Diangen Lin

ORCID: 0000-0002-6778-8563
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About
Contact & Profiles
Research Areas
  • Protein Structure and Dynamics
  • Heat shock proteins research
  • RNA and protein synthesis mechanisms
  • Machine Learning in Bioinformatics
  • Genomics and Phylogenetic Studies
  • SARS-CoV-2 and COVID-19 Research
  • Microbial Inactivation Methods
  • Viral Infectious Diseases and Gene Expression in Insects
  • Microfluidic and Bio-sensing Technologies
  • Scientific Computing and Data Management
  • Molecular Biology Techniques and Applications
  • RNA modifications and cancer
  • Genetics, Bioinformatics, and Biomedical Research
  • Machine Learning in Materials Science
  • thermodynamics and calorimetric analyses

University of Chicago
2022-2024

Argonne National Laboratory
2022-2023

We seek to transform how new and emergent variants of pandemic-causing viruses, specifically SARS-CoV-2, are identified classified. By adapting large language models (LLMs) for genomic data, we build genome-scale (GenSLMs) which can learn the evolutionary landscape SARS-CoV-2 genomes. pre-training on over 110 million prokaryotic gene sequences fine-tuning a SARS-CoV-2-specific model 1.5 genomes, show that GenSLMs accurately rapidly identify concern. Thus, our knowledge, represents one first...

10.1177/10943420231201154 article EN The International Journal of High Performance Computing Applications 2023-10-27

We seek to transform how new and emergent variants of pandemic-causing viruses, specifically SARS-CoV-2, are identified classified. By adapting large language models (LLMs) for genomic data, we build genome-scale (GenSLMs) which can learn the evolutionary landscape SARS-CoV-2 genomes. pre-training on over 110 million prokaryotic gene sequences fine-tuning a SARS-CoV-2-specific model 1.5 genomes, show that GenSLMs accurately rapidly identify concern. Thus, our knowledge, represents one first...

10.1101/2022.10.10.511571 preprint EN cc-by-nc-nd bioRxiv (Cold Spring Harbor Laboratory) 2022-10-11

In the upcoming decade, deep learning may revolutionize natural sciences, enhancing our capacity to model and predict occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors from drug development renewable energy. To answer this call, we present DeepSpeed4Science initiative (deepspeed4science.ai) which aims build unique capabilities through AI system technology innovations help domain experts unlock today's biggest science...

10.48550/arxiv.2310.04610 preprint EN cc-by arXiv (Cornell University) 2023-01-01
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