Exploring the impact of bioactive peptides from fermented Milk proteins: A review with emphasis on health implications and artificial intelligence integration DOI
Hosam M. Habib, Rania Ismail, Mahmoud Agami

и другие.

Food Chemistry, Год журнала: 2025, Номер unknown, С. 144047 - 144047

Опубликована: Март 1, 2025

Язык: Английский

Mass-spectrometry-based proteomics: from single cells to clinical applications DOI
Tiannan Guo, Judith A. Steen, Matthias Mann

и другие.

Nature, Год журнала: 2025, Номер 638(8052), С. 901 - 911

Опубликована: Фев. 26, 2025

Язык: Английский

Процитировано

2
Machine learning to dissect perturbations in complex cellular systems DOI Creative Commons
Pablo Monfort-Lanzas,

Katja Rungger,

Leonie Madersbacher

и другие.

Computational and Structural Biotechnology Journal, Год журнала: 2025, Номер 27, С. 832 - 842

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
Unifying Genetic and Chemical Perturbagen Representation through a Hybrid Deep Learning Framework DOI Open Access
Yiming Li, Jun Zhu, Linjing Liu

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Фев. 7, 2025

The integration of genetic and chemical perturbations has driven transformative advances in elucidating cellular mechanisms accelerating drug discovery. However, the lack a unified representation for diverse perturbagen types limits comprehensive analysis joint modeling multi-domain perturbation agents (molecular cause space) their resulting phenotypes (phenotypic effect spaces). Here, we present UniPert, hybrid deep learning framework that encodes perturbagens into shared semantic space. UniPert employs tailored encoders to address inherent molecular-scale differences across leverages contrastive with experiment-driven compound-target interactions bridge these domains. Extensive experiments validate UniPert’s versatility application. generated representations effectively capture hierarchical pharmacological relationships perturbagens, facilitating annotations understudied targets compounds. can be plugged advanced frameworks enhance performance both outcome prediction tasks. Notably, paves way cross-domain modeling, driving novel genetic-to-chemical transfer paradigm, boosting context-specific silico screening efficiency development personalized therapies.

Язык: Английский

Процитировано

0
Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review DOI
Avirup Guha, Viraj Shah,

Tarek Nahle

и другие.

Current Cardiology Reports, Год журнала: 2025, Номер 27(1)

Опубликована: Фев. 19, 2025

Язык: Английский

Процитировано

0
Grow AI virtual cells: three data pillars and closed-loop learning DOI Creative Commons
Liujia Qian, Zhen Dong, Tiannan Guo

и другие.

Cell Research, Год журнала: 2025, Номер unknown

Опубликована: Март 25, 2025

Язык: Английский

Процитировано

0
Exploring the impact of bioactive peptides from fermented Milk proteins: A review with emphasis on health implications and artificial intelligence integration DOI
Hosam M. Habib, Rania Ismail, Mahmoud Agami

и другие.

Food Chemistry, Год журнала: 2025, Номер unknown, С. 144047 - 144047

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0