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#bioinformatics

18 posts17 participants1 post today
Public *
Luke Pembleton

Ahhh all I want for my team is MacBooks for local bioinformatics dev and some local Linux server compute power. Seems to be a never ending back and forth with IT… The challenge is bioinformatics is very different to the often assumed traditional data science or software dev… 😮‍💨
#bioinformatics

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Peter Cock

Abstract submitted with hours to spare - fingers crossed for #BOSC2025 / #ISMB2025 / #ECCB2025 and plenty of interesting #OpenSource #Bioinformatics presentations! ⏳

open-bio.org/events/bosc-2025/

Open Bioinformatics FoundationSubmit to BOSC 2025BOSC 2025 Abstract Submission BOSC 2025, the 26th annual Bioinformatics Open Source Conference, will take place July 21-22, 2025 in Liverpool, UK (as part of ISMB/ECCB 2025). BOSC covers all aspects of open source bioinformatics and open science. Abstract submission is now open! We welcome submission of your 1-2 page abstract for consideration as a talk or poster. The second day of BOSC 2025 will be a joint session with the newly renamed Bio-Ontologies and Knowledge Representation COSI (BOKR).
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Luke Pembleton

Anyone have any experience using #AWS workspaces or VNC as a #rstats or #bioinformatics dev environment. I much prefer the idea of keeping dev on a local laptop and avoiding potential latency issues, keeping a single file system, and the ability to work on the go even without internet, but would be interested to hear others experiences.

Public
Blue Headline - Tech News

Can sugar fuel cancer?
A new bioinformatics study shows that high blood sugar triggers specific genes that make breast cancer more aggressive and harder to treat.

🧬 Genes like CCNB2, XRCC2, and CENPI respond directly to glucose—supercharging tumor growth, DNA repair, and mutation.

It’s not just a diet issue—it’s molecular.

💬 Should we be treating metabolism as a cancer risk factor?

📖 Read more: blueheadline.com/tech-breakthr

#Technology#Bioinformatics#CancerResearch
Continued thread
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Melbourne Bioinformatics

📌 Please note: You MUST register for this event using an affiliated institutional email address or your registration may be cancelled.

For more information, please contact Melbourne Bioinformatics at: bioinformatics-training@unimelb.edu.au

#Bioinformatics #GalaxyAustralia

eventbrite.com.au/e/intro-to-g

EventbriteIntro to Galaxy for BioinformaticsEmpower your Bioinformatics research using Galaxy Australia
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Mark Ziemann 🇺🇦

🔬 𝗛𝗲𝗹𝗽 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝘁𝗵𝗲 𝗥𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝗳 𝗕𝗶𝗼𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗰𝘀 𝗧𝗼𝗼𝗹𝘀 𝗶𝗻 𝗕𝗶𝗼𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗦𝗰𝗶𝗲𝗻𝗰𝗲𝘀 + 𝗪𝗶𝗻 𝗮 𝗩𝗼𝘂𝗰𝗵𝗲𝗿! 🎟️

My wonderful PhD student (not on fedi) is runnning a survey about researcher views about pathway enrichment analysis.

If you are a life scientist who has ever done a pathway analysis this survey is for you.

Survey Link🔗 [lnkd.in/gHNMSD89]

📊 Deakin University ethics approval (ref:2024/HE000066).

First 150 participants get a $10 AUD gift voucher.

Flier to promote a research survey aimed at life scientists interested in pathway analysis. It is being administered by Anusuiya Bora at Deakin University and in collaboration with Burnet Institute. There is a QR code that links to the survey: https://researchsurveys.deakin.edu.au/jfe/form/SV_204lFSzkFaqLQTI
#Genomics#Bioinformatics#Biology
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Ivan Rossi :debian:

[2504.06806] Mass Balance Approximation of Unfolding Improves Potential-Like Methods for Protein Stability Predictions

arxiv.org/abs/2504.06806

arXiv logo
arXiv.orgMass Balance Approximation of Unfolding Improves Potential-Like Methods for Protein Stability PredictionsThe prediction of protein stability changes following single-point mutations plays a pivotal role in computational biology, particularly in areas like drug discovery, enzyme reengineering, and genetic disease analysis. Although deep-learning strategies have pushed the field forward, their use in standard workflows remains limited due to resource demands. Conversely, potential-like methods are fast, intuitive, and efficient. Yet, these typically estimate Gibbs free energy shifts without considering the free-energy variations in the unfolded protein state, an omission that may breach mass balance and diminish accuracy. This study shows that incorporating a mass-balance correction (MBC) to account for the unfolded state significantly enhances these methods. While many machine learning models partially model this balance, our analysis suggests that a refined representation of the unfolded state may improve the predictive performance.
#AI#bioinformatics
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Ivan Rossi :debian:

[2504.03278] JanusDDG: A Thermodynamics-Compliant Model for Sequence-Based Protein Stability via Two-Fronts Multi-Head Attention

arxiv.org/abs/2504.03278

arXiv logo
arXiv.orgJanusDDG: A Thermodynamics-Compliant Model for Sequence-Based Protein Stability via Two-Fronts Multi-Head AttentionUnderstanding how residue variations affect protein stability is crucial for designing functional proteins and deciphering the molecular mechanisms underlying disease-related mutations. Recent advances in protein language models (PLMs) have revolutionized computational protein analysis, enabling, among other things, more accurate predictions of mutational effects. In this work, we introduce JanusDDG, a deep learning framework that leverages PLM-derived embeddings and a bidirectional cross-attention transformer architecture to predict $ΔΔG$ of single and multiple-residue mutations while simultaneously being constrained to respect fundamental thermodynamic properties, such as antisymmetry and transitivity. Unlike conventional self-attention, JanusDDG computes queries (Q) and values (V) as the difference between wild-type and mutant embeddings, while keys (K) alternate between the two. This cross-interleaved attention mechanism enables the model to capture mutation-induced perturbations while preserving essential contextual information. Experimental results show that JanusDDG achieves state-of-the-art performance in predicting $ΔΔG$ from sequence alone, matching or exceeding the accuracy of structure-based methods for both single and multiple mutations.
#AI#llm#bioinformatics
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