Research Object Crate for HPPIDiscovery - Scientific workflow to augment, predict and evaluate host-pathogen protein-protein interactions
type: Nanopublication
has assertion: assertion
has provenance: provenance
has publication info: pubinfo
type: RoCrateNanopub
created: 2026-03-03T15:15:57.612+01:00
creator: RoCrateBot

WorkflowHub
type: Organization

Workflow RO-Crate Profile
type: CreativeWork
version: 0.2.0

Yasmmin Martins
type: Person

Małgorzata Wolniewicz
type: Agent

Snakemake
type: ComputerLanguage

organism folders directory
type: Phrase
norm score: 1.9624945486262537
score: 4.5

training ppi augmentation
type: Phrase
norm score: 7.283035324901875
score: 16.7

environment
type: Lemma
norm score: 2.3600185099490973
score: 5.1

Then it proceeds parsing and cleaning the HPIDB results and downloading the protein interactions of the found organisms from the STRING database.
type: Sentence
norm score: 11.815561959654177
score: 12.3

Scientific workflow to augment, predict and evaluate host-pathogen protein-protein interactions
type: Sentence
norm score: 18.443804034582133
score: 19.2

http
type: Lemma
norm score: 3.0078667283664973
score: 6.5

host
type: Concept
norm score: 3.6437246963562755
score: 13.5

step
type: Lemma
norm score: 12.216566404442387
score: 26.4

law
type: Domain
norm score: 1.7699115044247788
score: 1.4

data
type: Concept
norm score: 6.4507422402159245
score: 23.9

rete
type: Concept
norm score: 4.8582995951417
score: 18.0

https://w3id.org/ro-id/1a95b293-a74b-4489-88f1-e9a78678cec7
type: Sentence
name: ## Resources - [How to Contribute to Open Source](https://opensource.guide/how-to-contribute/) - [Using Pull Requests](https://help.github.com/articles/about-pull-requests/) - [GitHub Help](https://help.github.com)
norm score: 12.199807877041305
score: 12.7

protein
type: Concept
norm score: 11.470985155195681
score: 42.5

## Contributing [fork]: /fork [pr]: /compare [style]: https://standardjs.com/ [code-of-conduct]: CODE_OF_CONDUCT.md
type: Sentence
norm score: 9.414024975984631
score: 9.8

preparation instruction
type: Phrase
norm score: 1.439162668992586
score: 3.3

data acquisition step
type: Phrase
norm score: 1.9188835586567816
score: 4.4

pull
type: Lemma
norm score: 2.822767237390097
score: 6.1

interaction
type: Concept
norm score: 8.367071524966262
score: 31.0

Genetics
type: IPTC
path: Science and technology/Natural science/Biology/Genetics

pathogen
type: Concept
norm score: 4.993252361673414
score: 18.5

environment
type: Concept
norm score: 1.9163292847503375
score: 7.1

Genetics
type: IPTC
path: Science and technology/Natural science/Biology/Genetics

training
type: Lemma
norm score: 4.257288292457195
score: 9.2

work in progress pull request
type: Phrase
norm score: 18.796336676842564
score: 43.1

visualization step
type: Phrase
norm score: 2.0497165285651984
score: 4.7

IT-computer sciences
type: IPTC
path: Science and technology/Technology and engineering/IT-computer sciences

query protein ID
type: Phrase
norm score: 3.6633231574356735
score: 8.4

using pull request
type: Phrase
norm score: 4.361098996947231
score: 10.0

pull request 1
type: Phrase
norm score: 4.53554295682512
score: 10.4

oceanography
type: FieldOfResearch
norm score: 100.0
score: 1.8297315537929535

software
type: Domain
norm score: 22.629582806573957
score: 17.9

string protein identifier
type: Phrase
norm score: 3.445268207588312
score: 7.8999999999999995

life sciences
type: NASA
norm score: 83.09801429584705
score: 1.9117830991744995

Work in Progress pull requests are also welcome to get feedback early on, or if there is something blocked you.
type: Sentence
norm score: 8.549471661863592
score: 8.9

host
type: Lemma
norm score: 6.247107820453494
score: 13.5

npm test
type: Phrase
norm score: 1.70082860880942
score: 3.9

data
type: Lemma
norm score: 7.265155020823693
score: 15.7

folder
type: Concept
norm score: 2.4831309041835357
score: 9.2

IT-computer sciences
type: IPTC
path: Science and technology/Technology and engineering/IT-computer sciences

computer science
type: Domain
norm score: 40.07585335018964
score: 31.700000000000003

computer programming
type: Domain
norm score: 17.825537294563844
score: 14.1

step
type: Concept
norm score: 6.828609986504723
score: 25.3

target
type: Concept
norm score: 2.2132253711201075
score: 8.2

database
type: Domain
norm score: 11.630847029077117
score: 9.2

identifier
type: Lemma
norm score: 5.414160111059695
score: 11.7

interaction data
type: Phrase
norm score: 0.9158307893589184
score: 2.1

http
type: Concept
norm score: 2.294197031039136
score: 8.5

2023
type: TimeReference

Public relations
type: IPTC
path: Economy, business and finance/Economic sector/Media/Public relations

host-pathogen protein-protein interaction
type: Phrase
norm score: 8.242477104230264
score: 18.9

system
type: Concept
norm score: 2.42914979757085
score: 9.0

earth sciences
type: FieldOfResearch
norm score: 100.0
score: 1.8297315537929535

computer programming and software
type: NASA
norm score: 16.901985704152953
score: 0.3888532221317291

method
type: Lemma
norm score: 1.8509949097639982
score: 4.0

Health
type: IPTC
path: Health

information technology
type: Domain
norm score: 4.677623261694058
score: 3.7

acquisition step
type: Phrase
norm score: 13.170518970780636
score: 30.2

Diseases and conditions
type: IPTC
path: Health/Diseases and conditions

update test
type: Phrase
norm score: 3.270824247710423
score: 7.5

home
type: Lemma
norm score: 4.211013419713096
score: 9.1

organism
type: Concept
norm score: 4.561403508771929
score: 16.9

Food
type: IPTC
path: Economy, business and finance/Economic sector/Consumer goods/Food

pathogen
type: Lemma
norm score: 5.460434983803795
score: 11.8

evaluation visualization step
type: Phrase
norm score: 10.248582642825992
score: 23.5

pull
type: Concept
norm score: 2.2672064777327936
score: 8.4

Research Object Crate for HPPIDiscovery - Scientific workflow to augment, predict and evaluate host-pathogen protein-protein interactions.
type: Sentence
norm score: 19.788664745437078
score: 20.6

instruction
type: Lemma
norm score: 1.897269782508098
score: 4.1

identifier
type: Concept
norm score: 4.804318488529015
score: 17.8

life sciences (general)
type: NASA
norm score: 83.09801429584705
score: 1.9117830991744995

host proteins information
type: Phrase
norm score: 2.878325337985172
score: 6.6

request
type: Lemma
norm score: 6.802406293382694
score: 14.7

This option is extremely useful when you are not sure that your organism has validated protein interactions, then it finds validated interactions from the closest proteins in the database.
type: Sentence
norm score: 19.788664745437078
score: 20.6

pathogen network
type: Phrase
norm score: 8.068033144352377
score: 18.5

interaction
type: Lemma
norm score: 6.756131420638593
score: 14.6

addestramento
type: Concept
norm score: 2.4021592442645074
score: 8.9

Research Object Crate for HPPIDiscovery - Scientific workflow to augment, predict and evaluate host-pathogen protein-protein interactions
type: ResearchObject
type: LiveRO
type: Dataset
imported by: Małgorzata Wolniewicz
author: Yasmmin Martins
content size: 73915655
creative work status: Stable
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:49.625147+00:00
date modified: 2024-03-05 12:23:14.770896+00:00
date published: 2023-10-20 08:40:49.625147+00:00
description: ## Summary HPPIDiscovery is a scientific workflow to augment, predict and perform an insilico curation of host-pathogen Protein-Protein Interactions (PPIs) using graph theory to build new candidate ppis and machine learning to predict and evaluate them by combining multiple PPI detection methods of proteins according to three categories: structural, based on primary aminoacid sequence and functional annotations. HPPIDiscovery contains three main steps: (i) acquirement of pathogen and host proteins information from seed ppis provided by HPIDB search methods, (ii) Model training and generation of new candidate ppis from HPIDB seed proteins' partners, and (iii) Evaluation of new candidate ppis and results exportation. (i) The first step acquires the identification of the taxonomy ids of the host and pathogen organisms in the result files. Then it proceeds parsing and cleaning the HPIDB results and downloading the protein interactions of the found organisms from the STRING database. The string protein identifiers are also mapped using the id mapping tool of uniprot API and we retrieve the uniprot entry ids along with the functional annotations, sequence, domain and kegg enzymes. (ii) The second step builds the training dataset using the non redundant hpidb validated interactions of each genome as positive set and random string low confidence ppis from each genome as negative set. Then, PredPrin tool is executed in the training mode to obtain the model that will evaluate the new candidate PPIs. The new ppis are then generated by performing a pairwise combination of string partners of host and pathogen hpidb proteins. Finally, (iii) in the third step, the predprin tool is used in the test mode to evaluate the new ppis and generate the reports and list of positively predicted ppis. The figure below illustrates the steps of this workflow. ## Requirements: * Edit the configuration file (config.yaml) according to your own data, filling out the following fields: - base_data: l…
encoding format: application/ld+json
is based on: https://github.com/YasCoMa/hppidiscovery
license: no-permission
main entity: workflow_hpAugmentation/Snakefile
domain: law
domain: software
domain: computer science
domain: computer programming
domain: database
domain: information technology
domain: computer hardware
concept: host
concept: data
concept: rete
concept: protein
concept: interaction
concept: pathogen
concept: environment
concept: folder
concept: step
concept: target
concept: http
concept: system
concept: organism
concept: pull
concept: identifier
concept: addestramento
concept: petition
concept: genome
concept: home
field of research: oceanography
field of research: earth sciences
IPTC: Genetics
IPTC: IT-computer sciences
IPTC: Public relations
IPTC: Health
IPTC: Diseases and conditions
IPTC: Food
lemma: environment
lemma: http
lemma: step
lemma: pull
lemma: training
lemma: host
lemma: data
lemma: identifier
lemma: method
lemma: home
lemma: pathogen
lemma: instruction
lemma: request
lemma: interaction
lemma: PR
lemma: protein
lemma: organism
lemma: target
lemma: network
NASA: life sciences
NASA: computer programming and software
NASA: life sciences (general)
NASA: mathematical and computer sciences
phrase: organism folders directory
phrase: training ppi augmentation

workflow_hpAugmentation
type: Folder
type: Dataset

log
type: Folder
type: Dataset

training_data
type: Folder
type: Dataset

input_example
type: Folder
type: Dataset

.snakemake
type: Folder
type: Dataset

__pycache__
type: Folder
type: Dataset

s_aureus
type: Folder
type: Dataset

CODE_OF_CONDUCT.md
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 3220
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.857721+00:00
date modified: 2023-10-20 08:40:52.945411+00:00
encoding format: text/markdown
license: no-permission
sd date published: 2023-10-20 08:40:50.857721+00:00

LICENSE.md
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 1055
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.859933+00:00
date modified: 2023-10-20 08:40:53.383379+00:00
encoding format: text/markdown
license: no-permission
sd date published: 2023-10-20 08:40:50.859933+00:00

s_aureus.tar.xz
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 11917056
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.969541+00:00
date modified: 2023-10-20 08:40:54.972720+00:00
encoding format: application/x-tar
license: no-permission
sd date published: 2023-10-20 08:40:50.969541+00:00

dag.pdf
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 13959
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.986923+00:00
date modified: 2023-10-20 08:41:00.215086+00:00
encoding format: application/pdf
license: no-permission
sd date published: 2023-10-20 08:40:50.986923+00:00

get_metrics.py
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 371
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.990475+00:00
date modified: 2023-10-20 08:41:01.207300+00:00
encoding format: text/x-python
license: no-permission
sd date published: 2023-10-20 08:40:50.990475+00:00

HPPIDiscovery - Scientific workflow to augment, predict and evaluate host-pathogen protein-protein interactions
type: Resource
type: MediaObject
type: SoftwareSourceCode
type: ComputationalWorkflow
conforms to: https://bioschemas.org/profiles/ComputationalWorkflow/1.0-RELEASE/
author: Yasmmin Martins
content size: 10251
creator: Yasmmin Martins
date created: 2023-10-19 23:56:34+00:00
date modified: 2023-10-20 08:41:12.795284+00:00
description: ## Summary HPPIDiscovery is a scientific workflow to augment, predict and perform an insilico curation of host-pathogen Protein-Protein Interactions (PPIs) using graph theory to build new candidate ppis and machine learning to predict and evaluate them by combining multiple PPI detection methods of proteins according to three categories: structural, based on primary aminoacid sequence and functional annotations. HPPIDiscovery contains three main steps: (i) acquirement of pathogen and host proteins information from seed ppis provided by HPIDB search methods, (ii) Model training and generation of new candidate ppis from HPIDB seed proteins' partners, and (iii) Evaluation of new candidate ppis and results exportation. (i) The first step acquires the identification of the taxonomy ids of the host and pathogen organisms in the result files. Then it proceeds parsing and cleaning the HPIDB results and downloading the protein interactions of the found organisms from the STRING database. The string protein identifiers are also mapped using the id mapping tool of uniprot API and we retrieve the uniprot entry ids along with the functional annotations, sequence, domain and kegg enzymes. (ii) The second step builds the training dataset using the non redundant hpidb validated interactions of each genome as positive set and random string low confidence ppis from each genome as negative set. Then, PredPrin tool is executed in the training mode to obtain the model that will evaluate the new candidate PPIs. The new ppis are then generated by performing a pairwise combination of string partners of host and pathogen hpidb proteins. Finally, (iii) in the third step, the predprin tool is used in the test mode to evaluate the new ppis and generate the reports and list of positively predicted ppis. The figure below illustrates the steps of this workflow. ## Requirements: * Edit the configuration file (config.yaml) according to your own data, filling out the following fields: - base_data: l…
keywords: Bioinformatics, Protein-Protein interaction prediction, host-pathogen PPIs, proteins network augmentation
license: no-permission
producer: yPublish - Bioinfo tools
programming language: #snakemake
sd date published: 2023-10-19 23:56:34+00:00
sd publisher: https://about.workflowhub.eu/
version: 1

params_example.tsv
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 101
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:51.046715+00:00
date modified: 2023-10-20 08:41:03.530610+00:00
encoding format: text/tab-separated-values
license: no-permission
sd date published: 2023-10-20 08:40:51.046715+00:00

ro-crate-preview.html
type: Resource
type: CreativeWork
type: MediaObject
about: https://ror.org/https://workflowhub.eu/workflows/611
author: Małgorzata Wolniewicz
content size: 21508
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:51.464954+00:00
date modified: 2023-10-20 08:41:12.435693+00:00
encoding format: text/html
license: no-permission
sd date published: 2023-10-20 08:40:51.464954+00:00

2023-10-19T182635.539173.snakemake.log
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 1363
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.975444+00:00
date modified: 2023-10-20 08:40:56.724742+00:00
license: no-permission
sd date published: 2023-10-20 08:40:50.975444+00:00

reset_input.zip
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 1064201
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.872283+00:00
date modified: 2023-10-20 08:40:54.128939+00:00
encoding format: application/zip
license: no-permission
sd date published: 2023-10-20 08:40:50.872283+00:00

clean_steps.py
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 239
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.983565+00:00
date modified: 2023-10-20 08:40:59.133120+00:00
encoding format: text/x-python
license: no-permission
sd date published: 2023-10-20 08:40:50.983565+00:00

workflow.png
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 83820
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:51.464082+00:00
date modified: 2023-10-20 08:41:06.681827+00:00
encoding format: image/png
license: no-permission
sd date published: 2023-10-20 08:40:51.464082+00:00

data_preprocessing_v1.py
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 20900
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.988797+00:00
date modified: 2023-10-20 08:41:00.874151+00:00
encoding format: text/x-python
license: no-permission
sd date published: 2023-10-20 08:40:50.988797+00:00

clean_outputs.sh
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 110
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.982729+00:00
date modified: 2023-10-20 08:40:58.947089+00:00
encoding format: text/x-sh
license: no-permission
sd date published: 2023-10-20 08:40:50.982729+00:00

2023-10-19T183224.995853.snakemake.log
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 24
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.977115+00:00
date modified: 2023-10-20 08:40:57.078758+00:00
license: no-permission
sd date published: 2023-10-20 08:40:50.977115+00:00

test_Snakefile
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 2496
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:51.461457+00:00
date modified: 2023-10-20 08:41:05.738227+00:00
license: no-permission
sd date published: 2023-10-20 08:40:51.461457+00:00

training_ppi_augmentation.py
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 10280
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:51.462174+00:00
date modified: 2023-10-20 08:41:06.292490+00:00
encoding format: text/x-python
license: no-permission
sd date published: 2023-10-20 08:40:51.462174+00:00

readme.md
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 5541
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:51.047714+00:00
date modified: 2023-10-20 08:41:03.749741+00:00
encoding format: text/markdown
license: no-permission
sd date published: 2023-10-20 08:40:51.047714+00:00

config_laptop.yaml
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 488
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.986013+00:00
date modified: 2023-10-20 08:41:00.028715+00:00
license: no-permission
sd date published: 2023-10-20 08:40:50.986013+00:00

params_augmentation.tsv
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 236
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:51.045900+00:00
date modified: 2023-10-20 08:41:03.332334+00:00
encoding format: text/tab-separated-values
license: no-permission
sd date published: 2023-10-20 08:40:51.045900+00:00

CITATION.cff
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 400
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.856652+00:00
date modified: 2023-10-20 08:40:52.747708+00:00
license: no-permission
sd date published: 2023-10-20 08:40:50.856652+00:00

results.zip
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 1062372
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.884026+00:00
date modified: 2023-10-20 08:40:54.363194+00:00
encoding format: application/zip
license: no-permission
sd date published: 2023-10-20 08:40:50.884026+00:00

hp_ppi_augmentation.yml
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 8116
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.991348+00:00
date modified: 2023-10-20 08:41:01.394077+00:00
license: no-permission
sd date published: 2023-10-20 08:40:50.991348+00:00

evaluation_visualization.py
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 11043
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.989648+00:00
date modified: 2023-10-20 08:41:01.034064+00:00
encoding format: text/x-python
license: no-permission
sd date published: 2023-10-20 08:40:50.989648+00:00

evaluation_visualization.cpython-38.pyc
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 7351
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.981025+00:00
date modified: 2023-10-20 08:40:58.581977+00:00
encoding format: application/x-python-code
license: no-permission
sd date published: 2023-10-20 08:40:50.981025+00:00

config.yaml
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 172
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.984377+00:00
date modified: 2023-10-20 08:40:59.599519+00:00
license: no-permission
sd date published: 2023-10-20 08:40:50.984377+00:00

reduced_workflow.png
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 54971
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:51.048967+00:00
date modified: 2023-10-20 08:41:03.961153+00:00
encoding format: image/png
license: no-permission
sd date published: 2023-10-20 08:40:51.048967+00:00

model_trained.joblib
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 30231
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.862139+00:00
date modified: 2023-10-20 08:40:53.921361+00:00
license: no-permission
sd date published: 2023-10-20 08:40:50.862139+00:00

2023-10-19T182926.274815.snakemake.log
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 24
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.976290+00:00
date modified: 2023-10-20 08:40:56.889005+00:00
license: no-permission
sd date published: 2023-10-20 08:40:50.976290+00:00

taxdump.tar.gz
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 60418600
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:51.460476+00:00
date modified: 2023-10-20 08:41:04.169671+00:00
encoding format: application/x-tar
license: no-permission
sd date published: 2023-10-20 08:40:51.460476+00:00

CONTRIBUTING.md
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 2238
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.858823+00:00
date modified: 2023-10-20 08:40:53.151499+00:00
encoding format: text/markdown
license: no-permission
sd date published: 2023-10-20 08:40:50.858823+00:00

model_trained.joblib
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 30231
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.971316+00:00
date modified: 2023-10-20 08:40:55.814044+00:00
license: no-permission
sd date published: 2023-10-20 08:40:50.971316+00:00

training_ppi_augmentation.cpython-38.pyc
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 7912
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.981927+00:00
date modified: 2023-10-20 08:40:58.762999+00:00
encoding format: application/x-python-code
license: no-permission
sd date published: 2023-10-20 08:40:50.981927+00:00

readme.md
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 5834
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.972235+00:00
date modified: 2023-10-20 08:40:56.050012+00:00
encoding format: text/markdown
license: no-permission
sd date published: 2023-10-20 08:40:50.972235+00:00

workflow.cwl
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 0
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:51.462840+00:00
date modified: 2023-10-20 08:41:06.496060+00:00
license: no-permission
sd date published: 2023-10-20 08:40:51.462840+00:00

data_preprocessing.cpython-38.pyc
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 13756
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.980132+00:00
date modified: 2023-10-20 08:40:58.274102+00:00
encoding format: application/x-python-code
license: no-permission
sd date published: 2023-10-20 08:40:50.980132+00:00

config_example.yaml
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 121
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.985206+00:00
date modified: 2023-10-20 08:40:59.847114+00:00
license: no-permission
sd date published: 2023-10-20 08:40:50.985206+00:00

2023-10-19T201611.869180.snakemake.log
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 24
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.977925+00:00
date modified: 2023-10-20 08:40:57.564724+00:00
license: no-permission
sd date published: 2023-10-20 08:40:50.977925+00:00

mapping_geneName_uniprot.tsv
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 3311534
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:51.044984+00:00
date modified: 2023-10-20 08:41:02.435885+00:00
encoding format: text/tab-separated-values
license: no-permission
sd date published: 2023-10-20 08:40:51.044984+00:00

list_virulence_factors_full.tsv
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 6489975
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:51.016019+00:00
date modified: 2023-10-20 08:41:01.639459+00:00
encoding format: text/tab-separated-values
license: no-permission
sd date published: 2023-10-20 08:40:51.016019+00:00

.gitignore
type: Resource
type: MediaObject
author: Małgorzata Wolniewicz
content size: 162
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.855228+00:00
date modified: 2023-10-20 08:40:52.443929+00:00
license: no-permission
sd date published: 2023-10-20 08:40:50.855228+00:00

reduced_workflow.png
type: Resource
type: ImageObject
type: MediaObject
type: WorkflowSketch
author: Małgorzata Wolniewicz
content size: 54971
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.973456+00:00
date modified: 2023-10-20 08:40:56.508828+00:00
encoding format: image/png
license: no-permission
sd date published: 2023-10-20 08:40:50.973456+00:00

data_preprocessing.py
author: Małgorzata Wolniewicz
content size: 22743
creator: Małgorzata Wolniewicz
date created: 2023-10-20 08:40:50.987866+00:00
date modified: 2023-10-20 08:41:00.412406+00:00
encoding format: text/x-python
type: Resource
type: MediaObject
license: no-permission
sd date published: 2023-10-20 08:40:50.987866+00:00

petition
type: Concept
norm score: 5.155195681511471
score: 19.1

PR
type: Lemma
norm score: 1.989819527996298
score: 4.3

pathogen hpidb protein
type: Phrase
norm score: 2.049716528565198
score: 4.699999999999999

Diseases and conditions
type: IPTC
path: Health/Diseases and conditions

Health
type: IPTC
path: Health

genome
type: Concept
norm score: 1.9163292847503375
score: 7.1

protein
type: Lemma
norm score: 8.97732531235539
score: 19.4

mathematical and computer sciences
type: NASA
norm score: 16.901985704152953
score: 0.3888532221317291

home
type: Concept
norm score: 2.42914979757085
score: 9.0

computer hardware
type: Domain
norm score: 1.3906447534766122
score: 1.1

organism
type: Lemma
norm score: 5.043961129106895
score: 10.9

target
type: Lemma
norm score: 3.840814437760297
score: 8.3

network
type: Lemma
norm score: 8.005552984729292
score: 17.3

yPublish - Bioinfo tools
type: Organization
type: Project

sig
has algorithm: RSA
signed by: RoCrateBot