Biomedical data are becoming increasingly multimodal and thereby capture the underlying complex relationships among biological processes. Deep learning (DL)-based data fusion …
M Picard, MP Scott-Boyer, A Bodein, O Périn… - Computational and …, 2021 - Elsevier
Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological …
D Leng, L Zheng, Y Wen, Y Zhang, L Wu, J Wang… - Genome biology, 2022 - Springer
Background A fused method using a combination of multi-omics data enables a comprehensive study of complex biological processes and highlights the interrelationship of …
Machine learning approaches are powerful techniques commonly employed for developing cancer prediction models using associated gene expression and mutation data. This …
T Athaya, RC Ripan, X Li, H Hu - Briefings in Bioinformatics, 2023 - academic.oup.com
Integrating single-cell multi-omics data is a challenging task that has led to new insights into complex cellular systems. Various computational methods have been proposed to effectively …
X Li, L Yang, X Jiao - Academic Radiology, 2023 - Elsevier
Rationale and Objectives Accurate identification of axillary lymph node (ALN) status in breast cancer patients is important for determining treatment options and avoiding axillary …
S Sushanki, AK Bhandari, AK Singh - Archives of Computational Methods …, 2024 - Springer
Breast cancer is a kind of cancer that develops and propagates from tissues of the breast and slowly transcends the whole body, this type of tumor is found in both sexes. Early …
Medical discoveries mainly depend on the capability to process and analyze biological datasets, which inundate the scientific community and are still expanding as the cost of next …
Abstracts: Deep learning, a branch of artificial intelligence, excavates massive data sets for patterns and predictions using a machine learning method known as artificial neural …