GeoMx Digital Spatial Profiler (DSP)
Quickly resolve tissue heterogeneity and the complexity of microenvironments with the GeoMx Digital Spatial Profiler (DSP), the most flexible and robust spatial multi-omic platform for analysis of FFPE and fresh frozen tissue sections. GeoMx is the only spatial biology platform that non-destructively profiles the expression of RNA and protein from distinct tissue compartments and cell populations with an automated and scalable workflow that integrates with standard histology staining. Spatially profile the whole transcriptome and 570+ protein targets separately or simultaneously from your choice of sample inputs: whole tissue sections, tissue microarrays (TMAs), or organoids. Make GeoMx DSP your spatial biology platform of choice for biomarker discovery and hypothesis testing. Decide where to draw the line and let the tissue be your guide with biology-driven profiling that empowers you to choose the tissue microenvironments and cell types that matter most to you.
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ESMC
ESMC is the latest in the ESM family of protein language models, establishing a new frontier in representation learning for protein biology. Trained on billions of evolutionary sequences, it learns representations that reflect a mechanistic reduction of protein structure and function. The model is built on a transformer architecture, supports sequences as its core modality, and is trained on up to 6 billion proteins. ESMC is designed for protein science research, including structure prediction, function annotation, protein design, and understanding evolutionary relationships between proteins. It can generate novel proteins from partial sequence, structure, or functional constraints, helping researchers explore new possibilities in protein design and biological discovery. The Biohub Platform provides access to ESMC through the API and the ESM Python package, with quickstart resources for installing the package, creating an API key, connecting to the platform.
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GPT-Rosalind
GPT-Rosalind is a purpose-built frontier reasoning model developed by OpenAI to accelerate scientific research across biology, drug discovery, and translational medicine. It is designed specifically for life sciences workflows, where researchers must navigate large volumes of literature, experimental data, and specialized databases to generate and validate new ideas. It combines deep domain understanding in areas such as chemistry, genomics, protein engineering, and disease biology with advanced tool-use capabilities, allowing it to interact with scientific databases, analyze experimental outputs, and support complex, multi-step reasoning tasks. It can assist with evidence synthesis, hypothesis generation, literature review, sequence interpretation, and experimental planning, helping scientists move faster from raw data to actionable insights. GPT-Rosalind transforms complex, time-intensive research processes into more efficient AI-assisted workflows.
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GPT-3.5
GPT-3.5 is the next evolution of GPT 3 large language model from OpenAI.
GPT-3.5 models can understand and generate natural language. We offer four main models with different levels of power suitable for different tasks. The main GPT-3.5 models are meant to be used with the text completion endpoint. We also offer models that are specifically meant to be used with other endpoints. Davinci is the most capable model family and can perform any task the other models can perform and often with less instruction. For applications requiring a lot of understanding of the content, like summarization for a specific audience and creative content generation, Davinci is going to produce the best results. These increased capabilities require more compute resources, so Davinci costs more per API call and is not as fast as the other models.
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