25 papers · ranked by Valyu relevance
Arran Hodgkinson, Dumitru Trucu, Matthieu Lacroix, Laurent Le Cam + 1 more
Cutaneous melanoma is a highly invasive tumor and, despite the development of recent therapies, most patients with advanced metastatic melanoma have a poor clinical outcome. The most frequent mutations in melanoma affect the BRAF oncogene, a protein kinase of the MAPK signaling pathway. Therapies targeting both BRAF…
Heinz Holling, Katrin Jansen, Walailuck Böhning, Dankmar Böhning + 2 more
'Susan Martin' 'Patarawan Sangnawakij'] The paper outlines several approaches for dealing with meta-analyses of count outcome data. These counts are the accumulation of occurred events, and these events might be rare, so a special feature of the meta-analysis is dealing with low counts including zero-count studies.…
Zachary Jackson, BingKan Xue
Ecosystems are formed by networks of species and their interactions. Traditional models of such interactions assume a constant interaction strength between a given pair of species. However, there is often significant trait variation among individual organisms even within the same species, causing heterogeneity in their…
Rudolf Debelak, Thorsten Meiser, Alicia Gernand
Item response tree (IRTree) models form a family of psychometric models that allow researchers to control for multiple response processes, such as different sorts of response styles, in the measurement of latent traits. While IRTree models can capture quantitative individual differences in both the latent traits of…
Thomas O. Hancock, John Buckell
Arguably the key issue in modelling discrete choice data is capturing preference heterogeneity. This can be through observed characteristics, and/or using techniques for capturing random heterogeneity across respondents. On the latter, in health economics, the two main approaches are the mixed multinomial logit (MMNL)…
Rachel S. Lee, Ben Engelhard, Ilana B. Witten, Nathaniel D. Daw
The hypothesis that midbrain dopamine (DA) neurons broadcast an error signal for the prediction of reward (reward prediction error, RPE) is among the great successes of computational neuroscience^1–3^. However, recent results contradict a core aspect of this theory: that the neurons uniformly convey a scalar, global…
Kim Luijken, Jia Song, Rolf H. H. Groenwold
Background When a predictor variable is measured in similar ways at the derivation and validation setting of a prognostic prediction model, yet both differ from the intended use of the model in practice (i.e., “predictor measurement heterogeneity”), performance of the model at implementation needs to be inferred. This…
Anja F. Ernst, Jonas M. B. Haslbeck
Time-series data have become ubiquitous in psychological research, allowing us to study detailed within-person dynamics and their heterogeneity across persons. Vector autoregressive (VAR) models have become a popular choice as a first approximation of these dynamics. The VAR model for each person and heterogeneity…
Anik Chaudhuri, Shabnam Choudhury, Anwoy Kumar Mohanty, Manoranjan Satpathy + 2 more
Studying the heterogeneity in cancerous tissue is challenging in cancer research. It is vital to process the real-world data efficiently to understand the heterogeneous nature of cancer tissue. GPU compatible models, which can estimate the subpopulation of cancerous tissue, are fast if the size of input data, i.e., the…
Philip B. Vinh, Brad Verhulst, Hermine H. M. Maes, Conor V. Dolan + 1 more
'Michael C. Neale'] Causal inference is inherently complex and relies on key assumptions that can be difficult to validate. One strong assumption is population homogeneity, which assumes that the causal direction remains consistent across individuals. However, there may be variation in causal directions across…
Stephanie Wankowicz, James Fraser
In their folded state, biomolecules exchange between multiple conformational states, crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules more accurately, we must move towards modeling and predicting…
Philip Vinh, Brad Verhulst, Conor V Dolan, Michael C Neale + 1 more
Causal inference is inherently complex, often dependent on key assumptions that are sometimes overlooked. One such assumption is the potential for unidirectional or bidirectional causality, while another is population homogeneity, which suggests that the causal direction between two variables remains consistent across…
Stephanie Wankowicz, James Fraser
In their folded state, biomolecules exchange between multiple conformational states, crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules more accurately, we must move towards modeling and predicting…
Stephane Hess, Sander van Cranenburgh
Models allowing for random heterogeneity, such as mixed logit and latent class, are generally observed to obtain superior model fit and yield detailed insights into unobserved preference heterogeneity. Using theoretical arguments and two case studies on revealed and stated choice data, this paper highlights that these…
Luca Ferretti, Tanya Golubchik, Francesco Di Lauro, Mahan Ghafari + 4 more
A standard method in phylogenetic reconstruction for representing variation in substitution rates between sites in the genome is the discrete Gamma model (DGM). Relative rates are assumed to be distributed according to a discretised Gamma distribution, where the probabilities that a site is included in each class are…
Shadi Zabad, Yue Li, Simon Gravel
With the increasing availability of high quality genomic data from diverse cohorts, polygenic scores (PRS) have become a mainstay of genetic analyses of complex traits and diseases. Despite their proliferation in numerous research domains, a major obstacle to wider adoption in clinical settings has been the…
Yilin Gao, Fengzhu Sun
Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging and critical step for developing machine learning algorithms with…
Diego Vallarino
Understanding consumer choice is fundamental to marketing and management research, as firms increasingly seek to personalize offerings and optimize customer engagement. Traditional choice modeling frameworks, such as multinomial logit (MNL) and mixed logit models, impose rigid parametric assumptions that limit their…
Cameron Pellett, Rubén Valbuena
Understanding the effect of heterogeneity is fundamental to numerous fields. In community ecology, classical theory postulates that habitat heterogeneity determines niche dimensionality and drives biodiversity. However, disparate heterogeneity-diversity relationships have been empirically observed, generating…
Adam J. Stone, John Paul Gosling
This paper introduces the Heteroscedastic AddiVortes model, a Bayesian nonparametric regression framework that simultaneously models the conditional mean and variance of a response variable using adaptive Voronoi tessellations. By employing a sum-of-tessellations approach for the mean and a product-of-tessellations…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Martin Bicher, Claire Rippinger, Christoph Urach, Dominik Brunmeir + 2 more
'Melanie Zechmeister' 'Niki Popper'] Continued model-based decision support is associated with particular challenges, especially in long-term projects. Due to the regularly changing questions and the often changing understanding of the underlying system, the models used must be regularly re-evaluated, -modelled and…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
A. Feder Cooper, Katherine Lee, Madiha Zahrah Choksi, Solon Barocas + 5 more
'Christopher De' 'James Grimmelmann' 'Jon Kleinberg' 'Siddhartha Sen' 'Baobao Zhang'] | A. Feder Cooper,∗ | Katherine Lee, | Madiha Zahrah Choksi, | Solon Barocas, | | --- | --- | --- | --- | | The GenLaw Center | The GenLaw Center | Cornell University | Microsoft Research | | Cornell University | Cornell University |…
Authors not listed
Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…