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 light of 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
Method: We focus on the most recent developments for cancer research in
precision medicine using machine learning and predictive algorithms,
together with multi-omics data.
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
multi-omics studies. Finally, we address a discussion of future directions
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.