| scholarly article | Q13442814 |
| P50 | author | Kuo-Chen Chou | Q30069869 |
| Hao Lin | Q38549110 | ||
| P2093 | author name string | Wei Chen | |
| Dian-Chuan Jin | |||
| Tian-Yu Lei | |||
| P2860 | cites work | Protein Remote Homology Detection by Combining Chou's Pseudo Amino Acid Composition and Profile-Based Protein Representation | Q39537162 |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | web server | Q11288 |
| P304 | page(s) | 53-60 | |
| P577 | publication date | 2014-04-13 | |
| P1433 | published in | Analytical Biochemistry | Q485215 |
| P1476 | title | PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. | |
| P478 | volume | 456 |
| Q33633983 | 2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function |
| Q38884774 | A comprehensive overview of computational resources to aid in precision genome editing with engineered nucleases |
| Q57805423 | A computational method for prediction of xylanase enzymes activity in strains of Bacillus subtilis based on pseudo amino acid composition features |
| Q36118064 | A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs |
| Q41368257 | A survey of tools for analysing DNA fingerprints |
| Q30983367 | Benchmark data for identifying DNA methylation sites via pseudo trinucleotide composition |
| Q35950707 | Benchmark data for identifying N(6)-methyladenosine sites in the Saccharomyces cerevisiae genome |
| Q47173127 | BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches |
| Q90182696 | CRIP: predicting circRNA-RBP-binding sites using a codon-based encoding and hybrid deep neural networks |
| Q47678064 | CarSite: identifying carbonylated sites of human proteins based on a one-sided selection resampling method |
| Q38835001 | Cell-Peptide Specific Interaction Can Inhibit Mycobacterium tuberculosis H37Rv Infection. |
| Q90450324 | Characterization of human proteins with different subcellular localizations by topological and biological properties |
| Q90347835 | Characterization of proteins in different subcellular localizations for Escherichia coli K12 |
| Q52674909 | Characterize the relationship between essential and TATA-containing genes for S. cerevisiae by network topologies in the perturbation sensitivity network. |
| Q90608588 | Circular RNA-protein interactions: functions, mechanisms, and identification |
| Q39606528 | Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots |
| Q36086528 | Comparison of genomic data via statistical distribution |
| Q27335183 | Decomposition of RNA methylome reveals co-methylation patterns induced by latent enzymatic regulators of the epitranscriptome |
| Q41353622 | Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines |
| Q47958519 | EnhancerPred2.0: predicting enhancers and their strength based on position-specific trinucleotide propensity and electron-ion interaction potential feature selection. |
| Q42354636 | EnhancerPred: a predictor for discovering enhancers based on the combination and selection of multiple features |
| Q92150872 | Ensemble of Deep Recurrent Neural Networks for Identifying Enhancers via Dinucleotide Physicochemical Properties |
| Q36060603 | Environmental genes and genomes: understanding the differences and challenges in the approaches and software for their analyses |
| Q46420725 | Evolutionary mechanism and biological functions of 8-mers containing CG dinucleotide in yeast |
| Q92906098 | FKRR-MVSF: A Fuzzy Kernel Ridge Regression Model for Identifying DNA-Binding Proteins by Multi-View Sequence Features via Chou's Five-Step Rule |
| Q37690994 | Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human |
| Q92257983 | Graph Theory-Based Sequence Descriptors as Remote Homology Predictors |
| Q36379689 | Handling High-Dimension (High-Feature) MicroRNA Data |
| Q64268401 | Identification of D Modification Sites by Integrating Heterogeneous Features in |
| Q46834389 | Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach |
| Q30386998 | Identification of Damaging nsSNVs in HumanERCC2 Gene. |
| Q50965878 | Identification of protein-interacting nucleotides in a RNA sequence using composition profile of tri-nucleotides. |
| Q40534208 | Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition |
| Q28651066 | Identification of real microRNA precursors with a pseudo structure status composition approach |
| Q47108002 | Identification of recent cases of hepatitis C virus infection using physical-chemical properties of hypervariable region 1 and a radial basis function neural network classifier |
| Q93029037 | Identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma via Chou's 5-steps rule |
| Q55008814 | Identifying RNA N6-Methyladenosine Sites in Escherichia coli Genome. |
| Q41903687 | Identifying new targets in leukemogenesis using computational approaches |
| Q37224739 | Identifying the Types of Ion Channel-Targeted Conotoxins by Incorporating New Properties of Residues into Pseudo Amino Acid Composition. |
| Q50062451 | Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams. |
| Q31144037 | In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth data |
| Q33677773 | Molecular classification of prostate adenocarcinoma by the integrated somatic mutation profiles and molecular network |
| Q42583554 | Molecular science for drug development and biomedicine |
| Q37325385 | PAI: Predicting adenosine to inosine editing sites by using pseudo nucleotide compositions |
| Q89621448 | Predicting lncRNA-Protein Interactions With miRNAs as Mediators in a Heterogeneous Network Model |
| Q38732163 | Predicting protein lysine phosphoglycerylation sites by hybridizing many sequence based features |
| Q47602547 | Predicting the Organelle Location of Noncoding RNAs Using Pseudo Nucleotide Compositions. |
| Q51046323 | Prediction of CpG island methylation status by integrating DNA physicochemical properties. |
| Q55490568 | Prediction of DNase I hypersensitive sites by using pseudo nucleotide compositions. |
| Q49205554 | Prediction of HIV-1 and HIV-2 proteins by using Chou's pseudo amino acid compositions and different classifiers |
| Q98155076 | Prediction of N7-methylguanosine sites in human RNA based on optimal sequence features |
| Q36034999 | Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes |
| Q92600598 | Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components |
| Q41472472 | Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression |
| Q37253984 | ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier |
| Q91869076 | Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction |
| Q51585994 | Protein binding site prediction by combining hidden Markov support vector machine and profile-based propensities. |
| Q50927764 | Protein remote homology detection by combining Chou's distance-pair pseudo amino acid composition and principal component analysis. |
| Q36246564 | Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods |
| Q35810290 | Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences |
| Q30459140 | PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions |
| Q58696200 | PseUI: Pseudouridine sites identification based on RNA sequence information |
| Q38534551 | Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences |
| Q91699024 | RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule |
| Q28830645 | RAMPred: identifying the N(1)-methyladenosine sites in eukaryotic transcriptomes |
| Q55083143 | Recent Advances in Identification of RNA Modifications. |
| Q60960974 | Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics |
| Q90659183 | Structural Variability in the RLR-MAVS Pathway and Sensitive Detection of Viral RNAs |
| Q40244157 | Surveying and benchmarking techniques to analyse DNA gel fingerprint images. |
| Q47116051 | UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences. |
| Q92975578 | Using extreme gradient boosting to identify origin of replication in Saccharomyces cerevisiae via hybrid features |
| Q90069170 | csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou's 5-step rule |
| Q28658162 | iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels |
| Q37376337 | iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC. |
| Q34128762 | iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition |
| Q47557569 | iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. |
| Q40420289 | iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition |
| Q37536872 | iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC. |
| Q47437268 | iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier |
| Q41489664 | iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach |
| Q57022615 | iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou's PseAAC to pseudo-tri-nucleotide composition |
| Q39392218 | iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition |
| Q50729299 | iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets. |
| Q90358625 | iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition |
| Q34677601 | iPTM-mLys: identifying multiple lysine PTM sites and their different types |
| Q37588275 | iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier |
| Q38794654 | iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory |
| Q57927521 | iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features |
| Q91832728 | iProEP: A Computational Predictor for Predicting Promoter |
| Q90180026 | iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features |
| Q47316901 | iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. |
| Q57928510 | iPromoter-FSEn: Identification of bacterial σ promoter sequences using feature subspace based ensemble classifier |
| Q62729918 | iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites |
| Q55162021 | iRNA-3typeA: Identifying Three Types of Modification at RNA's Adenosine Sites. |
| Q42315793 | iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. |
| Q42075633 | iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC. |
| Q42319212 | iRNA-PseU: Identifying RNA pseudouridine sites |
| Q90457322 | iRNA-m7G: Identifying N7-methylguanosine Sites by Fusing Multiple Features |
| Q41093262 | iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition |
| Q37376225 | iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition |
| Q37264647 | iRSpot-DACC: a computational predictor for recombination hot/cold spots identification based on dinucleotide-based auto-cross covariance |
| Q39484248 | iRSpot-EL: identify recombination spots with an ensemble learning approach |
| Q40593974 | iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples |
| Q55513264 | iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. |
| Q57929342 | iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components |
| Q38622969 | iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder. |
| Q33747883 | iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. |
| Q89584745 | iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components |
| Q98944942 | miRNALoc: predicting miRNA subcellular localizations based on principal component scores of physico-chemical properties and pseudo compositions of di-nucleotides |
| Q47657928 | pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. |
| Q47309310 | pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information |
| Q91282652 | pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset |
| Q39646826 | pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC. |
| Q58802255 | piRNN: deep learning algorithm for piRNA prediction |
| Q35518468 | repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects |
| Q45954150 | repRNA: a web server for generating various feature vectors of RNA sequences. |
Search more.