26 papers · ranked by Valyu relevance
Sherif Sultan, Yogesh Acharya, Mohamed S. Sultan, Omnia Zayed + 1 more
Background Vascular diseases are increasing in Ireland as well as worldwide alongside an ageing society, posing a growing demand for trained and qualified healthcare professionals. In this study, we have analysed current practices of vascular interventions by using the data from the vascular tertiary centre to predict…
Maerziya Yusufujiang, Constanza L Andaur Navarro, Johanna AA Damen, Toshihiko Takada + 6 more
Objectives The rise in popularity and off-the-shelf availability of machine learning (ML) and AI-based methodology to develop new prediction models provides developers with ample choices to compare and select the best performing model out of many possible models. Many studies have shown that such comparisons on any…
Jaroslav Mašek, Lucia Duricova, Juraj Čamaj, Hamed Aghaei
Railway accidents, particularly suicides and suicide attempts, significantly disrupt operations, cause delays in passenger and freight services, and result in varying degrees of infrastructure damage. This study focuses on identifying the relationship between suicide-related railway incidents, as the most frequent type…
Dan Tulpan, Luis O Tedeschi, Hector Menendez, Ricardo Augusto M Vieira
Integrating open-source tools and machine learning (ML) pipelines into livestock data analysis transforms research, education, and decision-making in animal science. This study presents a comprehensive, end-to-end regression pipeline implemented in Python, designed to predict outcome variables from structured input…
Aida Seyedsalehi, Giulio Scola, Seena Fazel
The Feature article by Krishnadas and colleagues1 brings a welcome focus on causal prediction modelling - an emerging field at the intersection of prediction research and causal inference that enables risk prediction under hypothetical interventions - and provides several helpful insights into the potential for these…
Authors not listed
Metal–organic frameworks (MOFs) represent a versatile class of porous materials, yet efficiently exploring their vast chemical space for target gas adsorption properties remains a major challenge. MOFid, a text-based encoding of MOF structures, has enabled large-scale data mining using natural language processing (NLP)…
Raquel Costa, Bruno de Sousa, Thomas Kneib, Rui Martins + 1 more
Clinical prediction models play a crucial role in advancing personalized care for mental health disorders, providing essential insights for diagnosis, prognosis and intervention planning. This work examines the current methodological approaches used to develop such models, emphasizing their application to mental health…
Claudio Bosco, Umberto Minora, Daniele de Rigo, J. Pingsdorf + 1 more
This paper presents a mixed-methodology to forecast illegal border crossings in Europe across five key migratory routes, with a one-year time horizon. The methodology integrates machine learning techniques with qualitative insights from migration experts. This approach aims at improving the predictive capacity of…
Jansson, Fredrik
Formal modelling provides a toolkit for understanding cultural dynamics, from individual decisions to recurring patterns of change. This chapter explains what models are and why they matter. Using a precise, shared language, they aid thinking and communication by turning fuzzy assumptions into clear, comparable…
Santiago Herce Castañon, Christopher R. Stephens
Predicting and understanding behaviour is a primary objective of many disciplines, especially human behaviour, as it is the cause of many of the world’s most pressing problems. Although it is a fundamental concept in multiple disciplines, there is no agreed operational definition of what it is. Neither is there a…
Authors not listed
Accurately predicting chemical reaction yields in silico is a long-standing goal in organic chemistry that, if achieved, would revolutionize synthesis design, op-timization, and discovery. The vast reaction data within scientific literature rep-resents a rich resource for training predictive machine learning models…
Stefano Damato, Nicolò Rubattu, Dario Azzimonti, Giorgio Corani
Intermittent time series, characterised by the presence of a significant amount of zeros, constitute a large percentage of inventory items in supply chain. Probabilistic forecasts are needed to plan the inventory levels; the predictive distribution should cover non-negative values, have a mass in zero and a long upper…
Jan Linnenbrink, Jakub Nowosad, Marvin Ludwig, Anna Frederike Jablotschkin + 3 more
Spatio-temporal machine-learning modelling is an important tool in environmental research. However, machine-learning models are highly sensitive to both the characteristics of the training data, such as its distribution, and methodological choices, including the cross-validation strategy. Each decision has impact and…
Authors not listed
The vast and expanding chemical universe contains hundreds of thousands of substances, yet experimental data on their environmental persistence are available for only a small fraction (<3%). This gap limits our ability to identify persistent organic contaminants. Models have been developed to predict the persistence…
Giovanni Palla, Alexander Hillsley, Yang-Joon Kim, Loic A. Royer
Predicting how cells respond to genetic and chemical perturbations is a central challenge in drug discovery and functional genomics. A growing ecosystem of specialized single-cell foundation models has been developed to address this problem, yet their practical advantage over domain-agnostic approaches remains unclear.…
Authors not listed
Theoretical prediction of enantioselectivity for broad range of substrates in a given reaction has long been a formidable challenge, traditionally replaced by labor-intensive screening of multiple conditions. Until recently this remained an unaddressed problem in asymmetric catalysis under data-limited scenarios, yet…
Zichao Jin, Jiaoru Wang, Wenjiang Huang, Jingcheng Zhang + 2 more
Accurate, reliable, large-scale disease predictions are essential to ensure rice production. Existing disease prediction models often face a trade-off between interpretability and predictive capability, necessitating the integration of mechanistic knowledge and data-driven learning within a modelling framework.…
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…
Merve Nur Güler, Maris Alver, Toomas Haller, Flora Jay + 3 more
Circulating metabolites capture clinically relevant physiological variation and contribute to disease aetiology yet are mostly studied as biomarkers rather than prediction targets. Although genome-wide association studies have identified genetic determinants of metabolites, marginal associations do not show how…
Authors not listed
This research investigates predicting the Highest Occupied Molecular Orbital and the Lowest Unoccupied Molecular Orbital (HOMO-LUMO; short HL) gap of natural compounds, a crucial property for understanding molecular electronic behavior relevant to cheminformatics and materials science. To address the high computational…
Authors not listed
Nickel/photoredox catalysis in cross-coupling reactions has recently emerged as a powerful tool for efficient C–C bond formation as it enables mild operating conditions, thus expanding synthetic scope to molecules of pharmaceutical interest. Successful routine implementation of such reactions is limited, due to the…
Mengyi Gong, Rebecca Killick, Andrew Hirons
Efficient irrigation management is crucial to agriculture, forestry and horticulture, especially under climate change. Developments in novel sensors and Internet of Things technology provide an opportunity to carry out real-time monitoring of tree sap flux density, which, when coupled with advanced modelling…
John M. Drake
Modelling has become a routine part of ecological and evolutionary research, yet its practice often lacks a clear conceptual framework. I propose that modelling can be fruitfully understood as experimentation. Like empirical studies, modelling projects involve treatments, levels and responses: parameter regimes or data…
Benjamin Avanzi, Matthew Lambrianidis, Greg Taylor, Bernard Wong
The use of neural networks trained on individual claims data has become increasingly popular in the actuarial reserving literature. We consider how to best input historical payment data in neural network models. Additionally, case estimates are also available in the format of a time series, and we extend our analysis…
D. Samuel Schwarzkopf, Ecem Altan, Steven C. Dakin, Catherine A. Morgan
Population receptive field (pRF) modelling is a ubiquitous tool in sensory neuroscience for estimating the functional architecture of the human brain. Most pRF models assume a canonical hemodynamic response function (HRF) to account for neurovascular effects. But how does this assumption affect results in practice?…
I. David Elder, Juan Moreno-Cruz, Cameron Wade, Sylvia Sleep + 4 more
Energy systems optimisation models are a leading tool for informing decisions in the energy transition. However, these models often remain opaque, and results are frequently presented without a clear discussion of their epistemic limitations. We propose Diagnostic Modelling as a framework wherein modellers critically…