Results 211 to 220 of about 4,772,239 (326)

Conditional Variational AutoEncoder to Predict Suitable Conditions for Hydrogenation Reactions. [PDF]

Molecules
Mazitov D, Gimadiev T, Poyezzhayeva A, Afonina V, Madzhidov T.   +4 more
europepmc   +1 more source

Beyond Prediction: Data, Baselines, Explanation, and Causation in Machine Learning for Food Insecurity

Food Safety and Health, EarlyView.
The gains from machine learning in nowcasting and forecasting food insecurity are still small and limited and cannot be observed in all countries. This review summarizes the public resources for data, corrects common misconceptions about model requirements, and establishes baseline requirements, explanations, causal inference, and equity in operational
Shabnam Mehboob, Azman Abdullah, Gunesh Kumar, Samreen Memon, Naila Afghan   +4 more
wiley   +1 more source

DCVBin: a novel binning method for single-sample metagenomes based on DNA language model and variational autoencoder. [PDF]

Brief Bioinform
Wang J, Liu Y, Liu F, Hou T, Chen S, Liu S, Liu Y.   +6 more
europepmc   +1 more source

Citrus Essential Oils in Food Safety and Health: Advances in AI‐Assisted Authentication, Antimicrobial Applications, Sustainable Extraction, and Regulatory Translation

Food Safety and Health, EarlyView.
Citrus essential oils combine sustainable extraction, AI‐assisted authentication, multi‐omics profiling, antimicrobial activity, and regulatory safety to enhance food quality, detect adulteration, and promote consumer health, and support intelligent, sustainable food systems through innovative industrial applications and circular bioeconomy strategies.
Md. Hassan Bin Nabi, Liza Akter, Yusuf Chowdhury, Mohima Akther Any, Nazmul Islam, Erni Sofia Murtini, Nurul Huda, Wahidu Zzaman   +7 more
wiley   +1 more source

scZiva: imputation method for single-cell RNA-seq data with zero-inflated variational autoencoder. [PDF]

BMC Bioinformatics
Vo LT, Le VV, Ha QT, Nguyen AQ.
europepmc   +1 more source

Analyzing Multimodal Integration in the Variational Autoencoder from an Information-Theoretic Perspective

ANALYZING MULTIMODAL INTEGRATION IN THE VARIATIONAL AUTOENCODER FROM AN INFORMATION-THEORETIC PERSPECTIVE Analyzing Multimodal Integration in the Variational Autoencoder from an Information-Theoretic Perspective / Langer, Carlotta (Rights reserved) (-
Langer, Carlotta, Porohovoj, Ilja, Georgie, Yasmin Kim, Hafner, Verena Vanessa, Ay, Nihat   +4 more
core  

From Physiology to Bioheat Simulation: A Data‐Driven Framework for Core Temperature Initialization

Heat Transfer, EarlyView.
ABSTRACT Accurate initialization of core body temperature is a critical yet often overlooked component of bioheat transfer models, particularly when such models are applied to heterogeneous populations and real‐world environmental conditions. Conventional bioheat simulations typically rely on fixed or idealized core body temperature values derived from
David S. Rodríguez, Ana S. Acosta, Carol D. Vergara, Daniela Martínez, Sebastián Bermúdez, Cristian F. Rodríguez, Juan C. Cruz   +6 more
wiley   +1 more source

Uncovering Heart Rate Response Patterns to Threat Pictures Through Deep Latent Representation Learning with a Variational Autoencoder. [PDF]

Psychophysiology
Moratti S, García SFC.
europepmc   +1 more source

Impact of Single‐Cell RNA Reference Selection for the Deconvolution of Breast Cancer Spatial Transcriptomics Datasets

International Journal of Cancer, EarlyView.
ABSTRACT Spot‐based spatial transcriptomics (ST) allows for unbiased gene expression analysis within tissue architecture, overcoming the limitations of single‐cell RNA sequencing (scRNA‐seq) by preserving spatial context. However, the high spatial resolution in ST leads to cellular heterogeneity within spots, requiring computational deconvolution to ...
Stefan Altendorfer, Scott J. Walker, Carsten O. Daub   +2 more
wiley   +1 more source

Machine learning to classify left ventricular hypertrophy using electrocardiographic feature extraction by variational autoencoder. [PDF]

Heart Rhythm O2
Gupta A, Harvey CJ, DeBauge A, Shomaji S, Yao Z, Lee Y, Noheria A.   +6 more
europepmc   +1 more source