Pingjun Chen

🚀 New Tool Release: scGCL — Single-Cell Graph Contrastive Learning Framework Excited to share the release of scGCL, a graph-based contrastive learning framework designed for advanced single-cell transcriptomics analysis and cell-type clustering. 🔬 What is scGCL? scGCL is a computational framework that leverages graph contrastive learning to improve representation learning and clustering of single-cell RNA-seq data. By integrating graph neural networks with contrastive learning strategies, the tool enhances detection of subtle cellular heterogeneity and improves clustering robustness across complex datasets. 💡 Key Features • Graph-based representation learning for single-cell data • Contrastive learning for improved clustering accuracy • Scalable architecture for large single-cell datasets • Modular and reproducible pipeline for research use • Designed for integrative multi-omics and AI-driven analyses 🧬 Applications • Cell-type identification and annotation • Tumor microenvironment heterogeneity • Developmental biology studies • Multi-omics integration • Precision medicine research 🔗 GitHub Repository: https://lnkd.in/dXpDwTn7 #Bioinformatics #SingleCell #scRNAseq #AIinGenomics #GraphNeuralNetworks #ComputationalBiology #OpenScience #PrecisionMedicine

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MOREL (Multi-Output REgression and Learning) is a machine learning framework we developed to tackle the long-standing challenge of missing data in DNA methylation arrays. By combining multi-output regression with ensemble and deep learning models, MOREL can accurately restore systematically missing CpG sites caused by differences between array versions or even across platforms like RRBS and arrays. Implemented in the open-source ultra-impute package, MOREL not only outperforms traditional imputation methods but also preserves biological complexity, enabling seamless integration of legacy and modern methylation datasets and improving downstream analyses such as epigenetic clock predictions. The MOREL methods are available in our Ultra-Impute package: https://lnkd.in/gy47h_R6

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Cell Press just published something really interesting (and wild!) today: Scientists from UIUC simulated a complete living cell for the first time. Every molecule, every reaction, from DNA replication to cell division. The paper (https://lnkd.in/ess6SwFK) used JCVI-Syn3A — a synthetic minimal bacterium with fewer than 500 genes. A 3D+time simulation of the full 105-minute cell cycle: DNA replication, protein translation, metabolism, division. Every gene, protein, RNA, and chemical reaction tracked through physical space. It took years to build. Multiple GPUs. Six days of compute time per run. And this is the simplest possible cell. A human cell has ~20,000 genes. It lives in tissue. It interacts with neighbors. It differentiates. It responds to drugs in ways that depend on context we haven't fully measured. Mechanistic simulation of the minimal cell costs 6 GPU-days for 105 minutes of biology. You cannot scale that to human cells. The complexity isn't 40x harder. It's exponentially harder. This is why the field pivoted to data-driven models. You can't hand-encode the regulatory wiring of a human hepatocyte. But you can learn it — if you have the right perturbation data collected across enough diverse biological contexts. The two approaches aren't competing. Papers like this generate the ground truth that future ML models need for validation. But the path to a clinically useful virtual cell runs through foundation models, not through scaling up mechanistic simulation. Amazing work!

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Large Language Models (LLMs) in Drug Discovery & Clinical Trials 🔹 What Are Large Language Models? Large Language Models (LLMs) — like GPT, Claude, or Gemini — are neural networks trained on vast text corpora to learn the statistical relationships between words, phrases, and concepts. They don’t just memorize language — they learn to represent meaning, context, and reasoning across domains. Mathematically, they model the probability of a text sequence: n P(w1​,w2​,…,wn​) = ∏ P(wt​ ∣ w1:t−1​) t=1 At scale (hundreds of billions of parameters), this allows them to: Summarize and explain scientific literature Generate new hypotheses Interpret complex data narratives Interact in natural language 🔹 What Can LLMs Do in Drug Discovery? LLMs are revolutionizing how biomedical knowledge is accessed and synthesized. 1. Knowledge Mining from Biomedical Literature LLMs can read and summarize millions of PubMed abstracts, patents, and trial reports to: Identify potential drug–target interactions Detect repurposing opportunities Summarize mechanisms of action 🧠 Example: BioGPT (Microsoft) and Galactica (Meta) trained on biomedical text to generate mechanistic summaries and suggest novel hypotheses. 2. Target Discovery and Mechanistic Insights Integrate omics data and textual knowledge to uncover biological pathways. Link genetic variants (e.g., from GWAS) to functional targets. 📘 Example: LLMs like EVA Pharma’s BioNLP pipeline and Insilico Medicine’s PandaOmics use domain-tuned LLMs to rank and prioritize novel drug targets. 3. Clinical Trial Optimization LLMs can: Read prior clinical trial protocols to design new ones Extract inclusion/exclusion criteria Match patients to trials based on EHR narratives Predict dropout risks and adverse events 💡 Real-world example: Pfizer has piloted GPT-based tools for protocol writing and trial summarization, Medidata uses LLMs for automatic adverse event classification. 4. Regulatory and Safety Applications Automate generation of clinical study reports and lay summaries Interpret spontaneous safety reports Harmonize data to CDISC and ICH standards 🔹 How to Use LLMs in Practice Fine-tune foundation models on biomedical text (PubMed, CT.gov, EHRs). Use retrieval-augmented generation (RAG) to ensure traceability — cite real evidence. Combine with structured data (omics, assays, trial metadata) for hybrid reasoning. Apply human-in-the-loop review for safety and scientific accuracy. Large Language Models (LLMs) are transforming how we discover and test new drugs. 🚀 The future of drug discovery is not just data-driven — it’s language-driven.

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Rethinking tokenisation for genomic sequence data 🧬: our AI research Team at M42 Health has integrated biological context into the modelling process, significantly enhancing how we represent and analyse genomic information. Read some of the key highlights in the post below.

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🚀 New GitHub Repositories: Bioinformatics Algorithms from Scratch 🧬💻 I’ve recently published a series of bioinformatics and computational biology repositories implementing core sequence alignment and phylogenetic algorithms from first principles in R. These projects were developed with an emphasis on algorithmic transparency, biological interpretation, and computational complexity, making them useful for both education and research. These projects were completed as apart of my Bio Seq class at GMU for my PhD. 🔬 What’s included: Smith–Waterman (Local Sequence Alignment) and Needleman-Wunsch (Global Sequence Alignment) Dynamic programming implementation with full traceback, scoring matrices, and runtime complexity discussion. UPGMA & WPGMA Distance-based hierarchical clustering methods illustrating molecular clock assumptions and their impact on tree topology. Neighbor-Joining (NJ) A biologically realistic, clock-free phylogenetic method with explicit branch length estimation and Q-matrix construction. 📊 Each repository includes: Clean, well-annotated code Runtime and space complexity analysis GitHub-ready documentation (README.md) Visualizations 🎓 These projects reflect my ongoing work at the intersection of bioinformatics education, computational biology, and reproducible research, and align closely with my teaching and research interests. 🔗 GitHub: https://lnkd.in/eefXHdAg Always happy to connect with others interested in bioinformatics, genomics, phylogenetics, or computational methods in biology. #Bioinformatics #ComputationalBiology #Phylogenetics #Genomics #GitHub #RStats #STEMEducation #ReproducibleResearch #TeachingBioinformatics

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𝗧𝗵𝗲 𝗲𝗿𝗮 𝗼𝗳 𝘀𝗶𝗹𝗼𝗲𝗱 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 𝗶𝗻 𝗰𝗮𝗻𝗰𝗲𝗿 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗶𝘀 𝗼𝘃𝗲𝗿. The transition from classical machine learning to 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗙𝗠𝘀) marks a fundamental shift in how we approach precision medicine. Integrating genomics, proteomics, imaging, and clinical data is no longer optional. It is the only way to unravel the complexity of cancer. I am thrilled to share that our latest research, "𝗙𝗿𝗼𝗺 𝗰𝗹𝗮𝘀𝘀𝗶𝗰𝗮𝗹 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘁𝗼 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀: 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗰𝗮𝗻𝗰𝗲𝗿 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵", has officially been published in 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗥𝗲𝘃𝗶𝗲𝘄. In this paper, we provide the first systematic map of the transition from conventional machine learning to advanced 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗙𝗠𝘀). We explore: 🚀 The shift from task-specific models to large-scale pre-trained backbones. 🧬 Holistic strategies for multi-omics and advanced imaging integration. 🗺️ A roadmap for the forthcoming era of large-scale AI models in oncology. 🎯 Our goal is to provide researchers and clinicians with the essential groundwork to revolutionize cancer diagnosis, prognosis, and biomarker discovery. 📖 𝗙𝘂𝗹𝗹-𝘁𝗲𝘅𝘁 𝗔𝗰𝗰𝗲𝘀𝘀: 🔗 https://rdcu.be/e73oj 💻 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗼𝘂𝗿 𝗚𝗶𝘁𝗛𝘂𝗯 𝗿𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆: https://lnkd.in/eGNbBBN9 A huge thank you to my incredible co-authors: Muhammad Waqas Sheikh, PhD, Maliazurina B. Saad, Eman Showkatian, Rukhmini Roy, Hui Xu, Wentao Li, Joe Chang, Zhongxing Liao, Cara Haymaker, Luisa M. Solis Soto, @Carol C. Wu @Natalie I. Vokes, Xiuning Le, @Lauren A. Byers @Don Gibbons @John V. Heymach, Jianjun Zhang, Jia Wu #AI #MachineLearning #CancerResearch #FoundationModels #DeepLearning #PrecisionMedicine #MultimodalData #AIinMedicine #SpringerNature #Oncology

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