We have 2 open RA+PhD positions in our AI-driven Bioinformatics & Biomed Lab @MonashBDI, for onshore students with strong CS, SE/ EE background & interest in medical imaging, starting any time soon, supported by Monash Major & Seed IDR Grants. Welcome to contact me if interested.

RT @NatComputSci: 📢Xiangliang Zhang and colleagues evaluate bias in AI-generated medical text, revealing disparities across race, sex, and….

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RT @QinMaBMBL: Happy to announce our special issue on "Application of large language models in genome analysis", now live on @GenomeBiology….

RT @naturemethods: Two absolutely fantastic bioimage analysis papers out today offering exceptional, generalizable tools for segmentation--….

RT @tbepler1: Excited to share PoET-2, our next breakthrough in protein language modeling. It represents a fundamental shift in how AI lear….

RT @BiologyAIDaily: Self-iterative multiple instance learning enables the prediction of CD4+ T cell immunogenic epitopes. 1. ImmuScope is a….

RT @ItaiYanai: How to write a grant?.1. Write it for the reviewer, not you, the applicant. 2. Communicate in stories. 3. Make your story co….

- DrugDAGT accurately predicts synergistic drug combinations for SARS-CoV-2 treatment. - We expect DrugDAGT to accelerate the discovery of safe and effective drug combinations for complex therapeutic applications. @YaojiaChen0807.

- We expect DrugDAGT to accelerate the discovery of safe and effective drug combinations for complex therapeutic applications. Paper: Code:

- DrugDAGT effectively identifies key local structures critical to DDI outcomes, as evidenced in interactions involving ketoconazole and loxoprofen with various drugs. - DrugDAGT accurately predicts synergistic drug combinations for SARS-CoV-2 treatment.

- DrugDAGT demonstrates significant improvements in DDI prediction for both warm-start and cold-start scenarios, leveraging the combined strengths of the graph transformer and contrastive learning frameworks.

We introduce DrugDAGT, a new machine learning-based tool that leverages a dual-attention graph transformer framework with contrastive learning to predict multiple types of drug-drug interactions (DDIs). Work published in @BMC Biology.

- Fine-tuning EPACT on 3D data identifies binding hotspots within the TCR-pMHC complex and corroborates the presence of T cell cross-reactivity. - EPACT is anticipated to accelerate the development of TCR-based immunotherapies and vaccines for infectious diseases and cancers.

- It substantially improves the prediction of TCR binding specificity for unseen epitopes using the contrastive and transfer learning framework. - The predictions for SARS-CoV-2 responsive T cells align well with the surge in spike-specific immune responses after vaccination.

Our latest work published @NatMachIntell introduces a bioinformatic tool to decipher the interaction between paired CD8+ T cell receptors and peptide-MHC complexes, and can predict for unseen epitopes. @Jamie_Rossjohn @MarkGerstein . Paper:

RT @YumingGuo007: Our global analyses find despite a decline in absolute PM2.5 concentrations, suburban and urban areas in very-high-HDI re….

RT @LeoTZ03: Genomics 2 Proteins portal: a resource and discovery tool for linking genetic screening outputs to protein sequences and struc….

RT @ItaiYanai: It’s a lonely job, leading a research group (professor, principal investigator, group leader – whatever you call it). You ta….

RT @BiologyAIDaily: Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects @Brief….

RT @greeninglab: We want @MonashUni to be the place to be for microbiology in the post-metagenomics era. So much going on.

RT @MonashBDI: 1/Unlocking the secrets of how the third form of life —archaea— makes energy, with potential applications for transitioning….