Atalay Eshetie Demilie, Zewditu Abdissa Denu, Yosef Belay Bizuneh, Endale Gebreegziabher Gebremedhn
Incidence and factors associated with failed spinal anaesthesia among patients undergoing surgery: a multi- center prospective observational study
BMC anesthesiology
molecular recognition features morfs particular functional segments disordered proteins play crucial roles regulating phase transition membraneless organelles frequently serve central sites cellular interaction networks association disordered proteins severe diseases continues discovered identifying morfs gained growing significance due limited number experimentally validated morfs performance existing morfs prediction algorithms good enough still needs improved research present model named morfesm utilizes deeplearning protein representations predict morfs disordered proteins approach employs pretrained esm2 protein language model generate embedding representations residues form attention map matrices representations combined selflearned textcnn model feature extraction prediction addition averaging step incorporated end morfesm model refine output generate final prediction results comparison impressive methods benchmark datasets morfesm approach demonstrates stateoftheart performance achieving formula see text higher auc methods tested test1 achieving formula see text higher auc methods tested test2 results imply combination esm2 textcnn effectively extract deep evolutionary features related protein structure function along capturing shallow pattern features located protein sequences well qualified prediction task morfs given esm2 highly versatile protein language model methodology proposed study readily applied tasks involving classification protein sequences