23 papers · ranked by Valyu relevance
Jong-Wook Ban, Lucy Abel, Richard Stevens, Rafael Perera + 1 more
External validation is a stage of clinical prediction rule (CPR) development where a CPR derived in a population is evaluated in different populations with a comparable health condition to the health condition of the derivation population (e.g., in another country) . An external validation study can generate evidence…
Richard D Riley, Lucinda Archer, Kym I E Snell, Joie Ensor + 4 more
External validation studies are an important but often neglected part of prediction model research. In this article, the second in a series on model evaluation, Riley and colleagues explain what an external validation study entails and describe the key steps involved, from establishing a high quality dataset to…
Raheleh Khorsan, Cindy Crawford
Background. Evidence rankings do not consider equally internal (IV), external (EV), and model validity (MV) for clinical studies including complementary and alternative medicine/integrative medicine (CAM/IM) research. This paper describe this model and offers an EV assessment tool (EVAT(c)) for weighing studies…
Giuseppe Gallitto, Robert Englert, Balint Kincses, Raviteja Kotikalapudi + 4 more
Multivariate predictive models play a crucial role in enhancing our understanding of complex biological systems and in developing innovative, replicable tools for translational medical research. However, the complexity of machine learning methods and extensive data pre-processing and feature engineering pipelines can…
Matthew Rosenblatt, Link Tejavibulya, Chris C. Camp, Rongtao Jiang + 3 more
Identifying reproducible and generalizable brain-phenotype associations is a central goal of neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype models in unseen data. Most prediction studies train and evaluate a model in the same dataset. However, external validation, or the…
Alex Youssef, Michael Pencina, Anshul Thakur, Tingting Zhu + 2 more
'David A. Clifton' 'Nigam H. Shah'] Stanford Bioengineering Department, Stanford University, Stanford, CA, USA Department of Engineering Science, University of Oxford, Oxford, UK Duke University School of Medicine, Durham, NC, USA Oxford-Suzhou Centre for Advanced Research, Suzhou, China Center for Biomedical…
Linda Nab, Maarten van Smeden, Ruth H. Keogh, Rolf H. H. Groenwold
1Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands 2Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands 3Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom 4Department…
Andres Jung, Julia Balzer, Tobias Braun, Kerstin Luedtke
Background Internal and external validity are the most relevant components when critically appraising randomized controlled trials (RCTs) for systematic reviews. However, there is no gold standard to assess external validity. This might be related to the heterogeneity of the terminology as well as to unclear evidence…
Zexiang Li, Donna Spiegelman, Molin Wang, Zuoheng Wang + 1 more
In epidemiology, obtaining accurate individual exposure measurements can be costly and challenging. Thus, these measurements are often subject to error. Regression calibration with a validation study is widely employed as a study design and analysis method to correct for measurement error in the main study due to its…
Tieu-Long Phan, Hoang-Son Lai Le, Gia-Bao Truong, The-Chuong Trinh + 4 more
HIV-1 (Human immunodeficiency virus-1) has been causing severe pandemics by attacking the immune system of its host. Left untreated, it can lead to AIDS (acquired immunodeficiency syndrome), where death is inevitable due to opportunistic diseases. Therefore, discovering new antiviral drugs against HIV-1 is crucial.…
Sarit Agami
Measurement error in the covariate of main interest (e.g. the exposure variable, or the risk factor) is common in epidemiologic and health studies. It can effect the relative risk estimator or other types of coefficients derived from the fitted regression model. In order to perform a measurement error analysis, one…
Lorenzo Trippa, Levi Waldron, Curtis Huttenhower, Giovanni Parmigiani
'Giovanni Parmigiani'] > We consider comparisons of statistical learning algorithms using multiple data sets, via leave-one-in cross-study validation: each of the algorithms is trained on one data set; the resulting model is then validated on each remaining data set. This poses two statistical challenges that need to…
Zhina Mohamadi, Erfan Abtahi, Zahra sadat Shayegh, Mehrafrin Ataei Kachouei + 8 more
Cancer is a major source of mortality and morbidity all over the world that has caused more than 19 million new cases and nearly 10 million deaths in 2020. Although there are so many advances in cancer diagnosis, previous methods such as imaging and serum biomarkers more often lack the necessary sensitivity and…
Mohammad Samha, Lucas Karas, David Vogt, Emmanuel Odogwu + 4 more
Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide goal is the development of workflows that integrate computational chemistry and data science tools with high-throughput experimentation as…
Lucas Gren
Most reviewers and authors in the behavioral soware engineering research eld, base their concept of validity on two publications, namely Wohlin et al. [20] and Runeson and Host ¨ [19]. While I highly appreciate the authors' work in these publications, I am afraid the four categories of validity threats promoted for…
Matutini Florence, Jacques Baudry, Guillaume Pain, Morgane Sineau + 1 more
Species distribution models (SDM) have been increasingly developed in recent years but their validity is questioned. Their assessment can be improved by the use of independent data but this can be difficult to obtain and prohibitive to collect. Protocoled data from citizen science may be used to establish external…
Jose Carlos Gómez-Tamayo, Lili Cao, Mazen Ahmad, Gary Tresadern
Protein-ligand binding affinity prediction is one of the major challenges in computational assisted drug discovery. An active area of research uses machine learning (ML) models trained on 3D structures of protein ligand complexes to predict binding modes, discriminate active and inactives, or predict affinity.…
Hans Ulrich Burger, Christoph Gerlinger, Chris Harbron, Armin Koch + 3 more
'Martin Posch' 'Justine Rochon' 'Anja Schiel'] 1) Hoffmann-La Roche AG, 2) Statistics and Data Insights, Bayer AG and Gynecology, Obstetrics and Reproductive Medicine, University Medical School of Saarland , 3) Roche Products, 4) Medizinische Hochschule Hannover, 5) Center for Medical Statistics, Informatics, and…
David A. Cook, Rose Hatala
Background Simulation plays a vital role in health professions assessment. This review provides a primer on assessment validation for educators and education researchers. We focus on simulation-based assessment of health professionals, but the principles apply broadly to other assessment approaches and topics. Key…
Brittany Haas, Melissa Hardy, Shree Sowndarya S. V., Keir Adams + 3 more
Data-driven reaction discovery and development is a growing field that relies on the use of molecular descriptors to capture key information about substrates, ligands, and targets. Broad adaptation of this strategy is hindered by the associated computational cost of descriptor calculation, especially when considering…
Xuan-Truc Dinh Tran, Tieu-Long Phan, Van-Thinh To, Ngoc-Vi Nguyen Tran + 4 more
3D pharmacophore models describe the ligand’s chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in Drug Design. Our research summarized the key studies for applying 3D pharmacophore…
Authors not listed
Automation of experiments in cloud laboratories promises to revolutionize scientific research by enabling remote experimentation and improving reproducibility. However, maintaining quality control without constant human oversight remains a critical challenge. Here, we present a novel machine learning framework for…
Ya Chen, Thomas Seidel, Roxane Axel Jacob, Steffen Hirte + 6 more
The ability to determine and predict metabolically labile atom positions in a molecule (also called “sites of metabolism” or “SoMs”) is of high interest to the design and optimization of bioactive compounds such as drugs, agrochemicals, and cosmetics. In recent years, several in silico models for SoM prediction have…