[HTML][HTML] Integration strategies of multi-omics data for machine learning analysis
Increased availability of high-throughput technologies has generated an ever-growing
number of omics data that seek to portray many different but complementary biological …
number of omics data that seek to portray many different but complementary biological …
[HTML][HTML] Machine learning: a new prospect in multi-omics data analysis of cancer
B Arjmand, SK Hamidpour, A Tayanloo-Beik… - Frontiers in …, 2022 - frontiersin.org
Cancer is defined as a large group of diseases that is associated with abnormal cell growth,
uncontrollable cell division, and may tend to impinge on other tissues of the body by different …
uncontrollable cell division, and may tend to impinge on other tissues of the body by different …
[HTML][HTML] Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials
Y Zheng, Y Liu, J Yang, L Dong, R Zhang, S Tian… - Nature …, 2024 - nature.com
Abstract Characterization and integration of the genome, epigenome, transcriptome,
proteome and metabolome of different datasets is difficult owing to a lack of ground truth …
proteome and metabolome of different datasets is difficult owing to a lack of ground truth …
Drug repurposing for viral cancers: A paradigm of machine learning, deep learning, and virtual screening‐based approaches
Cancer management is major concern of health organizations and viral cancers account for
approximately 15.4% of all known human cancers. Due to large number of patients, efficient …
approximately 15.4% of all known human cancers. Due to large number of patients, efficient …
[HTML][HTML] A systematic review of computational approaches to understand cancer biology for informed drug repurposing
Cancer is the second leading cause of death globally, trailing only heart disease. In the
United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for …
United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for …
Tutorial: integrative computational analysis of bulk RNA-sequencing data to characterize tumor immunity using RIMA
Abstract RNA-sequencing (RNA-seq) has become an increasingly cost-effective technique
for molecular profiling and immune characterization of tumors. In the past decade, many …
for molecular profiling and immune characterization of tumors. In the past decade, many …
Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration
Multiomics data including imaging radiomics and various types of molecular biomarkers
have been increasingly investigated for better diagnosis and therapy in the era of precision …
have been increasingly investigated for better diagnosis and therapy in the era of precision …
[HTML][HTML] Aberrant protein glycosylation: Implications on diagnosis and Immunotherapy
Glycosylation-mediated post-translational modification is critical for regulating many
fundamental processes like cell division, differentiation, immune response, and cell-to-cell …
fundamental processes like cell division, differentiation, immune response, and cell-to-cell …
Omics-based deep learning approaches for lung cancer decision-making and therapeutics development
Lung cancer has been the most common and the leading cause of cancer deaths globally.
Besides clinicopathological observations and traditional molecular tests, the advent of …
Besides clinicopathological observations and traditional molecular tests, the advent of …
[HTML][HTML] Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology
V Raufaste-Cazavieille, R Santiago… - Frontiers in Molecular …, 2022 - frontiersin.org
The acceleration of large-scale sequencing and the progress in high-throughput
computational analyses, defined as omics, was a hallmark for the comprehension of the …
computational analyses, defined as omics, was a hallmark for the comprehension of the …