# DNA Computing: Concepts for Medical Applications
## 2 Machine-Learning Approaches in Nucleotide Sequence Data Mining
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Paper** & **Method** & **Type of Input** & **Target** & **Dataset** & **Result** \\ \hline \multirow{2}{*}{[PERSON] et al. (2007) [15]} & Multiple & \multirow{2}{*}{DNA sequence} & \multirow{2}{*}{Imprint status of human genes} & \multirow{2}{*}{Ensembl} & \multirow{2}{*}{156 imprinted genes identified} \\  & classification & & & & \\  & algorithms & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. (2016) [16]} & \multirow{3}{*}{Hierarchical neural networks} & \multirow{3}{*}{ cDNA microarrays} & Molecular signal transduction & \multirow{3}{*}{PUMAdb} & \multirow{3}{*}{Novel model for evaluating the machinery regulating gene expression} \\  & & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. (2016) [17]} & \multirow{3}{*}{Convolutional neural networks} & \multirow{3}{*}{Genome sequence} & To annotate and & \multirow{3}{*}{DNaseal-seq peak} & \multirow{3}{*}{Nencoding genome parts annotated} \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. (2018) [18]} & \multirow{3}{*}{Long short-term memory} & \multirow{3}{*}{Genome sequence} & Annotate genome sequences & NCBI genome- & \multirow{3}{*}{Native 125 cells} \\  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. (2018) [19]} & Natural language processing & \multirow{3}{*}{Gene sequence} & Enhancer-promoter interactions & Various databases from TargetFinder & \multirow{3}{*}{Framework EP2 vec} \\ \cline{1-1}  & & & & & \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] (2019) [20]} & Convolutional neural network & \multirow{3}{*}{RNA sequences} & Gene–gene relationships & scRNA-seq and bulk RNA-seq & \multirow{3}{*}{Framework CNNC} \\ \cline{1-1}  & & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. (2020) [21]} & \multirow{3}{*}{Convolutional neural network} & \multirow{3}{*}{DNA sequences} & \multirow{3}{*}{Genome folding} & Five Hi-C & \multirow{3}{*}{Akita network} \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the existing machine-learning approaches for DNA sequence mining. computers. It is important to find unique patterns for the DNA sequences tested so that genetic differentiation of the investigated organisms is possible. DNA fragments encode various information, e.g., they encode amino acids that make up proteins. Therefore, comparing genomes based on specific DNA fragments makes it possible to find similarities between the tested organisms or to differentiate them with respect to the occurrence of unique nucleotide sequence arrangements. The problem of finding patterns in large sets of biological data that contain genomic sequences [9; 23] is a challenge both for computer scientists and mathematicians, but also for biologists. Figure 1 presents the main idea of finding patterns in various nucleotide sequences that are analyzed by applying machine-learning methods to files containing nucleotide sequences. In our approach to the use of biomolecular computers, we amplify the unique DNA fragments by PCR (fragments number 2 and number 4 to be exact); then, we read the amplified DNA fragments using a properly designed biomolecular computer. Thus, the step of finding unique patterns using machine-learning methods is a key stage in the application of biomolecular computers for molecular diagnostics. Machine-learning algorithms are of particular interest, as they allow classifying DNA sequences, for example, the use of convolutional neural networks to analyze DNA sequences [14]. The classification of DNA sequences is very useful in understanding the relationship between DNA sequences encoding different proteins, as well as the relationships between proteins [13]. The main problem with these studies is that the functions of DNA fragments are not fully understood and the relationships between DNA fragments are still being discovered.