a large number of neurons in the hidden layer, overfitting is more likely and thus unnecessary information can deterioriate the performance of the neural network. To solve this problem, a pruning method is proposed,

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Soleh, Agus M. "Statistical Downscaling to Predict Monthly Rainfall Using Generalized Linear Model with Gamma Distribution." Informatika Pertanian 24, no. 2 (2016): 215. http://dx.doi.org/10.21082/ip.v24n2.2015.p215-222.

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Statistical Downscaling (SDS) models might involve ill-conditioned covariates (large dimension and high correlation/multicollinear). This problem could be solved by a variable selection technique using L1 regularization/LASSO or a dimension reduction approach using principal component analysis (PCA). In this paper, both methods were applied to generalized linear modeling with gamma distribution and compared to predict rainfall models at 11 rain posts in Indramayu. More over, generalized linear model with gamma distribution was used to obtain non-negative rainfall prediction and compared with p

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Khattak, Afaq, Jianping Zhang, Pak-Wai Chan, Feng Chen, and Abdulrazak H. Almaliki. "Aviation Safety at the Brink: Unveiling the Hidden Dangers of Wind-Shear-Related Aircraft-Missed Approaches." Aerospace 12, no. 2 (2025): 126. https://doi.org/10.3390/aerospace12020126.

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Aircraft-missed approaches pose significant safety challenges, particularly under adverse weather conditions like wind shear. This study examines the critical factors influencing wind-shear-related missed approaches at Hong Kong International Airport (HKIA) using Pilot Report (PIREP) data from 2015 to 2023. A Binary Logistic Model (BLM) with L1 (Lasso) and L2 (Ridge) regularization was applied to both balanced and imbalanced datasets, with the balanced dataset created using the Synthetic Minority Oversampling Technique (SMOTE). The performance of the BLM on the balanced data demonstrated a goo

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Li, Qing, and Steven Liang. "Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach." Algorithms 11, no. 11 (2018): 184. http://dx.doi.org/10.3390/a11110184.

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Aimed at the issue of estimating the fault component from a noisy observation, a novel detection approach based on augmented Huber non-convex penalty regularization (AHNPR) is proposed. The core objectives of the proposed method are that (1) it estimates non-zero singular values (i.e., fault component) accurately and (2) it maintains the convexity of the proposed objective cost function (OCF) by restricting the parameters of the non-convex regularization. Specifically, the AHNPR model is expressed as the L1-norm minus a generalized Huber function, which avoids the underestimation weakness of t

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Kocbek, Primoz, Nino Fijacko, Cristina Soguero-Ruiz, et al. "Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data." Computational and Mathematical Methods in Medicine 2019 (February 19, 2019): 1–13. http://dx.doi.org/10.1155/2019/2059851.

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This study describes a novel approach to solve the surgical site infection (SSI) classification problem. Feature engineering has traditionally been one of the most important steps in solving complex classification problems, especially in cases with temporal data. The described novel approach is based on abstraction of temporal data recorded in three temporal windows. Maximum likelihood L1-norm (lasso) regularization was used in penalized logistic regression to predict the onset of surgical site infection occurrence based on available patient blood testing results up to the day of surgery. Prio

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Xie, Tongji, Yan Li, Lin Yang, et al. "A random forest model to predict the efficacy of anti-PD-1/PD-L1 monoclonal antibody in lung adenocarcinoma." Journal of Clinical Oncology 42, no. 16_suppl (2024): e20558-e20558. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.e20558.

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e20558 Background: Not all patients with lung adenocarcinoma (LUAD) could benefit from anti-PD-1/PD-L1 monoclonal antibody therapy, and existing biomarkers cannot explain all clinical benefits. Therefore, an effective model to predict the efficacy of anti-PD-1/PD-L1 monoclonal antibody is necessary. Methods: DNA methylation sequencing was used in LUAD patients’ FFPE samples, who received anti-PD-1/PD-L1 monoclonal antibody. The DNA methylation level of each gene was transformed into binary format according to its median value. The progression-free survival (PFS) was defined as the duration fro

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Cardall, Anna Catherine, Riley Chad Hales, Kaylee Brooke Tanner, Gustavious Paul Williams, and Kel N. Markert. "LASSO (L1) Regularization for Development of Sparse Remote-Sensing Models with Applications in Optically Complex Waters Using GEE Tools." Remote Sensing 15, no. 6 (2023): 1670. http://dx.doi.org/10.3390/rs15061670.

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Remote-sensing data are used extensively to monitor water quality parameters such as clarity, temperature, and chlorophyll-a (chl-a) content. This is generally achieved by collecting in situ data coincident with satellite data collections and then creating empirical water quality models using approaches such as multi-linear regression or step-wise linear regression. These approaches, which require modelers to select model parameters, may not be well suited for optically complex waters, where interference from suspended solids, dissolved organic matter, or other constituents may act as “confuse

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Liu, Yongshi, Xiaodong Yu, Jianjun Zhao, Changchun Pan, and Kai Sun. "Development of a Robust Data-Driven Soft Sensor for Multivariate Industrial Processes with Non-Gaussian Noise and Outliers." Mathematics 10, no. 20 (2022): 3837. http://dx.doi.org/10.3390/math10203837.

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Industrial processes are often nonlinear and multivariate and suffer from non-Gaussian noise and outliers in the process data, which cause significant challenges in data-driven modelling. To address these issues, a robust soft-sensing algorithm that integrates Huber’s M-estimation and adaptive regularisations with multilayer perceptron (MLP) is proposed in this paper. The proposed algorithm, called RAdLASSO-MLP, starts with an initially well-trained MLP for nonlinear data-driven modelling. Subsequently, the residuals of the proposed model are robustified with Huber’s M-estimation to improve th

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Nguyen, Linh, Dung K. Nguyen, Thang Nguyen, Binh Nguyen, and Truong X. Nghiem. "Analysis of Microalgal Density Estimation by Using LASSO and Image Texture Features." Sensors 23, no. 5 (2023): 2543. http://dx.doi.org/10.3390/s23052543.

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Monitoring and estimating the density of microalgae in a closed cultivation system is a critical task in culturing algae since it allows growers to optimally control both nutrients and cultivating conditions. Among the estimation techniques proposed so far, image-based methods, which are less invasive, nondestructive, and more biosecure, are practically preferred. Nevertheless, the premise behind most of those approaches is simply averaging the pixel values of images as inputs of a regression model to predict density values, which may not provide rich information of the microalgae presenting i

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Lo Russo, Giuseppe, Arsela Prelaj, James Dolezal, et al. "PEOPLE (NTC03447678), a phase II trial to test pembrolizumab as first-line treatment in patients with advanced NSCLC with PD-L1 <50%: a multiomics analysis." Journal for ImmunoTherapy of Cancer 11, no. 6 (2023): e006833. http://dx.doi.org/10.1136/jitc-2023-006833.

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BackgroundChemoimmunotherapy represents the standard of care for patients with advanced non-small cell lung cancer (NSCLC) and programmed death-ligand 1 (PD-L1) <50%. Although single-agent pembrolizumab has also demonstrated some activity in this setting, no reliable biomarkers yet exist for selecting patients likely to respond to single-agent immunotherapy. The main purpose of the study was to identify potential new biomarkers associated with progression-free-survival (PFS) within a multiomics analysis.MethodsPEOPLE (NTC03447678) was a prospective phase II trial evaluating first-line pembr

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Yu, Yunfang, Wenda Zhang, Qiyun Ou, et al. "Development and validation of novel microenvironment-based immune molecular subtypes of breast cancer: Implications for immunotherapy." Journal of Clinical Oncology 37, no. 15_suppl (2019): 1094. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.1094.

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1094 Background: Breast cancer treatment with immunotherapy can improve clinical benefits, but the majority of patients did not respond to the treatment. To understand tumor–immune interactions in breast cancer, we identified novel microenvironment-based immune molecular subtypes. Methods: A training cohort of 1,394 breast cancer patients from the Molecular Taxonomy of Breast Cancer International Consortium profiled by RNA and DNA sequencing data were analyzed to calculate immune-related gene biomarkers and to assign prognostic categories using LASSO Cox regression model. Additionally, 969 pat

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Bellec, Pierre C. "Out-of-sample error estimation for M-estimators with convex penalty." Information and Inference: A Journal of the IMA 12, no. 4 (2023): 2782–817. http://dx.doi.org/10.1093/imaiai/iaad031.

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Abstract A generic out-of-sample error estimate is proposed for $M$-estimators regularized with a convex penalty in high-dimensional linear regression where $(\boldsymbol{X},\boldsymbol{y})$ is observed and the dimension $p$ and sample size $n$ are of the same order. The out-of-sample error estimate enjoys a relative error of order $n^{-1/2}$ in a linear model with Gaussian covariates and independent noise, either non-asymptotically when $p/n\le \gamma $ or asymptotically in the high-dimensional asymptotic regime $p/n\to \gamma ^{\prime}\in (0,\infty )$. General differentiable loss functions $

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Schmutz, Hugo, Pierre-Alexandre Mattei, Pierre Tricarico, et al. "Predicting non-small cell lung cancer response to immune checkpoint inhibitors with machine learning based on heterogeneous biomarkers." Journal of Clinical Oncology 41, no. 16_suppl (2023): e21068-e21068. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e21068.

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e21068 Background: In patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICPI), individual clinical, biological, and imaging prognostic biomarkers have recently been identified. However, the combination of these biomarkers has not yet been studied. This study aims to analyse various clinical, biological and 18FDG PET/CT parameters using machine-learning algorithms to build more accurate prognostic models of NSCLC response to ICPI. Methods: The exploratory cohort consisted of patients with metastatic NSCLC, treated with either pembrolizumab or nivolumab

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Lee, Jeunghoon, Yongsung Park, Peter Gerstoft, and Kyungjun Lee. "Non-convex sparse beamformer." Journal of the Acoustical Society of America 157, no. 5 (2025): 3345–57. https://doi.org/10.1121/10.0036563.

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Sparse beamforming techniques, such as least absolute shrinkage and selection operator (LASSO) and fused least absolute shrinkage and selection operator (FL), underestimate source amplitude due to the soft-thresholding effect of the l1-norm regularization. For sparse beamforming, a new set of non-convex regularizers is introduced. They are designed to accurately recover source amplitudes—including those of extended sources—by reducing the shrinkage imposed on large coefficients. Integrating these non-convex penalties (NCP) within the FL framework, we propose the non-convex fused least absolute

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Zeng, Zhimin, Yuxia Liang, Jia Shi, et al. "Identification and Application of a Novel Immune-Related lncRNA Signature on the Prognosis and Immunotherapy for Lung Adenocarcinoma." Diagnostics 12, no. 11 (2022): 2891. http://dx.doi.org/10.3390/diagnostics12112891.

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Background: Long non-coding RNA (lncRNA) participates in the immune regulation of lung cancer. However, limited studies showed the potential roles of immune-related lncRNAs (IRLs) in predicting survival and immunotherapy response of lung adenocarcinoma (LUAD). Methods: Based on The Cancer Genome Atlas (TCGA) and ImmLnc databases, IRLs were identified through weighted gene coexpression network analysis (WGCNA), Cox regression, and Lasso regression analyses. The predictive ability was validated by Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves in the internal dataset, exter

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Turgut, Sebahattin Serhat, Erdogan Kucukoner, Erkan Karacabey, and Aberham Hailu Feyissa. "Probabilistic-Mechanistic Evaluation of a Hybrid Drying System Using l1 Norm Regularisation: Simultaneous Ohmic Heating and Convection Drying." Journal of Food Processing and Preservation 2024 (January 18, 2024): 1–18. http://dx.doi.org/10.1155/2024/5887654.

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Ohmic-assisted drying (OAD) is a novel drying system that combines ohmic heating and convection drying simultaneously. The present study is aimed at evaluating the mechanism of OAD system behaviours against the combined impact of operational and model uncertainties. Moreover, the dynamic (time-dependent), as well as static (end-of-drying) spatial homogeneity of the model predictions, was quantitatively described for the first time in the literature using Buzas and Gibson’s evenness value (an α-diversity index). The Monte Carlo simulation approach was used to propagate the uncertainty of random

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Ahmad, Neda, and Vandana Nath. "Evaluating Nine Machine Learning Algorithms for GaN HEMT Small Signal Behavioral Modeling through K-fold Cross-Validation." Engineering, Technology & Applied Science Research 14, no. 4 (2024): 15784–90. http://dx.doi.org/10.48084/etasr.7726.

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This paper presents an investigation into the modeling of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) using multiple Machine Learning (ML) algorithms. Despite the documented use of various ML techniques, a thorough comparison and performance analysis under different operating conditions were lacking. This study fills this gap by conducting a rigorous evaluation of nine ML models using TCAD-generated data of Pseudomorphic AlGaN/InGaN/GaN HEMT. The research focuses on Small Signal Behavioral Modeling and examines regression techniques such as Multiple Linear Regression (MLR)

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Lin, Pao-Chun, Wei-Shan Chang, Kai-Yuan Hsiao, et al. "Development of a Machine Learning Algorithm to Correlate Lumbar Disc Height on X-rays with Disc Bulging or Herniation." Diagnostics 14, no. 2 (2024): 134. http://dx.doi.org/10.3390/diagnostics14020134.

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Lumbar disc bulging or herniation (LDBH) is one of the major causes of spinal stenosis and related nerve compression, and its severity is the major determinant for spine surgery. MRI of the spine is the most important diagnostic tool for evaluating the need for surgical intervention in patients with LDBH. However, MRI utilization is limited by its low accessibility. Spinal X-rays can rapidly provide information on the bony structure of the patient. Our study aimed to identify the factors associated with LDBH, including disc height, and establish a clinical diagnostic tool to support its diagno

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Zhang, Bin, Ma-yi-di-li Ni-jia-Ti, Ruike Yan, et al. "CT-based radiomics for predicting the rapid progression of coronavirus disease 2019 (COVID-19) pneumonia lesions." British Journal of Radiology 94, no. 1122 (2021): 20201007. http://dx.doi.org/10.1259/bjr.20201007.

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Objectives: To develop and validate a radiomic model to predict the rapid progression (defined as volume growth of pneumonia lesions > 50% within seven days) in patients with coronavirus disease 2019 (COVID-19). Methods: Patients with laboratory-confirmed COVID-19 who underwent longitudinal chest CT between January 01 and February 18, 2020 were included. A total of 1316 radiomic features were extracted from the lung parenchyma window for each CT. The least absolute shrinkage and selection operator (LASSO), Relief, Las Vegas Wrapper (LVW), L1-norm-Support Vector Machine (L1-norm-SVM), and re

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Ogundimu, Emmanuel O. "On Lasso and adaptive Lasso for non-random sample in credit scoring." Statistical Modelling, May 9, 2022, 1471082X2210921. http://dx.doi.org/10.1177/1471082x221092181.

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Prediction models in credit scoring are often formulated using available data on accepted applicants at the loan application stage. The use of this data to estimate probability of default (PD) may lead to bias due to non-random selection from the population of applicants. That is, the PD in the general population of applicants may not be the same with the PD in the subpopulation of the accepted applicants. A prominent model for the reduction of bias in this framework is the sample selection model, but there is no consensus on its utility yet. It is unclear if the bias-variance trade- off of re

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Moudafi, Abdellatif. "Difference of two norms-regularizations for Q-Lasso." Applied Computing and Informatics ahead-of-print, ahead-of-print (2020). http://dx.doi.org/10.1016/j.aci.2018.07.002.

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The focus of this paper is in Q-Lasso introduced in Alghamdi et al. (2013) which extended the Lasso by Tibshirani (1996). The closed convex subset Q belonging in a Euclidean m-space, for m∈IN, is the set of errors when linear measurements are taken to recover a signal/image via the Lasso. Based on a recent work by Wang (2013), we are interested in two new penalty methods for Q-Lasso relying on two types of difference of convex functions (DC for short) programming where the DC objective functions are the difference of l1 and lσq norms and the difference of l1 and lr norms with r>1. By means

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Dupont, Clémentine, Jules Moreau, and Amélie Fournier. "Protein Structure Prediction through Lasso Regression with L1 Regularization." Journal of Computational Biology and Medicine 4, no. 1 (2024). https://doi.org/10.71070/jcbm.v4i1.95.

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Protein structure prediction plays a crucial role in understanding biological functions and drug design. However, the current methods face challenges in accuracy and efficiency due to the complexity of protein structures. This paper addresses the limitations by proposing a novel approach utilizing Lasso regression with L1 regularization. By incorporating the sparsity-inducing property of L1 regularization, our method efficiently selects relevant features and improves prediction accuracy. The research results demonstrate that our approach outperforms existing methods in both accuracy and comput

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Grechkin, Maxim, Maryam Fazel, Daniela Witten, and Su-In Lee. "Pathway Graphical Lasso." Proceedings of the AAAI Conference on Artificial Intelligence 29, no. 1 (2015). http://dx.doi.org/10.1609/aaai.v29i1.9636.

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Graphical models provide a rich framework for summarizing the dependencies among variables. The graphical lasso approach attempts to learn the structure of a Gaussian graphical model (GGM) by maximizing the log likelihood of the data, subject to an l1 penalty on the elements of the inverse covariance matrix. Most algorithms for solving the graphical lasso problem do not scale to a very large number of variables. Furthermore, the learned network structure is hard to interpret. To overcome these challenges, we propose a novel GGM structure learning method that exploits the fact that for many rea

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