Fig. 1
From: Learning to predict single-wall carbon nanotube-recognition DNA sequences
Overall scheme to develop a model to predict and test DNA recognition sequences. First, the training data set is collected using the ATP technique. If the DNA/CNT hybrid can allow partitioning one type of SWCNT in either the top or the bottom phase, that sequence is labeled as a recognition sequence (“Y”). This is done via the NIR absorbance spectra of sorted fractions. Once the data are collected, the DNA sequences and their labels are encoded to a numeric vector, which is called input feature construction. Then, the models with three different types of classification algorithms are trained using the training set feature vectors. A generated query sequence set including all possible sequences (~212) in the 12 mer C/T library are then classified using the trained models. Limitations due to small data set size are mitigated by choosing the consensus of a number of models. The predicted recognition sequences are tested using the ATP technique again. The new data are added to the existing labeled sequence data and the models are retrained. This procedure was repeated twice
