LiCo: A supervised method for measurement of DNA heterogeneity

In the context of clinical decision support system it is an important task to measure gene similarity effectively for comparative effectiveness studies. It helps clinicians to assess the likely outcomes resulting from their decisions and actions by enabling the capture of past experience as manifested in the collective longitudinal DNA records of genes. But research works about the measurement of gene similarity of heterogeneous DNA records are still few. In order to determine gene similarity of heterogeneous DNA records, we propose a supervised method LiCo combined with three methods namely-Locally Supervised Metric Learning (LSML), interactive Metric learning (iMet) and Composite Distance Integration (Comdi). Our goal is to devise a clinically relevant distance metric to measure gene similarity of heterogeneous DNA records. LSML method generalizes a Mahalanobis distance which is classified the genes of heterogeneous DNA sequences. Interactive Metric learning (iMet) updates the existing metric of genes in heterogeneous DNAs including new records. Finally, Comdi combines multiple similarities from multiple heterogeneous DNA records. We have found accurate solution of large scale DNA records and trims time complexity.