24 papers · ranked by Valyu relevance
Maider Aguerralde-Martin, Mónica Clemente-Císcar, Ana Conesa, Sonia Tarazona
The identification of phenotype-specific regulatory mechanisms is crucial for understanding the molecular basis of diseases and other complex traits. However, the lack of tools capable of constructing multi-omic, condition-specific regulatory networks remains a significant limitation. He re, we introduce MO RE…
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…
Sidharth SS
CurvPy is an open-source Python library for automated curve fitting and regression analysis, aiming to make advanced statistical and machine learning techniques more accessible. This paper explores the mathematical foundations and implementation of key CurvPy components for optimization, smoothing, imputation…
Xinyu Zhou, Pengtao Dang, Haixu Tang, Laura Xianlu Peng + 6 more
Spatial transcriptomics (ST) data demands models that recover how associations among molecular and cellular features change across tissue while contending with noise, collinearity, cell mixing, and thousands of predictors. We present Spatially Smooth Sparse Regression (S3R), a general framework that estimates…
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…
Aiman Tahir, Maryam Ilyas, Mohamed R. Abonazel
The inferential results regarding estimates of Support Vector Regression (SVR) are highly influenced by anomalies and ill-conditioned predictors. Excessive dimensions of data also make the model complex. To improve estimation accuracy, this paper introduces two modelling frameworks, Principal Component Robust Support…
Onorato d’Angelis, Chi Whan Choi, Hariharan Sureshkumar, Mario Merone + 2 more
Accurate estimation of body segment inertial properties is essential for biomechanical analyses, yet commonly used scaling methods rely on limited datasets and do not generalize well across diverse adult body morphologies. We developed a data-driven framework that estimates segment lengths, masses, centers of mass, and…
Authors not listed
Plastic mechanical recycling is the conventional technological step towards circularity. In such aspects, complex mixtures of polyolefin blends are often fed into mechanical recycling systems, resulting in moulded products with uncertain quality. To add to the difficulty of heterogeneous feedstocks, the testing of…
Changquan Huang, Yu Gu, Silvia Grassi
Meat adulteration is a global problem which undermines market fairness and harms people with allergies or certain religious beliefs. In this study, a novel framework in which a one-dimensional convolutional neural network (1DCNN) serves as a backbone and a random forest regressor (RFR) serves as a regressor, named…
Neel Desai, Veera Baladandayuthapani, Russell T. Shinohara, Jeffrey S. Morris
Assessing how brain functional connectivity networks vary across individuals promises to uncover important scientific questions such as patterns of healthy brain aging through the lifespan or dysconnectivity associated with disease. In this article we introduce a general regression framework, Connectivity Regression…
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…
Ayon Roy, Tausif Al Zubayer, Nafisa Tabassum, Muhammad Nazrul Islam + 1 more
'Abdus Sattar'] Regression analysis is a well known quantitative research method that primarily explores the relationship between one or more independent variables and a dependent variable. Conducting regression analysis manually on large datasets with multiple independent variables can be tedious. An automated system…
Luca Patelli, Michela Cameletti, Natalia Golini, Rosaria Ignaccolo
Random Forest (RF) is a well-known data-driven algorithm applied in several fields thanks to its flexibility in modeling the relationship between the response variable and the predictors, also in case of strong non-linearities. In environmental applications, it often occurs that the phenomenon of interest may present…
Tom Dupré la Tour, Michael Eickenberg, Anwar O. Nunez-Elizalde, Jack L. Gallant
'Jack L. Gallant'] Encoding models provide a powerful framework to identify the information represented in brain recordings. In this framework, a stimulus representation is expressed within a feature space and is used in a regularized linear regression to predict brain activity. To account for a potential…
Jonelle Angelo Cenita, Paul Richie Asuncion, Jayson Victoriano
**Special Issue on International Research Conference on Computer Engineering and Technology Education 2023 (IRCCETE 2023). Guest Associate Editors: Dr. Nelson C. Rodelas, PCpE (Computer Engineering Department, College of Engineering, University of the East-Caloocan City; nelson.rodelas@ue.edu.ph) and Engr. Ana…
Tom Dupré la Tour, Michael Eickenberg, Anwar O. Nunez-Elizalde, Jack L. Gallant
Encoding models provide a powerful framework to identify the information represented in brain recordings. In this framework, a stimulus representation is expressed within a feature space and is used in a regularized linear regression to predict brain activity. To account for a potential complementarity of different…
Authors not listed
Electrochemical impedance spectroscopy (EIS) coupled with distribution of relaxation times (DRT) analysis is a robust framework for characterizing electrochemical systems. However, DRT deconvolution is often plagued by spurious peaks, hindering accurate process identification and quantitative parameter estimation. To…
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…
Shovan Chowdhury, Yuxiao Lin, Bor Yann Liaw, Leslie Kerby
Battery performance datasets are typically non-normal and multicollinear. Extrapolating such datasets for model predictions needs attention to such characteristics. This study explores the impact of data normality in building machine learning models. In this work, tree-based regression models and multiple linear…
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.…
Stamatia Zavitsanou, Zonghua Bo, Emanuele Casali, Matthew Langton + 1 more
Machine learning (ML) is currently transforming the field of chemistry by offering unparalleled efficiency in addressing complex challenges. Despite the progress made, a notable gap persists in the availability of user-friendly tools tailored to chemical problems involving small and sparse datasets. Here, we introduce…
Helle W. van den Maagdenberg, Martin Šícho, David Alencar Araripe, Sohvi Luukkonen + 9 more
Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous.…
Authors not listed
This study presents a novel application of Multi-Objective Bayesian Optimization (MOBO) to enhance the formulation of flame-retardant polypropylene (PP) composites. Our goal was to optimize the chemical composition of intumescent polypropylene (PP) formulations by maximizing the Limiting Oxygen Index (LOI) and…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…