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25 papers · ranked by Valyu relevance

01
PPubMed13 Jul 2021
Scientific and regulatory evaluation of mechanistic <i> in silico </i> drug and disease models in drug development: Building model credibility MUSUAMBA et al.
Flora T. Musuamba, Ine Skottheim Rusten, Raphaëlle Lesage, Giulia Russo + 16 more
'Giulia Russo' 'Roberta Bursi' 'Luca Emili' 'Gaby Wangorsch' 'Efthymios Manolis' 'Kristin E. Karlsson' 'Alexander Kulesza' 'Eulalie Courcelles' 'Jean‐Pierre Boissel' 'Cécile F. Rousseau' 'Emmanuelle M. Voisin' 'Rossana Alessandrello' 'Nuno Curado' 'Enrico Dall’ara' 'Blanca Rodriguez' 'Francesco Pappalardo' 'Liesbet…
87relevance
02
AarXiv28 Oct 2019
POSITION PAPER : Credibility of in silico trial technologies - a theoretical framing
Marco Viceconti, Miguel A. Juárez, Cristina Curreli, Marzio Pennisi + 2 more
'Giulia Russo' 'Francesco Pappalardo'] Abstract— Different research communities have developed various approaches to assess the credibility of predictive models. Each approach usually works well for a specific type of model, and under some epistemic conditions that are normally satisfied within that specific research…
85relevance
03
BbioRxiv3 May 2020
Computational verification of large logical models - application to the prediction of T cell response to checkpoint inhibitors
Céline Hernandez, Morgane Thomas-Chollier, Aurélien Naldi, Denis Thieffry
At the crossroad between biology and mathematical modelling, computational systems biology can contribute to a mechanistic understanding of high-level biological phenomenon. But as our knowledge accumulates, the size and complexity of mathematical models increase, calling for the development of efficient dynamical…
85relevance
04
PPubMed14 Jan 2020
<i> In silico </i> trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products
Marco Viceconti, Francesco Pappalardo, Blanca Rodriguez, Marc Horner + 2 more
'Jeff Bischoff' 'Flora Musuamba Tshinanu'] Title: Highlights 1. • Regulators now consider also evidences produced in silico. 2. • We need accepted methods to evaluate the credibility of models. 3. • In this paper we describe the use of the ASME V&V-40 technical standard. 4. • We also discuss its application to various…
82relevance
05
PPubMed30 Sep 2020
Computational Verification of Large Logical Models—Application to the Prediction of T Cell Response to Checkpoint Inhibitors
Céline Hernandez, Morgane Thomas-Chollier, Aurélien Naldi, Denis Thieffry
'Denis Thieffry'] At the crossroad between biology and mathematical modeling, computational systems biology can contribute to a mechanistic understanding of high-level biological phenomenon. But as knowledge accumulates, the size and complexity of mathematical models increase, calling for the development of efficient…
82relevance
06
BbioRxiv20 Jan 2025
Verification and reproducible curation of the BioModels repository
Lucian Smith, Rahuman S. Malik-Sheriff, Tung V. N. Nguyen, Henning Hermjakob + 13 more
The BioModels Repository contains over 1000 manually curated mechanistic models from published literature, most often encoded in the Systems Biology Markup Language (SBML). This community-based standard formally specifies each model, but does not describe the computational experimental conditions to run a simulation.…
81relevance
07
AarXiv25 Apr 2025
Verification and Validation for Trustworthy Scientific Machine Learning
John D. Jakeman, Lorena A. Barba, Joaquim R. R. A. Martins, Thomas O'Leary-Roseberry
"Thomas O'Leary-Roseberry"] Scientific machine learning (SciML) models are transforming many scientific disciplines. However, the development of good modeling practices to increase the trustworthiness of SciML has lagged behind its application, limiting its potential impact. The goal of this paper is to start a…
81relevance
08
PPubMed18 Nov 2010
Validation of population-based disease simulation models: a review of concepts and methods
Jacek A Kopec, Philippe Finès, Douglas G Manuel, David L Buckeridge + 9 more
Background Computer simulation models are used increasingly to support public health research and policy, but questions about their quality persist. The purpose of this article is to review the principles and methods for validation of population-based disease simulation models. Methods We developed a comprehensive…
80relevance
09
PPubMed13 May 2020
A content analysis-based approach to explore simulation verification and identify its current challenges Exploring the past six decades of simulation verification using content analysis to reveal current challenges
Christopher J. Lynch, Saikou Y. Diallo, Hamdi Kavak, Jose J. Padilla + 1 more
'Itzhak Benenson'] Verification is a crucial process to facilitate the identification and removal of errors within simulations. This study explores semantic changes to the concept of simulation verification over the past six decades using a data-supported, automated content analysis approach. We collect and utilize a…
80relevance
10
AarXiv9 Sep 2024
Model Input Verification of Large Scale Simulations
Rumyana Neykova, Derek Groen
Reliable simulations are critical for analyzing and understanding complex systems, but their accuracy depends on correct input data. Incorrect inputs such as invalid or out-of-range values, missing data, and format inconsistencies can cause simulation crashes or unnoticed result distortions, ultimately undermining the…
79relevance
11
PPubMed14 Nov 2018
A probabilistic metric for the validation of computational models A computational model validation metric
Ksenija Dvurecenska, Steve Graham, Edoardo Patelli, Eann A. Patterson
'Eann A. Patterson'] A new validation metric is proposed that combines the use of a threshold based on the uncertainty in the measurement data with a normalized relative error, and that is robust in the presence of large variations in the data. The outcome from the metric is the probability that a model's predictions…
78relevance
12
BbioRxiv1 Nov 2022
Best Practices for Interpretable Machine Learning in Computational Biology
Valerie Chen, Muyu Yang, Wenbo Cui, Joon Sik Kim + 2 more
Advances in machine learning (ML) have enabled the development of next-generation prediction models for complex computational biology problems. These developments have spurred the use of interpretable machine learning (IML) to unveil fundamental biological insights through data-driven knowledge discovery. However, in…
77relevance
13
PPubMed15 Feb 2018
Validation and Trustworthiness of Multiscale Models of Cardiac Electrophysiology
Pras Pathmanathan, Richard A. Gray
Computational models of cardiac electrophysiology have a long history in basic science applications and device design and evaluation, but have significant potential for clinical applications in all areas of cardiovascular medicine, including functional imaging and mapping, drug safety evaluation, disease diagnosis…
74relevance
14
BbioRxiv20 Aug 2025
rbio1 - training scientific reasoning LLMs with biological world models as soft verifiers
Ana-Maria Istrate, Fausto Milletari, Fabrizio Castrotorres, Jakub M. Tomczak + 3 more
Reasoning Models are typically trained against verification mechanisms in formally specified systems such as code or symbolic math. However, in open domains like biology, we do not generally have access to exact rules facilitating formal verification at scale, and oftentimes resolve to testing hypotheses in the lab to…
71relevance
15
AarXiv2023
Of (Biological) Models and Simulations
Maurice HT Ling
Modeling and simulation are recognized as important aspects of the scientific method for more than 70 years but its adoption in biology has been slow. Debates on its representativeness, usefulness, and whether the effort spent on such endeavors is worthwhile, exist to this day. Here, I argue that most of learning is…
69relevance
16
AarXiv2022
Modelleme ve Simulasyon
Serdar Abut
Bilgisayar modellemesi ve simülasyonu, sistem davranışlarının analiz etmek ve tanımlayıcı veya tahmine dayalı modlarda işleyişindeki stratejileri değerlendirmek için kullanılmaktadır (Abar ve ark., 2017). Model kavramı, halihazırda var olan veya henüz planlanmış belirli bir gerçekliğin soyut ve basitleştirilmiş bir…
69relevance
17
AarXiv29 Aug 2014
Validating predictions of unobserved quantities
Todd Oliver, Gabriel Terejanu, Christopher S. Simmons, Robert Moser
The ultimate purpose of most computational models is to make predictions, commonly in support of some decision-making process (e.g., for design or operation of some system). The quantities that need to be predicted (the quantities of interest or QoIs) are generally not experimentally observable before the prediction…
68relevance
18
CchemRxiv18 Jan 2024
Novel machine learning approach toward classification model of HIV-1 integrase inhibitors
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.…
62relevance
19
CchemRxiv10 Jul 2025
Automated Generation of Mechanistic Models for Chemical Process Digital Twins using Reinforcement Learning - Part I: Conceptual Framework and Equation Generation
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
60relevance
20
CchemRxiv15 Nov 2024
Robust Protein-Ligand Interaction Modeling Integrating Physical Laws and Geometric Knowledge for Absolute Binding Free Energy Calculation
Authors not listed
Accurate estimation of protein-ligand (PL) binding free energies is a crucial task in medicinal chemistry and a critical measure of PL interaction modeling effectiveness. However, traditional computational methods are often computationally expensive and prone to errors. Recently, deep learning (DL)-based approaches for…
58relevance
21
CchemRxiv20 May 2025
A Hybrid Kinetic Machine Learning Model for Accelerating Cell Line Selection
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)…
57relevance
22
CchemRxiv15 Feb 2023
Characterizing Uncertainty in Machine Learning for Chemistry
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
55relevance
23
CchemRxiv27 Mar 2025
Automated Generation of Mechanistic Models for Chemical Process Digital Twins using Reinforcement Learning - Part II: Compartmentalization and Learning-Based Recalibration
Authors not listed
Digital twins are virtual companions for the design, scale-up, and control of chemical processes. Equipping digital twins with mechanistic models of their mirrored unit operation expands their range of applicability compared to pure data-driven models. As constructing mechanistic models requires time, effort, and…
54relevance
24
CchemRxiv8 Feb 2023
Characterizing Uncertainty in Machine Learning for Chemistry
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
53relevance
25
CchemRxiv11 Dec 2023
Attention towards chemistry agnostic and explainable battery lifetime prediction
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…
50relevance

Results for model verification

Scientific and regulatory evaluation of mechanistic <i> in silico </i> drug and disease models in drug development: Building model credibility MUSUAMBA et al.

POSITION PAPER : Credibility of in silico trial technologies - a theoretical framing

Computational verification of large logical models - application to the prediction of T cell response to checkpoint inhibitors

<i> In silico </i> trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products

Computational Verification of Large Logical Models—Application to the Prediction of T Cell Response to Checkpoint Inhibitors

Verification and reproducible curation of the BioModels repository

Verification and Validation for Trustworthy Scientific Machine Learning

Validation of population-based disease simulation models: a review of concepts and methods

A content analysis-based approach to explore simulation verification and identify its current challenges Exploring the past six decades of simulation verification using content analysis to reveal current challenges

Model Input Verification of Large Scale Simulations

A probabilistic metric for the validation of computational models A computational model validation metric

Best Practices for Interpretable Machine Learning in Computational Biology

Validation and Trustworthiness of Multiscale Models of Cardiac Electrophysiology

rbio1 - training scientific reasoning LLMs with biological world models as soft verifiers

Of (Biological) Models and Simulations

Modelleme ve Simulasyon

Validating predictions of unobserved quantities

Novel machine learning approach toward classification model of HIV-1 integrase inhibitors

Automated Generation of Mechanistic Models for Chemical Process Digital Twins using Reinforcement Learning - Part I: Conceptual Framework and Equation Generation

Robust Protein-Ligand Interaction Modeling Integrating Physical Laws and Geometric Knowledge for Absolute Binding Free Energy Calculation

A Hybrid Kinetic Machine Learning Model for Accelerating Cell Line Selection

Characterizing Uncertainty in Machine Learning for Chemistry

Automated Generation of Mechanistic Models for Chemical Process Digital Twins using Reinforcement Learning - Part II: Compartmentalization and Learning-Based Recalibration

Characterizing Uncertainty in Machine Learning for Chemistry

Attention towards chemistry agnostic and explainable battery lifetime prediction

Scientific and regulatory evaluation of mechanistic <i> in silico </i> drug and disease models in drug development: Building model credibility MUSUAMBA et al.

POSITION PAPER : Credibility of in silico trial technologies - a theoretical framing

Computational verification of large logical models - application to the prediction of T cell response to checkpoint inhibitors

<i> In silico </i> trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products

Computational Verification of Large Logical Models—Application to the Prediction of T Cell Response to Checkpoint Inhibitors

Verification and reproducible curation of the BioModels repository

Verification and Validation for Trustworthy Scientific Machine Learning

Validation of population-based disease simulation models: a review of concepts and methods

A content analysis-based approach to explore simulation verification and identify its current challenges Exploring the past six decades of simulation verification using content analysis to reveal current challenges

Model Input Verification of Large Scale Simulations

A probabilistic metric for the validation of computational models A computational model validation metric

Best Practices for Interpretable Machine Learning in Computational Biology

Validation and Trustworthiness of Multiscale Models of Cardiac Electrophysiology

rbio1 - training scientific reasoning LLMs with biological world models as soft verifiers

Of (Biological) Models and Simulations

Modelleme ve Simulasyon

Validating predictions of unobserved quantities

Novel machine learning approach toward classification model of HIV-1 integrase inhibitors

Automated Generation of Mechanistic Models for Chemical Process Digital Twins using Reinforcement Learning - Part I: Conceptual Framework and Equation Generation

Robust Protein-Ligand Interaction Modeling Integrating Physical Laws and Geometric Knowledge for Absolute Binding Free Energy Calculation

A Hybrid Kinetic Machine Learning Model for Accelerating Cell Line Selection

Characterizing Uncertainty in Machine Learning for Chemistry

Automated Generation of Mechanistic Models for Chemical Process Digital Twins using Reinforcement Learning - Part II: Compartmentalization and Learning-Based Recalibration

Characterizing Uncertainty in Machine Learning for Chemistry

Attention towards chemistry agnostic and explainable battery lifetime prediction