🧬 Category

q-bio

4605 papers

🧬 biology

Single-Cell Foundation Models in Biotechnology: A Scoping Review and Bibliometric Analysis of Multimodal AI for Cell-State, Perturbation, and Biomarker Prediction

This scoping review and bibliometric analysis of 1,042 studies reveals that while single-cell foundation models are rapidly expanding in biotechnology, the field suffers from inconsistent reproducibility, poor benchmark rigor, and a lack of open resources, with simple baselines often proving competitive against complex architectures.

Behin Omidi, Mohammmad Mahdi Hemati Aalm, Arshia Farmahini Farahani, Amir Reza Hassan Poor Barkadehi2026-06-30
🧬 biology

Identification of Differentially Expressed Genes Related to UFMylation Modifications in Periodontitis: Integrated Insights from Transcriptomics and Clinical Experiments

This study integrates transcriptomic analysis, machine learning, and clinical validation to identify C4A, GANAB, and TUBA4A as key UFMylation-related diagnostic biomarkers for periodontitis, elucidating their roles in immune and metabolic pathways and proposing potential therapeutic targets.

Changqing Mu, Juan Liu, Kaining Liu, Xiaofeng Huang2026-06-30
🧬 biology

Timing of menstrual cups to prevent transition from optimal to not optimal vaginal microbiome community state type: Results from a 6.5-year prospective observational cohort

In a 6.5-year prospective cohort study of Kenyan secondary schoolgirls, menstrual cup use significantly reduced the risk of transitioning from an optimal *L. crispatus*-dominant vaginal microbiome to a non-optimal state, suggesting that providing cups before sexual debut is crucial for maintaining vaginal health.

Supriya Mehta2026-06-30
🧬 biology

Ethnicity-Stratified Inflammatory Phenotyping Using GMM Clustering and XGBoost Regression: A Cross-Ethnicity Nearest-Neighbour Framework for Precision CRP Estimation in NHANES 2017–2023

This study utilizes a two-stage machine learning framework combining GMM clustering and XGBoost regression on NHANES 2017–2023 data to reveal that macronutrient-driven CRP responses vary significantly by metabolic phenotype and ethnicity, highlighting a "Fibre-Resilience Paradox" in Non-Hispanic Black populations and advocating for a stratified precision nutrition approach.

Anshul Iyer, Vania Sharma2026-06-30
🧬 biology

EEG Signal Variance as a Biomarker for Ictal State Classification: Subject-Level Validation of a Random Forest Classifier using the University of Bonn Dataset

This study demonstrates that EEG signal variance is a robust biomarker for distinguishing ictal states and validates that a Random Forest classifier, trained on the University of Bonn dataset with strict subject-level data partitioning to prevent leakage, achieves high accuracy (96.2%) and sensitivity (98.3%) in seizure detection.

Md. Ahasanul Al Hasib Ayon2026-06-30
🧬 biology

Differential Signaling in Adaptive Immunity: Deciphering the Mechanism to Identify the Physical and Evolutionary Bases of Non‑Self

This paper proposes a mechanophysical framework identifying differential mechanical displacement as the fundamental variable enabling adaptive immune receptors to discriminate self from non-self by measuring abrupt deviations in physical force fields at amino acid resolution, thereby unifying humoral and cellular recognition through a progressive decision cascade that resolves long-standing paradoxes in immune specificity.

Yasir Arafat Maassoom2026-06-30
🧬 biology

Fungal diversity in forest's soil: Moderate-term post-thinning effect of a Mediterranean mature Pinus halepensis forest.

A study of a Mediterranean *Pinus halepensis* forest in Israel reveals that while moderate thinning slightly reduces total fungal diversity, complete tree removal significantly alters soil characteristics and shifts fungal communities from symbiotic, pine-associated species to saprotrophic and pathogenic taxa typical of non-forested areas.

Segula Masaphy, Noam Levi, Limor Zabari, Ezra Orlofsky2026-06-30
🧬 biology

Integrative single-cell and bulk transcriptomic analysis with Shennong machine learning reveals the landscape of calmodulin associated prognostic genes in breast cancer

This study integrates single-cell and bulk transcriptomic analyses with Shennong machine learning to identify an eight-gene calmodulin-related prognostic signature in breast cancer, revealing its association with epithelial cell dynamics, specific signaling pathways, and potential therapeutic targets.

Jian Chen, Wenhui Lai, Can Yang2026-06-30