21 papers · ranked by Valyu relevance
Karel G. M. Moons, Joris A. H. de Groot, Walter Bouwmeester, Yvonne Vergouwe + 4 more
'Yvonne Vergouwe' 'Susan Mallett' 'Douglas G. Altman' 'Johannes B. Reitsma' 'Gary S. Collins'] Carl Moons and colleagues provide a checklist and background explanation for critically appraising and extracting data from systematic reviews of prognostic and diagnostic prediction modelling studies. Please see later in the…
Ricardo J. Pais, Pietro Pinoli, Anna Bernasconi
Clinical bioinformatics is a newly emerging field that applies bioinformatics techniques for facilitating the identification of diseases, discovery of biomarkers, and therapy decision. Mathematical modelling is part of bioinformatics analysis pipelines and a fundamental step to extract clinical insights from genomes…
Giacomo Welsch, Peter Kowalczyk
Prediction-oriented machine learning is becoming increasingly valuable to organizations, as it may drive applications in crucial business areas. However, decision-makers from companies across various industries are still largely reluctant to employ applications based on modern machine learning algorithms. We ascribe…
Skyler Cranmer, Bruce Desmarais
The large majority of inferences drawn in empirical political research follow from model-based associations (e.g. regression). Here, we articulate the benefits of predictive modeling as a complement to this approach. Predictive models aim to specify a probabilistic model that provides a good fit to testing data that…
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.…
Philip Greulich
Purpose of Review This article gives a broad overview of quantitative modelling approaches in biology and provides guidance on how to employ them to boost stem cell research, by helping to answer biological questions and to predict the outcome of biological processes. Recent Findings The twenty-first century has seen a…
Alicja Wolny–Dominiak, Tomasz Żądło
strategy Authors: ['Alicja Wolny–Dominiak' 'Tomasz Żądło'] This paper addresses the topic of choosing a prediction strategy when using parametric or nonparametric regression models. It emphasizes the importance of ex ante prediction accuracy, ensemble approaches, and forecasting not only the values of the dependent…
Lazaros Belbasis, Orestis A. Panagiotou
The field of health services research studies the health care system by examining outcomes relevant to patients and clinicians but also health economists and policy makers. Such outcomes often include health care spending, and utilization of care services. Building accurate prediction models using reproducible research…
D O’Connor, C Horien, F Mandino, RT Constable
Conceptually brain states reflect some combination of the internal mental process of a person, and the influence of their external environment. Importantly, for neuroimaging, brain states may impact brain-behavior modeling of a person’s traits, which should be independent of moment-to-moment changes in behavior. A…
Riddhi Chawla, S. Balaji, Raed N. Alabdali, Ibrahim A. Naguib + 2 more
'Nadir O. Hamed' 'Heba Y. Zahran'] A variety of receptor and donor characteristics influence long-and short-term kidney graft survival. It is critical to predict the effectiveness of kidney transplantation to optimise organ allocation. This would allow patients to choose the best accessible kidney donor and the optimal…
John L Mbotwa, Marc de Kamps, Paul D Baxter, George TH Ellison + 1 more
The present study aimed to compare the predictive acuity of latent class regression (LCR) modelling with: standard generalised linear modelling (GLM); and GLMs that include the membership of subgroups/classes (identified through prior latent class analysis; LCA) as alternative or additional candidate predictors. Using…
Authors not listed
The use of hybrid models, combing mechanistic and machine learning (ML), has emerged as a promising approach, contributing to the development of Industry 4.0. This work presents a hybrid model that forecasts minibioreactor (MBR) production runs of mammalian cell culture recombinant for monoclonal antibodies (mAbs)…
Nan van Geloven, Ruth H. Keogh, Wouter van Amsterdam, Giovanni Cinà + 11 more
'Jesse H. Krijthe' 'Niels Peek' 'Kim Luijken' 'Sara Magliacane' 'Paweł Morzywołek' 'Thijs van Ommen' 'Hein Putter' 'Matthew Sperrin' 'Junfeng Wang' 'Daniala L. Weir' 'Vanessa Didelez'] Nan van Geloven (1*), Ruth H Keogh (2), Wouter van Amsterdam (3), Giovanni Cinà (4,5,6), Jesse H. Krijthe (7), Niels Peek (8,9), Kim…
Marjolein Fokkema, Carolin Strobl
The authors would like to thank Benjamin Christoffersen for his contributions to the development of package pre. The authors would like to thank Susan Niessen for granting access to the dataset from Example 2 (Predicting Academic Achievement). Example 3 (Predicting Substance Use) results from secondary analyses of data…
Adarsh Sivasankaran, Eric Williams, Martin Maiers, Vladimir Cherkassky
Unrelated Donor selection for a Hematopoietic Stem Cell Transplant is a complex multi-stage process. Choosing the most suitable donor from a list of Human Leukocyte Antigen (HLA) matched donors can be challenging to even the most experienced physicians and search coordinators. The process involves experts sifting…
Abdullahi Ali, Nasir Ahmad, Elgar de Groot, Marcel A. J. van Gerven + 1 more
Predictive coding represents a promising framework for understanding brain function. It postulates that the brain continuously inhibits predictable sensory input, ensuring a preferential processing of surprising elements. A central aspect of this view is its hierarchical connectivity, involving recurrent message…
Authors not listed
Methanol synthesis from syngas (CO/CO₂/H₂) is vital for sustainable chemical production; however, traditional kinetic models hinder rapid reactor optimisation [1]. We present a reproducible machine-learning pipeline to predict methanol yield in a double-pass plug-flow reactor, utilising a synthetic dataset (n = 5,000)…
Nina Kusch, Andreas Schuppert
Drug sensitivity prediction models for human cancer cell lines constitute important tools in identifying potential driving factors of responsiveness in a pre-clinical setting. Integrating information derived from a range of heterogeneous data is crucial, but remains non-trivial, as differences in data structures may…
Authors not listed
Integrating machine learning (ML) into drug discovery has ushered in a new era of innovation, dramatically enhancing the efficiency and precision of identifying and developing new therapeutics. This review provides a comprehensive analysis of the current applications of machine learning in drug discovery, focusing on…
Willson Gaul, Dinara Sadykova, Hannah J. White, Lupe León-Sánchez + 3 more
Biological records are often the data of choice for training predictive species distribution models (SDMs), but spatial sampling bias is pervasive in biological records data at multiple spatial scales and is thought to impair the performance of SDMs. We simulated presences and absences of virtual species as well as the…
Emma King-Smith
Data-driven chemistry has garnered much interest concurrent with improvements in hardware and the development of new machine learning models. However, a notable bottleneck for data-driven chemistry specifically is the challenge in obtaining sufficiently large, accurate datasets of a desired chemical outcome. Herein, I…