run_confidentPrediction: Confident Prediction of RNA-Protein Interaction Using...

In HAN-Siyu/ncProR: Predicting Long non-coding RNA-Protein Interaction

Confident Prediction of RNA-Protein Interaction Using Multiple Methods Simultaneously

Description

This function can predict lncRNA/RNA-protein interactions using all supported methods, which is useful to have a high-confident prediction.

Usage

run_confidentPrediction(
  seqRNA,
  seqPro,
  label = NULL,
  methods = c("RPISeq_web", "RPISeq_retrain", "lncPro_original", "lncPro_retrain",
    "rpiCOOL_retrain", "LncADeep_retrain", "LION"),
  RPISeq.mod = NULL,
  lncPro.mod = NULL,
  rpiCOOL.mod = NULL,
  LncADeep.mod = NULL,
  LION.mod = NULL,
  parallel.cores = 2,
  cl = NULL
)

Arguments

seqRNA	RNA sequences loaded by function read.fasta from seqinr-package. Or a list of RNA/protein sequences. RNA sequences will be converted into lower case letters.
seqPro	protein sequences loaded by function read.fasta from seqinr-package. Or a list of protein sequences. Protein sequences will be converted into upper case letters. Each sequence should be a vector of single characters.
label	optional. A string or a vector of strings or NULL. Used to give labels or notes to the output result. Default: NULL.
methods	strings. Indicate the method(s) to be used for prediction. Can be: "RPISeq_web", "RPISeq_retrain", "lncPro_original", "lncPro_retrain", "rpiCOOL_retrain", "LncADeep_retrain" and "LION".
RPISeq.mod, lncPro.mod, rpiCOOL.mod, LncADeep.mod, LION.mod	use default retrained model (if NULL) or assign a new retrained model? New retrained model can be generated with run_RPISeq, run_lncPro, run_rpiCOOL, run_LncADeep and run_LION.
parallel.cores	an integer that indicates the number of cores for parallel computation. Default: 2. Set parallel.cores = -1 to run with all the cores. parallel.cores should be == -1 or >= 1.
cl	parallel cores to be passed to this function.

Value

A list containing the predicted results.

References

Han S, Yang X, Sun H, et al. LION: an integrated R package for effective prediction of ncRNA–protein interaction. Briefings in Bioinformatics. 2022; 23(6):bbac420

Examples

# Following codes only show how to use this function
# and cannot reflect the genuine performance of tools or classifiers.

data(demoPositiveSeq)
seqRNA <- demoPositiveSeq$RNA.positive
seqPro <- demoPositiveSeq$Pro.positive

# Using methods RPISeq (retrained model) and rpiCOOL (retrained model):

Res_confidence <- run_confidentPrediction(seqRNA = seqRNA, seqPro = seqPro,
                                          methods = c("RPISeq_retrain",
                                          "rpiCOOL_retrain", "LION"),
                                          label = "Interact", # label is optional
                                          parallel.cores = 2)
# Convert to data frame:

Res_confidence_df <- do.call("cbind", Res_confidence)
Res_confidence_df <- Res_confidence_df[!duplicated(names(Res_confidence_df))]

HAN-Siyu/ncProR documentation built on Nov. 3, 2023, 12:08 a.m.