Protein Secondary Structure Prediction On 1

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Q3	Q8	Paper Title	Repository
ProtT5-XL-UniRef50	0.86	0.74	ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing
ProtT5-XL-BFD	0.84	0.71	ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing
Porter5	-	0.74	Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction
LucaAngioloni-WindowCNN	-	0.684	Protein secondary structure prediction using deep convolutional neural fields
Advanced ACNN	-	0.733	-	-
ACNN	-	0.697	Protein secondary structure prediction using deep convolutional neural fields
PS4-Mega	0.868	0.763	PS4: a Next-Generation Dataset for Protein Single Sequence Secondary Structure Prediction
PS4-Conv	0.863	0.756	PS4: a Next-Generation Dataset for Protein Single Sequence Secondary Structure Prediction
ProtBert-BFD	0.83	0.7	ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing
DistilProtBert	0.79	-	DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts

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