26 papers · ranked by Valyu relevance
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…
Shabbar I. Ranapurwala, Joseph E. Cavanaugh, Tracy Young, Hongqian Wu + 2 more
'Hongqian Wu' 'Corinne Peek-Asa' 'Marizen R. Ramirez'] Background The goal of predictive modelling is to identify the likelihood of future events, such as the predictive modelling used in climate science to forecast weather patterns and significant weather occurrences. In public health, increasingly sophisticated…
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…
Bo Cao, Russell Greiner, Andrew Greenshaw, Jie Sui + 1 more
'Amaryllis Mavragani'] Title: Abstract Recent applications of artificial intelligence (AI) and machine learning in medicine, psychology, and social sciences have led to common terminological confusions. In this paper, we review emerging evidence from systematic reviews documenting widespread misuse of key terms…
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…
Roemer J Janse, Ameen Abu-Hanna, Iacopo Vagliano, Vianda S Stel + 5 more
'Kitty J Jager' 'Giovanni Tripepi' 'Carmine Zoccali' 'Friedo W Dekker' 'Merel van Diepen'] Title: ABSTRACT An artificial intelligence boom is currently ongoing, mainly due to large language models, leading to significant interest in artificial intelligence and subsequently also in machine learning (ML). One area where…
Galit Shmueli, Ali Tafti
Many internet platforms that collect behavioral big data use it to predict user behavior for internal purposes and for their business customers (e.g., advertisers, insurers, security forces, governments, political consulting firms) who utilize the predictions for personalization, targeting, and other decision-making.…
Joshua P. Jahner, C. Alex Buerkle, Dustin G. Gannon, Eliza M. Grames + 11 more
The proliferation of biological data with large numbers of samples and many dimensions is kindling hope that life scientists will be able to fit statistical and machine learning models that are highly predictive and interpretable. However, large biological data sets are commonly burdened with an inherent trade-off…
Anja K. Leist, Matthias Klee, Jung Hyun Kim, David H. Rehkopf + 3 more
'Stéphane P. A. Bordas' 'Graciela Muniz-Terrera' 'Sara Wade'] Machine learning (ML) methodology used in the social and health sciences needs to fit the intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive, systematic meta-mapping of research questions in the…
Lilli Heinen, Robert L. Larson, Brad J. White, Sofia Alves-Pimenta
Title: Simple Summary Predictive models use historical data to make future predictions. These tools have become more common in the cattle industry in recent years. Their applications are broad but they are especially useful in the prediction of disease. This review explores published studies that use predictive models…
Casey C. Bennett, Thomas W. Doub, Rebecca Selove
- EHRs are increasingly likely to contain data and functionality that can support computational approaches to healthcare. - Predictive modeling of EHR data has achieved 70-72% accuracy in predicting individualized treatment response at baseline. - Clinical decision support can be conceptualized as a form of artificial…
Ramyaa Ramyaa, Omid Hosseini, Giri P Krishnan, Sridevi Krishnan
Nutritional phenotyping is a promising approach to achieve personalized nutrition. While conventional statistical approaches haven’t enabled personalizing well yet, machine-learning tools may offer solutions that haven’t been evaluated yet. The primary aim of this study was to use energy balance components – input…
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…
Danilo Bzdok, Denis Engemann, Olivier Grisel, Gaël Varoquaux + 1 more
In the 20^th^ century many advances in biological knowledge and evidence-based medicine were supported by p-values and accompanying methods. In the beginning 21^st^ century, ambitions towards precision medicine put a premium on detailed predictions for single individuals. The shift causes tension between traditional…
Theo Knijnenburg, Gunnar Klau, Francesco Iorio, Mathew Garnett + 3 more
Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. Finding ‘actionable knowledge’ is becoming more important, but also more challenging as datasets grow in size and complexity. We…
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…
Authors not listed
Kinetic modeling is essential for predicting changes in food quality during processing and storage. This study evaluates the application of physics-informed neural networks (PINN) for food kinetic modeling, integrating kinetic insights into neural network frameworks. Based on three case studies, namely seed drying…
Authors not listed
Accurate prediction of melting points for pure molecules remains a significant challenge in predictive chemistry, with implications across various scientific fields, including materials science, drug discovery, and separations chemistry. Traditional methods, such as group contribution (GC) techniques, have shown…
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…
Authors not listed
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…
Michal Bozděch
Not only in sports is a neural network the most used type of artificial intelligence. With software development, anyone can create a neural network model, but little is known about how to prepare the data and how to set up the model algorithms to their maximum performance. For these reasons, this study aims to…
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)…
Jaron Arbet, Cole Brokamp, Jareen Meinzen-Derr, Katy E. Trinkley + 1 more
Machine learning (ML) provides the ability to examine massive datasets and uncover patterns within data without relying on a priori assumptions such as specific variable associations, linearity in relationships, or prespecified statistical interactions. However, the application of ML to healthcare data has been met…
Authors not listed
Accurate prediction of battery behavior under different dynamic operating conditions is critical for both fundamental research and practical applications. However, the diversity of emerging materials and cell architectures presents significant challenges to the generalizability of conventional prognostic approaches.…
Tianfan Jin, Brett M Savoie
Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive…