
Bioinformatics; PostDoc @BostonChildrens; PhD @Helmholtz_HIRI; #CRISPR genome-wide screening; #scWGS #scRNA in #bacteria; fighting #AMR
Boston, MA
Joined August 2014
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We need more and better tools to understand the microbes. CRISPR tools including CRISPRi are great candidates! It was an enriching journey as we explored the methods to improve the guide efficiency prediction in CRISPRi for bacteria. Check out our study and join the exploration!
Interested in applying CRISPRi to bacteria? Check out our new machine learning method to predict guide efficiency and recover rational design rules from @YanyingYu, out now in @GenomeBiology link.springer.com/article/10…
Yanying Yu retweeted
Interested in predicting AMR in bacteria? We show ignoring phylogenetic structure in genome collections leads to overly optimistic evaluations of machine learning methods for AMR prediction. With
@YanyingYu and @nwheeler443.
biorxiv.org/content/10.1101/…
Yanying Yu retweeted
Pseudomonas aeruginosa faces a fitness trade-off between mucosal colonization and antibiotic tolerance during airway infection | Nature Microbiology nature.com/articles/s41564-0…
Yanying Yu retweeted
🚨BREAKING: the @royalsociety publishes "Science in the Age of AI - How AI is changing the nature and method of scientific research," and it's a must-read for everyone interested in AI & science. Important information:
➡️According to the official release, the report addresses the following questions:
➵ How are AI-driven technologies transforming the methods and nature of scientific research?
➵ What are the opportunities, limitations, and risks of these technologies for scientific research?
➵ How can relevant stakeholders (governments, universities, industry, research funders, etc) best support the development, adoption, and uses of AI-driven technologies in scientific research?
➡️Some of the key findings are:
"Beyond landmark cases like AlphaFold, AI applications can be found across all STEM fields, with a concentration in fields such as medicine, materials science, robotics, agriculture, genetics, and computer science. The most prominent AI techniques across STEM fields include artificial neural networks, deep learning, natural language processing and image recognition"
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"China contributes approximately 62% of the patent landscape. Within Europe, the UK has the second largest share of AI patents related to life sciences after Germany, with academic institutions such as the University of Oxford, Imperial College, and Cambridge University featuring prominently among the top patent filers in the UK. Companies such as Alphabet, Siemens, IBM, and Samsung appear to exhibit considerable influence across scientific and engineering fields."
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"Interdisciplinary collaboration is essential to bridge skill gaps and optimise the benefits of AI in scientific research. By sharing knowledge and skills from each other’s fields, collaboration between AI and domain subject experts (including researchers from the arts, humanities, and social sciences) can help produce more effective and accurate AI models. This is being prevented, however, by siloed research environments and an incentive structure that does not reward interdisciplinary collaboration in terms of contribution towards career progression."
➡️Link to the full report below.
➡️To stay up to date with the latest developments in AI policy & regulation, subscribe to my weekly newsletter (link below).
Yanying Yu retweeted
Network depth affects inference of gene sets from bacterial transcriptomes using denoising autoencoders" 👩💻🧬 This paper explores how neural network architecture impacts gene set inference from bacterial expression data! 🔬 doi.org/10.1093/bioadv/vbae0… #DeepLearning #Bioinformatics #Transcriptomics
@LBarquist
Yanying Yu retweeted
What if there was an antibiotic that doesn't disrupt the gut microbiome?
There is now.
A discovery published @Nature today
nature.com/articles/s41586-0… @PaulHergie and colleagues @UofIllinois @justsaysinmice
Yanying Yu retweeted
Latest from the group on inference of bacterial regulation from RNA-seq data using deep learning: Network depth affects inference of gene sets from bacterial transcriptomes using denoising autoencoders academic.oup.com/bioinformat…
Yanying Yu retweeted
Interested in RNA-targeting antisense technologies to manipulate and kill bacteria? Come join us in Würzburg this September -- registration is open (and free!) helmholtz-hiri.de/en/asobiot…
Yanying Yu retweeted
In case you missed this: WHO 2024 update on the priority pathogen groups for which new treatments are needed who.int/publications/i/item/…
Yanying Yu retweeted
DECODING GENOMES - from sequences to phylodynamics: We are proud to present our book on how to analyse genomic sequences with an evolutionary perspective. See decodinggenomes.org for a free pdf, Amazon links for print orders, and further information. Great team effort 1/2
Postdoctoral Position on the Genomics of Infectious Diseases and Antimicrobial Resistance jobs.sciencecareers.org/job/…
Yanying Yu retweeted
Very excited to finally see our study of global RNA stability in Salmonella out today in
@PNASnews, where we use Bayesian statistical modeling to show bacterial transcripts have subminute half-lives, far shorter than previously thought. pnas.org/doi/10.1073/pnas.23… 1/n
Yanying Yu retweeted
The findings were published today in @NAR_Open: doi.org/10.1093/nar/gkae281
Congratulations to all authors! 👏🥳 @YanyingYu @BeatrizBeamud @LisaOsbelt @StrowigLab @sylvainbrisse @LBarquist @UdiQimron @dbikard @BeiselLab
Yanying Yu retweeted
We just published a short paper on @iMetaScience to introduce SeqKit's new functionality, performance optimizations, and improved user-friendliness, since the initial publication 7 years ago. onlinelibrary.wiley.com/doi/…
Yanying Yu retweeted
Antibiotic class with potent in vivo activity targeting lipopolysaccharide synthesis in Gram-negative bacteria | PNAS pnas.org/doi/10.1073/pnas.23…
Yanying Yu retweeted
Looking to perform genome-wide screens in bacteria? 🦠 Look no further than base editors—with a few tweaks #HIRI’s @BeiselLab @LBarquist @YanyingYu introduced to enhance the number of targetable sites. #baseediting #CRISPR #Cas9 doi.org/10.1093/nar/gkae174
Yanying Yu retweeted
Latest with @BeiselLab and @YanyingYu: Expanding the flexibility of base editing for high-throughput screens in bacteria.
academic.oup.com/nar/advance…
Yanying Yu retweeted
So excited to share that our preprint discovering KhpA/KhpB as broad RBPs in fusobacteria are now out in @NAR_Open. Big thanks to my supervisor @JoergLab and @AnkeSparmann, TeamFuso @PonathF , as well as all supports from Vogel Lab @Helmholtz_HIRI!
academic.oup.com/nar/advance…





















