Ashwin Mukund
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View article: Self-Normalizing Multi-Omics Neural Network for Pan-Cancer Prognostication
Self-Normalizing Multi-Omics Neural Network for Pan-Cancer Prognostication Open
Prognostic markers such as overall survival (OS) and tertiary lymphoid structure (TLS) ratios, alongside diagnostic signatures like primary cancer-type classification, provide critical information for treatment selection, risk stratificati…
View article: Self-Normalizing Deep Learning for Enhanced Multi-Omics Data Analysis in Oncology
Self-Normalizing Deep Learning for Enhanced Multi-Omics Data Analysis in Oncology Open
Investigating multi-omics data is crucial for unraveling the complex biological mechanisms underlying cancer, thereby enabling effective strategies for prevention, early detection, diagnosis, and treatment. However, predicting patient outc…
View article: Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence
Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence Open
Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the most formidable challenges in oncology, characterized by its late detection and poor prognosis. Artificial intelligence (AI) and machine learning (ML) are emerging as pivotal tools…
View article: Self-Normalizing Foundation Model for Enhanced Multi-Omics Data Analysis in Oncology
Self-Normalizing Foundation Model for Enhanced Multi-Omics Data Analysis in Oncology Open
Multi-omics research has enhanced our understanding of cancer heterogeneity and progression. Investigating molecular data through multi-omics approaches is crucial for unraveling the complex biological mechanisms underlying cancer, thereby…