# DNA Computing: Concepts for Medical Applications
## 3 New Concept of Designing Diagnostic Biochips  
Extraction of DNA sequence patterns with the use of machine-learning methods provides the background for the construction of diagnostic biochips based on biomolecular computers. In the proposed concept of designing diagnostic biochips based on biomolecular computers, unique DNA fragments play a special role, as they enable proper programming of a biomolecular computer in such a way that it reads unique DNA nucleotide sequences. In the previous section, we discussed different machine-learning approaches to nucleotide sequence data mining. In our new concept of designing diagnostic biochips, it is possible to use artificial neural networks, for example, recurrent neural network (RNN) [18], at the stage of machine learning in the sequence data mining. RNNs can be used for data containing ordered strings, e.g., nucleotide sequences. From the point of view of the research methodology involving the RNN, it is important that the nucleotide sequences of the studied living organism genome are the input layer of such a network. RNNs can be used to generate output based on a nucleotide sequence of a given length. For example, RNNs can analyze nucleotide sequences that are characteristic of protein-coding genes and identify promoter sequences [10]. As part of the methodology of working with sequence extraction machine-learning methods, classic elements of machine learning, such as the process of training, validation, and testing, should be distinguished in individual steps. In the first step, it is necessary to well understand the set of input data and then to formulate an appropriate research question. In the next step, the data should be divided into training, testing, and validation sets. The next step is to choose the most suitable model for the research question. At this stage, it is especially important to check assumptions on the possibility of using the model. From the point of view of machine-learning methodology, it is also important to fine-tune the hyperparameters for the methods used. It is worth noting that in recent years, the model called transformers have attracted a lot of interest from researchers, as it allows better accuracy when studying character strings such as nucleotide sequences [27,28]. We propose a new approach to biochip design with biomolecular computing as the hardware and software (Figure 2) to enable DNA-level diagnostics. In this approach, the main mechanism is based on the use of biomolecular computers, built of appropriately encoded DNA chains as the software, and restriction enzymes and ligase as the hardware. In this approach, the DNA fragments, for example, unique to a pathogenic virus, are determined by means of machine-learning methods in the sequence data mining. Figure 2: Schematic diagram of the new approach to using biomolecular computers as diagnostic biochips. Knowledge on unique DNA fragments, e.g., viral genomes, can come from analyzing an open access genetic sequence database, such as the GenBank database. Particularly interesting studies in this area include fingerprinting, which enables detection of the specific DNA fragments (obtained by PCR) characteristic of the investigated organisms. Recent years have also seen the development of methods based on artificial intelligence, which enable an automatic retrieval of information from genetic data. To this end, we propose the development of various machine-learning methods enabling detection of those DNA fragments that are unique with reference to the tested species. An important part of this approach is the software that will enable automatic encoding of the indispensable parts of diagnostic biochips based on biomolecular computers. This type of software will allow highly precise designing of indispensable components of the diagnostic biochips.