Chayaporn Nok Suphavilai
@nokcs
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Joined June 2009
🎉 Exciting News! 🎉 We are thrilled to announce that Dr. Suphavilai Chayaporn has been awarded the GIS Innovation Fellow FY24! 🏆 Dr. Suphavilai is spearheading an innovative project at GIS, aiming to develop clinical-grade metagenomic diagnostic systems for effective
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Find out how our researchers at @astar_gis—Associate Director @NiranjanTW, Scientist and GIS Innovation Fellow @nokcs and Dr Karrie Ko—discovered a new variation of 𝘊𝘢𝘯𝘥𝘪𝘥𝘢 𝘢𝘶𝘳𝘪𝘴 (𝘊.𝘢𝘶𝘳𝘪𝘴)—𝗰𝗹𝗮𝗱𝗲 𝗩𝗜—a drug-resistant yeast that kept infectious disease
research.a-star.edu.sg
Scientists identify a new, distinct group of the deadly yeast Candida auris, and urge the expansion of global surveillance efforts.
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Tech Alert! 🚀🧬 We can now determine the sequence of DNA with non-canonical bases in a direct and high-throughput manner with Nanopore sequencing. Check out our preprint for details:
biorxiv.org
The discovery of synthetic xeno-nucleic acids (XNAs) that can basepair as unnatural bases (UBs) to expand the genetic alphabet has spawned interest in many applications, from synthetic biology to DNA...
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Senior scientist at @astar_gis Dr. Chayaporn Suphavilai @nokcs is one of Singapore's Top 100 Women in Tech 2023 honorees. She is passionate about applying genomic sequencing, analyses, and artificial intelligence techniques to make tangible differences in the healthcare domain.
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Excited to share our work on city-wide metagenomic surveillance of hawker centres in Singapore! w/@macadology Several surprising findings here ... see thread https://t.co/q8fCeajxNk
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Check out our paper now on @LancetMicrobe ! We @nokcs @NiranjanTW @JacquesMeis @km_tsui made the discovery in June 2023 and made the data available for the community as a preprint in August 2023. Now it is finally here! https://t.co/TVZ3Bmwd2A
#candidia #candidaauris #Genomics
thelancet.com
The discovery of a new C auris clade in Singapore and Bangladesh in the Indomalayan zone, showing a close relationship to clade IV members most commonly found in South America, highlights the unknown...
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MetageNN - a memory efficient taxonomic classifier - is now out in BMC Bioinformatics #metagenomics #AI MetageNN outperforms other machine learning-based metagenomic classifiers, and shows higher sensitivity than kmer-based tools @rafaelperes @nokcs
https://t.co/uXsyCCe3sW
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The MetageNN paper is finally out on bioRxiv! MetageNN outperforms recent machine learning based approaches for taxonomic classification, and shows higher sensitivity than kmer based tools for novel taxa ... w/ @rafaelperes @nokcs
biorxiv.org
Background With the rapid increase in throughput of long-read sequencing technologies, recent studies have explored their potential for taxonomic classification by using alignment-based approaches to...
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By bringing #genomics tools to the bedside, researchers successfully tracked emergent mutations in immunocompromised #COVID19 patients and modified infection control measures to better suit their needs. More on this study in our latest article ⬇️ https://t.co/ynxKoNu9iA
research.a-star.edu.sg
With near-real-time genomic monitoring, researchers catch emergent, treatment-evading SARS-CoV-2 mutations in immunocompromised patients with chronic infections.
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Brilliant work and collaboration with @nokcs @karriekkko @JacquesMeis @NiranjanTW Discovery of the sixth Candida auris clade in Singapore
medrxiv.org
Background The emerging fungal pathogen Candida auris poses a serious threat to global public health due to its worldwide distribution, multidrug-resistance, high transmissibility, propensity to...
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Discovery of the sixth Candida auris clade in Singapore https://t.co/wOeN5SfUs2 We are excited to share our recent discovery of the sixth major Candida auris clade in Singapore @10minus6cosm @nokcs @JacquesMeis @NiranjanTW @km_tsui
medrxiv.org
Background The emerging fungal pathogen Candida auris poses a serious threat to global public health due to its worldwide distribution, multidrug-resistance, high transmissibility, propensity to...
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We integrated raw inconsistent drug response data to build an integrative pharmacogenomics database. CREAMMIST provides easy-to-use statistics and uncertainty info for various downstream analyses, such as identifying biomarkers and machine learning models.
academic.oup.com
Abstract. Extensive in vitro cancer drug screening datasets have enabled scientists to identify biomarkers and develop machine learning models for predicti
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@nokcs, @karriekkko : Sentinel-site sequencing in near real-time for detecting clonal outbreak clusters and providing alerts. Our new case study with @nanopore sequencing for whole-genome characterization of Shigella flexneri isolates https://t.co/Tg8s3Vvpap
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Our preprint on characterizing gut microbial diversity in Southeast Asians is finally out! Short summary: Quality is better than quantity for deriving population-specific references for metagenomics https://t.co/1AWsAydAls
biorxiv.org
Despite extensive efforts to address it, the vastness of uncharacterized ‘dark matter’ microbial genetic diversity can impact short-read sequencing based metagenomic studies. Population-specific...
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Imagine a technology that will let you see all microbes everywhere... well we already have it in the form of #metagenomics! Its time to push the frontiers and deploy it so that we aren't blind to our microbial world, its huge potential & occasional dangers https://t.co/ozDhLLsN82
nature.com
Nature Microbiology - Metagenomics-based surveillance could transform global efforts to detect risks to human health within a One Health framework.
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Fruitful collaboration with @karriekkko! An interactive interface for local hospitals to rapidly characterize and inspect healthcare-associated SARS-CoV-2 transmission. And thanks for the helpful advice from @NiranjanTW. https://t.co/Ab8zSUolNb
frontiersin.org
Background:The ongoing COVID-19 pandemic is a global health crisis caused by the spread of SARS-CoV-2. Establishing links between known cases is crucial for ...
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What a pleasant surprise to see this out in @GenomeMedicine! Great collaboration with @nokcs @asharmaiisc @DasguptaRam & Shumei Chia
Get all your #SingleCell #cancerresearch news here @GenomeMedicine ! @NiranjanTW, @DasguptaRam, & co leverage scRNA-seq with recommender system CaDRReS-Sc to predict drug response in the presence of tumor transcriptomic heterogeneity. Read more here: https://t.co/xZNHXjab4n
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We have some "strong" transfer learning mojo for you ... w/ @rafaelperes @nokcs TUGDA: task uncertainty guided domain adaptation for robust generalization of cancer drug response prediction from in vitro to in vivo settings
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Our new framework, CaDRReS-Sc, for predicting cancer drug response in heterogeneous tumors based on single-cell data. https://t.co/hQc5BB2VRQ
https://t.co/X3GlrcpEGt I’m so grateful for the support and guidance from @NiranjanTW @rdasgupt @asharmaiisc :)
biorxiv.org
While understanding heterogeneity in molecular signatures across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Single-cell...
Predicting cancer drug response for heterogenous tumors from single-cell data! Exciting collaboration with @rdasgupt @asharmaiisc @nokcs and great to have this out as a preprint: https://t.co/Yvf5eYZcwy…. Try CaDRReS-Sc out: https://t.co/JobwJzH2gB and we welcome feedback.
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