proteina_net
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ProteinNet è un set di dati standardizzato per l'apprendimento automatico della struttura delle proteine. Fornisce sequenze proteiche, strutture (secondarie e terziarie), allineamenti di sequenze multiple (MSA), matrici di punteggio specifiche per posizione (PSSM) e divisioni standardizzate di addestramento/validazione/test. ProteinNet si basa sulle valutazioni CASP biennali, che eseguono previsioni cieche di strutture proteiche recentemente risolte ma non disponibili pubblicamente, per fornire set di test che spingono le frontiere della metodologia computazionale. È organizzato come una serie di set di dati, che vanno da CASP 7 a 12 (coprendo un periodo di dieci anni), per fornire una gamma di dimensioni di set di dati che consentono la valutazione di nuovi metodi in regimi relativamente poveri di dati e ricchi di dati.
FeaturesDict({
'evolutionary': Tensor(shape=(None, 21), dtype=float32),
'id': Text(shape=(), dtype=string),
'length': int32,
'mask': Tensor(shape=(None,), dtype=bool),
'primary': Sequence(ClassLabel(shape=(), dtype=int64, num_classes=20)),
'tertiary': Tensor(shape=(None, 3), dtype=float32),
})
- Documentazione delle funzionalità :
Caratteristica | Classe | Forma | Tipo D | Descrizione |
---|
| CaratteristicheDict | | | |
evolutivo | Tensore | (Nessuno, 21) | galleggiante32 | |
id | Testo | | corda | |
lunghezza | Tensore | | int32 | |
maschera | Tensore | (Nessuno,) | bool | |
primario | Sequenza(EtichettaClasse) | (Nessuno,) | int64 | |
terziario | Tensore | (Nessuno, 3) | galleggiante32 | |
@article{ProteinNet19,
title = { {ProteinNet}: a standardized data set for machine learning of protein structure},
author = {AlQuraishi, Mohammed},
journal = {BMC bioinformatics},
volume = {20},
number = {1},
pages = {1--10},
year = {2019},
publisher = {BioMed Central}
}
protein_net/casp7 (configurazione predefinita)
Diviso | Esempi |
---|
'test' | 93 |
'train_100' | 34.557 |
'train_30' | 10.333 |
'train_50' | 13.024 |
'train_70' | 15.207 |
'train_90' | 17.611 |
'train_95' | 17.938 |
'validation' | 224 |
protein_net/casp8
Diviso | Esempi |
---|
'test' | 120 |
'train_100' | 48.087 |
'train_30' | 13.881 |
'train_50' | 17.970 |
'train_70' | 21,191 |
'train_90' | 24.556 |
'train_95' | 25.035 |
'validation' | 224 |
protein_net/casp9
Diviso | Esempi |
---|
'test' | 116 |
'train_100' | 60.350 |
'train_30' | 16.973 |
'train_50' | 22.172 |
'train_70' | 26.263 |
'train_90' | 30.513 |
'train_95' | 31.128 |
'validation' | 224 |
protein_net/casp10
Diviso | Esempi |
---|
'test' | 95 |
'train_100' | 73,116 |
'train_30' | 19.495 |
'train_50' | 25.897 |
'train_70' | 31.001 |
'train_90' | 36.258 |
'train_95' | 37.033 |
'validation' | 224 |
protein_net/casp11
Diviso | Esempi |
---|
'test' | 81 |
'train_100' | 87.573 |
'train_30' | 22.344 |
'train_50' | 29.936 |
'train_70' | 36.005 |
'train_90' | 42.507 |
'train_95' | 43.544 |
'validation' | 224 |
protein_net/casp12
Diviso | Esempi |
---|
'test' | 40 |
'train_100' | 104.059 |
'train_30' | 25.299 |
'train_50' | 34.039 |
'train_70' | 41.522 |
'train_90' | 49.600 |
'train_95' | 50.914 |
'validation' | 224 |
Salvo quando diversamente specificato, i contenuti di questa pagina sono concessi in base alla licenza Creative Commons Attribution 4.0, mentre gli esempi di codice sono concessi in base alla licenza Apache 2.0. Per ulteriori dettagli, consulta le norme del sito di Google Developers. Java è un marchio registrato di Oracle e/o delle sue consociate.
Ultimo aggiornamento 2022-12-16 UTC.
[null,null,["Ultimo aggiornamento 2022-12-16 UTC."],[],[],null,["# protein_net\n\n\u003cbr /\u003e\n\n- **Description**:\n\nProteinNet is a standardized data set for machine learning of protein structure.\nIt provides protein sequences, structures (secondary and tertiary), multiple\nsequence alignments (MSAs), position-specific scoring matrices (PSSMs), and\nstandardized training / validation / test splits. ProteinNet builds on the\nbiennial CASP assessments, which carry out blind predictions of recently solved\nbut publicly unavailable protein structures, to provide test sets that push the\nfrontiers of computational methodology. It is organized as a series of data\nsets, spanning CASP 7 through 12 (covering a ten-year period), to provide a\nrange of data set sizes that enable assessment of new methods in relatively data\npoor and data rich regimes.\n\n- **Homepage** :\n \u003chttps://github.com/aqlaboratory/proteinnet\u003e\n\n- **Source code** :\n [`tfds.datasets.protein_net.Builder`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/protein_net/protein_net_dataset_builder.py)\n\n- **Versions**:\n\n - **`1.0.0`** (default): Initial release.\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n No\n\n- **Feature structure**:\n\n FeaturesDict({\n 'evolutionary': Tensor(shape=(None, 21), dtype=float32),\n 'id': Text(shape=(), dtype=string),\n 'length': int32,\n 'mask': Tensor(shape=(None,), dtype=bool),\n 'primary': Sequence(ClassLabel(shape=(), dtype=int64, num_classes=20)),\n 'tertiary': Tensor(shape=(None, 3), dtype=float32),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|--------------|----------------------|------------|---------|-------------|\n| | FeaturesDict | | | |\n| evolutionary | Tensor | (None, 21) | float32 | |\n| id | Text | | string | |\n| length | Tensor | | int32 | |\n| mask | Tensor | (None,) | bool | |\n| primary | Sequence(ClassLabel) | (None,) | int64 | |\n| tertiary | Tensor | (None, 3) | float32 | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `('primary', 'tertiary')`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n Not supported.\n\n- **Citation**:\n\n @article{ProteinNet19,\n title = { {ProteinNet}: a standardized data set for machine learning of protein structure},\n author = {AlQuraishi, Mohammed},\n journal = {BMC bioinformatics},\n volume = {20},\n number = {1},\n pages = {1--10},\n year = {2019},\n publisher = {BioMed Central}\n }\n\nprotein_net/casp7 (default config)\n----------------------------------\n\n- **Download size** : `3.18 GiB`\n\n- **Dataset size** : `2.53 GiB`\n\n- **Splits**:\n\n| Split | Examples |\n|----------------|----------|\n| `'test'` | 93 |\n| `'train_100'` | 34,557 |\n| `'train_30'` | 10,333 |\n| `'train_50'` | 13,024 |\n| `'train_70'` | 15,207 |\n| `'train_90'` | 17,611 |\n| `'train_95'` | 17,938 |\n| `'validation'` | 224 |\n\n- **Examples** ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\nprotein_net/casp8\n-----------------\n\n- **Download size** : `4.96 GiB`\n\n- **Dataset size** : `3.55 GiB`\n\n- **Splits**:\n\n| Split | Examples |\n|----------------|----------|\n| `'test'` | 120 |\n| `'train_100'` | 48,087 |\n| `'train_30'` | 13,881 |\n| `'train_50'` | 17,970 |\n| `'train_70'` | 21,191 |\n| `'train_90'` | 24,556 |\n| `'train_95'` | 25,035 |\n| `'validation'` | 224 |\n\n- **Examples** ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\nprotein_net/casp9\n-----------------\n\n- **Download size** : `6.65 GiB`\n\n- **Dataset size** : `4.54 GiB`\n\n- **Splits**:\n\n| Split | Examples |\n|----------------|----------|\n| `'test'` | 116 |\n| `'train_100'` | 60,350 |\n| `'train_30'` | 16,973 |\n| `'train_50'` | 22,172 |\n| `'train_70'` | 26,263 |\n| `'train_90'` | 30,513 |\n| `'train_95'` | 31,128 |\n| `'validation'` | 224 |\n\n- **Examples** ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\nprotein_net/casp10\n------------------\n\n- **Download size** : `8.65 GiB`\n\n- **Dataset size** : `5.57 GiB`\n\n- **Splits**:\n\n| Split | Examples |\n|----------------|----------|\n| `'test'` | 95 |\n| `'train_100'` | 73,116 |\n| `'train_30'` | 19,495 |\n| `'train_50'` | 25,897 |\n| `'train_70'` | 31,001 |\n| `'train_90'` | 36,258 |\n| `'train_95'` | 37,033 |\n| `'validation'` | 224 |\n\n- **Examples** ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\nprotein_net/casp11\n------------------\n\n- **Download size** : `10.81 GiB`\n\n- **Dataset size** : `6.72 GiB`\n\n- **Splits**:\n\n| Split | Examples |\n|----------------|----------|\n| `'test'` | 81 |\n| `'train_100'` | 87,573 |\n| `'train_30'` | 22,344 |\n| `'train_50'` | 29,936 |\n| `'train_70'` | 36,005 |\n| `'train_90'` | 42,507 |\n| `'train_95'` | 43,544 |\n| `'validation'` | 224 |\n\n- **Examples** ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\nprotein_net/casp12\n------------------\n\n- **Download size** : `13.18 GiB`\n\n- **Dataset size** : `8.05 GiB`\n\n- **Splits**:\n\n| Split | Examples |\n|----------------|----------|\n| `'test'` | 40 |\n| `'train_100'` | 104,059 |\n| `'train_30'` | 25,299 |\n| `'train_50'` | 34,039 |\n| `'train_70'` | 41,522 |\n| `'train_90'` | 49,600 |\n| `'train_95'` | 50,914 |\n| `'validation'` | 224 |\n\n- **Examples** ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples..."]]