Related references
Note: Only part of the references are listed.ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides
Bing Rao et al.
BRIEFINGS IN BIOINFORMATICS (2020)
In Vitro and MD Simulation Study to Explore Physicochemical Parameters for Antibacterial Peptide to Become Potent Anticancer Peptide
Rui Ma et al.
MOLECULAR THERAPY-ONCOLYTICS (2020)
Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning
Jielu Yan et al.
MOLECULAR THERAPY-NUCLEIC ACIDS (2020)
Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model
Qingwen Li et al.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY (2020)
Anti-cancer peptides: classification, mechanism of action, reconstruction and modification
Mingfeng Xie et al.
OPEN BIOLOGY (2020)
Effect of sequence padding on the performance of deep learning models in archaeal protein functional prediction
Angela Lopez-del Rio et al.
SCIENTIFIC REPORTS (2020)
DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm
Lezheng Yu et al.
MOLECULAR THERAPY-NUCLEIC ACIDS (2020)
ACP-GCN: The Identification of Anticancer Peptides Based on Graph Convolution Networks
Bing Rao et al.
IEEE ACCESS (2020)
Drug discovery with explainable artificial intelligence
Jose Jimenez-Luna et al.
NATURE MACHINE INTELLIGENCE (2020)
Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery
Qihui Wu et al.
CURRENT TOPICS IN MEDICINAL CHEMISTRY (2019)
Encodings and models for antimicrobial peptide classification for multi-resistant pathogens
Sebastian Spaenig et al.
BIODATA MINING (2019)
PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning
Leyi Wei et al.
BIOINFORMATICS (2019)
A Comprehensive Review on Current Advances in Peptide Drug Development and Design
Andy Chi-Lung Lee et al.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2019)
De novo design of anticancer peptides by ensemble artificial neural networks
Francesca Grisoni et al.
JOURNAL OF MOLECULAR MODELING (2019)
ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides
Nalini Schaduangrat et al.
MOLECULES (2019)
ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation
Hai-Cheng Yi et al.
MOLECULAR THERAPY-NUCLEIC ACIDS (2019)
PTPD: predicting therapeutic peptides by deep learning and word2vec
Chuanyan Wu et al.
BMC BIOINFORMATICS (2019)
Design, synthesis, and anticancer activity of iridium(III) complex-peptide hybrids that contain hydrophobic acyl groups at the N-terminus of the peptide units
Kana Naito et al.
JOURNAL OF INORGANIC BIOCHEMISTRY (2019)
In silico design and optimization of selective membranolytic anticancer peptides
Gisela Gabernet et al.
SCIENTIFIC REPORTS (2019)
Effects of N-terminal and C-terminal modification on cytotoxicity and cellular uptake of amphiphilic cell penetrating peptides
Mehdi Soleymani-Goloujeh et al.
ARTIFICIAL CELLS NANOMEDICINE AND BIOTECHNOLOGY (2018)
ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides
Leyi Wei et al.
BIOINFORMATICS (2018)
iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences
Zhen Chen et al.
BIOINFORMATICS (2018)
Structure-based design for binding peptides in anti-cancer therapy
Sheng-Hung Wang et al.
BIOMATERIALS (2018)
Cell Penetrating Peptides as Molecular Carriers for Anti-Cancer Agents
Antonella Borrelli et al.
MOLECULES (2018)
AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest
Pratiti Bhadra et al.
SCIENTIFIC REPORTS (2018)
Artificial Intelligence in Drug Design
Gerhard Hessler et al.
MOLECULES (2018)
MLACP: machine-learning-based prediction of anticancer peptides
Balachandran Manavalan et al.
ONCOTARGET (2017)
Peptides with Dual Antimicrobial and Anticancer Activities
Mario R. Felicio et al.
FRONTIERS IN CHEMISTRY (2017)
Membranolytic anticancer peptides
G. Gabernet et al.
MEDCHEMCOMM (2016)
IACP: a sequence-based tool for identifying anticancer peptides
Wei Chen et al.
ONCOTARGET (2016)
Global Cancer Statistics, 2012
Lindsey A. Torre et al.
CA-A CANCER JOURNAL FOR CLINICIANS (2015)
Peptide-Based Treatment: A Promising Cancer Therapy
Yu-Feng Xiao et al.
JOURNAL OF IMMUNOLOGY RESEARCH (2015)
ACPP: A Web Server for Prediction and Design of Anti-cancer Peptides
Saravanan Vijayakumar et al.
INTERNATIONAL JOURNAL OF PEPTIDE RESEARCH AND THERAPEUTICS (2015)
CancerPPD: a database of anticancer peptides and proteins
Atul Tyagi et al.
NUCLEIC ACIDS RESEARCH (2015)
Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via Ames test
Zohre Hajisharifi et al.
JOURNAL OF THEORETICAL BIOLOGY (2014)
iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition
Peng-Mian Feng et al.
ANALYTICAL BIOCHEMISTRY (2013)
In Silico Models for Designing and Discovering Novel Anticancer Peptides
Atul Tyagi et al.
SCIENTIFIC REPORTS (2013)
From antimicrobial to anticancer peptides. A review
Diana Gaspar et al.
FRONTIERS IN MICROBIOLOGY (2013)
Cancer drug resistance: an evolving paradigm
Caitriona Holohan et al.
NATURE REVIEWS CANCER (2013)
Proteochemometric Modeling of the Bioactivity Spectra of HIV-1 Protease Inhibitors by Introducing Protein-Ligand Interaction Fingerprint
Qi Huang et al.
PLOS ONE (2012)
Oncolytic Activities of Host Defense Peptides
Sammy Al-Benna et al.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2011)
Studies on Mechanism of Action of Anticancer Peptides by Modulation of Hydrophobicity Within a Defined Structural Framework
Yi-bing Huang et al.
MOLECULAR CANCER THERAPEUTICS (2011)
Incorporating Distant Sequence Features and Radial Basis Function Networks to Identify Ubiquitin Conjugation Sites
Tzong-Yi Lee et al.
PLOS ONE (2011)
Structure and function of a custom anticancer peptide, CB1a
Jiun-Ming Wu et al.
PEPTIDES (2009)
Computational identification of ubiquitylation sites from protein sequences
Chun-Wei Tung et al.
BMC BIOINFORMATICS (2008)
Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: Focusing on applicability domain and overfitting by variable selection
Igor V. Tetko et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2008)
AAindex: amino acid index database, progress report 2008
Shuichi Kawashima et al.
NUCLEIC ACIDS RESEARCH (2008)
Predictive QSAR Modeling workflow, model applicability domains, and virtual screening
Alexander Tropsha et al.
CURRENT PHARMACEUTICAL DESIGN (2007)
Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences
Weizhong Li et al.
BIOINFORMATICS (2006)
Antibacterial peptides and proteins with multiple cellular targets
L Otvos
JOURNAL OF PEPTIDE SCIENCE (2005)
Rational selection of training and test sets for the development of validated QSAR models
A Golbraikh et al.
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2003)
Prediction of protein cellular attributes using pseudo-amino acid composition
KC Chou
PROTEINS-STRUCTURE FUNCTION AND GENETICS (2001)
Share Paper
