title: A reductionist approach to extract robust molecular markers from microarray data series - Isolating markers to track osseointegration.
abstract: Complexities in the full genome expression studies hinder the extraction of tracker genes to analyze the course of biological events. In this study, we demonstrate the applications of supervised machine learning methods to reduce the irrelevance in microarray data series and thereby extract robust molecular markers to track biological processes. The methodology has been illustrated by analyzing whole genome expression studies on bone-implant integration (ossointegration). Being a biological process, osseointegration is known to leave a trail of genetic footprint during the course. In spite of existence of enormous amount of raw data in public repositories, researchers still do not have access to a panel of genes that can definitively track osseointegration. The results from our study revealed panels comprising of matrix metalloproteinases and collagen genes were able to track osseointegration on implant surfaces (MMP9 and COL1A2 on micro-textured; MMP12 and COL6A3 on superimposed nano-textured surfaces) with 100% classification accuracy, specificity and sensitivity. Further, our analysis showed the importance of the progression of the duration in establishment of the mechanical connection at bone-implant surface. The findings from this study are expected to be useful to researchers investigating osseointegration of novel implant materials especially at the early stage. The methodology demonstrated can be easily adapted by scientists in different fields to analyze large databases for other biological processes.
authors.affiliation: School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India.
authors.forename: Anwesha
authors.initials: A
authors.lastname: Barik
authors.affiliation: School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India.
authors.forename: Satarupa
authors.initials: S
authors.lastname: Banerjee
authors.affiliation: School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India.
authors.forename: Santanu
authors.initials: S
authors.lastname: Dhara
authors.affiliation: School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India. Electronic address: nishant@smst.iitkgp.ernet.in.
authors.forename: Nishant
authors.initials: N
authors.lastname: Chakravorty
mesh_terms: D015415:Biomarkers; D001842:Bone and Bones; D005819:Genetic Markers; D000069550:Machine Learning; D016348:Osseointegration; D019736:Prostheses and Implants; D014025:Titanium
chemical_list: D015415:Biomarkers; D005819:Genetic Markers; D014025:Titanium
keywords: Gene ontology; Microarray data; Osseointegration; Statistical classifiers; Surface texture