Biology subjects

Hsieh, T.-Y.

Publications and source records attributed to Hsieh, T.-Y..

2 recordsLinked to original sources

bioRxiv2020

Hidden introductions of freshwater red algae via the aquarium trade exposed by DNA barcodes

ABSTRACTThe global aquarium trade can introduce alien freshwater invaders, potentially impacting local aquatic ecosystems and their biodiversity. The role of the aquarium trade in spreading freshwater red macroalgae that hitchhike on ornamental aquatic plants and animals is unassessed. We investigated this human-mediated phenomenon via a broad biodiversity survey and genetic analysis of freshwater red algae in the field and aquarium shops in East Asia. Using rbcL-based DNA barcoding, we surveyed 125 samples from 46 field sites and 88 samples from 53 aquarium shops (213 samples in total) mostly across Taiwan – a key hub in the global aquarium trade – as well as in Hong Kong, Okinawa (Japan), the Philippines, and Thailand. We augmented our rbcL sequences with GenBank rbcL sequences that represent 40 additional countries globally. We found 26 molecular operational taxonomic units (mOTUs) in Taiwan, some of which are cryptic. Phylogeographical analysis revealed three potential introduced mOTUs in Taiwan, which exhibit no local genetic variation in Taiwan and are distributed across continents. Also, we posit that some presumably endangered freshwater red algae may be preserved in aquaria, an unintentional ex situ conservation site for these organisms that are vulnerable to water pollution from anthropogenic disturbances. Collectively, these data suggest that freshwater red algae have been hitchhiking and dispersed via the aquarium trade, an important and overlooked mechanism of introduction of these organisms across the globe.Competing Interest StatementThe authors have declared no competing interest.View Full Text

ecology↗

bioRxiv2018

Min-Redundancy and Max-Relevance Multi-view Feature Selection for Predicting Ovarian Cancer Survival using Multi-omics Data

BackgroundLarge-scale collaborative precision medicine initiatives (e.g., The Cancer Genome Atlas (TCGA)) are yielding rich multi-omics data. Integrative analyses of the resulting multi-omics data, such as somatic mutation, copy number alteration (CNA), DNA methylation, miRNA, gene expression, and protein expression, offer the tantalizing possibilities of realizing the potential of precision medicine in cancer prevention, diagnosis, and treatment by substantially improving our understanding of underlying mechanisms as well as the discovery of novel biomarkers for different types of cancers. However, such analyses present a number of challenges, including the heterogeneity of data types, and the extreme high-dimensionality of omics data.\n\nMethodsIn this study, we propose a novel framework for integrating multi-omics data based on multi-view feature selection, an emerging research problem in machine learning research. We also present a novel multi-view feature selection algorithm, MRMR-mv, which adapts the well-known Min-Redundancy and Maximum-Relevance (MRMR) single-view feature selection algorithm for the multi-view settings.\n\nResultsWe report results of experiments on the task of building a predictive model of cancer survival from an ovarian cancer multi-omics dataset derived from the TCGA database. Our results suggest that multi-view models for predicting ovarian cancer survival outperform both view-specific models (i.e., models trained and tested using one multi-omics data source) and models based on two baseline data fusion methods.\n\nConclusionsOur results demonstrate the potential of multi-view feature selection in integrative analyses and predictive modeling from multi-omics data.

bioinformatics↗