# Heterofusion: Fusing genomics data of different measurement scales

## TL;DR

This paper addresses the challenge of integrating genomics data measured at different scales, proposing three fusion methods, including two novel approaches, and demonstrating their application with real-world data.

## Contribution

The paper introduces two new data fusion methods for heterogeneity of measurement scales in genomics, expanding tools available for systems biology research.

## Key findings

- Two new fusion methods demonstrated on real genomics data
- Methods effectively handle binary, ordinal, interval, and ratio data
- Enhanced integration of heterogeneous biological measurements

## Abstract

In systems biology, it is becoming increasingly common to measure biochemical entities at different levels of the same biological system. Hence, data fusion problems are abundant in the life sciences. With the availability of a multitude of measuring techniques, one of the central problems is the heterogeneity of the data. In this paper, we discuss a specific form of heterogeneity, namely that of measurements obtained at different measurement scales, such as binary, ordinal, interval and ratio-scaled variables. Three generic fusion approaches are presented of which two are new to the systems biology community. The methods are presented, put in context and illustrated with a real-life genomics example.