Early detection of cancer is very critical because it can reduce the treatment risk and cost. MicroRNAs (miRNAs) have been introduced in recent years as an efficient class of biomarkers for cancer early detection. Now, real-time polymerase chain reaction has been used to profile the miRNA expression, which is costly, time consuming and low accuracy. Most recently, DNA logic gates are used to detect the miRNA expression level that is more accurate and faster than previous methods. The DNA-based logic gates face with serious challenges such as the large complexity and low scalability. In this study, the authors proposed a methodology to design multi-threshold and multi-input DNA-based logic gates in response to specific miRNA inputs in live mammalian cells. The proposed design style can simultaneously recognise multiple miRNAs with different rising and falling thresholds. The design style has been evaluated on the lung cancer biomarkers and the experimental results show the efficiency of the proposed method in terms of accuracy, efficiency and speed.

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Early detection of diseases is very important to increase the life quality and reduce the treatment cost for the patient. MicroR-NAs have been introduced in recent years as an efficient class of biomarkers for detecting the risky situation of many diseases such as cancers, Multiple sclerosis (MS), and heart attacks, and other diseases. Now, real-time PCR has been used to profile the microRNA expression, which is expensive, time-consuming, and has low accuracy. Most recently, DNA logic gates are used to detect the MicroRNA expression level that is more accurate and faster than previous methods. In this paper, we improved the design of multi-threshold and multi-input DNA-based logic gates in response to specific microRNA (miRNA) inputs. The proposed design style can simultaneously recognize multiple miRNAs with different rising and falling thresholds. The proposed structure in this paper is used to diagnose Multiple Sclerosis (MS) as a case study. We simulated this system to understand its performance and compare it with other existing methods. The simulation results show the efficiency of the proposed method in terms of accuracy, efficiency, and speed. In this analysis, unwanted reactions, fault positive, and the probability of generating the final output using the formal method are investigated in depth. Finally, the proposed solutions are improved based on the results of these analyses. The analytic approach of this paper helps to design the DNA-based logic gates for real diseases.