NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides

[1]  Phasit Charoenkwan,et al.  Large-scale comparative review and assessment of computational methods for phage virion proteins identification , 2022, EXCLI journal.

[2]  P. Lio’,et al.  SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids , 2022, Pharmaceutics.

[3]  Balachandran Manavalan,et al.  STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction , 2021, Briefings Bioinform..

[4]  V. Lee,et al.  A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides , 2021, Scientific Reports.

[5]  P. Lio’,et al.  StackDPPIV: a novel computational approach for accurate prediction of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides. , 2021, Methods.

[6]  Balachandran Manavalan,et al.  UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning , 2021, International Journal of Molecular Sciences.

[7]  Phasit Charoenkwan,et al.  Review and comparative analysis of machine learning-based predictors for predicting and analyzing of anti-angiogenic peptides. , 2021, Current medicinal chemistry.

[8]  Chanin Nantasenamat,et al.  StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides , 2021, Briefings Bioinform..

[9]  E. Kondo,et al.  Tumor‐homing peptide and its utility for advanced cancer medicine , 2021, Cancer science.

[10]  T. Santhoshkumar,et al.  Geometry encoded functional programming of tumor homing peptides for targeted drug delivery. , 2021, Journal of controlled release : official journal of the Controlled Release Society.

[11]  Z. Li,et al.  A single nucleotide mutation drastically increases the expression of tumor-homing NGR-TNFα in the E. coli M15-pQE30 system by improving gene transcription , 2021, Applied Microbiology and Biotechnology.

[12]  Hao Lv,et al.  DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops , 2020, Briefings Bioinform..

[13]  Hao Lv,et al.  Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method , 2020, Briefings Bioinform..

[14]  Jiangning Song,et al.  DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy , 2020, Briefings Bioinform..

[15]  Jiangning Song,et al.  Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework , 2020, Briefings Bioinform..

[16]  Wei Chen,et al.  Design powerful predictor for mRNA subcellular location prediction in Homo sapiens , 2020, Briefings Bioinform..

[17]  OUP accepted manuscript , 2021, Briefings In Bioinformatics.

[18]  T. Teesalu,et al.  Homing Peptides for Cancer Therapy. , 2021, Advances in experimental medicine and biology.

[19]  Chanin Nantasenamat,et al.  In silico approaches for the prediction and analysis of antiviral peptides: a review. , 2020, Current pharmaceutical design.

[20]  Chanin Nantasenamat,et al.  iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides , 2020, J. Chem. Inf. Model..

[21]  Fu-Ying Dao,et al.  A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae , 2019, Briefings Bioinform..

[22]  Chanin Nantasenamat,et al.  iDPPIV-SCM: A sequence-based predictor for identifying and analyzing dipeptidyl peptidase IV (DPP-IV) inhibitory peptides using a scoring card method. , 2020, Journal of proteome research.

[23]  Chanin Nantasenamat,et al.  Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation , 2020, Journal of Computer-Aided Molecular Design.

[24]  Jiu-Xin Tan,et al.  Evaluation of different computational methods on 5-methylcytosine sites identification , 2020, Briefings Bioinform..

[25]  Guoying Zhang,et al.  ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides , 2019, Briefings Bioinform..

[26]  Nalini Schaduangrat,et al.  iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties , 2019, International journal of molecular sciences.

[27]  Wei Chen,et al.  iRNAD: a computational tool for identifying D modification sites in RNA sequence , 2019, Bioinform..

[28]  Wei Chen,et al.  i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome , 2019, Bioinform..

[29]  A. Hottinger,et al.  Phase I/II trial testing safety and immunogenicity of the multipeptide IMA950/poly-ICLC vaccine in newly diagnosed adult malignant astrocytoma patients. , 2019, Neuro-oncology.

[30]  Tur-Fu Huang,et al.  From Discovery of Snake Venom Disintegrins to A Safer Therapeutic Antithrombotic Agent , 2019, Toxins.

[31]  Wei Chen,et al.  iProEP: A Computational Predictor for Predicting Promoter , 2019, Molecular therapy. Nucleic acids.

[32]  Nalini Schaduangrat,et al.  THPep: A machine learning-based approach for predicting tumor homing peptides , 2019, Comput. Biol. Chem..

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

[34]  Yan Lin,et al.  iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators , 2018, Bioinform..

[35]  M. Schwaiger,et al.  External Beam Radiation Therapy Enhances Mesenchymal Stem Cell-Mediated Sodium-Iodide Symporter Gene Delivery. , 2018, Human gene therapy.

[36]  H. Gohlke,et al.  Relevance of N-terminal residues for amyloid-β binding to platelet integrin αIIbβ3, integrin outside-in signaling and amyloid-β fibril formation. , 2018, Cellular signalling.

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

[38]  J. Costello,et al.  Antigenic expression and spontaneous immune responses support the use of a selected peptide set from the IMA950 glioblastoma vaccine for immunotherapy of grade II and III glioma , 2017, Oncoimmunology.

[39]  Matthias Dehmer,et al.  Computational prediction of therapeutic peptides based on graph index , 2017, J. Biomed. Informatics.

[40]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[41]  A. Schmaier,et al.  Plasma kallikrein enhances platelet aggregation response by subthreshold doses of ADP. , 2017, Biochimie.

[42]  Qingsong Xu,et al.  Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds and their interactions , 2015, Bioinform..

[43]  G P S Raghava,et al.  Tumor homing peptides as molecular probes for cancer therapeutics, diagnostics and theranostics. , 2014, Current medicinal chemistry.

[44]  Robert L. Smith,et al.  Estimating confidence intervals for information transfer analysis of confusion matrices. , 2014, The Journal of the Acoustical Society of America.

[45]  Laurens van der Maaten,et al.  Accelerating t-SNE using tree-based algorithms , 2014, J. Mach. Learn. Res..

[46]  Rahul Kumar,et al.  Computational approach for designing tumor homing peptides , 2013, Scientific Reports.

[47]  Rahul Kumar,et al.  TumorHoPe: A Database of Tumor Homing Peptides , 2012, PloS one.

[48]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[49]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[50]  L. Jiang,et al.  PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence , 2006, Nucleic Acids Res..

[51]  Kuo-Chen Chou,et al.  Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes , 2005, Bioinform..

[52]  Leo Breiman,et al.  Stacked regressions , 2004, Machine Learning.

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

[54]  Minoru Kanehisa,et al.  AAindex: Amino Acid index database , 2000, Nucleic Acids Res..

[55]  R. Kodandapani,et al.  Crystal Structure of the OPG2 Fab , 1995, The Journal of Biological Chemistry.

[56]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.