25 papers · ranked by Valyu relevance
Juan Jovel, Russell Greiner
Machine learning (ML) approaches are a collection of algorithms that attempt to extract patterns from data and to associate such patterns with discrete classes of samples in the data-e.g., given a series of features describing persons, a ML model predicts whether a person is diseased or healthy, or given features of…
Wang, Jingyuan, Ji, Jiahao
This article serves as the regression analysis lecture notes in the Intelligent Computing course cluster (including the courses of Artificial Intelligence, Data Mining, Machine Learning, and Pattern Recognition) at the School of Computer Science and Engineering, Beihang University. It aims to provide students – who are…
Baidu Li, Xinhai Li
Linear models, including t-test, ANOVA, regression, ANCOVA, and generalized linear models, are foundational tools in statistical analysis. For large datasets, such as those involving tens of thousands of genes and millions of records, numerous advanced methods have been developed to improve both computational…
Ariel I. Mundo, John R. Tipton, Timothy J. Muldoon
In biomedical research, the outcome of longitudinal studies has been traditionally analyzed using the repeated measures analysis of variance (rm-ANOVA) or more recently, linear mixed models (LMEMs). Although LMEMs are less restrictive than rm-ANOVA in terms of correlation and missing observations, both methodologies…
Rezzy Eko Caraka, Rung-Ching Chen, Su-Wen Huang, Shyue-Yow Chiou + 2 more
'Prana Ugiana Gio' 'Bens Pardamean'] Background In heart data mining and machine learning, dimension reduction is needed to remove multicollinearity. Meanwhile, it has been proven to improve the interpretation of the parameter model. In addition, dimension reduction can also increase the time of computing in high…
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…
Marijn van Vliet, Riitta Salmelin
Linear machine learning models “learn” a data transformation by being exposed to examples of input with the desired output, forming the basis for a variety of powerful techniques for analyzing neuroimaging data. However, their ability to learn the desired transformation is limited by the quality and size of the example…
Authors not listed
We developed OpenStats, a user-friendly web application that brings the power of the R language to researchers through a high-level interface and broad support for statistical methods such as t-tests and ANOVA. OpenStats was integrated into our electronic lab notebook Chemotion ELN via its third-party API, enabling…
Wilhelm Grzesiak, Daniel Zaborski, Marcin Pluciński, Magdalena Jędrzejczak-Silicka + 3 more
'Magdalena Jędrzejczak-Silicka' 'Renata Pilarczyk' 'Piotr Sablik' 'Andrea Pezzuolo'] Title: Simple Summary The current trend in animal husbandry, including cattle farming, is toward increasing stocking density and automating individual activities in animal care. Various electro-optical, acoustic, mechanical, and…
Bernard M. S. van Praag, J. Peter Hop, William H. Greene
In the last few decades, the study of ordinal data in which the variable of interest is not exactly observed but only known to be in a specific ordinal category has become important. To emphasize that the problem is not specific to a specific discipline we will use the neutral term coarsened observation. For…
Mustafa Attallah
Pearson's correlation to select predictor variables for linear models Authors: ['Mustafa Attallah'] This article examines the limitations of Pearson's correlation in selecting predictor variables for linear models. Using mtcars and iris datasets from R, this paper demonstrates the limitation of this correlation measure…
Mohammad Hossein Golbabaei, Mohammadreza Saeidi Varnoosfaderani, Arsalan Zare, Hirad Salari + 4 more
'Arsalan Zare' 'Hirad Salari' 'Farshid Hemmati' 'Hamid Abdoli' 'Bejan Hamawandi' 'Vincenzo Baglio'] Prior to the long-term utilization of solid oxide fuel cell (SOFC), one of the most remarkable electrochemical energy conversion devices, a variety of difficult experimental validation procedures is required, so it would…
Jessica I. Murphy, Nicholas E. Weaver, Audrey E. Hendricks
Longitudinal mouse models are commonly used to study possible causal factors associated with human health and disease. However, the statistical models, such as two-way ANOVA, often applied in these studies do not appropriately model the experimental design, resulting in biased and imprecise results. Here, we describe…
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…
Aaditya Prasad Gupta
Biological systems, at all scales of organization from nucleic acids to ecosystems, are inherently complex and variable. Therefore mathematical models have become an essential tool in systems biology, linking the behavior of a system to the interaction between its components. Parameters in empirical mathematical models…
Yu Huo, Hongpei Li, Xiao Wang, Xiaochen Du + 1 more
When analysing two-dimensional data sets, scientists are often interested in regions where one variable depends linearly on the other. Typically they use an ad hoc method to do so. Here we develop a statistically rigorous, Bayesian approach to infer the optimal partitioning of a data set into contiguous piece-wise…
Jiwoong Kim
This article investigates a fast and stable method to solve Henderson's mixed model equation. The proposed algorithm is stable in that it avoids inverting a matrix of a large dimension and hence is free from the curse of dimensionality. This tactic is enabled through row operations performed on the design matrix.
Benjamin J. Heil, Jake Crawford, Casey S. Greene
Those building predictive models from transcriptomic data are faced with two conflicting perspectives. The first, based on the inherent high dimensionality of biological systems, supposes that complex non-linear models such as neural networks will better match complex biological systems. The second, imagining that…
Sanjar Adilov
Machine learning models for molecular-property prediction typically work with molecular representations in the form of fingerprints, descriptors, or graphs. In case of fingerprints and descriptors, molecular representations usually comprise thousands of features, which causes the curse of dimensionality for many…
Andrew McCluskey
The use of mathematical transformations to reduce non-linear functions to linear problems, which can be tackled with analytical linear regression, is commonplace in the chemistry curriculum. The linearization procedure, however, assumes an incorrect statistical model for real experimental data; leading to biased…
Nicholas C Chesnaye, Merel van Diepen, Friedo Dekker, Carmine Zoccali + 2 more
'Carmine Zoccali' 'Kitty J Jager' 'Vianda S Stel'] Title: ABSTRACT True linear relationships are rare in clinical data. Despite this, linearity is often assumed during analyses, leading to potentially biased estimates and inaccurate conclusions. In this introductory paper, we aim to first describe-in a non-mathematical…
Moustafa M. A. Ibrahim, Rikard Nordgren, Maria C. Kjellsson, Mats O. Karlsson
'Mats O. Karlsson'] We investigated the possible advantages of using linearization to evaluate models of residual unexplained variability (RUV) for automated model building in a similar fashion to the recently developed method “residual modeling.” Residual modeling, although fast and easy to automate, cannot identify…
Authors not listed
This paper presents the Multi Cell-line Kinetic Model (MCKM), a novel generalised kinetic mechanistic model specifically tailored for Ambr15™ fed-batch cultivations of multiple Chinese Hamster Ovary (CHO) cell lines producing different recombinant monoclonal antibodies (mAbs). Unlike traditional models that requires…
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…
Authors not listed
Inverse problems, where we seek the values of inputs to a model that lead to a desired set of outputs, are a challenges subset of problems in science and engineering. In this work we demonstrate the use of two generative AI methods to solve inverse problems. We compare this approach to two more conventional approaches…