Incorporating Machine Learning into Established Bioinformatics Frameworks

Abstract

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.

Keywords: bioinformatics methods; deep learning; machine learning; phylogenetics.

Figure 1

Machine learning algorithms frequently used in bioinformatics research. An example of the usage of each algorithm and the respective input data are indicated on the right. Abbreviations: SVM, support vector machines; KNN, K-nearest neighbors; CNN, convolutional neural networks; RNN, recurrent neural networks; PCA, principal component analysis; t-SNE, t-distributed stochastic neighbor embedding, NMF, non-negative matrix factorization.

Figure 2

Applications of integrated machine learning techniques with bioinformatics in molecular evolution, protein structure analysis, systems biology, and disease genomics.