Morgan

Applying Biological AI to population genetics @ Uni of Edinburgh

[Cell @CellCellPress]

Now online! Virtual Cell Challenge 2026: Benchmarking zero-shot generalization across cellular contexts https://t.co/VuNIsPFQqE

[Yifei Hu @hu_yifei]

common ml optimization trick: don't show loss curves on wide screens. You model learns slower if you screen is too wide.

[Anshul Kundaje @anshulkundaje]

@jennbrophy7 @pkoo562 If u have deep expertise in genomic data processing with agentic platforms, DNALMs, S2F models & interested in joining us please reach out. I'll be hiring 1-2 postdocs/staff scientists for this project. Please RT 7/

[Niko McCarty. @NikoMcCarty]

I'm starting a biotech journal committed to excellent prose. We'll build many tools to make papers useful and fun to read for humans.

I'm looking to connect with people who know how to navigate this, sort indexing, etc. If you'd like to work together on this, please DM!

[Morgan @MorganPHT]

@CrossCountryUK @thetrainline They never replied

[Morgan @MorganPHT]

Integrating computational pathology and multi-transcriptomics to characterize lung adenocarcinoma heterogeneity and prognostic modeling https://t.co/K6ZFj7HHtc

[Morgan @MorganPHT]

A Novel Method for Endometrial Cancer Patient Stratification Considering ARID1A Protein Expression and Activity with Effective Use of Multi-omics Data https://t.co/MOhCZ92tep

[Morgan @MorganPHT]

Bioinformatics-Based Screening and Validation of PANoptosis-Related Biomarkers in Periodontitis https://t.co/8KtDkdMi2w

[Morgan @MorganPHT]

Intervention of machine learning in bladder cancer research using multi-omics datasets: systematic review on biomarker identification https://t.co/xf3VwA3Xqq

[Morgan @MorganPHT]

Exercise-related immune gene signature for hepatocellular carcinoma: machine learning and multi-omics analysis https://t.co/aBOPC1RLno

[Morgan @MorganPHT]

Machine Learning Combined Multi-Omics Analysis to Explore Key Oxidative Stress Features in Systemic Lupus Erythematosus https://t.co/BJqdOZq1EU

[Morgan @MorganPHT]

Machine learning and multi-omics analysis reveal key regulators of proneural–mesenchymal transition in glioblastoma https://t.co/LSR5wpWZHk

[Morgan @MorganPHT]

Artificial intelligence for early prediction of alcohol-related liver disease: Advances, challenges, and clinical applications https://t.co/IpavZJSe4X

[Morgan @MorganPHT]

… m6A‐Related Genes in Tumor‐Associated Macrophages for Prognosis, Immunotherapy, and Drug Prediction in Lung Adenocarcinomas Based on Machine Learning … https://t.co/DlvygNAGlp

[Morgan @MorganPHT]

Treatment Resistant Persister Cells Exploit Macrophage Lipid Metabolism to Sustain Glioblastoma Growth https://t.co/6qHIqRr0Df

[Morgan @MorganPHT]

Study of combination CAR T-cell treatment for glioblastoma using mathematical modeling https://t.co/fxy5dy7XJ1

[Morgan @MorganPHT]

Robust self-supervised machine learning for single cell embeddings and annotations https://t.co/pXJIsGOasa

[Morgan @MorganPHT]

Preliminary insights into salivary proteomic versus targeted biomarker profiles associated with acute physical fatigue https://t.co/JHbojOuSTg

[Morgan @MorganPHT]

Positional frequency chaos game representation for machine learning-based classification of crop lncRNAs https://t.co/yZtBwozohd

[Morgan @MorganPHT]

The Neurofibrillary Tangle Maturity Scale: A Novel Framework for Tangle Pathology Evaluation in Alzheimer's Disease https://t.co/jR3zpyfXgm