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In current times people think that machine learning is the panacea, a belief that is far from the truth. At panacea lab we aim to build machine learning, computer vision, and NLP methods that help to generate insights from multi-modal large-scale data sources. With applications to precision medicine, medical informatics, astroinformatics and other domains, our work addresses domain-specific problems with data science methods and practices. Additionally, we are fully invested in helping science reproducibility by releasing (when possible) open-source and publicly available datasets and tools to aid reproducibility efforts.
With the new challenges brought by the data deluge in all fields, we are open to collaborations. Contact us here.
Our lab is supported by: Department of Veterans Affairs (Grant 1 I01 HX002487-01) and National Institute of Aging through Stanford Universitiy's Stanford Aging & Ethnogeriatrics Transdisciplinary Collaborative Center (SAGE) center (Grant 3P30AG059307-02S1)
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R package for Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation (APHRODITE). Built the OHDSI collaborative.
Github Link
ANANKE
R package for Mapping Between OHDSI Concept Identifiers to Unified Medical Language System (UMLS). Built for the OHDSI collaborative.
Github Link
AEOLUS
A curated and standardized adverse drug event resource to accelerate drug safety research. Built for the 旋风加速器专业版下载安装
Dryad Link | 旋风加速器下载
LSDO
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Harvard Dataverse Link | 旋风加速器ios下载网站
LIDDI
Provenance-centered dataset of drug-drug interactions.
Github Link
ImageFARMER
Introducing a Data Mining Framework for the Creation of Large-scale Content-based Image Retrieval Systems
Github Link
For a other code and datasets visit the lab's github page.
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- All
- Biomedical Informatics
- Solar Informatics
Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models
A large-scale solar dynamics observatory image dataset for computer vision applications
Characterizing treatment pathways at scale using the OHDSI network
Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network
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Regional content-based image retrieval for solar images: Traditional versus modern methods
Provenance-Centered Dataset of Drug-Drug Interactions
Solar Event Classification Using Deep Convolutional Neural Networks
For a full list of publications go here.
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Principal Investigator
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旋风加速器下载
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CS PhD Student
Bhaskar Ray
CS PhD Student
旋风加速器专业版下载安装
MS Student
Geeth Anirudh Suryadevara
MS Student
Kimberly Luviano-Garcia
UAP Scholar
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Contact info
25 Park Place - Suite 752,
Atlanta, GA 30303
jmbanda@panacealab.org
+1 404-413-5585
