Τίτλος:
Machine Learning Approaches on High Throughput NGS Data to Unveil
Mechanisms of Function in Biology and Disease
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
In this review, the fundamental basis of machine learning (ML) and data
mining (DM) are summarized together with the techniques for distilling
knowledge from state-of-the-art omics experiments. This includes an
introduction to the basic mathematical principles of
unsupervised/supervised learning methods, dimensionality reduction
techniques, deep neural networks architectures and the applications of
these in bioinformatics. Several case studies under evaluation mainly
involve next generation sequencing (NGS) experiments, like deciphering
gene expression from total and single cell (scRNA-seq) analysis; for the
latter, a description of all recent artificial intelligence (AI) methods
for the investigation of cell sub-types, biomarkers and imputation
techniques are described. Other areas of interest where various ML
schemes have been investigated are for providing information regarding
transcription factors (TF) binding sites, chromatin organization
patterns and RNA binding proteins (RBPs), while analyses on RNA sequence
and structure as well as 3D dimensional protein structure predictions
with the use of ML are described. Furthermore, we summarize the recent
methods of using ML in clinical oncology, when taking into consideration
the current omics data with pharmacogenomics to determine personalized
treatments. With this review we wish to provide the scientific community
with a thorough investigation of main novel ML applications which take
into consideration the latest achievements in genomics, thus, unraveling
the fundamental mechanisms of biology towards the understanding and cure
of diseases.
Συγγραφείς:
Pezoulas, Vasileios C.
Hazapis, Orsalia
Lagopati, Nefeli and
Exarchos, Themis P.
Goules, V, Andreas
Tzioufas, Athanasios G.
and Fotiadis, I, Dimitrios
Stratis, Ioannis G.
Yannacopoulos,
Athanasios N.
Gorgoulis, Vassilis G.
Περιοδικό:
Cancer Genomics & Proteomics
Εκδότης:
INT INST ANTICANCER RESEARCH
Λέξεις-κλειδιά:
Machine learning; supervised-unsupervised learning; NGS; gene
expression; scRNA-seq; TFs; RBPs; RNA structure; sequence motifs; review
