Analysis of machine learning algorithms on bioinformatics data of varying quality

One of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One particular challenge encountered when analyzing bioinformatics datasets is data noise, which refers to incorrect or missing values in datasets. Noise can be introduced as a result of experimental errors (e.g. faulty microarray chips, insufficient resolution, image corruption, and incorrect laboratory procedures), as well as other errors (errors
during data processing, transfer, and/or mining). A special type of data noise
called class noise, which occurs when an instance/example is mislabeled. Previous
research showed that class noise has a detrimental impact on machine learning algorithms (e.g. worsened classification performance and unstable feature selection). In
addition to data noise, gene expression datasets can suffer from the problems of high
dimensionality (a very large feature space) and class imbalance (unequal distribution
of instances between classes). As a result of these inherent problems, constructing accurate classification models becomes more challenging.