Healthcare
17 columns
105 rows
8 formats

Proteomics and Biomarker Discovery

This dataset provides comprehensive protein expression profiles, mass spectrometry results, post-translational modification details, and protein-protein interaction data, enabling robust biomarker discovery for disease research. It is designed for integrative analysis in proteomics, supporting both experimental and computational approaches to uncover disease mechanisms and candidate biomarkers.

last updated 2 Nov 2025
by GoMask

The brief that made it

Identification of candidate biomarkers for disease diagnosis and prognosis

Sample rows

preview · 8 of 105 rows · all 17 columns

protein_idstring	disease_statusstring	protein_abundancefloat	biomarker_candidateboolean	post_translational_modification_typestring	protein_namestring	gene_symbolstring	organismstring	sample_idstring	mass_spectrometry_methodstring	abundance_unitstring	modification_sitestring	interaction_partner_idstring	interaction_typestring	biomarker_evidence_levelstring	experiment_datedate	data_sourcestring
P00001	healthy	18452.65	false	phosphorylation	Serum albumin	ALB	Homo sapiens	SAMP001	LC-MS/MS	iBAQ	S34	P00007	complex	blank	2021-07-15	PRIDE
P00002	disease	321421.84	true	glycosylation	Alpha-1-antitrypsin	SERPINA1	Homo sapiens	SAMP002	MALDI-TOF	spectral count	N413	P00010	direct	experimental	2022-02-23	published study
Q9Y6R7	control	1853.46	false	blank	Vimentin	VIM	Homo sapiens	SAMP003	SWATH-MS	FPKM	blank	blank	blank	blank	2020-09-04	PRIDE
P00004	unknown	239.97	false	acetylation	Transferrin	TF	Homo sapiens	SAMP004	CE-MS	FPKM	K18	blank	blank	blank	2020-02-13	in-house
Q13509	disease	69437.4	true	phosphorylation	C-reactive protein	CRP	Homo sapiens	SAMP005	LC-MS/MS	spectral count	T87	P00001	complex	literature	2022-06-21	in-house
P00006	healthy	3561.12	false	blank	Tubulin beta chain	TUBB	Homo sapiens	SAMP006	SWATH-MS	iBAQ	blank	blank	blank	blank	2021-03-10	PRIDE
P00007	control	12489.75	true	methylation	Heat shock protein HSP 90	HSP90	Homo sapiens	SAMP007	LC-MS/MS	iBAQ	R51	P00015	transient	experimental	2023-04-09	published study
P00008	disease	254622.2	false	ubiquitination	Myosin heavy chain 9	MYH9	Homo sapiens	SAMP008	MALDI-TOF	spectral count	K120	P00010	complex	blank	2022-12-12	published study
Q96A58	disease	16042.32	false	blank	Hemoglobin subunit alpha	HBA1	Mus musculus	MICE001	LC-MS/MS	FPKM	blank	blank	blank	blank	2019-05-19	published study
P00010	disease	80212.17	true	acetylation	Actin cytoplasmic 1	ACTB	Homo sapiens	SAMP009	MALDI-TOF	spectral count	K32	P00004	direct	literature	2021-10-27	PRIDE
P00011	unknown	112.78	true	methylation	Histone H4	HIST4	Rattus norvegicus	RAT001	CE-MS	FPKM	K4	blank	blank	experimental	2018-11-08	in-house
Q9Z0H3	disease	1900.9	false	blank	Cytochrome c oxidase	COX1	Bos taurus	BOS001	MALDI-TOF	spectral count	blank	blank	blank	blank	2019-12-03	published study
P00013	control	8826.51	false	phosphorylation	Keratin type II cytoskeletal 1	KRT1	Homo sapiens	SAMP010	SWATH-MS	iBAQ	S16	P00007	transient	blank	2020-08-19	PRIDE
P00014	healthy	17523.33	false	glycosylation	Elongation factor 1-alpha	EEF1A1	Homo sapiens	SAMP011	LC-MS/MS	iBAQ	N284	blank	blank	blank	2023-01-30	in-house
Q8XJ01	unknown	15498.1	false	blank	DNA-directed RNA polymerase	RPOB	Escherichia coli	ECOL001	MALDI-TOF	spectral count	blank	blank	blank	blank	2018-06-15	in-house
P00016	disease	421859.86	true	phosphorylation	Glyceraldehyde-3-phosphate dehydrogenase	GAPDH	Homo sapiens	SAMP012	LC-MS/MS	iBAQ	S203	P00014	complex	computational	2023-08-11	PRIDE
Q14118	disease	218319.39	false	glycosylation	Collagen alpha-1(I)	COL1A1	Homo sapiens	SAMP013	MALDI-TOF	spectral count	N67	blank	blank	blank	2022-03-29	published study
P00018	control	1127.07	false	unknown	Pyruvate kinase	PKM	Gallus gallus	CHICK001	CE-MS	FPKM	blank	blank	unknown	unknown	2020-11-25	published study
P00019	disease	142190.57	true	phosphorylation	Ferritin heavy chain	FTH1	Homo sapiens	SAMP014	LC-MS/MS	spectral count	S51	P00021	complex	experimental	2023-03-18	in-house
P00020	healthy	4863.94	false	blank	Enolase 1	ENO1	Homo sapiens	SAMP015	SWATH-MS	iBAQ	blank	blank	blank	blank	2021-12-08	PRIDE

What the 105 rows show

from the 105-row sample

Disease (disease status) stands out: 33 of its 41 rows have biomarker_candidate = true, against 6 of 64 for the rest.

37%biomarker_candidate = true
8,827median protein_abundance
3abundance units
3data sources
4mass spectrometry methods
4interaction types

Biomarker candidate rate by disease_statusbiomarker_candidate = true

Disease (disease status)'s mean protein_abundance is 119,746, against 6,292 for healthy, 4,042 for control and 2,744 for unknown.

protein_abundance105 rows, in bands of 100k

Median 8,827, from 0.03 to 983,441.

post_translational_modification_type70 rows with a value · 35 left blank

phosphorylation26
glycosylation15
acetylation10
methylation8
ubiquitination5
oxidation3
hydroxylation2
unknown1

17 columns by typefrom the column list below

string 14
float 1
date 1
boolean 1

Columns

17 columns in four groups

blueprint · 17 columns
column	type	description	example
Text 14 columns
`protein_id`	string	Unique identifier for the protein (e.g., UniProt accession number)unique	P00001
`protein_name`	string	Standardized name of the protein	Serum albumin
`gene_symbol`	string	Gene symbol associated with the proteinoptional	ALB
`organism`	string	Organism from which the protein sample was derived7 organisms	Homo sapiens
`sample_id`	string	Identifier for the biological sample analyzed	SAMP001
`disease_status`	string	Disease status of the sample (e.g., healthy, disease, control)healthy · disease · control · unknown · optional	healthy
`mass_spectrometry_method`	string	Type of mass spectrometry used (e.g., LC-MS/MS, MALDI-TOF)4 methods · optional	LC-MS/MS
`abundance_unit`	string	Unit of measurement for protein abundance (e.g., FPKM, iBAQ, spectral count)3 units · optional	iBAQ
`post_translational_modification_type`	string	Type of post-translational modification detected (e.g., phosphorylation, glycosylation)8 types · optional	phosphorylation
`modification_site`	string	Amino acid residue and position of modification (e.g., S15 for serine at position 15)optional	S34
`interaction_partner_id`	string	Identifier for the protein interaction partner (e.g., UniProt accession number)optional	P00007
`interaction_type`	string	Type of protein-protein interaction (e.g., direct, complex, transient)direct · complex · transient · unknown · optional	complex
`biomarker_evidence_level`	string	Level of evidence supporting biomarker candidacy (e.g., experimental, computational, literature)experimental · computational · literature · unknown · optional	experimental
`data_source`	string	Source or repository of the data (e.g., PRIDE, in-house, published study)3 sources · optional	PRIDE
Numbers 1 column
`protein_abundance`	float	Quantitative measurement of protein abundance (e.g., intensity, spectral count)0 or more	18452.65
Dates and times 1 column
`experiment_date`	date	Date when the mass spectrometry experiment was performedoptional	2021-07-15
True or false 1 column
`biomarker_candidate`	boolean	Indicates if the protein is a candidate biomarker for disease	false

Use it for

A healthcare dashboard
The biomarker_candidate rate, protein_abundance by disease_status and a breakdown of post_translational_modification_type. Excel, Power BI or Tableau.
Why do 39 of 105 rows have biomarker_candidate = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Proteins105
P0000118452.65healthy
P00002321421.84disease
Q1350969437.4disease
A software demo
Believable proteins with protein_name, gene_symbol and organism to fill a screen in front of a buyer.

This dataset105 rows17 columns

Yours1,000 rows17 columnsmodification_site: UK only

blueprint · proteomics-and-biomarker-discovery

Behind this dataset

Same schema. As many rows as you need.

These 105 rows came out of a blueprint — 17 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Sample ID unique per specimen
Sample type: tissue, serum, plasma, urine, CSF
Mass spectrometry platform: Orbitrap, Q-TOF, triple quad
Quantification method: label-free, TMT, SILAC
Protein identification: UniProt ID
Peptide sequences and modifications
Protein abundance: spectral counts, intensity
Post-translational modifications: phosphorylation, acetylation, ubiquitination

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: proteomics-and-biomarker-discovery

What should your data show?

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Sample rows

What the 105 rows show

Columns

Use it for

A healthcare dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Healthcare

What should your data show?