Biology subjects

Zhuo, B.

Publications and source records attributed to Zhuo, B..

3 recordsLinked to original sources

bioRxiv2026

End-to-end bimodal anti-counterfeiting by informational DNA nanoparticles

The superior stealth properties and high information density make DNA a sought-after candidate in the field of molecular steganography. Here, we developed the InfinMark end-to-end DNA steganography framework for anti-counterfeiting applications by combining the characteristics of both the Internet of Things (IoT) and DNA-of-Things (DoT). InfinMark includes five modules: Information Transcoding, Fingerprint Writing, Nano-encapsulation, Invisible Marking, and Multi-level Rapid Authentication. It ensures precise anti-counterfeiting information reading and writing through a dynamic DNA-compatible transcoding algorithm, achieves seamless embedding by developing scalable nanoparticle manufacture methods, and supports cross-scenario on-site verification, ultimately granting it comprehensive anti-counterfeiting capabilities spanning from source labeling to terminal tracing. By addressing the bottlenecks in IoT and DoT integration, lifecycle tracking, as well on-site product authentication, this research constructs a full-chain bimodal anti-counterfeiting system, thereby showcasing the practical application of informational DNA nanoparticles in various aspects of production and daily life.

synthetic biology↗

bioRxiv2019

Test-statistic correlation and data-row correlation

When a statistical test is repeatedly applied to rows of a data matrix--such as in differential-expression analysis of gene expression data, correlations among data rows will give rise to correlations among corresponding test statistic values. Correlations among test statistic values create many inferential challenges in false-discovery-rate control procedures, gene-set enrichment analysis, or other procedures aiming to summarize the collection of test results. To tackle these challenges, researchers sometimes will--explicitly or implicitly--use the correlations (e.g., as measured by the Pearson correlation coefficients) among the data rows to approximate the correlations among the corresponding test statistic values. We show that, however, such approximations are only valid under limited settings. We investigate the relationship between the correlation coefficient between a pair of test statistics (test-statistic correlation) and the correlation coefficient between the two corresponding data rows (data-row correlation). We derive an analytical formula for the test-statistic correlation as a function of the data-row correlation for a general class of test statistics: in particular, two-sample t-test is a special case. The analytical formula implies that the test-statistic correlation is generally weaker than the corresponding data-row correlation, and in general, the latter will not well approximate the former when the involved null hypotheses are false. We verify our analytical results through simulations.

genetics↗

bioRxiv2017

MEACA: efficient gene-set interpretation of expression data using mixed models

Competitive gene-set analysis, also called enrichment analysis, is a widely used tool for functional interpretation of high-throughput biological data such as gene expression data. It aims at testing a known category (e.g. a pathway) of genes for enriched differential expression (DE) signals compared to genes not in the category. Most enrichment testing methods ignore the widespread correlations among genes, which has been shown to result in excessive false positives. We show, both theoretically and empirically, that existing methods to account for correlations, such as GSEA and CAMERA, can result in severely mis-calibrated type 1 error and/or considerable power loss due to the failure to properly accommodate the DE heterogeneity across genes. We propose MEACA, a new gene-set testing framework based on a mixed effects model. Our method flexibly incorporates the unknown distribution of DE effects, effectively adjusts for completely unknown, unstructured correlations among genes, and does not rely on time-consuming permutations. Compared to existing methods, MEACA enjoys robust type 1 error control in widely ranging scenarios and substantially improves power. Applications of MEACA to a Huntingtons disease study and a lymphoblastoid cell line data set demonstrate its ability to recover biologically meaningful relationships. MEACA is available as an R package.

bioinformatics↗