Clinical and Translational Science Institute
Advancing today’s discoveries to improve health for all.

All of Us Researcher Workbench Workshop at The Ohio State University

Cloud Infrastructure Development

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Cloud Infrastructure Development

  • NIH STRIDES

    An NIH program to allow researchers use their NIH funding directly to pay for cloud platform with a pricing discount.

    Contact: Qin Ma

  • Azure

    Azure is the cloud computing platform developed by Microsoft. Specially, it can support with the NIH STRIDES program at The Ohio State University Wexner Medical Center.

    Contact: Qin Ma

  • Amazon Web Services

    Amazon Web Services (AWS) is the cloud computing platform developed by Amazon. Specially, it can provide PHI data analysis and storage at Ohio State.

    Contact: Qin Ma

  • Terra

    Terra is a cloud-native platform for biomedical researchers to access data, run analysis tools and collaborate.

    Contact: Qin Ma

  • Brainlife.io

    A decentralized and open-source cloud platform to support neuroscience research.

    Reference: PMID: 38605111

    Contact: Qin Ma

  • DeepCell Kiosk

    A cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. Provided practical ways to deploy cloud deep learning infrastructure

    Reference: PMID: 33398191

    Contact: Qin Ma

  • ssREAD

    A single-cell and spatial RNA-seq database for Alzheimer’s disease (ssREAD) built on cloud. It offers a broader spectrum of AD-related datasets, an optimized analytical pipeline and improved usability over cloud.

    Reference: PMID: 38844475

    Contact: Qin Ma

Electronic Health Record (EHR) Predictive Analytical Models

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Electronic Health Record (EHR) Predictive Analytical Models

  • DeepIPW

    Treatment Effect Estimation based on deep learning framework.

    Reference: PMID: 35603127

    Contact: Ping Zhang

  • CURE

    Treatment Effect Estimation based on deep learning framework.

    Reference: PMID: 39005483

    Contact: Ping Zhang

  • KG-TREAT

    Treatment Effect Estimation based on deep learning framework.

    Reference: PMID: 38651015

    Contact: Ping Zhang

  • BiLSTM

    Predictive models based on deep learning.

    Reference: PMID: 33659912

    Contact: Ping Zhang

Pharmacology Knowledge Base

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Pharmacology Knowledge Base

  • MPRINT-KP Silver

    The Maternal and Pediatric Precision in Therapeutics (MPRINT) knowledge base is a crucial maternal and pediatric research informatics infrastructure. The silver version of the MPRINT knowledge base aims to assist researchers in discovering published maternal and pediatric research easily and quickly. The deep-learning model was trained using a comprehensive maternal and pediatric study corpus. Subsequently, PubMed abstracts were thoroughly screened using the established deep learning model to predict the publication study types and relevant populations. Users can retrieve studies based on the drugs, diseases, specific populations, and study types. 

    Reference: PMID: 36625779, https://ascpt.onlinelibrary.wiley.com/doi/10.1002/cpt.2835

    Contact: Lang Li

  • MPRINT-KP Gold

    The Maternal and Pediatric Precision in Therapeutics (MPRINT) knowledge base is a crucial maternal and pediatric research informatics infrastructure. The gold version of the MPRINT knowledge base aims to provide expert-curated knowledge from published maternal and pediatric studies. It primarily provides drug pharmacokinetic parameters and epidemiological evidence. Each piece of knowledge is well-annotated with relevant information, including demographic information, therapeutics, and other risk factors.

    Reference: PMID: 36625779, https://ascpt.onlinelibrary.wiley.com/doi/10.1002/cpt.2835

    Contact: Lang Li

  • DrugCombo

    DrugCombo is a comprehensive knowledge base that integrates drug toxicity and pharmacokinetic data for single drugs and drug combinations from the United States Food and Drug Administration Adverse Event Report System (FAERS), drug labeling from the National Library of Medicine’s DailyMed website, published study results in PubMed, and the DrugBank database. DrugCombo is the first database to contain crucial data, such as the maximum tolerable dose (MTD), dose-limiting toxicity (DLT), and dose range, for Phase I clinical trial design.  It also provides pharmacokinetic evidence of drug interactions between cancer drugs and other relevant knowledge. DrugCombo currently integrates 8577 drug/target interactions and 8797 drug interactions from DrugBank, 3995 severe adverse drug events (ADEs), 95,535 common ADEs from drug labels, 1,816,030 ADEs from FAERS, and MTD and DLTs from 2592 Phase I trials.

    Reference: https://doi.org/10.1158/1538-7445.AM2023-6573 , https://doi.org/10.1200/JCO.2023.41.16_suppl.1571 

    Contact: Lang Li

  • SLKB

    Synthetic lethality knowledge base (SLKB) is dedicated to curating CRISPR double knockout experiments (CDKO) aiming to identify synthetic lethal (SL) interactions between two genes. SL identification is highly context dependent, differing across pathways, gene targets, cell lines, and CDKO libraries. SLKB analysis pipeline, additionally distributed as a python package, allows SL score calculation for CDKO studies. Via SLKB, users can analyze their own CDKO data and browse their results.

    Reference: PMID: 37889037

    Contact: Lang Li

  • CTKG

    CTKG is a knowledge graph integrating a wide variety of information from clinical trials (e.g., interventions, drug side effects, disease symptoms). Together with computational tools to learn from knowledge graphs, CTKG faciliates knowledge discovery from clinical trials, and many relevant tasks such as information retrieval, drug repurposing, etc. 

    Reference: PMID: 35304504

    Contact: Xia Ning, Access Ning Lab

  • DRKG

    DRKG is a very large and comprehensive knowledge graph integrating a wide variety of entities, including drugs, diseases, symptoms, genes, proteins, etc. Together with computational tools to learn from knowledge graphs, DRKG facilitates drug repurposing, among other tasks such as information retrieval, etc. 

    Reference: https://github.com/gnn4dr/DRKG/blob/master/DRKG%20Drug%20Repurposing%20Knowledge%20Graph.pdf 

    Contact: Xia Ning

Natural Language Processing

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Natural Language Processing (NLP)

  • BioBERT

    BioBERT is Bidirectional Encoder Representations from Transformers for Biomedical Text Mining. It is a domain-specific language representation model pre-trained on large-scale biomedical corpora. BioBERT significantly outperforms many text mining methods on the following three representative biomedical text mining tasks: biomedical named entity recognition, biomedical relation extraction and biomedical question answering.

    Reference: PMID: 31501885

    Contact: Lang Li

  • BERT

    BERT is a transformer-based model that employs bidirectional training of Transformer encoders to achieve state-of-the-art performance in numerous natural language processing tasks at the time of its introduction. The development of BERT laid the groundwork for current generations of large language models, serving as a proof of concept for bidirectional transformer architectures. BERT is built upon the attention mechanism but deviates from the original encoder-decoder structure by focusing solely on the encoder. Its architecture consists of multiple layers of bidirectional self-attention and is pre-trained on a large corpus of text, including the entire Wikipedia and BookCorpus. BERT's key innovation is its ability to capture context from both directions, allowing for a deeper understanding of language semantics.

    Reference: https://arxiv.org/abs/1810.04805

    Contact: Lang Li

  • PubBERT

    PubBERT is a variant of BERT specifically fine-tuned on a corpus of biomedical literature, such as PubMed articles, to enhance its performance in biomedical text processing tasks. This adaptation allows PubBERT to better understand and generate language pertinent to the biomedical field, improving upon the general BERT model for domain-specific applications.

    Contact: Lang Li

  • Llama 2

    Llama 2 is a series of large language models by Meta AI, designed for various NLP tasks, available in three sizes: 7 billion, 13 billion and 70 billion parameters.

    Reference: https://arxiv.org/abs/2207.06947

    Contact: Ping Zhang

  • Mistral

    Mistral is an advanced language model designed for various natural language processing tasks, available in two sizes: 7 billion and 8x7 billion parameters.

    Reference: https://arxiv.org/abs/2307.02022

    Contact: Steve Rust

  • Mixtral

    Mixtral combines multiple 7 billion parameter models to enhance performance across various NLP tasks.

    Reference: https://arxiv.org/abs/2307.02022

    Contact: Steve Rust

  • Phi 2

    Phi 2 is a powerful language model designed for natural language understanding and generation.

    Reference: https://arxiv.org/abs/2306.11692

    Contact: Steve Rust

  • Flan-T5

    Flan-T5 is a fine-tuned language model from Google, optimized for various NLP tasks, available in sizes Lg 780M and XXL 11B.

    Reference: https://arxiv.org/abs/2210.11416

    Contact: Steve Rust

  • Falcon

    Falcon is a high-performance language model optimized for speed and efficiency, available in sizes 7 billion and 40 billion parameters.

    Reference: https://arxiv.org/abs/2208.13269

    Contact: Steve Rust

  • GatorTron

    GatorTron is a biomedical language model developed by the University of Florida, available in sizes S 345M, Medium 3.9B, and Large 9B.

    Reference: https://arxiv.org/abs/2207.07177

    Contact: Steve Rust

  • RoBERTa

    RoBERTa (Robustly optimized BERT approach) is a transformer-based model that improves on BERT by training with larger mini-batches and more data.

    Reference: https://arxiv.org/abs/1907.11692

    Contact: Steve Rust

  • Phi 3

    Phi 3 is the latest iteration of the Phi series, designed for advanced natural language processing and understanding.

    Reference: https://arxiv.org/abs/2308.11239

    Contact: Steve Rust

  • MPT

    MPT is a versatile transformer model available in sizes 7 billion and 30 billion parameters, designed for a wide range of NLP tasks.

    Reference: https://arxiv.org/abs/2307.11590

    Contact: Steve Rust

  • OPT

    OPT (Open Pre-trained Transformer) is a series of transformer models designed for various NLP tasks, available in multiple sizes.

    Reference: https://arxiv.org/abs/2205.01068

    Contact: Steve Rust

Clinical and Translational Science Pilot Program Ideation Session 2

Clinical and Translational Science Pilot Program Ideation Session 1

AI Informatics Tools

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AI Informatics Tools

These tools enable researchers to store, analyze and use data to improve healthcare by enhancing efficiency, maintaining accuracy and collaborating effectively throughout their projects. 

Doctor holding graphic of AI
AI for Drug Development
Data being organized
Bioinformatics Data Analysis Pipeline
Person putting data in the cloud
Cloud Infrastructure Development
Person using mobile health application
Dashboard and Mobile Health Application
Doctor using electronic health record
Electronic Health Record (EHR) Predictive Analytical Models
Person using AI assistant
Generative AI
People looking at charts
Image Data Analytics
Person displaying data
Natural Language Processing (NLP)
Doctor looking at pill boxes
Pharmacology Knowledge Base

Need Data Support?

The Ohio State University offers numerous data sources for researchers, but effectively navigating these resources can be daunting. To assist faculty and staff in this process our Data Navigator acts as the first point of contact. The Data Navigator provides a high-level explanation of regulatory and institutional processes regarding these databases, identifies potential collaborators and addresses initial data-related questions. The navigator helps clarify requests, coordinates efforts and links investigators with domain experts for different data sources.

Contact the Data Navigator

T32 Postdoctoral

T32 Predoctoral

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