Tootfinder

Understanding the mismatch between in-vivo and in-silico rhinomanometry
Marco Atzori, Gabriele Dini Ciacci, Maurizio Quadrio
https://arxiv.org/abs/2506.10865

Understanding the mismatch between in-vivo and in-silico rhinomanometry
Numerical simulations and clinical measurements of nasal resistance are in quantitative disagreement. Bias introduced by the design of medical devices has not been considered until now as a possible explanation. The aim of present paper is to study the effect of the location of the probe on the rhinomanometer that is meant to measure the ambient pressure. Rhinomanometry is carried out on a 3D silicone model of a patient-specific anatomy; a clinical device and dedicated sensors are employed side…

ChemReservoir -- An Open-Source Framework for Chemically-Inspired Reservoir Computing
Mehmet Aziz Yirik, Jakob Lykke Andersen, Rolf Fagerberg, Daniel Merkle
https://arxiv.org/abs/2506.04249

ChemReservoir -- An Open-Source Framework for Chemically-Inspired Reservoir Computing
Reservoir computing is a type of a recurrent neural network, mapping the inputs into higher dimensional space using fixed and nonlinear dynamical systems, called reservoirs. In the literature, there are various types of reservoirs ranging from in-silico to in-vitro. In cheminformatics, previous studies contributed to the field by developing simulation-based chemically inspired in-silico reservoir models. Yahiro used a DNA-based chemical reaction network as its reservoir and Nguyen developed a D…

Impact of friction force and retrieval speed on in silico mechanical thrombectomies: a sensitivity analysis
Mahesh S. Nagargoje, Virginia Fregona, Giulia Luraghi, Francesco Migliavacca, Demitria A Poulos, Bryan C Good, Jose Felix Rodriguez Matas
https://arxiv.org/abs/2506.03812

Impact of friction force and retrieval speed on in silico mechanical thrombectomies: a sensitivity analysis
Background: Mechanical Thrombectomy (MT) is a widely accepted first-line treatment for Acute Ischemic Stroke (AIS) and it has been studied using in vitro and in silico models. Thrombectomy outcomes have been performed for patient-specific cases using in silico models. However, until now, in vivo friction coefficients for stent-vessel, stent-clot, and clot-vessel interactions are unknown, but in vitro experiments have been attempted with significant standard deviations. These interactions and fr…

A novel sensitivity analysis method for agent-based models stratifies \emph{in-silico} tumor spheroid simulations
Edward H. Rohr, John T. Nardini
https://arxiv.org/abs/2506.00168 …

A novel sensitivity analysis method for agent-based models stratifies \emph{in-silico} tumor spheroid simulations
Agent-based models (ABMs) are widely used in biology to understand how individual actions scale into emergent population behavior. Modelers employ sensitivity analysis (SA) algorithms to quantify input parameters' impact on model outputs, however, it is hard to perform SA for ABMs due to their computational and complex nature. In this work, we develop the \textbf{S}imulate, \textbf{S}ummarize, \textbf{R}educe, \textbf{C}luster, and \textbf{A}nalyze (SSRCA) methodology, a machine-learning based …

Rapid cardiac activation prediction for cardiac resynchronization therapy planning using geometric deep learning
Ehsan Naghavi, Haifeng Wang, Vahid Ziaei Rad, Julius Guccione, Ghassan Kassab, Vishnu Boddeti, Seungik Baek, Lik-Chuan Lee
https://arxiv.org/abs/2506.08987

Rapid cardiac activation prediction for cardiac resynchronization therapy planning using geometric deep learning
Cardiac resynchronization therapy (CRT) is a common intervention for patients with dyssynchronous heart failure, yet approximately one-third of recipients fail to respond due to suboptimal lead placement. Identifying optimal pacing sites remains challenging, largely due to patient-specific anatomical variability and the limitations of current individualized planning strategies. In a step towards constructing an in-silico approach to help address this issue, we develop two geometric deep learnin…

Aligning Protein Conformation Ensemble Generation with Physical Feedback
Jiarui Lu, Xiaoyin Chen, Stephen Zhewen Lu, Aur\'elie Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang
https://arxiv.org/abs/2505.24203

Aligning Protein Conformation Ensemble Generation with Physical Feedback
Protein dynamics play a crucial role in protein biological functions and properties, and their traditional study typically relies on time-consuming molecular dynamics (MD) simulations conducted in silico. Recent advances in generative modeling, particularly denoising diffusion models, have enabled efficient accurate protein structure prediction and conformation sampling by learning distributions over crystallographic structures. However, effectively integrating physical supervision into these d…

This https://arxiv.org/abs/2506.00168 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

A novel sensitivity analysis method for agent-based models stratifies \emph{in-silico} tumor spheroid simulations
Agent-based models (ABMs) are widely used in biology to understand how individual actions scale into emergent population behavior. Modelers employ sensitivity analysis (SA) algorithms to quantify input parameters' impact on model outputs, however, it is hard to perform SA for ABMs due to their computational and complex nature. In this work, we develop the \textbf{S}imulate, \textbf{S}ummarize, \textbf{R}educe, \textbf{C}luster, and \textbf{A}nalyze (SSRCA) methodology, a machine-learning based …

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Christoph Brunken, Olivier Peltre, Heloise Chomet, Lucien Walewski, Manus McAuliffe, Valentin Heyraud, Solal Attias, Martin Maarand, Yessine Khanfir, Edan Toledo, Fabio Falcioni, Marie Bluntzer, Silvia Acosta-Guti\'errez, Jules Tilly

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Machine Learning Interatomic Potentials (MLIP) are a novel in silico approach for molecular property prediction, creating an alternative to disrupt the accuracy/speed trade-off of empirical force fields and density functional theory (DFT). In this white paper, we present our MLIP library which was created with two core aims: (1) provide to industry experts without machine learning background a user-friendly and computationally efficient set of tools to experiment with MLIP models, (2) provide m…

In silico tool for identification of colorectal cancer from cell-free DNA biomarkers
Kartavya Mathur (Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, School of Biotechnology, Gautam Buddha University, Uttar Pradesh), Shipra Jain (Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi), Nisha Bajiya (Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi), Nishant Ku…

In silico tool for identification of colorectal cancer from cell-free DNA biomarkers
Colorectal cancer remains a major global health concern, with early detection being pivotal for improving patient outcomes. In this study, we leveraged high throughput methylation profiling of cellfree DNA to identify and validate diagnostic biomarkers for CRC. The GSE124600 study data were downloaded from the Gene Expression Omnibus, as the discovery cohort, comprising 142 CRC and 132 normal cfDNA methylation profiles obtained via MCTA seq. After preprocessing and filtering, 97,863 CpG sites w…

This https://arxiv.org/abs/2502.09775 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Building a virtual cell capable of accurately simulating cellular behaviors in silico has long been a dream in computational biology. We introduce CellFlux, an image-generative model that simulates cellular morphology changes induced by chemical and genetic perturbations using flow matching. Unlike prior methods, CellFlux models distribution-wise transformations from unperturbed to perturbed cell states, effectively distinguishing actual perturbation effects from experimental artifacts such as …

This https://arxiv.org/abs/2502.09775 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Building a virtual cell capable of accurately simulating cellular behaviors in silico has long been a dream in computational biology. We introduce CellFlux, an image-generative model that simulates cellular morphology changes induced by chemical and genetic perturbations using flow matching. Unlike prior methods, CellFlux models distribution-wise transformations from unperturbed to perturbed cell states, effectively distinguishing actual perturbation effects from experimental artifacts such as …