23 papers · ranked by Valyu relevance
Fumiaki Katagiri
While combinatorial genetic data collection from biological systems in which quantitative phenotypes are controlled by functional and non-functional alleles of multiple genes (multi-gene systems) is becoming common, a standard analysis method for such data has not been established. A common additive model of the…
Gilles Eerlings, Sebe Vanbrabant, Jori Liesenborgs, Gustavo Rovelo Ruiz + 2 more
Informed and Transparent Decision-Making Authors: ['Gilles Eerlings' 'Sebe Vanbrabant' 'Jori Liesenborgs' 'Gustavo Rovelo Ruiz' 'Davy Vanacken' 'Kris Luyten'] Abstract. We present an approach, AI-Spectra, to leverage model multiplicity for interactive systems. Model multiplicity means using slightly different AI models…
Benjamin J. Burgess, Michelle C. Jackson, David J. Murrell
As most ecosystems are being challenged by multiple, co-occurring stressors, an important challenge is to understand and predict how stressors interact to affect biological responses. A popular approach is to design factorial experiments that measure biological responses to pairs of stressors and compare the observed…
Qingyan Xiang, Yubai Yuan, Dongyuan Song, Usman J. Wudil + 3 more
'Muktar H. Aliyu' 'C. William Wester' 'Bryan E. Shepherd'] > Causal inference literature has extensively focused on binary treatments, with relatively fewer methods developed for multi-valued treatments. In particular, methods for multiple simultaneously assigned treatments remain understudied despite their practical…
Luca Serena, Moreno Marzolla, Gabriele D’Angelo, Stefano Ferretti
—Multilevel modeling and simulation (M&S) is becoming increasingly relevant due to the benefits that this methodology offers. Multilevel models allow users to describe a system at multiple levels of detail. From one side, this can make better use of computational resources, since the more detailed and time-consuming…
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…
Priscilla Balestrucci, Marc Ernst
Many characteristics of sensorimotor adaptation are well predicted by the Kalman filter, a relatively simple learning algorithm for the optimal estimation of dynamic variables given noisy measurements. Yet not all Kalman filter predictions are confirmed empirically, suggesting that the model might be too simplistic to…
Zhixiao Zhu, Maria Christodoulou, David Steinsaltz
Many complex systems are modelled using modular models, where individual sub-models are estimated separately and then combined. While this simplifies inference, it fails to account for interactions between components. A natural solution is to estimate all components jointly, but this is often impractical due to…
Pol Capdevila, Duncan O’Brien, Valentina Marconi, Thomas F. Johnson + 3 more
Conservation policies aiming to halt biodiversity loss often focus on globally prevalent threats like habitat loss and exploitation, yet direct and interactive effects of multiple threats remain poorly quantified. Here, we go beyond prior meta-analyses or species-level studies by providing a global, population-level…
Theresa Ramelot, Roberto Tejero, Gaetano Montelione
Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations of biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. We also address the challenges…
Jerome J. Choi, Noah Cohen Kalafut, Tim Gruenloh, Corinne D. Engelman + 2 more
Single-omics approaches often provide a limited view of complex biological systems, whereas multiomics integration offers a more comprehensive understanding by combining diverse data views. However, integrating heterogeneous data types and interpreting the intricate relationships between biological features—both within…
Gunnar König, Eric Günther, Ulrike von Luxburg
In explainable machine learning, global feature importance methods try to determine how much each individual feature contributes to predicting the target variable, resulting in one importance score for each feature. But often, predicting the target variable requires interactions between several features (such as in the…
Winston Chen, Yifan Jiang, William Stafford Noble, Yang Young Lu
models Authors: ['Winston Chen' 'Yifan Jiang' 'William Stafford Noble' 'Yang Young Lu'] Machine learning (ML) models are powerful tools for detecting complex patterns within data, yet their "black box" nature limits their interpretability, hindering their use in critical domains like healthcare and finance. To address…
Ling Zhang, Zhichao Hou, Tingxiang Ji, Yuanyuan Xu + 1 more
Discovery Authors: ['Ling Zhang' 'Zhichao Hou' 'Tingxiang Ji' 'Yuanyuan Xu' 'Runze Li'] Model interpretability and explainability have garnered substantial attention in recent years, particularly in decision-making applications. However, existing interpretability tools often fall short in delivering satisfactory…
Bruno Stegani, Emanuele Scalone, Fran Bacic Toplek, Thomas Lohr + 4 more
The computational study of the binding of a ligand to a target protein provides mechanistic insight into the molecular determinants of this process and can improve the success rate of in silico drug design. All-atom molecular dynamics (MD) simulations can be used to evaluate the binding free energy, typically by…
Mingfei Han, Xiaoqing Chen, Xiao Li, Jie Ma + 6 more
'Chunyuan Yang' 'Juan Wang' 'Yingxing Li' 'Wenting Guo' 'Yunping Zhu'] Title: Abstract Gene expression involves complex interactions between DNA, RNA, proteins, and small molecules. However, most existing molecular networks are built on limited interaction types, resulting in a fragmented understanding of gene…
Viola Merhof, Thorsten Meiser
Responding to rating scale items is a multidimensional process, since not only the substantive trait being measured but also additional personal characteristics can affect the respondents’ category choices. A flexible model class for analyzing such multidimensional responses are IRTree models, in which rating responses…
Emilia M. Wysocka, Matthew Page, James Snowden, T. Ian Simpson
Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-state molecular…
Hugh Panton, Gavin Leech, Laurence Aitchison
The main obstacle to interpretability is interaction. If the component trees had depth d = 1, the 'decision stumps' of Iba and Langley (1992), then partial dependence offers a complete feature-wise summary of the model's behaviour (Friedman, 2001). For p features used in the splits, an array of p partial dependence…
Leif Jacobson, James Stevenson, Farhad Ramezanghorbani, Steven Dajnowicz + 1 more
Transferable neural network potentials have shown great promise as an avenue to increase the accuracy and applicability of existing atomistic force fields for organic molecules and inorganic materials. Training sets used to develop transferable potentials are very large, typically millions of examples, and as such, are…
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.…
Hyemin Han
In the present study, I developed and tested an R module to explore the best models within the context of multilevel modeling in research in public health. The module that I developed, explore.models, compares all possible candidate models generated from a set of candidate predictors with information criteria, Akaike…
Prashanth Athri, Vidhya Murali, Pradyumna Y Muralidhar, Cassandra Königs + 4 more
- 1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India - 2. PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, Bengaluru, India - 3. Bioinformatics and Medical Informatics, Bielefeld University…