25 papers · ranked by Valyu relevance
Rick Farouni
In general, we will be limiting our focus mainly to the the multivariate statistics setting in which we treat an observation as a multivariate random vector yn = (yn,1, . . . , yn,P ) consisting of P features and the data as a set of N observations y = {y1, · · · , yn, · · · , yN }. Accordingly, we can think of the…
Alexander von Eye, Wolfgang Wiedermann, Stefan von Weber
In this article, we demonstrate that latent variable analysis can be of great use in person-oriented research. Starting with exploratory factor analysis of metric variables, we present an example of the problems that come with generalization of aggregate-level results to subpopulations. Oftentimes, results that are…
Matthew R. Whiteway, Daniel A. Butts
The activity of sensory cortical neurons is not only driven by external stimuli, but is also shaped by other sources of input to the cortex. Unlike external stimuli these other sources of input are challenging to experimentally control or even observe, and as a result contribute to variability of neuronal responses to…
Daniel T. Chang
Latent representations are the essence of deep generative models and determine their usefulness and power. For latent representations to be useful as generative concept representations, their latent space must support latent space interpolation, attribute vectors and concept vectors, among other things. We investigate…
Klaus K. Holst, Esben Budtz‐Jørgensen
An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features are implemented including…
Matthew Farrell, Stefano Recanatesi, R. Clay Reid, Stefan Mihalas + 1 more
Spectacular advances in imaging and data processing techniques are revealing a wealth of information about brain connectomes. This raises an exciting scientific opportunity: to infer the underlying circuit function from the structure of its connectivity. A potential roadblock, however, is that – even with well…
Nicola Melillo, Adam S. Darwich
In drug development decision-making is often supported through model-based methods, such as physiologically-based pharmacokinetics (PBPK). Global sensitivity analysis (GSA) is gaining use for quality assessment of model-informed inference. However, the inclusion and interpretation of correlated factors in GSA has…
Angela Sorgente, Rossella Caliciuri, Matteo Robba, Margherita Lanz + 1 more
Latent class analysis (LCA) can help identify unobserved classes of individuals in a population based on collected categorical data. It is commonly used in psychology to test hypotheses about sources of heterogeneity and class characteristics. However, careful decision-making is required in the modeling process. Its…
Tetsuro Morimura, Tatsushi Oka, Suzuki, Yugo + 1 more
Latent variable models provide a powerful framework for incorporating and inferring unobserved factors in observational data. In causal inference, they help account for hidden factors influencing treatment or outcome, thereby addressing challenges posed by missing or unmeasured covariates. This paper proposes a new…
Tamara Schamberger, Florian Schuberth, Jörg Henseler, Yves Rosseel
Structural equation modeling (SEM) is a prevalent approach for studying constructs. Traditionally, these constructs are modeled as reflectively measured latent variables – common factors that account for the variance-covariance structure of their associated indicators. Over the past two decades, there has been growing…
Ji Hoon Ryoo, Cixin Wang, Susan M. Swearer, Michael Hull + 1 more
In the areas of social and behavioral sciences, factor analysis has been a long-standing analytic strategy to understand unobserved (or latent) constructs as well as their internal structure from observed data (Cudeck and MacCallum, ). When the unobserved constructs are introduced using factor analysis, researchers are…
Muhammad Ammar Malik, Tom Michoel
Random effect models are popular statistical models for detecting and correcting spurious sample correlations due to hidden confounders in genome-wide gene expression data. In applications where some confounding factors are known, estimating simultaneously the contribution of known and latent variance components in…
Bert van der Veen, Francis K.C. Hui, Knut A. Hovstad, Robert B. O’Hara
In community ecology, unconstrained ordination can be used to predict latent variables from a multivariate dataset, which generated the observed species composition. Latent variables can be understood as ecological gradients, which are represented as a function of measured predictors in constrained ordination, so that…
Isabella Gollini, Thomas Brendan Murphy
Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. However, model-based clustering methods for categorical data are less standard. Latent class analysis is a commonly used method for model-based clustering of binary data and/or categorical data…
Augustin Kelava, Holger Brandt
In the past 2 decades latent variable modeling has become a standard tool in the social sciences. In the same time period, traditional linear structural equation models have been extended to include non-linear interaction and quadratic effects (e.g., Klein and Moosbrugger, [41]), and multilevel modeling (Rabe-Hesketh…
Pär Jonsson, Benny Björkblom, Elin Chorell, Tommy Olsson + 1 more
Multivariate projection methods are unique in being both multivariable by combining many variables into stronger predictive features (latent variables), and multivariate for being able to model systematic variation both related and orthogonal to an observed response. Orthogonal partial least squares (OPLS) is a…
John M. Felt, Sarah Depaoli, Jitske Tiemensma
Objective: The stress response is a dynamic process that can be characterized by predictable biochemical and psychological changes. Biomarkers of the stress response are typically measured over time and require statistical methods that can model change over time. One flexible method of evaluating change over time is…
Ruofan Wang, Lei Fang, Yue Wang, Jin Jin
Leveraging observational data to understand the associations between risk factors and disease outcomes and conduct disease risk prediction is a common task in epidemiology. While traditional linear regression and other machine learning models have been extensively implemented for this task, the associations between…
Ranjani Srinivasan, Jaron Jia Rong Lee, Rohit Bhattacharya, Ilya Shpitser
'Ilya Shpitser'] Causal analyses of longitudinal data generally assume that the qualitative causal structure relating variables remains invariant over time. In structured systems that transition between qualitatively different states in discrete time steps, such an approach is deficient on two fronts. First…
Samuel Renaud, Rachael Mansbach
Current antibacterial treatments cannot overcome the rapidly growing resistance of bacteria to antibiotic drugs, and novel treatment methods are required. One option is the development of new antimicrobial peptides (AMPs), to which bacterial resistance build-up is comparatively slow. Deep generative models have…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…
Authors not listed
Computational methods for generating molecules with specific physiochemical properties or biolog- ical activity can greatly assist drug discovery efforts. Deep learning generative models constitute a significant step towards that direction. In this work, we introduce a novel approach that utilizes a Reinforcement…
Roberto Di Mari, Zsuzsa Bakk, Jennifer Oser, Jouni Kuha
We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates are added in the model, keeping the measurement model parameters fixed. We discuss model identification, and derive an…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
Tagir Akhmetshin, Arkadii Lin, Timur Madzhidov, Alexandre Varnek
Autoencoders represent a promising technique for the inverse quantitative structure-activity relationship (QSAR) task. However, undesirable bias, such as atom ordering, affects the neighbourhood behaviour of autoencoders’ latent space and, consequently, usage of the latent vectors as variables in machine-learning…