Results 221 to 230 of about 48,815 (295)

Genomic selection and association analyses for improving semen traits in Thai native roosters. [PDF]

Poult Sci
Juiputta J, Tuntiyasawasdikul V, Chankitisakul V, Boonkum W.   +3 more
europepmc   +1 more source

When Biology Meets Medicine: A Perspective on Foundation Models

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu, Yuxing Wang, Limin Zou, Jing Huang   +3 more
wiley   +1 more source

Faba bean populations already contain the inbreds needed for breeding. [PDF]

Theor Appl Genet
Laugel H, Link W.
europepmc   +1 more source

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

Advanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta, Madison Dautle, Shaoqiang Zhang, Yong Chen   +3 more
wiley   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

A breeding strategy for high-oleic acid content and high-yield peanut using Tag SNPs and genomic selection. [PDF]

BMC Plant Biol
Guo M, Miao J, Li Y, Yin J, Wang P, Luo F, Li S, Hu J, Liu W, Zhang T, Ren L, Deng L.   +11 more
europepmc   +1 more source

Spatially Informed Feature Selection and Machine Learning in Matrix‐Assisted Laser Desorption/Ionization Imaging for Cohort‐Scale Molecular Tissue Phenomics in Glioblastoma

Advanced Intelligent Discovery, EarlyView.
Matrix‐assisted laser desorption/ionization imaging‐based identification of reliable small molecule markers across heterogeneous glioblastoma cohorts is challenging with intensity‐only methods. We present spatially informed feature selection (SIFS), a spatially informed framework that prioritizes molecules consistently colocalizing with histopathology.
Shad A. Mohammed, Denis Abu Sammour, Dirk C. Hoffmann, Philipp Sievers, Tobias Bausbacher, Melissa Hahn, Yasemin Ucal, Iris Mildenberger, Christiane A. Opitz, Stefan Schmidt, Michael Platten, Oliver Wasenmüller, Wolfgang Wick, Andreas von Deimling, Tobias Kessler, Carsten Hopf   +15 more
wiley   +1 more source

A benchmarking of genomic selection models for predicting grain-yield related traits using haplotype-based and genome-wide association study-based markers in rice. [PDF]

Plant Genome
Hu X, Xian L, Cao G, Liu S, Wang D, Che Y, Huang W, Lin Y, He L.   +8 more
europepmc   +1 more source

An Attention‐Assisted Machine Learning System for Deep Microorganism Image Classification

Advanced Intelligent Systems, EarlyView.
An attention‐assisted DenseNet201 framework was developed for the classification of eight microorganism classes from microscopic images. The proposed model improved classification performance and achieved an accuracy of 87.38%. Advances in microbiology and environmental health fundamentally depend on precise and timely microorganism identification ...
Yujie Li, Ailing Gao, Guoxu Liu, Chunlei Chen, Yonghui Zhang, Sunkyoung Kang, M. Khosravi   +6 more
wiley   +1 more source

Optimising realised genetic gain with low forward predictive ability across cycles for cooking time and correlated traits in common bean based on multivariate genomic selection. [PDF]

Theor Appl Genet
Saradadevi R, Amongi W, Mukankusi C, Male AS, Rubyogo JC, Huttner E, Castro-Urrea FA, Li L, Siddique KHM, Cowling WA.   +9 more
europepmc   +1 more source