Research software and pipelines
A generalisable automated pipeline for predicting clinical outcomes using electronic health records (Ramakrishnaiah et al 2024).
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Electronic Health Records quality control pipeline (Ramakrishnaiah et al 2023).
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NLP and Transformer based genomic language modelling (Chen et al 2023).
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A user-friendly multi-omics data harmonisation R pipeline (Methods: Chen et al 2023, F1000Research).
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A deep learning model for functional annotation of long non-coding RNA (lncRNA). (Manuscripts: Ramakrishnaiah, Kuhlmann & Tyagi 2020; Ramakrishnaiah & Tyagi 2023).
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CID-miRNA implements a probabilistic model to predict pre-miRNA structures from the genomic data (Tyagi et al 2008; Dubrovski & Tyagi 2015; Stark, Tyagi et al 2010).
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Uncovering co-regulatory modules and gene regulatory networks in the heart through machine learning-based analysis of large-scale epigenomic data.
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Annotation of somatic and germline variants in WES or WGS data (Stark et al 2012 Nature genetics).
Noncoding variant detection and prioritisation in diseases (Manuscript: Chahal et al 2019, Tool: CARAVAN).
Publications
For in depth information about our lab, take a look at our recent publications.
