Fabrication and Decryption of a Microarray of Digital Dithiosuccinimide Oligomers.

Digital polymers with precisely arranged binary units provide an important option for information storage. This is especially true if the digital polymers are assembled in a device, as it would be of great benefit for data writing and reading in practice. Herein, inspired by the DNA microarray technique, the programmable information storing and reading on a mass spectrometry target plate is proposed. First, an array of 4-bit sequence-coded dithiosuccinimide oligomers is efficiently built through sequential thiol-maleimide Michael couplings with good sequence readability by tandem mass spectrometry (MS/MS). Then, toward engineering microarrays for information storage, a programmed robotic arm is specifically designed for precisely loading sequence-coded oligomers onto the target plate, and a decoding software is developed for efficient readout of the data from MS/MS sequencing. Notably, short sequence-coded oligomer chains can be used to write long strings of information, and extra error-correction codes are not required as usual due to the inherent concomitant fragmentation signals. Not only text but also bitimages can be automatically stored and decoded with excellent accuracy. This work provides a promising platform of digital polymers for programmable information storing and reading.

Multilevel Anti-counterfeiting Barcode with Enhanced Information Encryption Based on Stimulus-Responsive Digital Polymers.

In response to the escalating challenges of counterfeiting due to technological and socioeconomic advancements, a novel trilevel anti-counterfeiting Quick Response (QR) code system has been developed. This system integrates digital polymers with QR code and stimulus-responsive chromophores, i.e., rhodamine B (RB), rhodamine 6G (R6G), and spiropyran (SP), to provide a sophisticated security solution. This advanced barcode remains concealed until specific stimuli reveal it and can be scanned by a smartphone, enabling first and second level anti-counterfeiting. For the third level of security, the encrypted information within the digital polymers can only be deciphered using tandem mass spectrometry. This innovative approach not only enhances security features but also offers reversible visibility and a complex verification process. This trilevel system surpasses traditional single-level anti-counterfeiting methods and holds significant potential for future applications in protecting brand authenticity and managing data storage, contributing new concepts and techniques to the field of high-security anti-counterfeiting materials.

An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data.

MOTIVATION: Gene selection for cancer classification is one of the most important topics in the biomedical field. However, microarray data pose a severe challenge for computational techniques. We need dimension reduction techniques that identify a small set of genes to achieve better learning performance. From the perspective of machine learning, the selection of genes can be considered to be a feature selection problem that aims to find a small subset of features that has the most discriminative information for the target. RESULTS: In this article, we proposed an Ensemble Correlation-Based Gene Selection algorithm based on symmetrical uncertainty and Support Vector Machine. In our method, symmetrical uncertainty was used to analyze the relevance of the genes, the different starting points of the relevant subset were used to generate the gene subsets and the Support Vector Machine was used as an evaluation criterion of the wrapper. The efficiency and effectiveness of our method were demonstrated through comparisons with other feature selection techniques, and the results show that our method outperformed other methods published in the literature.

Multiclass cancer classification using a feature subset-based ensemble from microRNA expression profiles.

Cancer classification has been a crucial topic of research in cancer treatment. In the last decade, messenger RNA (mRNA) expression profiles have been widely used to classify different types of cancers. With the discovery of a new class of small non-coding RNAs; known as microRNAs (miRNAs), various studies have shown that the expression patterns of miRNA can also accurately classify human cancers. Therefore, there is a great demand for the development of machine learning approaches to accurately classify various types of cancers using miRNA expression data. In this article, we propose a feature subset-based ensemble method in which each model is learned from a different projection of the original feature space to classify multiple cancers. In our method, the feature relevance and redundancy are considered to generate multiple feature subsets, the base classifiers are learned from each independent miRNA subset, and the average posterior probability is used to combine the base classifiers. To test the performance of our method, we used bead-based and sequence-based miRNA expression datasets and conducted 10-fold and leave-one-out cross validations. The experimental results show that the proposed method yields good results and has higher prediction accuracy than popular ensemble methods. The Java program and source code of the proposed method and the datasets in the experiments are freely available at https://sourceforge.net/projects/mirna-ensemble/.

A New Direction of Cancer Classification: Positive Effect of Low-Ranking MicroRNAs.

OBJECTIVES: Many studies based on microRNA (miRNA) expression profiles showed a new aspect of cancer classification. Because one characteristic of miRNA expression data is the high dimensionality, feature selection methods have been used to facilitate dimensionality reduction. The feature selection methods have one shortcoming thus far: they just consider the problem of where feature to class is 1:1 or n:1. However, because one miRNA may influence more than one type of cancer, human miRNA is considered to be ranked low in traditional feature selection methods and are removed most of the time. In view of the limitation of the miRNA number, low-ranking miRNAs are also important to cancer classification. METHODS: We considered both high- and low-ranking features to cover all problems (1:1, n:1, 1:n, and m:n) in cancer classification. First, we used the correlation-based feature selection method to select the high-ranking miRNAs, and chose the support vector machine, Bayes network, decision tree, k-nearest-neighbor, and logistic classifier to construct cancer classification. Then, we chose Chi-square test, information gain, gain ratio, and Pearson's correlation feature selection methods to build the m:n feature subset, and used the selected miRNAs to determine cancer classification. RESULTS: The low-ranking miRNA expression profiles achieved higher classification accuracy compared with just using high-ranking miRNAs in traditional feature selection methods. CONCLUSION: Our results demonstrate that the m:n feature subset made a positive impression of low-ranking miRNAs in cancer classification.