Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine

I Zafar, S Anwar, W Yousaf, FU Nisa, T Kausar… - … Signal Processing and …, 2023 - Elsevier
The advancements in genomics and biomedical technologies have generated vast amounts
of biological and physiological data, which present opportunities for understanding human …

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks

M Nourbakhsh, K Degn, A Saksager… - Briefings in …, 2024 - academic.oup.com
The vast amount of available sequencing data allows the scientific community to explore
different genetic alterations that may drive cancer or favor cancer progression. Software …

Multi-omics integration analysis of GPCRs in pan-cancer to uncover inter-omics relationships and potential driver genes

S Li, X Chen, J Chen, B Wu, J Liu, Y Guo, M Li… - Computers in Biology …, 2023 - Elsevier
G protein-coupled receptors (GPCRs) are the largest drug target family. Unfortunately,
applications of GPCRs in cancer therapy are scarce due to very limited knowledge …

[HTML][HTML] A Web-Based Calculator to Predict Early Death Among Patients With Bone Metastasis Using Machine Learning Techniques: Development and Validation …

M Lei, B Wu, Z Zhang, Y Qin, X Cao, Y Cao… - Journal of Medical …, 2023 - jmir.org
Background Patients with bone metastasis often experience a significantly limited survival
time, and a life expectancy of< 3 months is generally regarded as a contraindication for …

A novel heterophilic graph diffusion convolutional network for identifying cancer driver genes

T Zhang, SW Zhang, MY Xie, Y Li - Briefings in Bioinformatics, 2023 - academic.oup.com
Identifying cancer driver genes plays a curial role in the development of precision oncology
and cancer therapeutics. Although a plethora of methods have been developed to tackle this …

SMG: self-supervised masked graph learning for cancer gene identification

Y Cui, Z Wang, X Wang, Y Zhang… - Briefings in …, 2023 - academic.oup.com
Cancer genomics is dedicated to elucidating the genes and pathways that contribute to
cancer progression and development. Identifying cancer genes (CGs) associated with the …

The Cancermuts software package for the prioritization of missense cancer variants: a case study of AMBRA1 in melanoma

M Tiberti, L Di Leo, MV Vistesen, RS Kuhre… - Cell Death & …, 2022 - nature.com
Cancer genomics and cancer mutation databases have made an available wealth of
information about missense mutations found in cancer patient samples. Contextualizing by …

Multi-Network Graph Contrastive Learning for cancer driver gene identification

W Peng, Z Zhou, W Dai, N Yu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Identifying driver genes contributing to the occurrence and development of cancers plays a
critical role in cancer research and treatment. Some recent computational approaches …

InDEP: an interpretable machine learning approach to predict cancer driver genes from multi-omics data

H Yang, Y Liu, Y Yang, D Li… - Briefings in Bioinformatics, 2023 - academic.oup.com
Cancer driver genes are critical in driving tumor cell growth, and precisely identifying these
genes is crucial in advancing our understanding of cancer pathogenesis and developing …

Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction

X Ma, Z Li, Z Du, Y Xu, Y Chen, L Zhuo, X Fu… - Computers in Biology and …, 2024 - Elsevier
Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment
and has recently received great attention from researchers. However, the high complexity …