Aptamers are short, single-stranded DNA, RNA, or synthetic XNA molecules that can be developed with high affinity and specificity to interact with any desired targets. They have been widely used in facilitating discoveries in basic research, ensuring food safety and monitoring the environment. Furthermore, aptamers play promising roles as clinical diagnostics and therapeutic agents. This review provides update on the recent advances in this rapidly progressing field of research with particular emphasis on generation of aptamers and their applications in biosensing, biotechnology and medicine. The limitations and future directions of aptamers in target specific delivery and real-time detection are also discussed.

MiRNAs are small noncoding RNA molecules, mainly responsible for post-transcriptional control of gene expressions. Machine learning is becoming more and more widely used in breast tumor classification and diagnosis. In this paper, we compared the performance of different machine learning methods, such as Random Forest (RF), eXtreme Gradient Boosting(XGBoost) and Light Gradient Boosting Machine(LightGBM), for miRNAs identification in breast cancer patients. The performance comparison of each algorithm was evaluated based on the accuracy and logistic loss and where LightGBM was found better performing in several aspects. hsa-mir-139 was found as an important target for the breast cancer classification. As a powerful tool, LightGBM can be used to identify and classify miRNA target in breast cancer.