CBKH: The Cornell Biomedical Knowledge Hub

Related tags

Deep LearningCBKH
Overview

Cornell Biomedical Knowledge Hub (CBKH)

CBKG integrates data from 18 publicly available biomedical databases. The current version of CBKG contains a total of 2,932,164 entities of 10 types. Specifically, the CBKH includes 22,963 anatomy entities, 18,774 disease entities, 36,522 drug entities, 87,942 gene entities, 2,065,015 molecule entities, 1,361 symptom entities, 4,101 DSI entities, 137,568 DSP entities, 605 TC entities and 2,970 pathway entities. For the relationships in the CBKG (Table 3), there are 100 relation types within 17 kinds of entity pairs, including Anatomy-Gene, Drug-Disease, Drug-Drug, Drug-Gene, Disease-Disease, Disease-Gene, Disease-Symptom, Gene-Gene, DSI-Disease, DSI-Symptom, DSI-Drug, DSI-Anatomy, DSI-DSP, DSI-TC, Disease-Pathway, Drug-Pathway and Gene-Pathway. In total, CBKH contains 49,541,938 relations.

Schema

Materials and Methods

Our ultimate goal was to build a biomedical knowledge graph via comprehensively incorporating biomedical knowledge as much as possible. To this end, we collected and integrated 18 publicly available data sources to curate a comprehensive one. Details of the used data resources were listed in Table.

Statistics of CBKH

Entity Type Number Included Identifiers
Anatomy 22,963 Uberon ID, BTO ID, MeSH ID, Cell Ontology ID
Disease 18,774 Disease Ontology ID, KEGG ID, PharmGKB ID, MeSH ID, OMIM ID
Drug 36,759 DrugBank ID, KEGG ID, PharmGKB ID, MeSH ID
Gene 87,942 HGNC ID, NCBI ID, PharmGKB ID
Molecule 2,065,015 CHEMBL ID, CHEBI ID
Symptom 1,361 MeSH ID
Dietary Supplement Ingredient 4,101 iDISK ID
Dietary Supplement Product 137,568 iDISK ID
Therapeutic Class 605 iDISK ID, UMLS CUI
Pathway 2,970 Reactome ID, KEGG ID
Total Entities 2,382,309 -
Relation Type Number
Anatomy-Gene 12,825,270
Drug-Disease 2,711,848
Drug-Drug 2,684,682
Drug-Gene 1,295,088
Disease-Disease 11,072
Disease-Gene 27,541,618
Disease-Symptom 3,357
Gene-Gene 1,605,716
DSI-Symptom 2,093
DSI-Disease 5,134
DSI-Anatomy 4,334
DSP-DSI 689,297
DSI-TC 5,430
Disease-Pathway 1,942
Drug-Pathway 3,231
Gene-Pathway 153,236
Drug-Side Effect 163,206
Total Relations 49,706,554

Licence

The data of CBKG is licensed under the MIT License. The CBKH integrated the data from many resources, and users should consider the licenses for each of them (see the detail in the table).

Cite

@article{su2021cbkh,
  title={CBKH: The Cornell Biomedical Knowledge Hub},
  author={Su, Chang and Hou, Yu and Guo, Winston and Chaudhry, Fayzan and Ghahramani, Gregory and Zhang, Haotan and Wang, Fei},
  journal={medRxiv},
  year={2021},
  publisher={Cold Spring Harbor Laboratory Press},
  url = {https://www.medrxiv.org/content/10.1101/2021.03.12.21253461v1}
}
A Deep Learning based project for creating line art portraits.

ArtLine The main aim of the project is to create amazing line art portraits. Sounds Intresting,let's get to the pictures!! Model-(Smooth) Model-(Quali

Vijish Madhavan 3.3k Jan 07, 2023
Numerical Methods with Python, Numpy and Matplotlib

Numerical Bric-a-Brac Collections of numerical techniques with Python and standard computational packages (Numpy, SciPy, Numba, Matplotlib ...). Diffe

Vincent Bonnet 10 Dec 20, 2021
Source codes for the paper "Local Additivity Based Data Augmentation for Semi-supervised NER"

LADA This repo contains codes for the following paper: Jiaao Chen*, Zhenghui Wang*, Ran Tian, Zichao Yang, Diyi Yang: Local Additivity Based Data Augm

GT-SALT 36 Dec 02, 2022
Codes and models of NeurIPS2021 paper - DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks

DominoSearch This is repository for codes and models of NeurIPS2021 paper - DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense n

11 Sep 10, 2022
The open-source and free to use Python package miseval was developed to establish a standardized medical image segmentation evaluation procedure

miseval: a metric library for Medical Image Segmentation EVALuation The open-source and free to use Python package miseval was developed to establish

59 Dec 10, 2022
PyTorch implementation for paper Neural Marching Cubes.

NMC PyTorch implementation for paper Neural Marching Cubes, Zhiqin Chen, Hao Zhang. Paper | Supplementary Material (to be updated) Citation If you fin

Zhiqin Chen 109 Dec 27, 2022
Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR

Official implementation for paper "Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR"

Ziyue Feng 72 Dec 09, 2022
Compositional and Parameter-Efficient Representations for Large Knowledge Graphs

NodePiece - Compositional and Parameter-Efficient Representations for Large Knowledge Graphs NodePiece is a "tokenizer" for reducing entity vocabulary

Michael Galkin 107 Jan 04, 2023
This is a custom made virus code in python, using tkinter module.

skeleterrorBetaV0.1-Virus-code This is a custom made virus code in python, using tkinter module. This virus is not harmful to the computer, it only ma

AR 0 Nov 21, 2022
The second project in Python course on FCC

Assignment Write a function named add_time that takes in two required parameters and one optional parameter: a start time in the 12-hour clock format

Denise T 1 Dec 13, 2021
The undersampled DWI image using Slice-Interleaved Diffusion Encoding (SIDE) method can be reconstructed by the UNet network.

UNet-SIDE The undersampled DWI image using Slice-Interleaved Diffusion Encoding (SIDE) method can be reconstructed by the UNet network. For Super Reso

TIANTIAN XU 1 Jan 13, 2022
Practical Single-Image Super-Resolution Using Look-Up Table

Practical Single-Image Super-Resolution Using Look-Up Table [Paper] Dependency Python 3.6 PyTorch glob numpy pillow tqdm tensorboardx 1. Training deep

Younghyun Jo 116 Dec 23, 2022
A crash course in six episodes for software developers who want to become machine learning practitioners.

Featured code sample tensorflow-planespotting Code from the Google Cloud NEXT 2018 session "Tensorflow, deep learning and modern convnets, without a P

Google Cloud Platform 2.6k Jan 08, 2023
Stroke-predictions-ml-model - Machine learning model to predict individuals chances of having a stroke

stroke-predictions-ml-model machine learning model to predict individuals chance

Alex Volchek 1 Jan 03, 2022
Cooperative Driving Dataset: a dataset for multi-agent driving scenarios

Cooperative Driving Dataset (CODD) The Cooperative Driving dataset is a synthetic dataset generated using CARLA that contains lidar data from multiple

Eduardo Henrique Arnold 124 Dec 28, 2022
Orthogonal Over-Parameterized Training

The inductive bias of a neural network is largely determined by the architecture and the training algorithm. To achieve good generalization, how to effectively train a neural network is of great impo

Weiyang Liu 11 Apr 18, 2022
Code for ViTAS_Vision Transformer Architecture Search

Vision Transformer Architecture Search This repository open source the code for ViTAS: Vision Transformer Architecture Search. ViTAS aims to search fo

46 Dec 17, 2022
Self-Supervised Learning for Domain Adaptation on Point-Clouds

Self-Supervised Learning for Domain Adaptation on Point-Clouds Introduction Self-supervised learning (SSL) allows to learn useful representations from

Idan Achituve 66 Dec 20, 2022
For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training.

LongScientificFormer For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training. Some code

Athar Sefid 6 Nov 02, 2022
Official implementation of "CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding" (CVPR, 2022)

CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding (CVPR'22) Paper Link | Project Page Abstract : Manual an

Mohamed Afham 152 Dec 23, 2022