{"count":6,"items":[{"article":"/blog/modele-prezentacji-antygenu","cancer_slugs":["pan-cancer"],"category":"immunopeptidomics","confidence":"high","developer":"Broad Institute / Dana-Farber Cancer Institute (Keskin, Wu, Carr labs)","hf":null,"kind":"task-model","license":"free web server; academic use","links":{"demo":"http://hlathena.tools/","docs":null,"doi":"10.1038/s41587-019-0322-9","github":null,"huggingface":null,"paper":"https://pmc.ncbi.nlm.nih.gov/articles/PMC7008090/","pmid":null},"modalities":["protein-sequence","immunopeptidomics","transcriptomics"],"name":"HLAthena","openness":"api-only","regulatory_status":"research-only","release_date":"2020-01-13","settings":["basic-research","vaccine-design","immunotherapy"],"slug":"hlathena","summary":"Model prezentacji trenowany na peptydomie monoallelicznym 95 linii kom\u00f3rkowych \u2014 186 464 peptydy dla 95 alleli HLA, z czego pi\u0119tna\u015bcie nie mia\u0142o wcze\u015bniej opisanego motywu \u2014 czyli na zbiorze, kt\u00f3ry zmieni\u0142 t\u0119 dziedzin\u0119 bardziej ni\u017c jakikolwiek pomys\u0142 architektoniczny.","tasks":["antigen-presentation","peptide-mhc-binding","neoantigen-prioritisation"],"url":"/ai-oncology/models/hlathena","verified_at":"2026-09-05T22:26:00.948597","version":null},{"article":"/blog/modele-prezentacji-antygenu","cancer_slugs":["pan-cancer"],"category":"immunopeptidomics","confidence":"high","developer":"O'Donnell, Rubinsteyn and Laserson (Mount Sinai / OpenVax)","hf":null,"kind":"task-model","license":"apache-2.0","links":{"demo":null,"docs":null,"doi":"10.1016/j.cels.2020.06.010","github":"https://github.com/openvax/mhcflurry","huggingface":null,"paper":"https://www.cell.com/cell-systems/fulltext/S2405-4712(20)30331-8","pmid":null},"modalities":["protein-sequence","immunopeptidomics"],"name":"MHCflurry 2.0","openness":"open-weights","regulatory_status":"research-only","release_date":"2020-07-08","settings":["basic-research","vaccine-design","immunotherapy"],"slug":"mhcflurry","summary":"Otwarto\u017ar\u00f3d\u0142owy pan-alleliczny model prezentacji, kt\u00f3rego wyr\u00f3\u017cnikiem jest osobny model przetwarzania antygenu: czyta peptyd razem z pi\u0119tnastoma aminokwasami kontekstu po ka\u017cdej stronie, bo ci\u0119cie proteasomem zale\u017cy od tego, co le\u017cy wok\u00f3\u0142 miejsca ci\u0119cia.","tasks":["antigen-presentation","peptide-mhc-binding","neoantigen-prioritisation"],"url":"/ai-oncology/models/mhcflurry","verified_at":"2026-09-05T22:26:00.839223","version":"2.0"},{"article":"/blog/modele-prezentacji-antygenu","cancer_slugs":["pan-cancer"],"category":"immunopeptidomics","confidence":"medium","developer":"Karchin lab, Johns Hopkins University","hf":null,"kind":"task-model","license":"apache-2.0","links":{"demo":null,"docs":null,"doi":"10.1158/2326-6066.CIR-19-0464","github":"https://github.com/KarchinLab/mhcnuggets","huggingface":null,"paper":"https://pmc.ncbi.nlm.nih.gov/articles/PMC7056596/","pmid":null},"modalities":["protein-sequence","immunopeptidomics"],"name":"MHCnuggets","openness":"open-weights","regulatory_status":"research-only","release_date":"2020-03-01","settings":["basic-research","vaccine-design","immunotherapy"],"slug":"mhcnuggets","summary":"Sieci LSTM osobne dla ka\u017cdego allelu \u2014 148 dla klasy I \u2014 czytaj\u0105ce peptyd litera po literze, wi\u0119c bez wyr\u00f3wnywania i dope\u0142niania; trenowane przez uczenie transferowe od allelu najbogatszego w dane.","tasks":["peptide-mhc-binding","antigen-presentation","neoantigen-prioritisation"],"url":"/ai-oncology/models/mhcnuggets","verified_at":"2026-09-05T22:26:00.909264","version":"2.x"},{"article":"/blog/modele-prezentacji-antygenu","cancer_slugs":["pan-cancer"],"category":"immunopeptidomics","confidence":"medium","developer":"Gfeller lab, Ludwig Institute for Cancer Research / University of Lausanne","hf":null,"kind":"task-model","license":"free for academic use (see repository terms)","links":{"demo":null,"docs":null,"doi":"10.1186/s13073-025-01449-1","github":"https://github.com/GfellerLab/MixMHCpred","huggingface":null,"paper":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11927126/","pmid":null},"modalities":["protein-sequence","immunopeptidomics"],"name":"MixMHCpred 3.0","openness":"open-weights","regulatory_status":"research-only","release_date":"2025-03-19","settings":["basic-research","vaccine-design","immunotherapy"],"slug":"mixmhcpred","summary":"Model oparty na macierzach pozycyjnych, zbudowany na spostrze\u017ceniu, \u017ce ligandy klasy I niemal nie wykazuj\u0105 zale\u017cno\u015bci mi\u0119dzy pozycjami; wersja 3.0 zatacza ko\u0142o, bo macierz przewiduje sie\u0107 neuronowa z tych samych 34 aminokwas\u00f3w rowka.","tasks":["antigen-presentation","peptide-mhc-binding","neoantigen-prioritisation"],"url":"/ai-oncology/models/mixmhcpred","verified_at":"2026-09-05T22:26:00.871048","version":"3.0"},{"article":"/blog/modele-prezentacji-antygenu","cancer_slugs":["pan-cancer"],"category":"immunopeptidomics","confidence":"medium","developer":"Health Tech, Technical University of Denmark (Nielsen lab)","hf":null,"kind":"task-model","license":"free for academic use; commercial licence required (DTU)","links":{"demo":null,"docs":"https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.0/","doi":"10.1093/nar/gkaa379","github":null,"huggingface":null,"paper":"https://academic.oup.com/nar/article/48/W1/W449/5837056","pmid":null},"modalities":["protein-sequence","immunopeptidomics"],"name":"NetMHCIIpan-4.0","openness":"api-only","regulatory_status":"research-only","release_date":"2020-05-22","settings":["basic-research","vaccine-design","immunotherapy"],"slug":"netmhciipan","summary":"Odpowiednik NetMHCpan dla klasy II, wydany w tej samej publikacji: przewiduje prezentacj\u0119 przez HLA-DR, -DQ i -DP, kt\u00f3rych rowek jest otwarty z obu ko\u0144c\u00f3w, wi\u0119c peptydy s\u0105 d\u0142u\u017csze, a rdze\u0144 wi\u0105\u017c\u0105cy trzeba znale\u017a\u0107 wewn\u0105trz d\u0142u\u017cszej sekwencji.","tasks":["antigen-presentation","peptide-mhc-binding","neoantigen-prioritisation"],"url":"/ai-oncology/models/netmhciipan","verified_at":"2026-09-05T22:26:00.814125","version":"4.0"},{"article":"/blog/modele-prezentacji-antygenu","cancer_slugs":["pan-cancer"],"category":"immunopeptidomics","confidence":"high","developer":"Health Tech, Technical University of Denmark (Nielsen lab)","hf":null,"kind":"task-model","license":"free for academic use; commercial licence required (DTU)","links":{"demo":null,"docs":"https://services.healthtech.dtu.dk/services/NetMHCpan-4.1/","doi":"10.1093/nar/gkaa379","github":null,"huggingface":null,"paper":"https://academic.oup.com/nar/article/48/W1/W449/5837056","pmid":null},"modalities":["protein-sequence","immunopeptidomics"],"name":"NetMHCpan-4.1","openness":"api-only","regulatory_status":"research-only","release_date":"2020-05-22","settings":["basic-research","vaccine-design","immunotherapy"],"slug":"netmhcpan","summary":"Referencyjny pan-alleliczny model prezentacji antygenu przez MHC klasy I: jedna niewielka sie\u0107 obs\u0142uguje ponad 11 000 cz\u0105steczek MHC, bo opis rowka jest cz\u0119\u015bci\u0105 wej\u015bcia \u2014 34-literowa pseudosekwencja obok peptydu.","tasks":["antigen-presentation","peptide-mhc-binding","neoantigen-prioritisation"],"url":"/ai-oncology/models/netmhcpan","verified_at":"2026-09-05T22:26:00.703103","version":"4.1"}],"standard":"https://cancer3.ai/docs/ai-model-card","vocab":{"confidence":["high","medium","low"],"dataset_access":["open","registration","dua","controlled","commercial"],"dataset_kinds":["dataset","registry","benchmark","biobank","corpus"],"formats":["SVS","TIFF","NDPI","DICOM","NIfTI","PNG","JPEG","CSV","TSV","JSON","Parquet","FASTQ","BAM","VCF","MAF","h5ad","PDB","mmCIF","text","other"],"kinds":["foundation","task-model","tool","framework","product"],"link_roles":["pretraining","training","finetuning","evaluation","benchmark"],"modalities":["histopathology","radiology-ct","radiology-mri","radiology-xray","mammography","pet","ultrasound","dermoscopy","endoscopy","genomics","transcriptomics","single-cell","proteomics","protein-sequence","molecules","clinical-text","literature","ehr","multimodal","immunopeptidomics"],"need_status":["open","in-progress","solved"],"openness":["open-weights","gated-weights","api-only","closed"],"regulatory":["research-only","fda-cleared","fda-de-novo","fda-pma","ce-marked","ce-ivdr","not-applicable"],"settings":["basic-research","screening","diagnosis","prognosis","treatment-planning","treatment-response","drug-discovery","clinical-trials","education","vaccine-design","immunotherapy"],"tasks":["feature-extraction","classification","segmentation","detection","screening","risk-prediction","prognosis","treatment-response","survival-analysis","image-text-retrieval","report-generation","question-answering","information-extraction","clinical-trial-matching","protein-structure","variant-effect","gene-expression","single-cell","drug-discovery","drug-repurposing","peptide-mhc-binding","antigen-presentation","neoantigen-prioritisation"]}}