Results 61 to 70 of about 53,428 (254)

Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis

BMC Medical Informatics and Decision Making, 2020
Background Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival lengths, indicating a need to identify prognostic biomarkers for personalized diagnosis and treatment ...
Li Tong, Jonathan Mitchel, Kevin Chatlin, May D. Wang   +3 more
doaj   +1 more source

Long‐Term Follow‐Up of Chemotherapy‐Associated Biological Aging in Women With Early Breast Cancer

Aging and Cancer, EarlyView.
Women threated with adjuvant chemotherapy for early breast cancer have sustained long‐term increase in p16INK4a,, a robust marker of cell senescence, suggesting a chemotherapy‐associated age acceleration. p16INK4a as well as other biomarkers may identify patients at greatest risk for senescence‐related diseases of aging.
Hyman B. Muss, Alexis C. Wardell, Allison M. Deal, Kirsten A. Nyrop, Allison Ross, Yara G. Abdou, Jeffrey D. Aldrich, Lisa A. Cary, E. Claire Dees, Katherine E. Reeder‐Hayes, Emily M. Ray, Natalia Mitin, Denis Tsygankov   +12 more
wiley   +1 more source

DBCL-DFNet: Dual-Branch Contrastive Learning for Multi-Omics Dynamic Fusion

Entropy
Multimodal omics data portray biological processes across molecular layers, yet their heterogeneity and high dimensionality hinder a unified representation. Existing integrative approaches either focus on local feature interactions or adopt static fusion,
Yun Dang, Xiaoran Yan, Li Zhou, Dongxi Li   +3 more
doaj   +1 more source

Methods for the integration of multi-omics data: mathematical aspects [PDF]

BMC Bioinformatics, 2016
Methods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number of biological samples make the analysis of multi-omics datasets a ...
Matteo Bersanelli, Ettore Mosca, Daniel Remondini, Enrico Giampieri, Claudia Sala, Gastone C. Castellani, Luciano Milanesi   +6 more
openaire   +4 more sources

Integrated PANoptosis Profiling Identifies Immunosuppressive Subtypes and a Prognostic Signature With Functional Validation of MLKL in Glioblastoma

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective The prognosis of glioblastoma (GBM) remains highly unfavorable, largely due to high tumor heterogeneity and an immunosuppressive microenvironment. However, the functional role of PANoptosis in this context is poorly understood. Methods Patients were stratified via K‐means clustering. A risk score model was constructed using prognosis‐
Langfei Tian, Minghui Zhao, Yuanbo Hu, Kaiyue Wang, Haiguang Liu, Zetong Bai, Kebin Zheng   +6 more
wiley   +1 more source

Engineering Nanoplatforms for Alzheimer's Disease Detection via Biomolecular Corona Proteomic and Lipidomic Profiling

Advanced Healthcare Materials, EarlyView.
Engineered nanoparticles capture disease‐specific biomolecular coronas that uncover hidden molecular features of Alzheimer's disease. Combined proteomic and lipidomic analyses reveal a characteristic shift in ribosomal machinery and energy metabolism, generating a multiomic fingerprint that supports accurate disease detection and opens new ...
Antonietta Greco, Roberto Baretta, Andrea Di Fonzo, Andrea Armirotti, Stefano Cinti, Mara Mirasoli, Marco Frasconi, Claudia Corbo   +7 more
wiley   +1 more source

scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration

Genome Biology
Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integration of multi-omics data presents challenges.
Xiuhui Yang, Koren K. Mann, Hao Wu, Jun Ding   +3 more
doaj   +1 more source

A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction

BMC Bioinformatics, 2023
Background Recent years have seen a surge of novel neural network architectures for the integration of multi-omics data for prediction. Most of the architectures include either encoders alone or encoders and decoders, i.e., autoencoders of various sorts,
Tony Hauptmann, Stefan Kramer
doaj   +1 more source

Intelligent Orthopedics: Machine Learning in Diagnosis of Bone Disease, Implants, and Bone Health Monitoring

Advanced Healthcare Materials, EarlyView.
Efficient recovery from traumatic or degenerative diseases is a great challenge, even after all the advancements in bone and cartilage regeneration. Machine learning (ML) algorithms have presented opportunities to enhance these aspects by accurately analyzing imaging data.
Maryam Kamaei, Hesam Mohammadi, Shamim Golafshan, Mojtaba Alehosseini, Mahsa Mohammadzadeh, Atefeh Golshirazi, Nasim Golafshan, Tatiane Eufrásio‐da‐Silva, Alireza Dolatshahi‐Pirouz, Gorka Orive   +9 more
wiley   +1 more source

Multi-omics strategies for biomarker discovery and application in personalized oncology

Molecular Biomedicine
Multi-omics strategies, integrating genomics, transcriptomics, proteomics, and metabolomics, have revolutionized biomarker discovery and enabled novel applications in personalized oncology.
Ziming Jiang, Haoxuan Zhang, Yibo Gao, Yingli Sun   +3 more
doaj   +1 more source