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Bin Liu's lab at Beijing Institute of Technology (BIT) is focusing on developing techniques grounded in the natural language processing (NLP) to uncover the meanings of "book of life". The research areas of Bin Liu's lab include:
1) Developing the Biological language models (BLMs);
2) Studying the natural language processing techniques;
3) Applying BLMs to biological sequence analysis;
4) Protein remote homology detection and fold recognition;
5) Predicting DNA/RNA binding proteins and their binding residues;
6) Disordered protein/region prediction based on sequence labelling models;
7) Predicting noncoding RNA-disease associations;
8) Identifying protein complexes;
9) DNA/RNA sequence analysis.
a platform for analyzing DNA, RNA, and protein sequences based on biological language models
An updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based ...
A platform for DNA, RNA and protein sequence analysis based on machine learning approaches
A web server for generating various modes of pseduo components of DNA, RNA, and protein sequences
A Python package for DNA/RNA and protein/peptide sequence analysis based on pseudo components and kernel methods
A Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined ...
Protein Remote Homology Detection by Combining PageRank and Hyperlink-Induced Topic Search
Protein remote homology detection by combining Long Short-Term Memory and ranking methods
Protein Remote Homology Detection based on Bidirectional Long Short-Term Memory
detecting remote homology proteins using profile-based pseudo protein sequence and rank aggregation
Application of Learning to Rank to protein remote homology detection
An improved method for protein remote homology detection by combining pseudo protein and supervised learning to rank
protein remote homology detection by incorporating profile-based features into Learning to Rank
A profile link based search method for protein remote homology detection
A novel supervised search framework based on PSI-BLAST for protein remote homology detection and its application to ...
protein fold recognition by combining cluster-to-cluster model and protein similarity network
protein fold recognition by combining Directed Fusion Graph and PageRank algorithm
Identification of Intrinsically Disordered Proteins and Regions based on Sequence to Sequence Learning
Reduce the false positive rates for intrinsically disordered protein and region prediction by incorporating ordered proteins
identifying nucleic acid binding residues in proteins based on multi-label sequence labeling model
identifying DNA-binding proteins and RNA-binding proteins based on multi-label learning model and motif-based ...
a new genome annotation predictor for identifying DNA-binding proteins and RNA-binding proteins using Convolutional ...
identifying DNA-binding proteins by combing position specific frequency matrix and distance-bigram transformation
a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC
a predictor for identifying promoters and their types by combining Smoothing Cutting Window algorithm and ...
identifying enhancers and their strength with ensemble learning approach
a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition
DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation
identify eseential genes based on Z curve pseudo k-tuple nucleotide composition
identify DNA replication origins based on Pseudo k-tuple GC Composition
Identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble ...
identifying Piwi-interacting RNA-disease associations based on Positive Unlabeled Learning
a new predictor for identifying lncRNA-disease associations by fusing biological feature blocks through deep neural network
identification of microRNA precursor with the degenerate K-tuple or Kmer strategy
microRNA precursor identification with a pseudo distance-pair composition approach
microRNA precursor identification based on distance structure status pairs
identification of the real microRNA precursors with a pseudo structure status composition approach
a two-layer ensemble classifier identifying piwi-interacting RNAs and their function
predict sgRNAs on-target activity based on Position Specific Mismatch
Using distances between Top-n-gram and residue pairs for protein remote homology detection
DNA-binding Protein Identification by Combining Chou's PseAAC and Physicochemical Distance Transformation
Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation
Improving the Identification of MicroRNA Precursors by Combining Negative Sets with Different Distributions
identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the ...
Identification of DNA-binding Proteins by Applying Ensemble Learning
Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote ...
Identifying nucleic acid-binding proteins based on PSSM and PSFM Cross Transformation
identifying DNA-binding proteins and RNA-binding proteins based on hierarchical ensemble learning
protein fold recognition based on residue-based and motif-based self-attention networks
identification of disordered flexible linker regions in proteins by combining sequence labeling and transfer learning
prediction of species-specific RNA-binding proteins based on transfer learning
identification of circRNA-disease associations based on learning to rank
Protein remote homology detection by combining classification methods and network methods via Learning to Rank
protein intrinsically disordered region prediction by combining convolutional attention network and hierarchical attention network.
Therapeutic peptides prediction based on auto-weighted multi-view learning
identifying snoRNA-disease associations based on local similarity constraint and global topological constraint
Identifying DNA- and RNA-binding residues in proteins based on induction and transfer framework
Prediction of Antimicrobial Peptides based on Graph Attention Network
Identifying DNA- and RNA- binding proteins based on extensible cubic hybrid framework
Protein Remote Homology Detection by Combing Motifs and Protein Cubic Language Model
Predicting protein disorder molecular functions based on protein cubic language model
lncRNA-disease association prediction based on graph auto-encoder and Learning to Rank
Identifying piwi-interacting RNA-disease associations based on learning to rank
Identification of piRNA-disease associations based on Graph Convolutional Network
Identifying snoRNA-disease associations based on multiple biological data by ranking framework
A novel ranking framework for improving the identification of miRNA-disease association
Identification of therapeutic peptides and their types using two-layer ensemble learning framework
A novel multi-label Subcellular Locality prediction model of ncRNA based on ensemble learning
Protein Function Prediction for Specific Ontology based on Multiple Sequence Alignment Reconstruction
Identification of piRNA-disease associations based on Supplementarily Weighted Graph Convolutional Network
Prediction of protein intrinsic disorder and disorder functions based on language models
miRNA subcellular localization prediction by combining miRNA-disease associations and graph convolutional networks
A ligand-protein interaction binding residue extractor based on Protein Data Bank
Protein intrinsically disordered region prediction by combining Neural Architecture Search and Multi-objective genetic algorithm
Please constact us by email
Prof. Dr. Bin Liu, email: firstname.lastname@example.org