IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier.

ABSTRACT:

Background

Identifying lncRNA-disease associations not only helps to better comprehend the underlying mechanisms of various human diseases at the lncRNA level but also speeds up the identification of potential biomarkers for disease diagnoses, treatments, prognoses, and drug response predictions. However, as the amount of archived biological data continues to grow, it has become increasingly difficult to detect potential human lncRNA-disease associations from these enormous biological datasets using traditional biological experimental methods. Consequently, developing new and effective computational methods to predict potential human lncRNA diseases is essential.

Results

Using a combination of incremental principal component analysis (IPCA) and random forest (RF) algorithms

SUBMITTER: Zhu R 

PROVIDER: S-EPMC8017839 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature