Protocol for clustering of non-unified protein sequences through memory-map guided deep learning

Protocol established and validated for clustering of non-unified protein sequences through memorymap guided deep learning. Data evaluated belongs to the disease causing proteins/genes from human hormonal system. Possibilities for future experiments validation was found for genes as: ACTHR, AGMX1, ATK, BPK, DPDE3, ERBA2, FSHB, GH1, GHSR, GNAS1, GSP, HANF, LCGR, LGR2, LGR3, LHRHR, NR1A2, PKR1, PRKAR1, RNF216, SBP2, SECISBP2, THR1, THRB, TPIT, TRIAD3, TSE1, UBCE7IP1, XAP2, and ZIN. Protocol is recommended for implementation with small to large dataset (protein/ DNA/ RNA sequences of unified or non-unified length) with unclassified data flags.