Reverse engineering of gene regulatory networks from biological data by Li-Zhi Liu, Fang-Xiang Wu, Wen-Jun Zhang. (Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Volume 2, Issue 5, pages 365–385, September/October 2012)
Abstract:
Reverse engineering of gene regulatory networks (GRNs) is one of the most challenging tasks in systems biology and bioinformatics. It aims at revealing network topologies and regulation relationships between components from biological data. Owing to the development of biotechnologies, various types of biological data are collected from experiments. With the availability of these data, many methods have been developed to infer GRNs. This paper firstly provides an introduction to the basic biological background and the general idea of GRN inferences. Then, different methods are surveyed from two aspects: models that those methods are based on and inference algorithms that those methods use. The advantages and disadvantages of these models and algorithms are discussed.
As you might expect, heterogeneous data is one topic of interest in this paper:
Models Based on Heterogeneous Data
Besides the dimensionality problem, the data from microarray experiments always contain many noises and measurement errors. Therefore, an accurate network can hardly be obtained due to the limited information in microarray data. With the development of technologies, a large amount of other diverse types of genomic data are collected. Many researchers are motivated to study GRNs by combining these data with microarray data. Because different types of the genomic data reflect different aspects of underlying networks, the inferences of GRNs based on the integration of different types of data are expected to provide more accurate and reliable results than based on microarray data alone. However, effectively integrating heterogeneous data is currently a hot research topic and a nontrivial task because they are generally collected along with much noise and related to each other in a complex way. (emphasis added)
Truth be known, high dimensionality and heterogeneous data are more accurate reflections of the objects of our study.
Conversely, the lower the dimensions of a model or the greater the homogeneity of the data, the less accurate they become.
Are we creating less accurate reflections to allow for the inabilities of our machines?
Will that make our machines less self-conscious about their limitations?
Or will that make us less self-conscious about our machines’ limitations?