Using machine learning approaches for multi-omics data analysis: A review
With the development of modern high-throughput omic measurement platforms, it has
become essential for biomedical studies to undertake an integrative (combined) approach to …
become essential for biomedical studies to undertake an integrative (combined) approach to …
Applications of support vector machine (SVM) learning in cancer genomics
S Huang, N Cai, PP Pacheco… - Cancer genomics & …, 2018 - cgp.iiarjournals.org
Machine learning with maximization (support) of separating margin (vector), called support
vector machine (SVM) learning, is a powerful classification tool that has been used for …
vector machine (SVM) learning, is a powerful classification tool that has been used for …
Unsupervised multi-omics data integration methods: a comprehensive review
N Vahabi, G Michailidis - Frontiers in genetics, 2022 - frontiersin.org
Through the developments of Omics technologies and dissemination of large-scale
datasets, such as those from The Cancer Genome Atlas, Alzheimer's Disease Neuroimaging …
datasets, such as those from The Cancer Genome Atlas, Alzheimer's Disease Neuroimaging …
Data-driven biological subtypes of depression: systematic review of biological approaches to depression subtyping
L Beijers, KJ Wardenaar, HM van Loo… - Molecular …, 2019 - nature.com
Research into major depressive disorder (MDD) is complicated by population heterogeneity,
which has motivated the search for more homogeneous subtypes through data-driven …
which has motivated the search for more homogeneous subtypes through data-driven …
Integrated multi‐omics approaches to understand microbiome assembly in Jiuqu, a mixed‐culture starter
The use of Jiuqu as a saccharifying and fermenting starter in the production of fermented
foods is a very old biotechnological process that can be traced back to ancient times. Jiuqu …
foods is a very old biotechnological process that can be traced back to ancient times. Jiuqu …
A deep learning approach for predicting antidepressant response in major depression using clinical and genetic biomarkers
In the wake of recent advances in scientific research, personalized medicine using deep
learning techniques represents a new paradigm. In this work, our goal was to establish deep …
learning techniques represents a new paradigm. In this work, our goal was to establish deep …
Artificial intelligence (AI)-based systems biology approaches in multi-omics data analysis of cancer
N Biswas, S Chakrabarti - Frontiers in oncology, 2020 - frontiersin.org
Cancer is the manifestation of abnormalities of different physiological processes involving
genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in …
genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in …
An introduction and overview of machine learning in neurosurgical care
JT Senders, MM Zaki, AV Karhade, B Chang… - Acta …, 2018 - Springer
Background Machine learning (ML) is a branch of artificial intelligence that allows computers
to learn from large complex datasets without being explicitly programmed. Although ML is …
to learn from large complex datasets without being explicitly programmed. Although ML is …
[HTML][HTML] Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance
COVID-19 was first discovered in December 2019 and has continued to rapidly spread
across countries worldwide infecting thousands and millions of people. The virus is deadly …
across countries worldwide infecting thousands and millions of people. The virus is deadly …
Artificial intelligence in rheumatoid arthritis: current status and future perspectives: a state-of-the-art review
Investigation of the potential applications of artificial intelligence (AI), including machine
learning (ML) and deep learning (DL) techniques, is an exponentially growing field in …
learning (ML) and deep learning (DL) techniques, is an exponentially growing field in …