Tootfinder

(SimPhon Speech Test): A Data-Driven Method for In Silico Design and Validation of a Phonetically Balanced Speech Test
Stefan Bleeck
https://arxiv.org/abs/2506.11620

(SimPhon Speech Test): A Data-Driven Method for In Silico Design and Validation of a Phonetically Balanced Speech Test
Traditional audiometry often provides an incomplete characterization of the functional impact of hearing loss on speech understanding, particularly for supra-threshold deficits common in presbycusis. This motivates the development of more diagnostically specific speech perception tests. We introduce the Simulated Phoneme Speech Test (SimPhon Speech Test) methodology, a novel, multi-stage computational pipeline for the in silico design and validation of a phonetically balanced minimal-pair speec…

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…

An in-silico lung phantom to assess the performance of pulmonary artery segmentation using angiogram
Sunder Neelakantan, Tanmay Mukherjee, Emilio A. Mendiola, Kyle Myers, Reza Avazmohammadi
https://arxiv.org/abs/2507.01867

An in-silico lung phantom to assess the performance of pulmonary artery segmentation using angiogram
Pulmonary hypertension (PH) can lead to significant vascular remodeling, resulting in altered pulmonary blood flow. Estimating the patient-specific contributions of each remodeling event is necessary to optimize and individualize clinical intervention strategies. In-silico modeling has emerged as a powerful tool to simulate pulmonary hemodynamics, and one of the primary requirements for robust in-silico modeling is an accurate representation of the pulmonary vasculature structure. Computed tomo…

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…

In situ fine-tuning of in silico trained Optical Neural Networks
Gianluca Kosmella, Ripalta Stabile, Jaron Sanders
https://arxiv.org/abs/2506.22122 https:/…

In situ fine-tuning of in silico trained Optical Neural Networks
Optical Neural Networks (ONNs) promise significant advantages over traditional electronic neural networks, including ultrafast computation, high bandwidth, and low energy consumption, by leveraging the intrinsic capabilities of photonics. However, training ONNs poses unique challenges, notably the reliance on simplified in silico models whose trained parameters must subsequently be mapped to physical hardware. This process often introduces inaccuracies due to discrepancies between the idealized…

In silico evaluation of pramlintide dosing algorithms in artificial pancreas systems
Borja Pons Torres, Iv\'an Sala Mira, Clara Furi\'o-Novejarque, Ricardo Sanz, Pedro Garc\'ia, Jos\'e-Luis D\'iez, Jorge Bondia
https://arxiv.org/abs/2506.17790

In silico evaluation of pramlintide dosing algorithms in artificial pancreas systems
Pramlintide's capability to delay gastric emptying has motivated its use in artificial pancreas systems, accompanying insulin as a control action. Due to the scarcity of pramlintide simulation models in the literature, in silico testing of insulin-plus-pramlintide strategies is not widely used. This work incorporates a recent pramlintide pharmacokinetics/pharmacodynamics model into the T1DM UVA/Padova simulator to adjust and validate four insulin-plus-pramlintide control algorithms. The proposa…

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…

Mix-Geneformer: Unified Representation Learning for Human and Mouse scRNA-seq Data
Yuki Nishio, Takayoshi Yamashita, Keita Ito, Tsubasa Hirakawa, Hironobu Fujiyoshi
https://arxiv.org/abs/2507.07454

Mix-Geneformer: Unified Representation Learning for Human and Mouse scRNA-seq Data
Single-cell RNA sequencing (scRNA-seq) enables single-cell transcriptomic profiling, revealing cellular heterogeneity and rare populations. Recent deep learning models like Geneformer and Mouse-Geneformer perform well on tasks such as cell-type classification and in silico perturbation. However, their species-specific design limits cross-species generalization and translational applications, which are crucial for advancing translational research and drug discovery. We present Mix-Geneformer, a …

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…

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 …

Replaced article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- In silico discovery of representational relationships across visual cortex
Alessandro T. Gifford, Maya A. Jastrz\k{e}bowska, Johannes J. D. Singer, Radoslaw M. Cichy

Development and in silico imaging trial evaluation of a deep-learning-based transmission-less attenuation compensation method for DaT SPECT
Zitong Yu, Md Ashequr Rahman, Zekun Li, Chunwei Ying, Hongyu An, Tammie L. S. Benzinger, Richard Laforest, Jingqin Luo, Scott A. Norris, Abhinav K. Jha
https://arxiv.org/abs/2506.20781

Development and in silico imaging trial evaluation of a deep-learning-based transmission-less attenuation compensation method for DaT SPECT
Quantitative measures of dopamine transporter (DaT) uptake in caudate, putamen, and globus pallidus derived from DaT-single-photon emission computed tomography (SPECT) images are being investigated as biomarkers to diagnose, assess disease status, and track the progression of Parkinsonism. Reliable quantification from DaT-SPECT images requires performing attenuation compensation (AC), typically with a separate X-ray CT scan. Such CT-based AC (CTAC) has multiple challenges, a key one being the n…

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 …

Diversity by Design: Addressing Mode Collapse Improves scRNA-seq Perturbation Modeling on Well-Calibrated Metrics
Gabriel M. Mejia, Henry E. Miller, Francis J. A. Leblanc, Bo Wang, Brendan Swain, Lucas Paulo de Lima Camillo
https://arxiv.org/abs/2506.22641

Diversity by Design: Addressing Mode Collapse Improves scRNA-seq Perturbation Modeling on Well-Calibrated Metrics
Recent benchmarks reveal that models for single-cell perturbation response are often outperformed by simply predicting the dataset mean. We trace this anomaly to a metric artifact: control-referenced deltas and unweighted error metrics reward mode collapse whenever the control is biased or the biological signal is sparse. Large-scale \textit{in silico} simulations and analysis of two real-world perturbation datasets confirm that shared reference shifts, not genuine biological change, drives hig…