Chayaporn Nok Suphavilai retweeted
🎉 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 infectious disease detection and management in healthcare settings. 🌟
Check out her 1-minute pitch 🎥of the project here: youtu.be/jZeYsPNEcQ8?si=OTyO…
📧 Interested in collaborating with Dr. @nokcs and the team? Reach out to us at [email protected].
#Innovation #Healthcare #Metagenomics #InfectiousDiseases #Collaboration
Chayaporn Nok Suphavilai retweeted
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 experts on high alert worldwide.
Read more👉Mapping an evolving fungal foe - A*STAR Research
research.a-star.edu.sg/artic…
Chayaporn Nok Suphavilai retweeted
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/content/10.1101/…
Chayaporn Nok Suphavilai retweeted
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. Currently, she leads a team in developing microbial genomic solutions specifically designed for infectious disease diagnostics and healthcare-associated infection outbreak investigation, such as the recent discovery of a new clade of Candida auris (a-star.edu.sg/gis/news-event…).
In this interview with SCS Women in Tech Chapter, she shares her personal experiences on managing challenges and moving out of comfort zones: linkedin.com/pulse/focus-sel…
Chayaporn Nok Suphavilai retweeted
Excited to share our work on city-wide metagenomic surveillance of hawker centres in Singapore! w/@macadology
Several surprising findings here ... see thread
medrxiv.org/content/10.1101/…
Chayaporn Nok Suphavilai retweeted
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!
thelancet.com/journals/lanmi… #candidia #candidaauris #Genomics
Chayaporn Nok Suphavilai retweeted
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
tinyurl.com/mvr5th8j
Chayaporn Nok Suphavilai retweeted
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/content/10.1101/…
Chayaporn Nok Suphavilai retweeted
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 ⬇️
bit.ly/3Pon2PZ
Chayaporn Nok Suphavilai retweeted
Brilliant work and collaboration with @nokcs @karriekkko @JacquesMeis @NiranjanTW
Discovery of the sixth Candida auris clade in Singapore medrxiv.org/content/10.1101/…
Chayaporn Nok Suphavilai retweeted
Discovery of the sixth Candida auris clade in Singapore medrxiv.org/content/10.1101/…
We are excited to share our recent discovery of the sixth major Candida auris clade in Singapore @10minus6cosm @nokcs @JacquesMeis @NiranjanTW @km_tsui
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/nar/advance…
Chayaporn Nok Suphavilai retweeted
@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
frontiersin.org/articles/10.…
Chayaporn Nok Suphavilai retweeted
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
biorxiv.org/content/10.1101/…
Chayaporn Nok Suphavilai retweeted
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
nature.com/articles/s41564-0…
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.
frontiersin.org/article/10.3…
Chayaporn Nok Suphavilai retweeted
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: bit.ly/3DWxztO
Chayaporn Nok Suphavilai retweeted
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 academic.oup.com/bioinformat…
Our new framework, CaDRReS-Sc, for predicting cancer drug response in heterogeneous tumors based on single-cell data. github.com/CSB5/CaDRReS-Sc biorxiv.org/content/10.1101/…
I’m so grateful for the support and guidance from @NiranjanTW @rdasgupt @asharmaiisc :)
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: biorxiv.org/content/10.1101/…….
Try CaDRReS-Sc out: github.com/CSB5/CaDRReS-Sc and we welcome feedback.