26 papers · ranked by Valyu relevance
Islam M. Hammam, Amin K. El-Kharbotly, Yomna M. Sadek
Accurate demand forecasting is essential for informed decision-making in today’s dynamic business environment, where product demand often follows diverse and shifting patterns throughout increasingly shorter life cycles driven by continuous product innovation. This study aims to develop a forecasting framework capable…
Jong Woo Nam, Eun Young Choi, Jennifer A. Ailshire, Yao-yi Chiang
As environmental hazards become more frequent, it is critically important to understand their health impacts and identify individuals at disproportionately higher risk. Moderated Multiple Regression (MMR) provides a straightforward approach for investigating population heterogeneity by incorporating interaction terms…
Jingyi Zhou, Senlin Luo, Haofan Chen, Alemayehu Getahun Kumela
Textemotion detection constitutes a crucial foundation for advancing artificial intelligence from basic comprehension to the exploration of emotional reasoning. Most existing emotion detection datasets rely on manual annotations, which are associated with high costs, substantial subjectivity, and severe label…
Deepani Hemachandra, Jagath Senarathne, Mahasen Dehideniya
Classical regression approaches, including ordinary least squares, rely on strong assumptions such as constant variance and normality of residuals, which are often violated in real-world data. Although log-transformation is commonly used to stabilise variance, it may introduce re-transformation bias and fail to address…
Ashish Bhatia, Renato Cordeiro de Amorim, Vito De Feo
Regression analysis is employed to examine and quantify the relationships between input variables and a dependent and continuous output variable. It is widely used for predictive modelling in fields such as finance, healthcare, and engineering. However, traditional methods often struggle with real-world data…
Simon Fontaine, Bing Li, Lingzhou Xue
Fréchet regression provides a versatile framework for modeling responses in metric spaces with Euclidean predictors, yet current methodologies rely almost exclusively on frequentist approaches. We propose a Bayesian framework for Fréchet regression that offers a principled way of incorporating prior information into…
Anirban Chakraborty, Chloe Mattila, Debashis Ghosh, Brian Neelon + 1 more
High-throughput bulk and single-cell omics technologies enable comprehensive molecular profiling, yet identifying compact, biologically interpretable marker sets that distinguish cell types, conditions, or disease states remains challenging. Standard pipelines rely on univariate differential expression tests, which…
B. S. Sindu, Jan Hamaekers
Epoxy polymers are widely used due to their multifunctional properties, however their complex 3D molecular structure, multi-component nature, and lack of curated datasets have limited the application of machine learning (ML) for these materials.Existing ML studies are largely restricted to simulation data, specific…
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Phase equilibrium calculations are crucial in chemical engineering design and optimization processes. The PC-SAFT equation of state (EoS) can precisely calculate phase equilibrium, but is relatively complex and computationally intensive. Surrogate models are mathematically simple models that map or regress the…
Souvik Seal, Anirban Chakraborty, Chloe Mattila, Mark Rubinstein + 4 more
High-throughput spatial omics technologies enable molecular profiling within intact tissue architecture, yet identifying concise, predictive, and biologically interpretable marker panels for cell types, tissue domains, and disease-associated tissue classes remains challenging. This limitation hinders the development of…
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Supervised deep learning has become a standard approach to deliver competitive predictive tools that allow relating the structure of molecules and their physicochemical features to properties such as binding to protein targets, performance as electronic materials, and reactivity. However, efforts to understand how…
Sendhil Nathan B, Veera Siva Reddy B, Chandrasekhara Sastry C, Sachin Salunkhe + 3 more
Accurate demand forecasting for spare parts under true cold-start conditions remains a fundamental challenge due to extreme demand sparsity, zero inflation, and the complete absence of historical demand information. Conventional time-series methods, single-stage machine learning models, and sequence-based probabilistic…
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Metal hydrides play a pivotal role in a wide range of applications, including hydrogen storage, compression, heat management, and catalysis, making them a central focus of interdisciplinary research spanning chemistry, materials science, and engineering. The performance of the metal hydride based systems is strongly…
Stephen Wright, Colin Paterson
Many mathematical modelling tasks (such as in Economics and Finance) are informed by data that is "found" rather than being the result of carefully designed experiments. (e.g., tax returns, reported sales or confidence surveys, etc.) This often results in data series that are short, noisy, multidimensional and…
Julian Hecker, Dmitry Prokopenko, Georg Hahn, Sanghun Lee + 2 more
Integrative omics analyses enhance our understanding of disease mechanisms and biomarkers by investigating relationships among traits, omics measurements, genetic variants, and epidemiological factors. Statistically, these analyses are challenging and require robust, flexible methodologies due to high dimensionality…
Hillary Muhanguzi, Francesca Bassi, Yeko Mwanga, James Wokadala + 1 more
This study assesses differentials in service delivery among Ugandan local governments through a composite indicator that consolidates essential performance data from the education, health, and water sectors. The composite indicator scores as an outcome variable and it is modeled against probable determinants using beta…
Theodoros Anagnostopoulos, Evanthia K. Zervoudi, Christos Anagnostopoulos, Apostolos Christopoulos + 1 more
Linear regression analysis focuses on predicting a numeric regressand value based on certain regressor values. In this context, k-Nearest Neighbors (k-NN) is a common non-parametric regression algorithm, which achieves efficient performance when compared with other algorithms in literature. In this research effort an…
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LC-HRMS is widely used in forensic toxicology for broad-scope screening. When a newly emerging or rarely encountered compound is tentatively identified, toxicologists must decide whether it may be relevant to the case and, if so, quantify it. Acquiring reference material for quantification is costly and time-consuming.…
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Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published…
Francesco Freni, A. Fries, Linus Kühne, Markus Reichstein + 1 more
We consider a regression setting where observations are collected in different environments modeled by different data distributions. The field of out-of-distribution (OOD) generalization aims to design methods that generalize better to test environments whose distributions differ from those observed during training.…
Art Taychameekiatchai, Xiaowei Zhan, Guanghua Xiao, Peifeng Ruan
Spatial transcriptomics technologies enable measurement of gene expression while preserving spatial tissue organization, but they remain highly sensitive to technical variability such as library size differences, slide-level effects, and spatial artifacts. Most existing normalization approaches treat normalization as a…
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Background: Batch reactor process optimization has traditionally relied on Analysis of Variance (ANOVA) for factor effect quantification. However, Structural Equation Modeling (SEM) and machine learning (ML) offer complementary mechanistic and predictive capabilities that remain underexplored in chemical engineering…
Hao Chen, Ge Han, Wenze Ding, Clara Grazian
Inferring gene regulatory networks (GRNs) from expression data is a fundamental problem in systems biology, but its accuracy is often undermined by structural noise arising from transitive correlations. These indirect interactions can obscure the true regulatory architecture, leading to a high rate of false positives.…
Jinghong Zeng, Amy Schwartz
The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use published the estimand framework in 2019. The estimand framework aims to clearly define a treatment effect for a clinical question through construction of estimands, and it has been widely applied in clinical trials…
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Computational methods for predictive modeling have been increasingly utilized in the early stages of drug discovery to supplement high-throughput screening. The advent of highly efficient and complex machine learning architectures necessitates new methods of collating the plethora of topological, geometrical, and…
Rongmei Tang, JianPing Liu, Pengfei Zhang, Xujun Liang
Gene regulatory networks are formed by complex regulatory relationships between transcription factors and their target genes. A systematic understanding of these regulatory relationships is crucial for deciphering the molecular mechanisms that underlie cell state transitions under physiological and pathological…