Add new ABLeS participants

participants/FHMRI.md

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+---
+title: FHMRI Bioinformatics
+description: FHMRI Bioinformatics is a new bioplatform to support bioinformatics analyses for research groups in the College of Medicine and Public Health at Flinders University.
+toc: false
+type: ABLeS Participant - Completed
+page_id: FHMRI
+---
+
+## Project title
+
+FHMRI Bioinformatics
+
+## Collaborators and funding
+
+Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University
+
+## Contact(s)
+
+-   Michael Roach, Flinders, <michael.roach@flinders.edu.au>
+-   Clarice Harker, Flinders, <clarice.cram@flinders.edu.au>
+
+## Project description and aims
+
+FHMRI Bioinformatics aims to support Flinders research groups with bioinformatics analyses, ranging from preprocessing large datasets with established pipelines through to hands-on analyses requiring custom software and tool development. We use where possible established nf-core pipelines or published and well-maintained pipelines preferably built using a workflow manager. We develop tools mostly with Snakemake to support custom applications for research groups. Aims and analyses are therefore quite varied, but impact is considerable as not all research groups can support a full time bioinformatician so having access to bioinformatics support when needed is greatly beneficial.
+
+
+## How is ABLeS supporting this work?
+
+
+
+## Expected outputs enabled by participation in ABLeS
+
+FHMRI Bioinformatics supports many research groups. We always strive for open access Q1 journals. All our code is made available open source (typically on GitHub) and where applicable published on appropriate repositories (PyPI, Bioconda, workflowhub, etc). 
+
+<br/>
+
+> _These details have been provided by project members at project initiation. For more information on the project, please consult the contact(s) or project links above._

participants/FightMND.md

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+---
+title: Beben Benyamin
+description: The project aims to investigate the roles of human endogenous retrovirus in motor neuron disease using multi-omics analysis.
+toc: false
+type: ABLeS Participant - Completed
+page_id: FightMND
+---
+
+## Project title
+
+Investigating the roles of human endogenous retroviruses in motor neuron disease
+
+## Collaborators and funding
+
+FightMND
+
+## Contact(s)
+
+Beben Benyamin, <beben.benyamin@adelaide.edu.au>
+
+## Project description and aims
+
+Project aims:
+1.        To test for differential expression of HERV-K annotated genomic sequences between MND cases and healthy controls.
+2.        To investigate whether HERV-K annotated methylation probes (epigenomic markers) are differently methylated in MND cases compared to healthy controls.
+3.        To identify predictors of MND and its survival by integrating machine learning and classical statistical approaches.
+
+## How is ABLeS supporting this work?
+
+
+
+## Expected outputs enabled by participation in ABLeS
+
+We expect to establish an association between a previously unidentified HERV-K subtype and MND. It is also expected to identify the differentially methylated HERV-K genome in MND. Finally, we expect to identify important predictors of MND using machine learning.
+
+<br/>
+
+> _These details have been provided by project members at project initiation. For more information on the project, please consult the contact(s) or project links above._

participants/HLA_prs.md

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+---
+title: Data Science Unit, Computational Systems Oncology, SAiGENCI, Adelaide
+description: The TCGA Prostate Adenocarcinoma (PRAD) cohort, with its rich germline SNP-array data linked to detailed clinical outcomes, offers a unique opportunity to investigate how germline variation influences both cancer biology and treatment response. By integrating SNP-based genome-wide risk profiling with high-resolution HLA imputation, we can build genetic models that not only improve prediction of prostate cancer recurrence and survival, but also reveal how inherited variation interacts with tumor subtypes, immune features, and treatment modalities such as surgery, radiation, or ADT.
+toc: false
+type: ABLeS Participant - Completed
+page_id: HLA_prs
+---
+
+## Project title
+
+Integrating HLA Variation and Polygenic Risk Scores to Predict Treatment Response and Clinical Outcomes in Prostate Cancer
+
+## Collaborators and funding
+
+SAiGENCI, Adelaide University
+
+## Contact(s)
+
+Simranjeet Kaur, Data Science Lead, SAiGENCI, <simran.kaur@adelaide.edu.au>
+
+## Project description and aims
+
+By integrating HLA-focused imputation and polygenic risk score (PRS) development, we aim to:
+1.        Identify germline HLA alleles and SNPs associated with prostate cancer susceptibility.
+2.        Construct and validate genome-wide and HLA-informed PRS.
+3.        Assess whether germline variation predicts recurrence, progression, or survival following primary therapies (surgery, radiation, ADT).
+4.        Explore whether germline variation interacts with tumor molecular subtypes (e.g., PTEN loss, ETS fusions, SPOP mutations) or immune features (tumor infiltration, checkpoint expression) that influence treatment sensitivity.
+The proposed study will provide foundational insights into how inherited variation shapes therapy response and prognosis in prostate cancer. 
+
+## How is ABLeS supporting this work?
+
+
+
+## Expected outputs enabled by participation in ABLeS
+
+Outputs include a clinically oriented GRS+HLA model, interaction evidence to guide treatment selection, and fully reproducible HPC pipeline, directly translatable to precision oncology workflows.
+Precisely: 
+-    Identification of HLA alleles and SNPs influencing recurrence and progression.
+-   Development of a validated PRS + HLA risk model for prostate cancer prognosis.
+-   Evidence for germline–treatment interactions that may guide therapy selection (e.g., surgery vs. radiation vs. ADT).
+-   Insights into host genetics–immune interactions with implications for immunotherapy.
+-   A reproducible, HPC-enabled analytic pipeline applicable to other TCGA cancer cohorts. 
+
+<br/>
+
+> _These details have been provided by project members at project initiation. For more information on the project, please consult the contact(s) or project links above._

participants/accelerator.md

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+---
+title: Omics Accelerator, Australia Wine Research Institute
+description: The Omics Accelerator is a NCRIS funded node that aims to develop and deploy multi-omic diagnostics for Australian industry. The requested project will utilize supercomputing resources for Omics accelerator and Metabolomics Australia method development.
+toc: false
+type: ABLeS Participant - Completed
+page_id: accelerator
+---
+
+## Project title
+
+Omics Accelerator
+
+## Collaborators and funding
+
+[Omics Accelerator/Metabolomics Australia](https://metabolomics.awri.com.au/)
+
+## Contact(s)
+
+-   Chris Ward, Omics Accelerator, <chris.ward@awri.com.au> 
+-   Anthony Borneman, Omics Accelerator, <anthony.borneman@awri.com.au>
+
+## Project description and aims
+
+The Omics Accelerator aims to develop multi-omic diagnostic solutions for deployment across Australian industries, with a focus on agriculture and the environment. The project will be crucial to developing multi-omic solutions within the Omics Accelerator. 
+
+Projects carried out will focus on the development and delivery of bioinformatics services that support applied research and enable the translation of data from a range of omics technologies into practical outcomes for industry, SMEs, and the broader research community
+
+Planned analyses include short-read population genomics, metagenomics, and quantitative genomics; genome assembly; and the integration of proteomic, genomic, and metabolomic datasets.
+
+## How is ABLeS supporting this work?
+
+
+
+## Expected outputs enabled by participation in ABLeS
+
+Outputs will be published and disseminated. Pipelines will be made available on the Omics Accelerator GitHub.
+
+<br/>
+
+> _These details have been provided by project members at project initiation. For more information on the project, please consult the contact(s) or project links above._

participants/aml.md

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+---
+title: Computational Drug Discovery Biology Group
+description: Small-molecule splicing modulators compounds may interact with recurrent AML mutations in spliceosome proteins like SF3B1, SRSF2, U2AF1, and cause synthetic lethality, killing mutant cells but spare healthy ones. In this project we will characterize the effect of 15 novel small-molecule splicing modulators compounds in the transcriptomes and RNA processing of cells using Nanopore single-molecule multimodal timing of in vivo mRNA synthesis protocol (https://www.biorxiv.org/content/10.1101/2025.04.27.650906v1.full.pdf).
+toc: false
+type: ABLeS Participant - Completed
+page_id: aml
+---
+
+## Project title
+
+Utilising splicing modulator induced poison exons as a therapy for high-risk AML
+
+## Collaborators and funding
+
+-   [Children's Cancer Institute](https://www.ccia.org.au/about-cci/our-people/antoine-de-weck), *The Druggable Transcriptome: Small Molecules Inducing Reading Frame Shifts (SMIRFS)*
+$1,313,681
+
+-   NHMRC Ideas Grant (01 Jan 2025 - 31 Dec 2027), CIA, *Treating Childhood Cancers by Small Molecule Mediated Reactivation of Tumour Suppressors*
+$500,000
+
+-   Luminesce Alliance (01 Jul 2025 - 30 Jun 2027), CIA, *Paralog-sparing targeting of H3-3A, the carrier of Diffuse Midline Glioma’s founding driver mutation H3K27M*
+£242,997
+
+-   CRUK Catalyst Grant (01 Nov 2025 - 31 May 2027), CIA, *Critical validation of our proprietary RNA splicing modulation discovery platform*
+$25,000
+
+-   CCI Seed Fund (01 Jun 2025 - 31 May 2026), CIB, *Exploring a Novel Alpha-Synuclein Isoform for Therapeutic Control of Its Expression in Parkinson's Disease Induced Pluripotent Stem Cells*
+£19,800
+
+-   Rosetrees / Seedcorn (01 Oct 2025 - 31 Sept 2026), CIB, *The ACRF Childhood Cancer Early Detection, Prevention and Treatment (ACCEPT) Program*
+$5,500,000
+
+-   ACRF (01 Feb 2025 - 31 Jan 2032), CIJ
+
+## Contact(s)
+
+-   Pablo Acera, Children's Cancer Institute, <paceramateos@ccia.org.au>
+-   Antoine de Weck, Children's Cancer Institute, <ADeWeck@ccia.org.au> 
+
+## Project description and aims
+
+**Overview**: This project will characterize the splicing effect of known splicing modulators on AML with splicing mutations (Aim1, transcriptomics). Next, we will generate (Aim2, medicinal chemistry) then characterize (Aim3, transcriptomics) novel related splicing modulator compounds with the most therapeutically promising ‘poison’ inclusion for synthetic lethal therapy in AML.
+-   Aim 1: Characterize known splicing modulators impact on AML with spliceosomal mutations.
+Known splicing modulators introduce splicing events specific to AML, due to their spliceosomal mutations. Through transcriptomics analysis (RNAseq & dFORCE) of cell lines with and without spliceosomal mutation, we can prioritize which splicing event to pursue for medicinal chemistry optimization. To do so we will use AML cell lines with and without splice mutations, and treat both conditions with Branaplam, Risdiplam and Votoplam. RNA-seq will provide a deep transcriptome wide assessment of splicing events, while dFORCE (https://doi.org/10.1101/2025.04.27.650906) will provide single-molecule long-read assessment of treatment.
+-   Aim 2: Generate novel compounds related to known splicing modulators
+Branaplam, Risdiplam and Votoplam are structurally similar. Nonetheless they introduce different splicing effects. A panel of ~20 structural analogues will be synthesized to investigate how structure affects splicing.
+In addition to completely novel structures, we will also survey existing literature/patents for interesting compounds. For instance, although compounds in the Branaplam patent may be weak splicing modulators of the intended target SMN2, they may strongly affect AML specific splicing events.
+-   Aim 3: Identify the most promising new compound for splice-mutant AML
+In this aim we will expand the characterization presented in Aim1 to the novel compounds generated in Aim2. By using the pioneering dFORCE technology in addition to RNAseq, we will generate the deepest characterization globally of splicing modulators. This new knowledge will identify which structures and compounds bias towards AML specific events.
+**Expected Outcome**: We will identify compounds representing precursor drugs ready for chemical optimization, preclinical development, and eventually full clinical development.
+
+## How is ABLeS supporting this work?
+
+
+
+## Expected outputs enabled by participation in ABLeS
+
+We expect our analysis to show that the novel analogs induce deleterious, cancer-specific splicing alterations, opening a new therapeutic strategy for hard-to-treat cancers such as AML.
+
+<br/>
+
+> _These details have been provided by project members at project initiation. For more information on the project, please consult the contact(s) or project links above._

participants/aware.md

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+---
+title: The University of Queensland
+description: This project develops GPU-accelerated, uncertainty-aware deep learning models for multimodal medical image segmentation by integrating imaging data with clinical signals and text using large language models (LLMs) and vision–language models (VLMs). The goal is to improve robustness, interpretability, and clinical reliability in computer-assisted diagnosis.
+toc: false
+type: ABLeS Participant - Completed
+page_id: aware
+---
+
+## Project title
+
+Uncertainty-aware Medical Data Analysis
+
+## Collaborators and funding
+
+The University of Queensland
+
+## Contact(s)
+
+Dr. Moloud Abdar, <m.abdar@uq.edu.au>
+
+## Project description and aims
+
+This project focuses on advancing medical image segmentation by developing multimodal deep learning frameworks that jointly model medical images (e.g., MRI, CT, ultrasound), auxiliary clinical signals, and textual information such as radiology reports and clinical annotations. The research addresses key challenges in clinical deployment, including limited labeled data, domain variability, and uncertainty in model predictions.
+
+The primary aim is to design GPU-accelerated segmentation models based on CNNs, transformers, and hybrid architectures, enhanced with Bayesian deep learning techniques for uncertainty quantification. Predictive uncertainty will be estimated using methods such as Monte Carlo dropout, Bayesian neural networks, and ensemble strategies to support safer and more interpretable clinical decision-making.
+
+A key innovation of this project is the integration of large language models (LLMs) and vision–language models (VLMs) to incorporate semantic and contextual knowledge from clinical text and multimodal signals. These models will be used for cross-modal alignment, prompt-based conditioning, and weakly supervised learning, enabling improved generalization and robustness in low-data and heterogeneous clinical settings.
+
+Planned analyses include large-scale training and evaluation on clinically relevant datasets, extensive hyperparameter optimization, and repeated stochastic inference to assess segmentation accuracy, calibration, and uncertainty. Performance will be measured using standard metrics such as Dice similarity coefficient and Intersection over Union (IoU), alongside uncertainty-aware evaluation measures.
+
+By leveraging large-scale GPU resources on the Gadi supercomputer, the project aims to deliver a robust, uncertainty-aware, multimodal medical image segmentation framework with significant impact on clinical reliability and real-world deployment.
+
+## How is ABLeS supporting this work?
+
+
+
+## Expected outputs enabled by participation in ABLeS
+
+Participation in ABLeS will enable the large-scale training and evaluation of GPU-intensive deep learning models for multimodal medical image segmentation and medical data analysis, which would not be feasible without access to national HPC resources. The expected outputs include:
+
+1. Peer-reviewed publications in leading machine learning and medical imaging venues (e.g., ICML, NeurIPS, MICCAI, IEEE TMI), describing novel uncertainty-aware segmentation methods and multimodal LLM/VLM-based frameworks.
+
+2. Open-source code repositories (e.g., GitHub) containing model implementations, training pipelines, and evaluation scripts to support reproducibility and reuse by the research community.
+
+3. Pretrained model checkpoints and configuration files (where permissible) to facilitate downstream research and benchmarking.
+
+4. Curated experimental results and metadata, including uncertainty estimates and evaluation metrics, stored in version-controlled repositories or institutional research data platforms.
+
+5. Technical reports and documentation summarizing experimental findings, best practices for multimodal training on HPC systems, and guidance for uncertainty-aware medical image analysis.
+
+<br/>
+
+> _These details have been provided by project members at project initiation. For more information on the project, please consult the contact(s) or project links above._