Results 171 to 180 of about 51,529 (289)

Microbial Community Shifts and Plant Performance Improvements Driven by Bacillus firmus in Pampa Agroecosystems. [PDF]

Environ Microbiol Rep
Fagundes D, Costa LMS, Cagliari A, de Vargas DP, Rieger A.   +4 more
europepmc   +1 more source

Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics

Advanced Intelligent Discovery, EarlyView.
A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha, Jesus Gil, Pedro J. Ballester   +2 more
wiley   +1 more source

A pilot study revealed the gut microbiota based on 16S rRNA metagenomics in gestational diabetes. [PDF]

BMC Genom Data
Kousar R, Latif S, Zahoor M, Tabassum S.
europepmc   +1 more source

Clustering in an Object-Oriented Environment

This paper describes the incorporation of seven stand-alone clustering programs into S-PLUS, where they can now be used in a much more flexible way.
Mia Hubert, Peter Rousseeuw, Anja Struyf
core  

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Urban forest restoration enhances soil microbial functional potential and functional insurance via shifts in β-diversity. [PDF]

Environ Microbiome
Brachmann S, Kiesewetter KN, Liddicoat C, Wallace KJ, Breed MF, Eisenhauer N, Barnes AD.   +6 more
europepmc   +1 more source

Adversarial Erasing Enhanced Multiple Instance Learning (siMILe): Discriminative Identification of Oligomeric Protein Structures in Single Molecule Localization Microscopy

Advanced Intelligent Systems, EarlyView.
Hallgrimson et al. introduce a machine learning algorithm, siMILe, that takes features of single‐molecule localization microscopy localization clusters (e.g., size and sphericity) and finds the clusters that are associated with certain cell conditions (such as differential protein expression or drug treatment).
Christian Hallgrimson, Y. Lydia Li, Claire A. Shou, Ben Cardoen, John Lim, Timothy H. Wong, Ismail M. Khater, Ivan Robert Nabi, Ghassan Hamarneh   +8 more
wiley   +1 more source

Lactic acid bacteria and endogenous ethanol mediate proton pump inhibitor-associated MASLD: a multicohort cross-sectional mediation analysis. [PDF]

Gut Microbes
Davids M, Herrema H, K Groen A, Galenkamp H, Zwinderman A, Palmu J, Havulinna A, Niiranen T, Knight R, Acherman Y, Franken R, Verheij J, Dukas M, Dutch Liver Pathology Panel, Bajaj J, Llorente C, Schnabl B, Nieuwdorp M, Meijnikman A.   +18 more
europepmc   +1 more source

OntoLogX: Ontology‐Guided Knowledge Graph Extraction From Cybersecurity Logs With Large Language Models

Advanced Intelligent Systems, EarlyView.
OntoLogX is an autonomous AI agent that uses large language models to transform unstructured cyber security logs into ontology grounded knowledge graphs. By integrating retrieval augmented generation, iterative correction, and a light‐weight log ontology, OntoLogX produces semantically consistent intelligence that links raw log events to MITRE ATT & CK
Luca Cotti, Idilio Drago, Anisa Rula, Devis Bianchini, Federico Cerutti   +4 more
wiley   +1 more source

Resource‐Aware Contrastive Scattering Meta‐Learning for Efficient Few‐Shot Acoustic Anomaly Detection

Advanced Intelligent Systems, EarlyView.
This paper introduces a resource‐aware Contrastive Scattering Meta‐Learning (CSML) framework for acoustic anomaly detection. By leveraging training‐free wavelet scattering and metric‐based meta‐learning, the model achieves competitive performance with only 50 K learnable parameters—a 98% reduction compared to state‐of‐the‐art frameworks—enabling ...
Rami Zewail, Bassem Mokhtar
wiley   +1 more source