20 papers · ranked by Valyu relevance
Nithisha Suryadevara, Vivek Reddy Srigiri
— Joint modeling of longitudinal and survival data has become increasingly important in medical research, particularly for understanding disease progression in chronic conditions where both repeated biomarker measurements and time-toevent outcomes are available. Traditional two-stage methods, which analyze longitudinal…
Félix Laplante, Christophe Ambroise
In this paper, we propose a general framework that unifies longitudinal biomarker modeling with multi-state event processes defined on arbitrary directed graphs. Our approach accommodates both Markovian and semi-Markovian transition structures, and extends classical joint models by coupling nonlinear mixed-effects…
Zilu Liang, Christos Thomadakis, Jessica Sena, Young Won Cho + 8 more
Background Missing data are inevitable in mobile health (mHealth) and ubiquitous health (uHealth) research and are often driven by distinct within- and between-person factors that influence compliance. Understanding these distinct mechanisms underlying nonresponse can inform strategies to improve compliance and…
Niek Stevenson, Steven Miletić, Birte U. Forstmann
Understanding how neural activity relates to behavior remains a central challenge in cognitive neuroscience. Joint modeling offers a principled method by simultaneously fitting behavioral and fMRI data and estimating the relations between them, accounting for measurement error, inter-individual variability, and shared…
Benjamin Christoffersen, Keith Humphreys, Alessandro Gasparini, Birzhan Akynkozhayev + 2 more
Joint models are well suited to modelling linked data from laboratories and health registers. However, there are few examples of joint models that allow for (a) multiple markers, (b) multiple survival outcomes (including terminal events, competing events, and recurrent events), (c) delayed entry and (d) scalability. We…
Svenja Elkenkamp, John Grosser, Kim Rand
The R package hyreg2 introduces a frequentist framework for estimating latent class models for mixed outcome types using a joint likelihood approach. The method combines continuous and dichotomous data under the assumption that both outcome types arise from a common underlying data-generating process. In the…
Linsell, Louise, Paracha, Noman + 16 more
Data were pooled from three phase I/II open-label trials evaluating larotrectinib in 196 patients with neurotrophic tyrosine receptor kinase fusion-positive (NTRK+) solid tumours followed up until July 2021. Bayesian joint modelling was used to obtain patient-specific predictions of OS using individual-level sum of…
J. Salomon, J. Enjalbert, T. Flutre
The genetics of interspecific groups remains largely unexplored, despite the central role of social (or indirect) genetic effects in shaping phenotypic expression within communities. Intercropping, i.e. the simultaneous cultivation of multiple crop species in the same field, offers a powerful model to harness these…
Markus Lindén, Tea Ammunét, Tommi Välikangas, Laura L. Elo + 1 more
Biomedical studies increasingly incorporate longitudinal data, enabling us to track individual disease processes over time at the molecular level, and to discover associations of the molecular profiles with the outcome of interest, such as the onset of a disease. Despite the potential of statistical methods that…
Hosein Mirazi, Scott T. Wood
Osteoarthritis (OA) is a multifactorial joint disease driven by complex interactions among chondrocytes, osteoblasts, fibroblasts, and immune cells across cartilage, bone, and synovial tissues. Conventional monoculture systems are unable to capture this crosstalk, limiting their physiological relevance. Building on our…
Amir Hosein Hadian Rasanan, Lukas Schumacher, Michael D. Nunez, Gabriel Weindel + 1 more
Over the past sixty years, evidence accumulation models have emerged as a dominant framework for explaining the neural and behavioral aspects of the process underlying decision making. These models have also been widely used as a measurement instrument to assess individual differences in latent cognitive constructs…
Authors not listed
Active learning is an emerging paradigm used to help accelerating drug discovery, but most prior applications seek solely to optimize potency, whereas multiple properties influence a compound’s utility as a drug candidate. We introduce a method for multiobjective ligand optimization, which is able to efficiently handle…
Niklas Neubrand, Timo Rachel, Tim Litwin, Jens Timmer + 2 more
Systems biology strives to unravel the complex dynamics of cellular processes, often with the help of ordinary differential equations (ODEs). However, the sparsity of measured data and the strong non-linearity of common ODEs introduce severe numerical problems in typical modeling tasks. This gave rise to the…
J. Crossa, J. Sun, A. Montesinos‐López, P. Pérez‐Rodríguez + 4 more
The rapid expansion of genomic, environmental, phenomic, and other high-dimensional data sources has transformed genomic prediction in plant breeding. However, the terms multimodal, interaction modeling, and multimodule architecture are often used inconsistently, generating ambiguity regarding whether they refer to…
Stephen B. Broomell, Sabina J. Sloman, Lisheng He
Behavioral models are instrumental for studying human cognition, yet many inferences derived from such models fail to generalize. We argue that this is driven in part by the increasing complexity of behavioral models, where non-linearities and discontinuities create dynamic parameter interactions that limit the…
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Accurate modeling of drug concentration--time (C--t) profiles is central to pharmacokinetics (PK) and plays a critical role in both early-stage compound selection and late-stage individualized dosing. Traditional PK model offer mechanistic interpretability but often rely on rigid assumptions, extensive…
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The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine learning-based methods, necessitates a new collaborative architecture to replace the isolated, traditional model of software development. The traditional approach often led to redundant engineering effort, high…
Priscilla Balestrucci, Maura Mezzetti, Barbara La Scaleia, Alessandro Moscatelli
Inferential models in psychophysics are essential for quantifying the relation between physical properties of the stimulus and their perceptual representations. The psychometric function is typically used to model the responses of individual participants in forced-choice experiments. The accuracy and the noise of the…
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Predicting drug-induced toxicity remains a central challenge in computational toxicology, particularly for organ-specific adverse effects that arise from diverse structural, biochemical, and mechanistic origins. Existing deep learning models excel at pattern recognition but often lack mechanistic interpretability…
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Accurate prediction of chemical reaction yields remains essential for accelerating synthesis optimization, yet current machine learning models face critical limitations in capturing temporal dynamics, providing calibrated uncertainty estimates, and explicitly modeling reactant-to-product transformations. Here we…