get_seq_encode_pad: Vectorization of characters of strings

In dongminjung/DeepPINCS: Protein Interactions and Networks with Compounds based on Sequences using Deep Learning

Description

A vectorization of characters of strings is necessary. Vectorized characters are padded or truncated.

Usage

get_seq_encode_pad(sequences, length_seq, ngram_max = 1, ngram_min = 1,
    lenc = NULL)

Arguments

sequences	SMILE strings or amino acid sequences
length_seq	length of input sequences
ngram_max	maximum size of an n-gram (default: 1)
ngram_min	minimum size of an n-gram (default: 1)
lenc	encoded labels for characters, LableEncoder object fitted by "CatEncoders::LabelEncoder.fit" (default: NULL)

Value

sequences_encode_pad	for each SMILES string, an encoded sequence which is padded or truncated
lenc	encoded labels for characters
num_token	total number of characters

Author(s)

Dongmin Jung

See Also

CatEncoders::LabelEncoder.fit, CatEncoders::transform, keras::pad_sequences, stringdist::qgrams, tokenizers::tokenize_ngrams

Examples

get_seq_encode_pad(example_cpi[1, 2], 10)

dongminjung/DeepPINCS documentation built on Dec. 20, 2021, 12:13 a.m.