As the understanding of a biological system advances, an enormous amount of data is generated on a daily basis. This vast data come from various input sources, for example, imaging data via high-throughput microscopic analysis in cell and developmental biological field and large-scale genomic-wide association studies, and so on [4]. Though manually handling these large numbers of data increases the risk of being biased, inefficient, costly and can produce errored results. In the analysis of genomics datasets, various practical aspects of machine learning algorithms are adopted, for example, analysis of DNA/RNA-binding proteins and other gene regulatory regions. However, the importance and challenges of machine learning in the analytical research of genomics, proteomics, and metabolomics fields are still considered seriously [5].
In general, machine learning algorithms are trained (or learned) with a sufficient amount of known data (labeled or tagged) so that outcomes of the unknown input data from the experiments can be predicted or interpreted. Based on the training module, these algorithm models predict the results as, for example, right or not right, favorable or unfavorable, in the given scenario. There are several number of tasks that can be performed using these trained algorithms. Therefore, I briefly explain the two broad categories of machine learning typically used when analyzing biological data. First, Supervised learning model, in which algorithms are trained with enough labeled datasets, and then used to predict the outcomes of the experiments as explained above. Second, Unsupervised learning model, here algorithms are not developed based on labeled datasets but instead trained to identify the unlabeled parts in the data and hoping to find something new. In such given scenarios, this model can help to discover the potential novel genetic elements in the genomic datasets.
Both computational and biological researchers have recently taken machine learning-based projects together and handshake for more interdisciplinary collaborations [1], therefore, machine learning-based approaches are, now, widely used to annotate the functions of several genes. Such an advanced level of work gives strong confidence to discover the potential roles and new other features of the annotated genes in the study; further, it will help to understand the locations and possible structural analysis of the gene in the whole genomic database [6]. In recent times, machine learning applications are aggressively invading in biology and medicine, and undoubtedly, revolutionizing the outcomes with significant and fruitful results around the globe.
Recent evidence suggests, machine learning-based tools are employed in biological studies, and their results have achieved significantly. Some examples are, PlasFlow is designed with machine learning’s advanced neural network algorithms and understand the bacterial plasmid sequences from the environmental samples, and as described, the accuracy in identifying the genomic signatures is leveled-up to 96 percent [7]. MetaBCC-LR in metagenomic binning studies, is developed based on k-mer coverage histograms and oligonucleotide composition [8]. Machine learning algorithms are expanding their use to estimate genetic relatedness using mitochondrial DNA (mtDNA) in humans, and this prediction is mainly based on the analysis of hypervariable region I sequences from African, Asian, and Caucasian genetic databases [9]. Because of the nature of this article and limited space, it is, indeed, not possible to list every biological study using machine learning approaches, as mentioned in just a few of them.