24 papers · ranked by Valyu relevance
David Higgins, Christian Johner
The introduction of artificial intelligence / machine learning (AI/ML) products to the regulated fields of pharmaceutical research and development (R&D) and drug manufacture, and medical devices (MD) and in-vitro diagnostics (IVD), poses new regulatory problems: a lack of a common terminology and understanding leads to…
David E Carlson, Ricardo Chavarriaga, Yiling Liu, Fabien Lotte + 1 more
'Bao-Liang Lu'] Title: Abstract Objective. Machine learning’s (MLs) ability to capture intricate patterns makes it vital in neural engineering research. With its increasing use, ensuring the validity and reproducibility of ML methods is critical. Unfortunately, this has not always been the case in practice, as there…
Zhe Wang, Ardan Patwardhan, Gerard J. Kleywegt
The Electron Microscopy Data Bank (EMDB) is the central archive of the electron cryo-microscopy (cryo-EM) community for storing and disseminating volume maps and tomograms. With input from the community, EMDB has developed new resources for validation of cryo-EM structures, focussing on the quality of the volume data…
Sterling Baird, Tran Diep, Taylor Sparks
We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR), a Python tool for identifying high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, and clustering. We introduce several…
Sterling Baird, Tran Diep, Taylor Sparks
We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR), a Python tool for identifying high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, and clustering. We introduce several…
Melinda Nicola, Helen Correia, Graeme Ditchburn, Peter D. Drummond
Purpose To validate an individual's feelings or behaviour is to sanction their thoughts or actions as worthy of social acceptance and support. In contrast, rejection of the individual's communicated experience indicates a denial of social acceptance, representing a potential survival threat. Pain-invalidation, though…
K. Larsen, R. Lukyanenko, Roland M. Mueller, V. Storey + 3 more
Researchers must ensure that the claims about the knowledge produced by their work are valid. However, validity is neither well-understood nor consistently established in design science, which involves the development and evaluation of artifacts (models, methods, instantiations, and theories) to solve problems. As a…
Karolina Kopańska, Thomas Hartung
Next generation risk assessment (NGRA) demands a fundamental transformation in how toxicological test methods are validated. Traditional validation approaches, designed for animal tests and mainly for simple in vitro methods, are increasingly inadequate for evaluating complex New Approach Methodologies (NAMs)…
Alvin Lal, Ashneel Sharan, Krishneel Sharma, Arishma Ram + 2 more
'Dilip Kumar Roy' 'Bithin Datta'] Groundwater salinity is a critical factor affecting water quality and ecosystem health, with implications for various sectors including agriculture, industry, and public health. Hence, the reliability and accuracy of groundwater salinity predictive models are paramount for effective…
Abdelsalam M. Maatuk, Sohil F. Alshareef, Tawfig M. Abdelaziz
Requirements engineering is a discipline of software engineering that is concerned with the identification and handling of user and system requirements. Aspect-Oriented Requirements Engineering (AORE) extends the existing requirements engineering approaches to cope with the issue of tangling and scattering resulted…
Evgueni Jacob, Angélique Perrillat-Mercerot, Jean-Louis Palgen, Adèle L’Hostis + 5 more
Over the past several decades, metrics have been defined to assess the quality of various types of models and to compare their performance depending on their capacity to explain the variance found in real-life data. However, available validation methods are mostly designed for statistical regressions rather than for…
Udit Surya Saha, Michele Vendruscolo, Anne E. Carpenter, Shantanu Singh + 2 more
Recent advances in machine learning methods for materials science have significantly enhanced accurate predictions of the properties of novel materials. Here, we explore whether these advances can be adapted to drug discovery by addressing the problem of prospective validation - the assessment of the performance of a…
Roy Eagleson, Leo Joskowicz, Nassir Navab, Philipp Fürnstahl + 3 more
'Mazda Farshad' 'Hooman Esfandiari' 'Matthias Seibold'] This paper presents a discussion about the fundamental principles of Analysis of Augmented and Virtual Reality (AR/VR) Systems for Medical Imaging and Computer-Assisted Interventions. The three key concepts of Analysis (Verification, Evaluation, and Validation)…
Olawale Salaudeen, Anka Reuel, Ahmed Ahmed, Suhana Bedi + 5 more
'Zachary Robertson' 'Sudharsan Sundar' 'Ben Domingue' 'Angelina Wang' 'Sanmi Koyejo'] While the capabilities and utility of AI systems have advanced, rigorous norms for evaluating these systems have lagged. Grand claims, such as models achieving general reasoning capabilities, are supported with model performance on…
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…
Sebastian Stock, Atif Mashkoor, Alexander Egyed
Formal methods play a fundamental role in asserting the correctness of requirements specifications. However, historically, formal method experts have primarily focused on verifying those specifications. Although equally important, validation of requirements specifications often takes the back seat. This paper…
Victor H. R. Nogueira, Rishabh Sharma, Rafael V. C. Guido, Michael J. Keiser
As efforts to improve the robustness of molecular representations advance, so does the need for methods to test and validate them. We use a Variational Auto-Encoder (VAE), an unsupervised deep learning model, to generate anomalous samples of a well-known molecular string format called SELF-referencIng Embedded Strings…
Dominique Sommers, Natalia Sidorova, Boudewijn van Dongen
The assessment of process mining techniques using real-life data is often compromised by the lack of ground truth knowledge, the presence of non-essential outliers in system behavior and recording errors in event logs. Using synthetically generated data could leverage ground truth for better evaluation. Existing log…
Junghee Lee, Joongbin Lim, Jeongho Lee, Juhan Park + 2 more
'Fernando José Aguilar'] As satellite launching increases worldwide, uncertainty quantification for satellite data becomes essential. Misunderstanding satellite data uncertainties can lead to misinterpretations of natural phenomena, emphasizing the importance of validation. In this study, we established a tower-based…
Yanlin Zhang, Jing Zhao
We formalize scientific methodology—the end-to-end process from question formulation to evidence-grounded writing—as a phase-gated research protocol with explicit return paths and persistent constraints, and instantiate it for general-purpose language models as executable protocol specifications. The formalization…
Katherine Blades, Michael Biddle, Robert Froud, Eva M. Krockow + 1 more
The experimental use of antibodies that have not been validated for context-specific use frequently misdirects biomedical research. Experimental results that derive from the use of inadequately validated antibodies are estimated to waste over $1 billion annually in the United States alone and to consume millions of…
Authors not listed
Approximately 40% of marketed drugs exhibit suboptimal pharmacokinetic profiles. Co-crystallization, where pairs of molecules form a multicomponent crystal, constitutes a promising strategy to enhance physicochemical properties without compromising the pharmacological activity. However, finding promising co-crystal…
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…
Authors not listed
This study presents a validation and refinement of the “yellow cards” error detection workflow that can be applied to any property connected to molecular structure. In our implementation the workflow employed 5 predictive models with each assigning a “yellow card” to 5% of the entries with worst prediction accuracy.…