Background: Evidence now suggests that precision medicine, which is sometimes
used interchangeably with personalized medicine, is becoming a cornerstone of
medical practices by providing the right patient with the right medication at the right dose
at the right time. In the light of the recent advances in biomedical computing and big data
science, more and more genetic variants associated with human diseases and treatment response
are being discovered in precision medicine applications by leveraging multi-omics
and machine learning approaches.
Objective: In this review, a key question is whether multi-omics approaches outperform
the traditional single data type analysis in various multi-omics studies.
Method: We focus on the most recent developments for cancer research in precision
medicine using machine learning and predictive algorithms, together with multi-omics
Results: First, we describe different machine learning approaches that are employed to assess
whether biomarkers are correlated with diseases and treatment responses in various
multi-omics studies. We also survey probable biomarkers that have been identified to be
involved in diseases and treatment responses such as recurrence and survival in ovarian
cancer. Furthermore, we summarize the limitations with respect to the mentioned multiomics
studies. Finally, we address a discussion of future directions and challenges.
Conclusion: Predictive models based on multi-omics data could be more powerful than
those based on a single data type. Future replication studies with much larger sample sizes
are essential to confirm the role of the biomarkers identified in these multi-omics studies
and will seemingly have key contributions for precision medicine.