seq_preprocessing: Preprocessing for SMILES strings and amino acid sequences

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

Description

Preprocessing helps make the data suitable for the model depending on the type of data the preprocessing works upon. Preprocessing is more time consuming for text data. The adjacency matrix and node feature, fingerprint, or string data are preprocessed from sequences.

Usage

seq_preprocessing(smiles = NULL,
    AAseq = NULL,
    type,
    convert_canonical_smiles,
    max_atoms,
    length_seq,
    lenc = NULL,
    ngram_max = 1,
    ngram_min = 1)

Arguments

smiles	SMILES strings (default: NULL)
AAseq	amino acid sequences (default: NULL)
type	"graph", "fingerprint" or "sequence"
convert_canonical_smiles	SMILES strings are converted to canonical SMILES strings if TRUE
max_atoms	maximum number of atoms for compounds
length_seq	length of compound or protein sequence
lenc	encoded labels for characters of SMILES strings or amino acid sequenes (default: NULL)
ngram_max	maximum size of an n-gram for protein sequences (default: 1)
ngram_min	minimum size of an n-gram for protein sequences (default: 1)

Value

canonical_smiles	canonical representation of SMILES
convert_canonical_smiles	canonical representation is used or not
A_pad	padded or turncated adjacency matrix of compounds if type is "graph"
X_pad	padded or turncated node features of compounds if type is "graph"
fp	fingerprint of compounds if type is "fingerprint"
sequences_encode_pad	encoded sequences which are padded or truncated
lenc	encoded labels for characters of SMILES strings or amino acid sequenes
length_seq	length of compound or protein sequence
num_tokens	total number of characters of compounds or proteins

Author(s)

Dongmin Jung

References

Dey, N., Wagh, S., Mahalle, P. N., & Pathan, M. S. (Eds.). (2019). Applied machine learning for smart data analysis. CRC Press.

Examples

seq_preprocessing(smiles = cbind(example_cpi[1, 1]),
    type = "fingerprint",
    convert_canonical_smiles = TRUE)

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