iBLP: An XGBoost-Based Predictor for Identifying Bioluminescent Proteins

[1]  Liao Fu Luo.  The degeneracy rule of genetic code , 2005, Origins of life and evolution of the biosphere.

[2]  W. Besio,et al.  Automatic Seizure Detection in Rats Using Laplacian EEG and Verification with Human Seizure Signals , 2012, Annals of Biomedical Engineering.

[3]  Abhigyan Nath,et al.  Unsupervised learning assisted robust prediction of bioluminescent proteins , 2016, Comput. Biol. Medicine.

[4]  Bohdan Schneider,et al.  A short survey on protein blocks , 2010, Biophysical Reviews.

[5]  Adam Godzik,et al.  Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences , 2006, Bioinform..

[6]  Yanxin Huang,et al.  Prediction of Bioluminescent Proteins Using Auto Covariance Transformation of Evolutional Profiles , 2012, International journal of molecular sciences.

[7]  Jack Y. Yang,et al.  Transcription factor and microRNA regulation in androgen-dependent and -independent prostate cancer cells , 2008, BMC Genomics.

[8]  Gajendra P S Raghava,et al.  Classification of Nuclear Receptors Based on Amino Acid Composition and Dipeptide Composition* , 2004, Journal of Biological Chemistry.

[9]  L. Eriksson,et al.  Extensions to amino acid description , 2010, Molecular Diversity.

[10]  Pier Luigi Martelli,et al.  DeepLGP: a novel deep learning method for prioritizing lncRNA target genes , 2020, Bioinform..

[11]  Hua Tang,et al.  Identification of Secretory Proteins in Mycobacterium tuberculosis Using Pseudo Amino Acid Composition , 2016, BioMed research international.

[12]  A. Heck,et al.  Protein Flexibility and Ligand Rigidity: A Thermodynamic and Kinetic Study of ITAM‐Based Ligand Binding to Syk Tandem SH2 , 2005, Chembiochem : a European journal of chemical biology.

[13]  Thomas Martinetz,et al.  BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selection , 2011, BMC Bioinformatics.

[14]  Ian H. Witten,et al.  Data mining in bioinformatics using Weka , 2004, Bioinform..

[15]  Osamu Shimomura,et al.  BIOLUMINESCENCE , 1983 .

[16]  E. Widder,et al.  Bioluminescence in the Ocean: Origins of Biological, Chemical, and Ecological Diversity , 2010, Science.

[17]  Meng Zhou,et al.  MetSigDis: a manually curated resource for the metabolic signatures of diseases , 2019, Briefings Bioinform..

[18]  Gwang Lee,et al.  SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome , 2019, Molecular therapy. Nucleic acids.

[19]  Leyi Wei,et al.  Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation , 2019, Molecular therapy. Nucleic acids.

[20]  K. Chou.  Prediction of protein cellular attributes using pseudo‐amino acid composition , 2001, Proteins.

[21]  Wei Chen,et al.  Classifying Included and Excluded Exons in Exon Skipping Event Using Histone Modifications , 2018, Front. Genet..

[22]  D. Huppert,et al.  Comparative study of the photoprotolytic reactions of D-luciferin and oxyluciferin. , 2012, The journal of physical chemistry. A.

[23]  M. Moline,et al.  Bioluminescence in the sea. , 2010, Annual review of marine science.

[24]  Hao Lin,et al.  XG-PseU: an eXtreme Gradient Boosting based method for identifying pseudouridine sites , 2019, Molecular Genetics and Genomics.

[25]  Hao Lv,et al.  Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique , 2018, Bioinform..

[26]  Jiu-Xin Tan,et al.  Identification of hormone binding proteins based on machine learning methods. , 2019, Mathematical biosciences and engineering : MBE.

[27]  R. Durbin,et al.  Pfam: A comprehensive database of protein domain families based on seed alignments , 1997, Proteins.

[28]  Ying-Mei Feng.  Gene Therapy on the Road , 2019, Current gene therapy.

[29]  Balachandran Manavalan,et al.  iGHBP: Computational identification of growth hormone binding proteins from sequences using extremely randomised tree , 2018, Computational and structural biotechnology journal.

[30]  Hiroyuki Kurata,et al.  Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework , 2020, Briefings Bioinform..

[31]  Chenglong Yu,et al.  A Novel Method of Characterizing Genetic Sequences: Genome Space with Biological Distance and Applications , 2011, PloS one.

[32]  Geoffrey I. Webb,et al.  iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences , 2018, Bioinform..

[33]  Wei Chen,et al.  Pro54DB: a database for experimentally verified sigma‐54 promoters , 2016, Bioinform..

[34]  Jason H. Moore,et al.  STatistical Inference Relief (STIR) feature selection , 2018, bioRxiv.

[35]  Qiang Cheng,et al.  The Fisher-Markov Selector: Fast Selecting Maximally Separable Feature Subset for Multiclass Classification with Applications to High-Dimensional Data , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Yan Huang,et al.  RNALocate: a resource for RNA subcellular localizations , 2016, Nucleic Acids Res..

[37]  S. Yau,et al.  Zika and Flaviviruses Phylogeny Based on the Alignment-Free Natural Vector Method. , 2017, DNA and cell biology.

[38]  Qian-Zhong Li,et al.  Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou's pseudo amino acid composition. , 2013, Journal of theoretical biology.

[39]  Hui Ding,et al.  Prediction of bacteriophage proteins located in the host cell using hybrid features , 2018, Chemometrics and Intelligent Laboratory Systems.

[40]  Hui Ding,et al.  An Overview on Predicting Protein Subchloroplast Localization by using Machine Learning Methods. , 2020, Current protein & peptide science.

[41]  Rong Chen,et al.  HBPred: a tool to identify growth hormone-binding proteins , 2018, International journal of biological sciences.

[42]  M. Kanehisa,et al.  Analysis of amino acid indices and mutation matrices for sequence comparison and structure prediction of proteins. , 1996, Protein engineering.

[43]  Hui Yang,et al.  iCarPS: a computational tool for identifying protein carbonylation sites by novel encoded features , 2020, Bioinform..

[44]  S. Daunert,et al.  Engineering bioluminescent proteins: expanding their analytical potential. , 2009, Analytical chemistry.

[45]  Fu-Ying Dao,et al.  Recent Development of Computational Predicting Bioluminescent Proteins. , 2019, Current pharmaceutical design.

[46]  Hua Tang,et al.  Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection technique. , 2016, Molecular bioSystems.

[47]  David L Streiner,et al.  What's under the ROC? An Introduction to Receiver Operating Characteristics Curves , 2007, Canadian journal of psychiatry. Revue canadienne de psychiatrie.

[48]  K. Khajeh,et al.  Light emission miracle in the sea and preeminent applications of bioluminescence in recent new biotechnology. , 2017, Journal of photochemistry and photobiology. B, Biology.

[49]  Zhiqiang Ma,et al.  Prediction of bioluminescent proteins by using sequence-derived features and lineage-specific scheme , 2017, BMC Bioinformatics.

[50]  Wei Chen,et al.  A Brief Survey of Machine Learning Application in Cancerlectin Identification. , 2018, Current gene therapy.

[51]  Zhangxin Chen,et al.  ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network , 2017, Molecules.

[52]  Changchuan Yin,et al.  Virus classification in 60-dimensional protein space. , 2016, Molecular phylogenetics and evolution.

[53]  The Uniprot Consortium,et al.  UniProt: a hub for protein information , 2014, Nucleic Acids Res..

[54]  Han Zhang,et al.  Gene Expression Value Prediction Based on XGBoost Algorithm , 2019, Front. Genet..

[55]  Vijayakumar Saravanan,et al.  SCLAP: an adaptive boosting method for predicting subchloroplast localization of plant proteins. , 2013, Omics : a journal of integrative biology.

[56]  John G. Lee.  Perspectives on Bioluminescence Mechanisms , 2017, Photochemistry and photobiology.

[57]  Minzhu Xie,et al.  XGBFEMF: An XGBoost-Based Framework for Essential Protein Prediction , 2018, IEEE Transactions on NanoBioscience.

[58]  Thomas L. Madden,et al.  Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.

[59]  Liang Cheng,et al.  Exposing the Causal Effect of C-Reactive Protein on the Risk of Type 2 Diabetes Mellitus: A Mendelian Randomization Study , 2018, Front. Genet..

[60]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[61]  Andreas Winkler,et al.  Molecular Mechanisms of Bacterial Bioluminescence , 2018, Computational and structural biotechnology journal.

[62]  I. Muchnik,et al.  Prediction of protein folding class using global description of amino acid sequence. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[63]  Hui-Ling Huang,et al.  Propensity Scores for Prediction and Characterization of Bioluminescent Proteins from Sequences , 2014, PloS one.

[64]  Wei Chen,et al.  iATP: A Sequence Based Method for Identifying Anti-tubercular Peptides , 2020, Medicinal Chemistry.

[65]  Hua Tang,et al.  Identification of Bacterial Cell Wall Lyases via Pseudo Amino Acid Composition , 2016, BioMed research international.