Papers with Code 아카이브
Protein Secondary Structure Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 8
- 보존된 평가 기록
- 24
Protein secondary structure prediction is a vital task in bioinformatics, aiming to determine the arrangement of amino acids in proteins, including α-helices, β-sheets, and coils. By analyzing amino acid sequences, computational algorithms and machine learning techniques predict these structural elements. This knowledge is crucial for understanding protein function and interactions. While progress has been made, challenges remain, especially with non-local interactions and low sequence homology. Advancements in machine learning hold promise for improving prediction accuracy, furthering our understanding of protein biology.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration
Approximate Conditional Coverage & Calibration via Neural Model Approximations
Adaptive Residue-wise Profile Fusion for Low Homologous Protein SecondaryStructure Prediction Using External Knowledge
DLPAlign: A Deep Learning based Progressive Alignment Method for Multiple Protein Sequences
PS8-Net: A Deep Convolutional Neural Network to Predict the Eight-State Protein Secondary Structure
ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing
Seq-SetNet: Exploring Sequence Sets for Inferring Structures
ProteinNet: a standardized data set for machine learning of protein structure
High Quality Prediction of Protein Q8 Secondary Structure by Diverse Neural Network Architectures
Predicting protein secondary structure with Neural Machine Translation
Reaching Optimized Parameter Set, Protein Secondary Structure Prediction Using Neural Network
MUFold-SS: Protein Secondary Structure Prediction Using Deep Inception-Inside-Inception Networks
Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure Prediction
Protein Secondary Structure Prediction Using Deep Multi-scale Convolutional Neural Networks and Next-Step Conditioning
Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks
Protein secondary structure prediction using deep convolutional neural fields
Protein Secondary Structure Prediction with Long Short Term Memory Networks
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.