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Quantitative Biology

arXiv preprints from January 1, 2026 through September 5, 2026 — 15:07:42 EST

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Posted in q-bio.QM · 2026-01-02 · Biraja Ghoshal

Quantum Simulation of Protein Fragment Electronic Structure Using Moment-based Adaptive Variational Quantum Algorithms

Background: Understanding electronic interactions in protein active sites is fundamental to drug discovery and enzyme engineering, but remains computationally challenging due to exponential scaling of quantum mechanical calculations. Results: We present a quantum-classical hybrid framework for simulating protein fragment electronic...

💬 0 commentsarXiv:2601.00656v1PDF
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Posted in q-bio.QM · 2026-01-02 · Vasiliki Tsampazi, Nicholas M. Glykos

Quantifying the uncertainty of molecular dynamics simulations : Good-Turing statistics revisited

We have previously shown that Good-Turing statistics can be applied to molecular dynamics trajectories to estimate the probability of observing completely new (thus far unobserved) biomolecular structures, and showed that the method is stable, dependable and its predictions verifiable. The major problem with that initial algorithm was...

💬 0 commentsarXiv:2601.00618v1PDF
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Posted in q-bio.OT · 2026-01-02 · Simon Lebech Cichosz, Stine Hangaard, Thomas Kronborg, Peter Vestergaard, Morten Hasselstrøm Jensen

Personalized Forecasting of Glycemic Control in Type 1 and 2 Diabetes Using Foundational AI and Machine Learning Models

Background: Accurate week-ahead forecasts of continuous glucose monitoring (CGM) derived metrics could enable proactive diabetes management, but relative performance of modern tabular learning approaches is incompletely defined. Methods: We trained and internally validated four regression models (CatBoost, XGBoost, AutoGluon,...

💬 0 commentsarXiv:2601.00613v1PDF
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Posted in q-bio.QM · 2026-01-02 · Clara Bender, Line Davidsen, Søren Schou Olesen, Simon Lebech Cichosz

Peak-Nadir Encoding for Efficient CGM Data Compression and High-Fidelity Reconstruction

Aim/background: Continuous glucose monitoring (CGM) generates dense time-series data, posing challenges for efficient storage, transmission, and analysis. This study evaluates novel encoding strategies that reduce CGM profiles to a compact set of landmark points while maintaining fidelity in reconstructed signals and derived glycemic...

💬 0 commentsarXiv:2601.00608v1PDF
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Posted in q-bio.PE · 2026-01-02 · Wenjie Zhang, Yusheng Li, Qin Li, Guojun Huang, Minyu Feng

Modeling Epidemic Dynamics of Mutant Strains with Evolutionary Game-based Vaccination Behavior

The outbreak of mutant strains and vaccination behaviors have been the focus of recent epidemiological research, but most existing epidemic models failed to simultaneously capture viral mutation and consider the complexity and behavioral dynamics of vaccination. To address this gap, we develop an extended SIRS model that distinguishes...

💬 0 commentsarXiv:2601.00757v1PDF
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Posted in q-bio.QM · 2026-01-02 · Xujun Che, Xiuxia Du, Depeng Xu

Comparative Analysis of Formula and Structure Prediction from Tandem Mass Spectra

Liquid chromatography mass spectrometry (LC-MS)-based metabolomics and exposomics aim to measure detectable small molecules in biological samples. The results facilitate hypothesis-generating discovery of metabolic changes and disease mechanisms and provide information about environmental exposures and their effects on human health....

💬 0 commentsarXiv:2601.00941v1PDF
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Posted in q-bio.NC · 2026-01-02 · Mohadeseh Shafiei Kafraj, Dmitry Krotov, Peter E. Latham

A Biologically Plausible Dense Associative Memory with Exponential Capacity

Krotov and Hopfield (2021) proposed a biologically plausible two-layer associative memory network with memory storage capacity exponential in the number of visible neurons. However, the capacity was only linear in the number of hidden neurons. This limitation arose from the choice of nonlinearity between the visible and hidden units,...

💬 0 commentsarXiv:2601.00984v2PDF
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Posted in q-bio.PE · 2026-01-02 · Deeptanshu Pandey, Dwipanjan Sanyal, Vladimir N. Uversky, Daniel C. Zielinski, Sourav Chowdhury

Evolutionary and Structural Constraints Define a Mutation-Resistant Catalytic Core in E. coli Serine Hydroxy methyltransferase (SHMT)

Serine hydroxymethyltransferase is an essential enzyme in the Escherichia coli folate pathway, yet it has not been adopted as an antibacterial target, unlike DHFR, DHPS, or thymidylate synthase. To investigate this discrepancy, we applied a multi-scale computational framework that integrates large-scale sequence analysis of 1000...

💬 0 commentsarXiv:2601.00769v1PDF
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Posted in q-bio.GN · 2026-01-01 · Gang Qu, Guanghao Li, Zhongming Zhao

MethConvTransformer: A Deep Learning Framework for Cross-Tissue Alzheimer's Disease Detection

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by progressive cognitive decline and widespread epigenetic dysregulation in the brain. DNA methylation, as a stable yet dynamic epigenetic modification, holds promise as a noninvasive biomarker for early AD detection. However, methylation signatures...

💬 0 commentsarXiv:2601.00143v1PDF
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Posted in q-bio.QM · 2026-01-01 · Ali Anaissi, Seid Miad Zandavi, Weidong Huang, Junaid Akram, Basem Suleiman, Ali Braytee, Jie Hua

Benchmarking Preprocessing and Integration Methods in Single-Cell Genomics

Single-cell data analysis has the potential to revolutionize personalized medicine by characterizing disease-associated molecular changes at the single-cell level. Advanced single-cell multimodal assays can now simultaneously measure various molecules (e.g., DNA, RNA, Protein) across hundreds of thousands of individual cells,...

💬 0 commentsarXiv:2601.00277v1PDF
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Posted in q-bio.CB · 2026-01-01 · Samuel W. S. Johnson, Ruth E. Baker, Philip K. Maini

The spontaneous emergence of leaders and followers in a mathematical model of cranial neural crest cell migration

Many agent-based mathematical models of cranial neural crest cell (CNCC) migration impose a binary phenotypic partition of cells into either leaders or followers. In such models, the movement of leader cells at the front of collectives is guided by local chemoattractant gradients, while follower cells behind leaders move according to...

💬 0 commentsarXiv:2601.00374v2PDF
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Posted in q-bio.NC · 2026-01-01 · Jacek Małecki, Alexander Mathiesen-Ohman

Rogue Variable Theory: A Quantum-Compatible Cognition Framework with a Rosetta Stone Alignment Algorithm

Many of the most consequential dynamics in human cognition occur \emph{before} events become explicit: before decisions are finalized, emotions are labeled, or meanings stabilize into narrative form. These pre-event states are characterized by ambiguity, contextual tension, and competing latent interpretations. Rogue Variable Theory...

💬 0 commentsarXiv:2601.00466v1PDF