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Deshpande2019 - Random Forest model to predict long non-coding RNAs from coding RNAs in Zea Mays plant transcriptomic data

ABSTRACT: This is a Random Forest algorithm-based machine learning model to predict lncRNAs from coding mRNAs in plant transcriptomic data. The model assigns 1 for coding sequences and 2 for long non-coding sequences. The prediction is performed using a combination of Open Reading Frame (ORF) based, Sequence-based and Codon-bias features. Users need to download the curated ONNX model and also need to convert the sequences into feature matrix as mentioned in PLIT paper (Deshpande et al. 2019) to make predictions on sequences from Zea Mays sequence data.

SUBMITTER: Sumukh Deshpande  

PROVIDER: BIOMD0000001067 | BioModels | 2024-12-09

REPOSITORIES: BioModels

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Dataset's files

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Action DRS
BIOMD0000001067?filename=AUC_curve_reprodued_ZM.pdf Pdf
BIOMD0000001067?filename=Dockerfile.zip Other
BIOMD0000001067?filename=Model_annotation_file.csv Csv
BIOMD0000001067?filename=PLIT_extractFeatures_testSet.sh Other
BIOMD0000001067?filename=PLIT_extractFeatures_trainingSet.sh Other
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