Galleria
Playlist immagini
Gruppi
Unisciti a un gruppo
Acquisizione
Nuovo oggetto
Mappe concettuali
Gestione
Tabelle (CRUD generico)
Ricerca tag
Elaborazioni
Somma di prova
Sistema
Impostazioni
Esci
☰
Oggetti
LOGISTAR
LMP
ospite
⋮
Profilo utente
Preferenze colonne
Guida programma
Segnala anomalia
Cambia utente
⌂
💾 Salva
↻ Aggiorna
☑ Lista
🏷 Righe
Id
Descrizione
dta reg (aaaa-mm-gg)
Body
<p> Genomic (DNA) sequences encode an enormous amount of information for gene regulation and protein synthesis. Similar to natural language models, researchers have proposed foundation models in genomics to learn generalizable features from unlabeled genome data that can then be fine-tuned for downstream tasks such as identifying regulatory elements. Due to the quadratic scaling of attention, previous Transformer-based genomic models have used 512 to 4k tokens as context (<0.001% of the human genome), significantly limiting the modeling of long-range interactions in DNA. In addition, these methods rely on tokenizers to aggregate meaningful DNA units, losing single nucleotide resolution where subtle genetic variations can completely alter protein function via single nucleotide polymorphisms (SNPs). Recently, Hyena, a large language model based on implicit convolutions was shown to match attention in quality while allowing longer context lengths and lower time complexity. Leveraging Hyenas new long-range capabilities, we present HyenaDNA, a genomic foundation model pretrained on the human reference genome with context lengths of up to 1 million tokens at the single nucleotide-level, an up to 500x increase over previous dense attention-based models. HyenaDNA scales sub-quadratically in sequence length (training up to 160x faster than Transformer), uses single nucleotide tokens, and has full global context at each layer. We explore what longer context enables - including the first use of in-context learning in genomics for simple adaptation to novel tasks without updating pretrained model weights. On fine-tuned benchmarks from the Nucleotide Transformer, HyenaDNA reaches state-of-the-art (SotA) on 12 of 17 datasets using a model with orders of magnitude less parameters and pretraining data. On the GenomicBenchmarks, HyenaDNA surpasses SotA on all 8 datasets on average by +9 accuracy points.</p>
tipo ogg.
cat
subcat
proprietario
azienda
ute
selez1
op1
codart
codice
codclifor
motivo
url
email
tag
imgsrc
sunto_breve
sunto_medio
note
sequencing (67.7%), genoma (64.1%), biobanca genomica (60.5%), genomica (59.2%), OGM (55.4%), trascrittomica (55.1%), genetica (52.2%), ancestry (51.9%), gene (49.2%), bioinformatica (48.0%), proteina (43.6%), epigenetica (43.1%), generative (41.1%), language_model (39.6%), text_generation (38.8%), diagnostica (37.8%), documentation (37.6%), neuroinformatica (37.4%), analisi (35.6%), biosensore (35.4%)
keyscrape
Lista tag dell'oggetto (oggettag)
cod_tag
value
dev
5
gen
5
genoma
5
gpt
5
llm
5
nn
5
tool
5
wpg
5
Lista delle righe dell'oggetto (oggettir)
tip_pro
cod_pro
descrizione
val_pro
pes_pro
---.