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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError Exception: DatasetGenerationCastError Message: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 14 new columns ({'ICD10_ID', 'DO_ID', 'DO_Name', 'OMIM_ID', 'ICD11_Title', 'MeSH_Name', 'MONDO_ID', 'MeSH_ID', 'ICD11_ID', 'SNOMEDCT_ID', 'MONDO_Name', 'UMLS_Name', 'SNOMEDCT_Name', 'UMLS_ID'}) and 1 missing columns ({'Type'}). This happened while the csv dataset builder was generating data using hf://datasets/FuhaiLiAiLab/BioMedGraphica/BioMedGraphica-Conn/Entity/Disease/BioMedGraphica_Conn_Disease.csv (at revision caa5502cca807df484eab5f6bcb8aa71c8ce7b4f) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations) Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1831, in _prepare_split_single writer.write_table(table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, in write_table pa_table = table_cast(pa_table, self._schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2218, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast BioMedGraphica_Conn_ID: string BioMedGraphica_ID: string SNOMEDCT_ID: string UMLS_Name: string MeSH_Name: string ICD11_ID: string ICD11_Title: string ICD10_ID: string DO_ID: string DO_Name: string UMLS_ID: string MeSH_ID: string OMIM_ID: string MONDO_ID: string MONDO_Name: string SNOMEDCT_Name: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2181 to {'BioMedGraphica_Conn_ID': Value('string'), 'BioMedGraphica_ID': Value('string'), 'Type': Value('string')} because column names don't match During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1451, in compute_config_parquet_and_info_response parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet( File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 994, in stream_convert_to_parquet builder._prepare_split( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1833, in _prepare_split_single raise DatasetGenerationCastError.from_cast_error( datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 14 new columns ({'ICD10_ID', 'DO_ID', 'DO_Name', 'OMIM_ID', 'ICD11_Title', 'MeSH_Name', 'MONDO_ID', 'MeSH_ID', 'ICD11_ID', 'SNOMEDCT_ID', 'MONDO_Name', 'UMLS_Name', 'SNOMEDCT_Name', 'UMLS_ID'}) and 1 missing columns ({'Type'}). This happened while the csv dataset builder was generating data using hf://datasets/FuhaiLiAiLab/BioMedGraphica/BioMedGraphica-Conn/Entity/Disease/BioMedGraphica_Conn_Disease.csv (at revision caa5502cca807df484eab5f6bcb8aa71c8ce7b4f) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
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BioMedGraphica_Conn_ID
string | BioMedGraphica_ID
string | Type
string |
---|---|---|
BMGC_PM00001
|
BMG_PM000001
|
Promoter
|
BMGC_PM00002
|
BMG_PM000002
|
Promoter
|
BMGC_PM00003
|
BMG_PM000003
|
Promoter
|
BMGC_PM00004
|
BMG_PM000004
|
Promoter
|
BMGC_PM00005
|
BMG_PM000005
|
Promoter
|
BMGC_PM00006
|
BMG_PM000006
|
Promoter
|
BMGC_PM00007
|
BMG_PM000007
|
Promoter
|
BMGC_PM00008
|
BMG_PM000017
|
Promoter
|
BMGC_PM00009
|
BMG_PM000018
|
Promoter
|
BMGC_PM00010
|
BMG_PM000019
|
Promoter
|
BMGC_PM00011
|
BMG_PM000020
|
Promoter
|
BMGC_PM00012
|
BMG_PM000022
|
Promoter
|
BMGC_PM00013
|
BMG_PM000023
|
Promoter
|
BMGC_PM00014
|
BMG_PM000024
|
Promoter
|
BMGC_PM00015
|
BMG_PM000025
|
Promoter
|
BMGC_PM00016
|
BMG_PM000027
|
Promoter
|
BMGC_PM00017
|
BMG_PM000030
|
Promoter
|
BMGC_PM00018
|
BMG_PM000038
|
Promoter
|
BMGC_PM00019
|
BMG_PM000040
|
Promoter
|
BMGC_PM00020
|
BMG_PM000041
|
Promoter
|
BMGC_PM00021
|
BMG_PM000042
|
Promoter
|
BMGC_PM00022
|
BMG_PM000043
|
Promoter
|
BMGC_PM00023
|
BMG_PM000044
|
Promoter
|
BMGC_PM00024
|
BMG_PM000045
|
Promoter
|
BMGC_PM00025
|
BMG_PM000046
|
Promoter
|
BMGC_PM00026
|
BMG_PM000047
|
Promoter
|
BMGC_PM00027
|
BMG_PM000048
|
Promoter
|
BMGC_PM00028
|
BMG_PM000049
|
Promoter
|
BMGC_PM00029
|
BMG_PM000050
|
Promoter
|
BMGC_PM00030
|
BMG_PM000051
|
Promoter
|
BMGC_PM00031
|
BMG_PM000052
|
Promoter
|
BMGC_PM00032
|
BMG_PM000053
|
Promoter
|
BMGC_PM00033
|
BMG_PM000054
|
Promoter
|
BMGC_PM00034
|
BMG_PM000055
|
Promoter
|
BMGC_PM00035
|
BMG_PM000056
|
Promoter
|
BMGC_PM00036
|
BMG_PM000057
|
Promoter
|
BMGC_PM00037
|
BMG_PM000058
|
Promoter
|
BMGC_PM00038
|
BMG_PM000059
|
Promoter
|
BMGC_PM00039
|
BMG_PM000060
|
Promoter
|
BMGC_PM00040
|
BMG_PM000061
|
Promoter
|
BMGC_PM00041
|
BMG_PM000062
|
Promoter
|
BMGC_PM00042
|
BMG_PM000063
|
Promoter
|
BMGC_PM00043
|
BMG_PM000064
|
Promoter
|
BMGC_PM00044
|
BMG_PM000065
|
Promoter
|
BMGC_PM00045
|
BMG_PM000066
|
Promoter
|
BMGC_PM00046
|
BMG_PM000067
|
Promoter
|
BMGC_PM00047
|
BMG_PM000068
|
Promoter
|
BMGC_PM00048
|
BMG_PM000069
|
Promoter
|
BMGC_PM00049
|
BMG_PM000070
|
Promoter
|
BMGC_PM00050
|
BMG_PM000071
|
Promoter
|
BMGC_PM00051
|
BMG_PM000072
|
Promoter
|
BMGC_PM00052
|
BMG_PM000073
|
Promoter
|
BMGC_PM00053
|
BMG_PM000074
|
Promoter
|
BMGC_PM00054
|
BMG_PM000075
|
Promoter
|
BMGC_PM00055
|
BMG_PM000076
|
Promoter
|
BMGC_PM00056
|
BMG_PM000077
|
Promoter
|
BMGC_PM00057
|
BMG_PM000078
|
Promoter
|
BMGC_PM00058
|
BMG_PM000079
|
Promoter
|
BMGC_PM00059
|
BMG_PM000080
|
Promoter
|
BMGC_PM00060
|
BMG_PM000081
|
Promoter
|
BMGC_PM00061
|
BMG_PM000082
|
Promoter
|
BMGC_PM00062
|
BMG_PM000083
|
Promoter
|
BMGC_PM00063
|
BMG_PM000084
|
Promoter
|
BMGC_PM00064
|
BMG_PM000085
|
Promoter
|
BMGC_PM00065
|
BMG_PM000086
|
Promoter
|
BMGC_PM00066
|
BMG_PM000087
|
Promoter
|
BMGC_PM00067
|
BMG_PM000088
|
Promoter
|
BMGC_PM00068
|
BMG_PM000089
|
Promoter
|
BMGC_PM00069
|
BMG_PM000090
|
Promoter
|
BMGC_PM00070
|
BMG_PM000091
|
Promoter
|
BMGC_PM00071
|
BMG_PM000092
|
Promoter
|
BMGC_PM00072
|
BMG_PM000093
|
Promoter
|
BMGC_PM00073
|
BMG_PM000094
|
Promoter
|
BMGC_PM00074
|
BMG_PM000095
|
Promoter
|
BMGC_PM00075
|
BMG_PM000096
|
Promoter
|
BMGC_PM00076
|
BMG_PM000097
|
Promoter
|
BMGC_PM00077
|
BMG_PM000098
|
Promoter
|
BMGC_PM00078
|
BMG_PM000099
|
Promoter
|
BMGC_PM00079
|
BMG_PM000100
|
Promoter
|
BMGC_PM00080
|
BMG_PM000101
|
Promoter
|
BMGC_PM00081
|
BMG_PM000102
|
Promoter
|
BMGC_PM00082
|
BMG_PM000103
|
Promoter
|
BMGC_PM00083
|
BMG_PM000104
|
Promoter
|
BMGC_PM00084
|
BMG_PM000105
|
Promoter
|
BMGC_PM00085
|
BMG_PM000106
|
Promoter
|
BMGC_PM00086
|
BMG_PM000107
|
Promoter
|
BMGC_PM00087
|
BMG_PM000108
|
Promoter
|
BMGC_PM00088
|
BMG_PM000109
|
Promoter
|
BMGC_PM00089
|
BMG_PM000110
|
Promoter
|
BMGC_PM00090
|
BMG_PM000111
|
Promoter
|
BMGC_PM00091
|
BMG_PM000112
|
Promoter
|
BMGC_PM00092
|
BMG_PM000113
|
Promoter
|
BMGC_PM00093
|
BMG_PM000114
|
Promoter
|
BMGC_PM00094
|
BMG_PM000115
|
Promoter
|
BMGC_PM00095
|
BMG_PM000116
|
Promoter
|
BMGC_PM00096
|
BMG_PM000117
|
Promoter
|
BMGC_PM00097
|
BMG_PM000118
|
Promoter
|
BMGC_PM00098
|
BMG_PM000119
|
Promoter
|
BMGC_PM00099
|
BMG_PM000120
|
Promoter
|
BMGC_PM00100
|
BMG_PM000121
|
Promoter
|
End of preview.
BioMedGraphica
BioMedGraphica is an all-in-one platform for biomedical data integration and knowledge graph generation. It harmonizes fragmented biomedical datasets into a unified, graph AI-ready resource that facilitates precision medicine, therapeutic target discovery, and integrative biomedical AI research.
Developed using data from 43 biomedical databases, BioMedGraphica integrates:
- 11 entity types
- 30 relation types
- Over 2.3 million entities and 27 million relations
β¨ Highlights
- Multi-omics integration: Genomic, transcriptomic, proteomic, metabolomic, microbiomic, exposomic
- Graph AI-ready: Outputs subgraphs ready for GNNs and ML models
- Soft matching: Uses BioBERT for fuzzy entity resolution (disease, phenotype, drug, exposure)
- GUI software: Provides Windows-based interface for end-to-end pipeline
- Connected graph variant: Isolated nodes removed for efficient downstream training
π Dataset Statistics
Metric | Count |
---|---|
Total Entities | 2,306,921 |
Total Relations | 27,232,091 |
Connected Entities | 834,809 |
Connected Relations | 27,087,971 |
Entity Types | 11 |
Relation Types | 30 |
𧬠Entity Types
Entity Type | Count | Percentage (%) | Connected Count | Connected (%) |
---|---|---|---|---|
Promoter | 230,358 | 9.99 | 86,238 | 10.33 |
Gene | 230,358 | 9.99 | 86,238 | 10.33 |
Transcript | 412,326 | 17.87 | 412,039 | 49.36 |
Protein | 173,978 | 7.54 | 121,419 | 14.54 |
Pathway | 6,793 | 0.29 | 1,930 | 0.23 |
Metabolite | 218,335 | 9.46 | 62,364 | 7.47 |
Microbiota | 621,882 | 26.96 | 1,119 | 0.13 |
Exposure | 1,159 | 0.05 | 1,037 | 0.12 |
Phenotype | 19,532 | 0.85 | 19,078 | 2.29 |
Disease | 118,814 | 5.15 | 22,429 | 2.69 |
Drug | 273,386 | 11.85 | 20,918 | 2.51 |
Total | 2,306,921 | 100 | 834,809 | 100 |
π Relation Types
Relation Type | Count | Percentage (%) |
---|---|---|
Promoter-Gene | 230,358 | 0.85 |
Gene-Transcript | 427,810 | 1.57 |
Transcript-Protein | 152,585 | 0.56 |
Protein-Protein | 16,484,820 | 60.53 |
Protein-Pathway | 152,912 | 0.56 |
Protein-Phenotype | 478,279 | 1.76 |
Protein-Disease | 143,394 | 0.53 |
Pathway-Protein | 176,133 | 0.65 |
Pathway-Drug | 1,795 | 0.01 |
Pathway-Exposure | 301,448 | 1.11 |
Metabolite-Protein | 2,804,430 | 10.30 |
Metabolite-Pathway | 12,198 | 0.04 |
Metabolite-Metabolite | 931 | 0.003 |
Metabolite-Disease | 24,970 | 0.09 |
Microbiota-Disease | 22,371 | 0.08 |
Microbiota-Drug | 866 | 0.003 |
Exposure-Gene | 28,982 | 0.11 |
Exposure-Pathway | 301,448 | 1.11 |
Exposure-Disease | 979,780 | 3.60 |
Phenotype-Phenotype | 23,427 | 0.09 |
Phenotype-Disease | 181,192 | 0.67 |
Disease-Phenotype | 181,192 | 0.67 |
Disease-Disease | 12,006 | 0.04 |
Drug-Protein | 84,859 | 0.31 |
Drug-Pathway | 3,065 | 0.01 |
Drug-Metabolite | 3,589 | 0.01 |
Drug-Microbiota | 866 | 0.003 |
Drug-Phenotype | 93,826 | 0.34 |
Drug-Disease | 39,977 | 0.15 |
Drug-Drug | 3,882,582 | 14.26 |
Total | 27,232,091 | 100 |
π¦ Access and Downloads
- Knowledge Graph Dataset: Hugging Face
- Software & Tutorials: GitHub
π§ͺ Validation
- Hard matching for structured identifiers (e.g. Ensembl, HGNC)
- BioBERT-based soft matching for flexible terms (e.g., diseases, phenotypes, drugs)
- Case study and benchmarking with Synapse dataset
π Citation
@article{zhang2024biomedgraphica,
title={BioMedGraphica: An All-in-One Platform for Biomedical Prior Knowledge and Omic Signaling Graph Generation},
author={Zhang, Heming and Liang, Shunning and Xu, Tim and Li, Wenyu and Huang, Di and Dong, Yuhan and Li, Guangfu and Miller, J Philip and Goedegebuure, S Peter and Sardiello, Marco and others},
journal={bioRxiv},
year={2024}
}
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