相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models
Ummu Gulsum Soylemez et al.
APPLIED SCIENCES-BASEL (2022)
AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning
Hongwu Lv et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2022)
AMPDeep: hemolytic activity prediction of antimicrobial peptides using transfer learning
Milad Salem et al.
BMC BIOINFORMATICS (2022)
Cooperativity in Bacterial Membrane Association Controls the Synergistic Activities of Antimicrobial Peptides
Thao N. Nguyen et al.
JOURNAL OF PHYSICAL CHEMISTRY B (2022)
Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework
Md Mehedi Hasan et al.
BRIEFINGS IN BIOINFORMATICS (2021)
Synergistic effect of two antimicrobial peptides, Nisin and P10 with conventional antibiotics against extensively drug-resistant Acinetobacter baumannii and colistin-resistant Pseudomonas aeruginosa isolates
Abolfazl Jahangiri et al.
MICROBIAL PATHOGENESIS (2021)
Computational Methods and Tools in Antimicrobial Peptide Research
Pietro G. A. Aronica et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2021)
DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics
Malak Pirtskhalava et al.
NUCLEIC ACIDS RESEARCH (2021)
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
Davide Chicco et al.
BMC GENOMICS (2020)
On the performance of Matthews correlation coefficient (MCC) for imbalanced dataset
Qiuming Zhu
PATTERN RECOGNITION LETTERS (2020)
Antimicrobial Peptides as Anticancer Agents: Functional Properties and Biological Activities
Anna Lucia Tornesello et al.
MOLECULES (2020)
A bioinformatic study of antimicrobial peptides identified in the Black Soldier Fly (BSF) Hermetia illucens (Diptera: Stratiomyidae)
Antonio Moretta et al.
SCIENTIFIC REPORTS (2020)
A high-bias, low-variance introduction to Machine Learning for physicists
Pankaj Mehta et al.
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS (2019)
Prevalence of antibiotic resistance in Escherichia coli strains simultaneously isolated from humans, animals, food, and the environment: a systematic review and meta-analysis
Ali Pormohammad et al.
INFECTION AND DRUG RESISTANCE (2019)
Efficient computational model for identification of antitubercular peptides by integrating amino acid patterns and properties
Shamima Khatun et al.
FEBS LETTERS (2019)
A Systematic Review on Imbalanced Data Challenges in Machine Learning: Applications and Solutions
Harsurinder Kaur et al.
ACM COMPUTING SURVEYS (2019)
Flagella-dependent inhibition of biofilm formation by sub-inhibitory concentration of polymyxin B in Vibrio cholerae
Sean Giacomucci et al.
PLOS ONE (2019)
Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria
Boris Vishnepolsky et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2018)
In Silico Approach for Prediction of Antifungal Peptides
Piyush Agrawal et al.
FRONTIERS IN MICROBIOLOGY (2018)
What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning?
Ernest Y. Lee et al.
INTERFACE FOCUS (2017)
CAMPR3: a database on sequences, structures and signatures of antimicrobial peptides
Faiza Hanif Waghu et al.
NUCLEIC ACIDS RESEARCH (2016)
APD3: the antimicrobial peptide database as a tool for research and education
Guangshun Wang et al.
NUCLEIC ACIDS RESEARCH (2016)
Bacterial strategies of resistance to antimicrobial peptides
Hwang-Soo Joo et al.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES (2016)
Mapping membrane activity in undiscovered peptide sequence space using machine learning
Ernest Y. Lee et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2016)
IACP: a sequence-based tool for identifying anticancer peptides
Wei Chen et al.
ONCOTARGET (2016)
Influence of hydrophobic residues on the activity of the antimicrobial peptide magainin 2 and its synergy with PGLa
Erik Strandberg et al.
JOURNAL OF PEPTIDE SCIENCE (2015)
THE NATURAL HISTORY OF MODEL ORGANISMS The unexhausted potential of E. coli
Zachary D. Blount
ELIFE (2015)
propy: a tool to generate various modes of Chou's PseAAC
Dong-Sheng Cao et al.
BIOINFORMATICS (2013)
A Critical Assessment of Feature Selection Methods for Biomarker Discovery in Clinical Proteomics
Christin Christin et al.
MOLECULAR & CELLULAR PROTEOMICS (2013)
Comparative study on classification performance between support vector machine and logistic regression
Abdallah Bashir Musa
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2013)
Role of lipids in the interaction of antimicrobial peptides with membranes
Vitor Teixeira et al.
PROGRESS IN LIPID RESEARCH (2012)
Pseudo Amino Acid Composition and its Applications in Bioinformatics, Proteomics and System Biology
Kuo-Chen Chou
CURRENT PROTEOMICS (2009)
Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances
Irith Wiegand et al.
NATURE PROTOCOLS (2008)
Role of peptide hydrophobicity in the mechanism of action of α-helical antimicrobial peptides
Yuxin Chen et al.
ANTIMICROBIAL AGENTS AND CHEMOTHERAPY (2007)
Length effects in antimicrobial peptides of the (RW)n series
Zhigang Liu et al.
ANTIMICROBIAL AGENTS AND CHEMOTHERAPY (2007)
PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence
Z. R. Li et al.
NUCLEIC ACIDS RESEARCH (2006)
Antibacterial peptides and proteins with multiple cellular targets
L Otvos
JOURNAL OF PEPTIDE SCIENCE (2005)
Antimicrobial peptides: Pore formers or metabolic inhibitors in bacteria?
KA Brogden
NATURE REVIEWS MICROBIOLOGY (2005)
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes
KC Chou
BIOINFORMATICS (2005)
Can we predict biological activity of antimicrobial peptides from their interactions with model phospholipid membranes?
N Papo et al.
PEPTIDES (2003)
Antimicrobial peptides of multicellular organisms
M Zasloff
NATURE (2002)
From innate immunity to de-novo designed antimicrobial peptides
Y Shai
CURRENT PHARMACEUTICAL DESIGN (2002)