TY - JOUR
T1 - Graph embedding on mass spectrometry- and sequencing-based biomedical data
AU - Alvarez-Mamani, Edwin
AU - Dechant, Reinhard
AU - Beltran-Castañón, César A.
AU - Ibáñez, Alfredo J.
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2024/12
Y1 - 2024/12
N2 - Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction, community detection, and visualization. Although typically used in the context of guessing friendships in social media, several applications for graph embedding techniques in biomedical data analysis have emerged. While these approaches remain computationally demanding, several developments over the last years facilitate their application to study biomedical data and thus may help advance biological discoveries. Therefore, in this review, we discuss the principles of graph embedding techniques and explore the usefulness for understanding biological network data derived from mass spectrometry and sequencing experiments, the current workhorses of systems biology studies. In particular, we focus on recent examples for characterizing protein–protein interaction networks and predicting novel drug functions.
AB - Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction, community detection, and visualization. Although typically used in the context of guessing friendships in social media, several applications for graph embedding techniques in biomedical data analysis have emerged. While these approaches remain computationally demanding, several developments over the last years facilitate their application to study biomedical data and thus may help advance biological discoveries. Therefore, in this review, we discuss the principles of graph embedding techniques and explore the usefulness for understanding biological network data derived from mass spectrometry and sequencing experiments, the current workhorses of systems biology studies. In particular, we focus on recent examples for characterizing protein–protein interaction networks and predicting novel drug functions.
KW - Biological network
KW - Biomedical data
KW - Graph embedding
UR - http://www.scopus.com/inward/record.url?scp=85181250854&partnerID=8YFLogxK
U2 - 10.1186/s12859-023-05612-6
DO - 10.1186/s12859-023-05612-6
M3 - Review article
C2 - 38166530
AN - SCOPUS:85181250854
SN - 1471-2105
VL - 25
JO - BMC Bioinformatics
JF - BMC Bioinformatics
IS - 1
M1 - 1
ER -