Search Results for author:
Found 16 papers, 4 papers with code
InstructBio: A Large-scale Semi-supervised Learning Paradigm for Biochemical Problems
In the field of artificial intelligence for science, it is consistently an essential challenge to face a limited amount of labeled data for real-world problems.
Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks
Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area.
Distance-based Protein Folding Powered by Deep Learning
We show that protein distance matrix can be predicted well by deep learning and then directly used to construct 3D models without folding simulation at all.
Biomolecules
Folding membrane proteins by deep transfer learning
Computational elucidation of membrane protein (MP) structures is challenging partially due to lack of sufficient solved structures for homology modeling.
Predicting membrane protein contacts from non-membrane proteins by deep transfer learning
Tested on 510 non-redundant MPs, our deep model (learned from only non-MPs) has top L/10 long-range contact prediction accuracy 0. 69, better than our deep model trained by only MPs (0. 63) and much better than a representative DCA method CCMpred (0. 47) and the CASP11 winner MetaPSICOV (0. 55).
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0. 5.
Network Inference by Learned Node-Specific Degree Prior
We propose a novel method for network inference from partially observed edges using a node-specific degree prior.
Protein secondary structure prediction using deep convolutional neural fields
Protein secondary structure (SS) prediction is important for studying protein structure and function.
Ranked #1 on
Protein Secondary Structure Prediction
on CullPDB
Learning structured densities via infinite dimensional exponential families
Learning the structure of a probabilistic graphical models is a well studied problem in the machine learning community due to its importance in many applications.
AUC-maximized Deep Convolutional Neural Fields for Sequence Labeling
Our experimental results confirm that maximum-AUC greatly outperforms the other two training methods on 8-state secondary structure prediction and disorder prediction since their label distributions are highly imbalanced and also have similar performance as the other two training methods on the solvent accessibility prediction problem which has three equally-distributed labels.
Learning Scale-Free Networks by Dynamic Node-Specific Degree Prior
Learning the network structure underlying data is an important problem in machine learning.
Exact Hybrid Covariance Thresholding for Joint Graphical Lasso
This paper proposes a novel hybrid covariance thresholding algorithm that can effectively identify zero entries in the precision matrices and split a large joint graphical lasso problem into small subproblems.
MRFalign: Protein Homology Detection through Alignment of Markov Random Fields
A sequence profile is usually represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection.
Protein Contact Prediction by Integrating Joint Evolutionary Coupling Analysis and Supervised Learning
To further improve the accuracy of the estimated precision matrices, we employ a supervised learning method to predict contact probability from a variety of evolutionary and non-evolutionary information and then incorporate the predicted probability as prior into our GGL framework.
Predicting protein contact map using evolutionary and physical constraints by integer programming (extended version)
Most existing methods predict the contact map matrix element-by-element, ignoring correlation among contacts and physical feasibility of the whole contact map.
Conditional Neural Fields
To model the nonlinear relationship between input features and outputs we propose Conditional Neural Fields (CNF), a new conditional probabilistic graphical model for sequence labeling.
