Machine learning in bioinformatics

Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining. Prior to the emergence of machine learning, bioinformatics algorithms had to be programmed by hand; for problems such as protein structure prediction, this proved difficult. Machine learning techniques such as deep learning can learn features of data sets rather than requiring the programmer to define them individually. The algorithm can further learn how to combine low-level features into more abstract features, and so on. This multi-layered approach allows such systems to make sophisticated predictions when appropriately trained. These methods contrast with other computational biology approaches which, while exploiting existing datasets, do not allow the data to be interpreted and analyzed in unanticipated ways.

Tasks Machine learning algorithms in bioinformatics can be used for prediction, classification, and feature detection and selection. Classification or recognition algorithms output a categorical class, while prediction algorithms output a numerical valued feature. Algorithms differ in the type of process used to build the predictive models from data using analogies, rules, neural networks, probabilities, and/or statistics. Due to the exponential growth of information technologies and applicable models, including artificial intelligence and data mining, in addition to the access to ever-more comprehensive and open data sets, new and better information analysis techniques have been created, based on their ability to learn.

Approaches

Artificial neural networks Artificial neural networks in bioinformatics have been used for:

Comparing and aligning RNA, protein, and DNA sequences. Identification of promoters and finding genes from sequences related to DNA. Interpreting the expression-gene and micro-array data. Identifying the network (regulatory) of genes. Learning evolutionary relationships by constructing phylogenetic trees. Classifying and predicting protein structure. Molecular design and docking

Feature engineering The way that features, often vectors in a many-dimensional space, are extracted from the domain data is an important component of learning systems. In genomics, a typical representation of a sequence is a vector of k-mers frequencies, which is a vector of dimension 4 k {\displaystyle 4^{k}} whose entries count the appearance of each subsequence of length k {\displaystyle k} in a given sequence. Since for a value as small as k = 12 {\displaystyle k=12} the dimensionality of these vectors is huge (e.g. in this case the dimension is 4 12 ≈ 16 × 10 6 {\displaystyle 4^{12}\approx 16\times 10^{6}} ), techniques such as principal component analysis are used to project the data to a lower dimensional space, thus selecting a smaller set of features from the sequences.

Classification In this type of machine learning task, the output is a discrete variable. One example of this type of task in bioinformatics is labeling new genomic data (such as genomes of unculturable bacteria) based on a model of already labeled data.

Hidden Markov models Hidden Markov models (HMMs) are a class of statistical models for sequential data (often related to systems evolving over time). An HMM is composed of two mathematical objects: an observed state‐dependent process X 1 , X 2 , … , X M {\displaystyle X_{1},X_{2},\ldots ,X_{M}} , and an unobserved (hidden) state process S 1 , S 2 , … , S T {\displaystyle S_{1},S_{2},\ldots ,S_{T}} . In an HMM, the state process is not directly observed – it is a 'hidden' (or 'latent') variable – but observations are made of a state‐dependent process (or observation process) that is driven by the underlying state process (and which can thus be regarded as a noisy measurement of the system states of interest). HMMs can be formulated in continuous time. HMMs can be used to profile and convert a multiple sequence alignment into a position-specific scoring system suitable for searching databases for homologous sequences remotely. Additionally, ecological phenomena can be described by HMMs.

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