inst/PlastoGram/detailed_results_desc.md

In BioGenies/PlastoGram: Prediction of Subchloroplast localization

Detailed results include predictions made by the lower-level models which were trained to distinguish specific classes of proteins. These predictions were used by the higher-level random forest-based model to make final predictions. - Nuclear_model - n-gram based random forest trained to differentiate nuclear-encoded (1) from plastid-encoded proteins (0). - Membrane_model - n-gram based random forest trained to identify integral membrane proteins. - N_E_vs_N_TM_model - n-gram based random forest trained to differentiate nuclear-encoded envelope proteins (1) from nuclear-encoded thylakoid membrane proteins (0). - Plastid_membrane_model - n-gram based random forest trained to distinguish plastid-encoded inner membrane proteins (1) from plastid-encoded thylakoid membrane proteins (0). - N_E_vs_N_S_model - n-gram based random forest trained to differentiate nuclear-encoded envelope proteins (1) from nuclear-encoded stromal proteins (0). - Nuclear_membrane_model - n-gram based random forest trained to distinguish nuclear-encoded membrane proteins (1) from all other proteins (0). - Sec_model - profile HMM model trained to identify proteins with signals responsible for targeting to the thylakoid lumen via Sec pathway - Tat_model - profile HMM model trained to identify proteins with signals responsible for targeting to the thylakoid lumen via Tat pathway

BioGenies/PlastoGram documentation built on May 25, 2023, 10:45 p.m.