Aashish Bhandari
@aashishbhandari.com
PhD student @ RMIT
#machinelearning #digitalhealth
#machinelearning #digitalhealth
Reposted by Aashish Bhandari
Good work by @aashishbhandari.com
#digitalhealth #missingdata #machinelearning #predictivemodeling #AIforHealth
#digitalhealth #missingdata #machinelearning #predictivemodeling #AIforHealth
March 18, 2025 at 1:45 AM
Reposted by Aashish Bhandari
New publication from the lab @tyagilab.bsky.social
Applications of linguistics in genome language modeling
academic.oup.com/biomethods/a...
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
May 12, 2025 at 1:06 AM
New publication from the lab @tyagilab.bsky.social
Applications of linguistics in genome language modeling
academic.oup.com/biomethods/a...
Applications of linguistics in genome language modeling
academic.oup.com/biomethods/a...
Reposted by Aashish Bhandari
New publication from the lab @tyagilab.bsky.social academic.oup.com/biomethods/a...
#multimidaldata #biomedicaldata #dataharmonisation #tyagilab
#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
May 4, 2025 at 7:50 PM
New publication from the lab @tyagilab.bsky.social academic.oup.com/biomethods/a...
#multimidaldata #biomedicaldata #dataharmonisation #tyagilab
#multimidaldata #biomedicaldata #dataharmonisation #tyagilab
Reposted by Aashish Bhandari
Research published in 2024 by our lab member @naimavahab.bsky.social and continue to use this for future work on elucidating biological regulatory pathways.
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
Check it out and explore: doi.org/10.1016/j.co...
#Genomics #Epigenomics #MachineLearning #CardiacResearch #OpenScience
@tyagilab.bsky.social @tsonika.bsky.social
September 25, 2025 at 4:24 AM
Research published in 2024 by our lab member @naimavahab.bsky.social and continue to use this for future work on elucidating biological regulatory pathways.
Reposted by Aashish Bhandari
DM us for online meeting link
October 13, 2025 at 10:26 AM
DM us for online meeting link
Reposted by Aashish Bhandari
Our latest paper combines multi-omics integration with genome-scale NLP models trained on DNA to uncover how S. aureus regulates infection, metabolism, and antibiotic resistance.
This unique organism agnostic method offers a new lens for systems-level biology.
🔗 www.nature.com/articles/s41...
This unique organism agnostic method offers a new lens for systems-level biology.
🔗 www.nature.com/articles/s41...
Understanding the regulatory grammar of sepsis-causing Staphylococcus aureus bacteria using contexualised DNA language models - Scientific Reports
Scientific Reports - Understanding the regulatory grammar of sepsis-causing Staphylococcus aureus bacteria using contexualised DNA language models
www.nature.com
October 14, 2025 at 9:11 PM
Our latest paper combines multi-omics integration with genome-scale NLP models trained on DNA to uncover how S. aureus regulates infection, metabolism, and antibiotic resistance.
This unique organism agnostic method offers a new lens for systems-level biology.
🔗 www.nature.com/articles/s41...
This unique organism agnostic method offers a new lens for systems-level biology.
🔗 www.nature.com/articles/s41...
Reposted by Aashish Bhandari
Our latest review explores how RNA foundation models are reshaping predictions of ncRNA structure & function.
We highlight key architectures, training strategies, and open challenges to guide the next phase of RNA-AI research.
Read here 👉 link.springer.com/article/10.1...
We highlight key architectures, training strategies, and open challenges to guide the next phase of RNA-AI research.
Read here 👉 link.springer.com/article/10.1...
Advancing non-coding RNA annotation with RNA sequence foundation models: structure and function perspectives - BMC Artificial Intelligence
Noncoding RNAs (ncRNAs) form the major part of the expressed transcriptome. These are critical in regulating gene expression and contributing to disease mechanisms, primarily through their complex sec...
link.springer.com
October 14, 2025 at 9:18 PM
Our latest review explores how RNA foundation models are reshaping predictions of ncRNA structure & function.
We highlight key architectures, training strategies, and open challenges to guide the next phase of RNA-AI research.
Read here 👉 link.springer.com/article/10.1...
We highlight key architectures, training strategies, and open challenges to guide the next phase of RNA-AI research.
Read here 👉 link.springer.com/article/10.1...
Reposted by Aashish Bhandari
Our latest paper presents EHR-QC 2.0, a major upgrade to our open-source pipeline for preparing and standardising biomedical & genomic EHR data for machine learning.
🔍 What’s new:
LLM-enabled clinical vocabulary mapping
Support for FHIR
A web-based interface
🔗 papers.ssrn.com/sol3/papers....
🔍 What’s new:
LLM-enabled clinical vocabulary mapping
Support for FHIR
A web-based interface
🔗 papers.ssrn.com/sol3/papers....
<span>An accessible pipeline for LLM-driven medical concept mapping, automated OMOP and FHIR conversion</span>
Background:Our previous work introduced the open-source EHR-QC pipeline. This pipeline implements extraction, transform and load (ETL), pre-processing and quali
papers.ssrn.com
October 14, 2025 at 9:25 PM
Our latest paper presents EHR-QC 2.0, a major upgrade to our open-source pipeline for preparing and standardising biomedical & genomic EHR data for machine learning.
🔍 What’s new:
LLM-enabled clinical vocabulary mapping
Support for FHIR
A web-based interface
🔗 papers.ssrn.com/sol3/papers....
🔍 What’s new:
LLM-enabled clinical vocabulary mapping
Support for FHIR
A web-based interface
🔗 papers.ssrn.com/sol3/papers....
🩺 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...
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
September 25, 2025 at 1:33 AM
🩺 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...
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...