#BiomedicalDataScience

Leeds Institute for Data Analytics

@lida-leeds.bsky.social

2 weeks ago

#NHSData #NHSCareers #BiomedicalDataScience #HealthDataScience #UKResearch #DataScienceCareers #EDIinSTEM #MentoringMatters
#LeadershipInScience

Leeds Institute for Data Analytics

@lida-leeds.bsky.social

1 month ago

📢 If you work in academia, industry, the NHS, or the public sector - your experience matters.

Please like or share to help us reach the wider community.
#BiomedicalDataScience #HealthDataScience #UKResearch #DataScienceCareers #EDIinSTEM #MentoringMatters #LeadershipInScience

BioniChaos

@bionichaos.bsky.social

3 months ago

Demystifying Biomedical Data: Exploring Open-Source Tools and Ethical Tech Frontiers YouTube video by BioniChaos

Demystifying biomedical data! 🧠 BioniChaos is democratizing EEG/ECG analysis using open-source web tools & Game-Based Learning. We dive into the tech & the ethics of neuroenhancement.
Link: youtu.be/yZ8_ZTLABx8
#BiomedicalDataScience #NeurotechEthics #OpenSource #BioniChaos

Leeds Institute for Data Analytics

@lida-leeds.bsky.social

3 months ago

Read the full article 👉 lida.leeds.ac.uk/news/promote...

#BiomedicalDataScience #LIDA #PROMOTE #MRC #ResearchCulture #DataCareers

BIOMICS_GIMM

@biomics-twinning.bsky.social

7 months ago

BIOMICS Biomedical Data Science Seminar
🎙 @pedrobeltrao.bsky.social from @ethz.ch
“The genetics of human trait variation across the scales of biological organization”
🗓 17 July | 12:00 (Lisbon)
📍 GIMM Lisboa, Egas Moniz Building - Aud. 58
💻 Zoom ⬇︎
#BIOMICS #Genetics #BiomedicalDataScience #ETHZurich

JMIR Publications

@jmirpub.bsky.social

8 months ago

Twenty-Five Years of AI in Neurology: The Journey of Predictive Medicine and Biological Breakthroughs Neurological disorders are the leading cause of physical and cognitive disability across the globe, currently affecting up to 15% of the world population, with the burden of chronic neurodegenerative diseases having doubled over the last 2 decades. Two decades ago, neurologists relying solely on clinical signs and basic imaging faced challenges in diagnosis and treatment. Today, the integration of artificial intelligence (AI) and bioinformatic methods is changing this landscape. This paper explores this transformative journey, emphasizing the critical role of AI in neurology, aiming to integrate a multitude of methods and thereby enhance the field of neurology. Over the past 25 years, integrating biomedical data science into medicine, particularly neurology, has fundamentally transformed how we understand, diagnose, and treat neurological diseases. Advances in genomics sequencing, the introduction of new imaging methods, the discovery of novel molecular biomarkers for nervous system function, a comprehensive understanding of immunology and neuroimmunology shaping disease subtypes, and the advent of advanced electrophysiological recording methods, alongside the digitalization of medical records and the rise of AI, all led to an unparalleled surge in data within neurology. In addition, telemedicine and web-based interactive health platforms, accelerated by the COVID-19 pandemic, have become integral to neurology practice. The real-world impact of these advancements is evident, with AI-driven analysis of imaging and genetic data leading to earlier and more accurate diagnoses of conditions such as multiple sclerosis, Parkinson disease, amyotrophic lateral sclerosis, Alzheimer disease, and more. Neuroinformatics is the key component connecting all these advances. By harnessing the power of IT and computational methods to efficiently organize, analyze, and interpret vast datasets, we can extract meaningful insights from complex neurological data, contributing to a deeper understanding of the intricate workings of the brain. In this paper, we describe the large-scale datasets that have emerged in neurology over the last 25 years and showcase the major advancements made by integrating these datasets with advanced neuroinformatic approaches for the diagnosis and treatment of neurological disorders. We further discuss challenges in integrating AI into neurology, including ethical considerations in data use, the need for further personalization of treatment, and embracing new emerging technologies like quantum computing. These developments are shaping a future where neurological care is more precise, accessible, and tailored to individual patient needs. We believe further advancements in AI will bridge traditional medical disciplines and cutting-edge technology, navigating the complexities of neurological data and steering medicine toward a future of more precise, accessible, and patient-centric health care.

Twenty-Five Years of AI in Neurology: The Journey of Predictive Medicine and Biological Breakthroughs #AIinNeurology #PredictiveMedicine #NeurodegenerativeDiseases #BiomedicalDataScience #NeurologyBreakthroughs

BIOMICS_GIMM

@biomics-twinning.bsky.social

9 months ago

😋As we prepare for upcoming meetings, training sessions, and public engagement activities, it's great to have tangible reminders of the identity and mission behind the BIOMICS Twinning Project. 🤩
#BIOMICS #BiomedicalDataScience #HorizonEurope

Annalisa Barla

@annalisabarla.bsky.social

6 years ago

our paper on aging and glucose metabolism is out! https://rdcu.be/bKGBU @F_Samu @malga_center @SciReports #biomedicaldatascience #machinelearning