naimavahab.bsky.social
@naimavahab.bsky.social
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tsonika.bsky.social
Sonika Tyagi @tsonika.bsky.social · Jan 31
#hotoffthepress #newpublication from the #TyagiLab

"EHR-ML: A Data-Driven Framework for Designing Machine Learning Applications with Electronic Health Records “

Pre-proof is online now: lnkd.in/gHHrFEGF
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Reposted
tyagilab.bsky.social
AI in Digital Health & Bioinformatics Lab @tyagilab.bsky.social · Mar 18
Good work by @aashishbhandari.com

#digitalhealth #missingdata #machinelearning #predictivemodeling #AIforHealth
tsonika.bsky.social
Sonika Tyagi @tsonika.bsky.social · Mar 17
www.researchsquare.com/article/rs-6...

@tyagilab.bsky.social
Mind the Gaps: Guess Less, Predict More with Missing Medical Data
Healthcare data, generally available as electronic health records (EHR), provide a rich profile of an individual’s health and lifestyle. This data can be harnessed for predictive modelling using machi...
www.researchsquare.com
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Reposted
tsonika.bsky.social
Sonika Tyagi @tsonika.bsky.social · May 12
New publication from the lab @tyagilab.bsky.social

Applications of linguistics in genome language modeling

academic.oup.com/biomethods/a...
Genome language modeling (GLM): a beginner’s cheat sheet
Abstract. Integrating genomics with diverse data modalities has the potential to revolutionize personalized medicine. However, this integration poses signi
academic.oup.com
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Reposted
tsonika.bsky.social
Sonika Tyagi @tsonika.bsky.social · May 4
New publication from the lab @tyagilab.bsky.social academic.oup.com/biomethods/a...

#multimidaldata #biomedicaldata #dataharmonisation #tyagilab
Navigating the Multiverse: a Hitchhiker’s guide to selecting harmonization methods for multimodal biomedical data
Abstract. The application of machine learning (ML) techniques in predictive modelling has greatly advanced our comprehension of biological systems. There i
academic.oup.com
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Reposted
tsonika.bsky.social
Sonika Tyagi @tsonika.bsky.social · Jun 18
New preprint from the lab

@tyagilab.bsky.social
medrxivpreprint.bsky.social
medRxivpreprint @medrxivpreprint.bsky.social · Jun 18
Semantic Encoding in Medical LLMs for Vocabulary Standardisation https://www.medrxiv.org/content/10.1101/2025.06.16.25329716v1
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Reposted
tyagilab.bsky.social
AI in Digital Health & Bioinformatics Lab @tyagilab.bsky.social · Jul 11
#Interpretable vs #explainable #AI

📖 An informative post by lab members @esha4.bsky.social and @tnavya.bsky.social 📖
esha4.bsky.social
esha4.bsky.social @esha4.bsky.social · Jul 11
New Blog: Interpretability vs Explainability in ML
Why understanding model decisions matters for ethical AI.

👉 aiineverything.blogspot.com/2025/07/mach...

With @tyagilab.bsky.social, A/Prof. Sonika Tyagi & Navya Tyagi
#XAI #AIethics #ML #TyagiLab
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Reposted
tyagilab.bsky.social
AI in Digital Health & Bioinformatics Lab @tyagilab.bsky.social · Jul 21
#foundationsofAI #AI #teaching
tsonika.bsky.social
Sonika Tyagi @tsonika.bsky.social · Jul 21
Discussing 4 approaches to defining #AI
"Understanding these four perspectives i.e. acting humanly, thinking humanly, thinking rationally, and acting rationally, gives us a framework to appreciate the richness and diversity of AI."
aiineverything.blogspot.com/2025/07/four...

@tyagilab.bsky.social
aiineverything.blogspot.com
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Reposted
tsonika.bsky.social
Sonika Tyagi @tsonika.bsky.social · 14d
Congratulations to @yashpalr.bsky.social on his graduation!
@tyagilab.bsky.social
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Reposted
aashishbhandari.com
Aashish Bhandari @aashishbhandari.com · 14d
🩺 Missing medical data isn't just something to fill in or ignore!
EHRs often have missing values. Common fix? Imputation. But filling gaps can mislead predictions.
We explore ML approaches to handle missingness while preserving the original data distribution.
📜 www.researchsquare.com/article/rs-6...
Mind the Gaps: Guess Less, Predict More with Missing Medical Data
Healthcare data, generally available as electronic health records (EHR), provide a rich profile of an individual’s health and lifestyle. This data can be harnessed for predictive modelling using machi...
www.researchsquare.com
2 1
naimavahab.bsky.social
naimavahab.bsky.social @naimavahab.bsky.social · 14d
A versatile machine learning pipeline to find co-regulatory modules (CRMs) in DNA.
Check it out and explore: doi.org/10.1016/j.co...

#Genomics #Epigenomics #MachineLearning #CardiacResearch #OpenScience

@tyagilab.bsky.social @tsonika.bsky.social
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