FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4226303323> ?p ?o ?g. }

• W4226303323 endingPage "3631" @default.
• W4226303323 startingPage "3631" @default.
• W4226303323 abstract "Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this study, we focused on the linear cationic peptides with non-hemolytic activity, which are downloaded from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). Referring to the MIC (Minimum inhibition concentration) values, we have assigned a positive label to a peptide if it shows antimicrobial activity; otherwise, the peptide is labeled as negative. Here, we focused on the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately, and we created two datasets accordingly. Ten different physico-chemical properties of the peptides are calculated and used as features in our study. Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross-Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. This prediction approach might be useful to evaluate the antibacterial potential of a candidate peptide sequence before moving to the experimental studies." @default.
• W4226303323 created "2022-05-05" @default.
• W4226303323 creator A5033545795 @default.
• W4226303323 creator A5063017519 @default.
• W4226303323 creator A5063633515 @default.
• W4226303323 creator A5075818577 @default.
• W4226303323 creator A5091900270 @default.
• W4226303323 date "2022-04-03" @default.
• W4226303323 modified "2023-09-26" @default.
• W4226303323 title "Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models" @default.
• W4226303323 cites W1486212524 @default.
• W4226303323 cites W1930624869 @default.
• W4226303323 cites W1964467472 @default.
• W4226303323 cites W1967320885 @default.
• W4226303323 cites W1971024387 @default.
• W4226303323 cites W1976178947 @default.
• W4226303323 cites W1979900513 @default.
• W4226303323 cites W1979973356 @default.
• W4226303323 cites W1992603413 @default.
• W4226303323 cites W1995002572 @default.
• W4226303323 cites W1998064689 @default.
• W4226303323 cites W1999656017 @default.
• W4226303323 cites W2006676204 @default.
• W4226303323 cites W2019513270 @default.
• W4226303323 cites W2024046085 @default.
• W4226303323 cites W2027731040 @default.
• W4226303323 cites W2035755292 @default.
• W4226303323 cites W2039175997 @default.
• W4226303323 cites W2046704370 @default.
• W4226303323 cites W2051725773 @default.
• W4226303323 cites W2052270758 @default.
• W4226303323 cites W2056234582 @default.
• W4226303323 cites W2059764020 @default.
• W4226303323 cites W2065677214 @default.
• W4226303323 cites W2068474065 @default.
• W4226303323 cites W2095407378 @default.
• W4226303323 cites W2097834518 @default.
• W4226303323 cites W2103743593 @default.
• W4226303323 cites W2112862199 @default.
• W4226303323 cites W2114723189 @default.
• W4226303323 cites W2125335678 @default.
• W4226303323 cites W2128101147 @default.
• W4226303323 cites W2133056039 @default.
• W4226303323 cites W2151084977 @default.
• W4226303323 cites W2156125289 @default.
• W4226303323 cites W2160257187 @default.
• W4226303323 cites W2163316049 @default.
• W4226303323 cites W2167101814 @default.
• W4226303323 cites W2294798173 @default.
• W4226303323 cites W2302124239 @default.
• W4226303323 cites W2345657752 @default.
• W4226303323 cites W2401903253 @default.
• W4226303323 cites W2474880329 @default.
• W4226303323 cites W2511835277 @default.
• W4226303323 cites W2563632637 @default.
• W4226303323 cites W2588028078 @default.
• W4226303323 cites W2588463038 @default.
• W4226303323 cites W2602538792 @default.
• W4226303323 cites W2611579076 @default.
• W4226303323 cites W2739955608 @default.
• W4226303323 cites W2766842911 @default.
• W4226303323 cites W2770714336 @default.
• W4226303323 cites W2779119857 @default.
• W4226303323 cites W2786810290 @default.
• W4226303323 cites W2788314593 @default.
• W4226303323 cites W2789857505 @default.
• W4226303323 cites W2791848964 @default.
• W4226303323 cites W2793168264 @default.
• W4226303323 cites W2794498378 @default.
• W4226303323 cites W2796712624 @default.
• W4226303323 cites W2796754201 @default.
• W4226303323 cites W2801924301 @default.
• W4226303323 cites W2807001312 @default.
• W4226303323 cites W2884267293 @default.
• W4226303323 cites W2890779120 @default.
• W4226303323 cites W2896101249 @default.
• W4226303323 cites W2898262866 @default.
• W4226303323 cites W2905198474 @default.
• W4226303323 cites W2917874934 @default.
• W4226303323 cites W2919451909 @default.
• W4226303323 cites W2920054351 @default.
• W4226303323 cites W2921372942 @default.
• W4226303323 cites W2924234733 @default.
• W4226303323 cites W2935465942 @default.
• W4226303323 cites W2940847052 @default.
• W4226303323 cites W2947626248 @default.
• W4226303323 cites W2948261638 @default.
• W4226303323 cites W2951765074 @default.
• W4226303323 cites W2958929976 @default.
• W4226303323 cites W2967908265 @default.
• W4226303323 cites W2967956546 @default.
• W4226303323 cites W2967960129 @default.
• W4226303323 cites W2986368198 @default.
• W4226303323 cites W2998197489 @default.
• W4226303323 cites W3003306158 @default.
• W4226303323 cites W3025501158 @default.
• W4226303323 cites W3036144909 @default.
• W4226303323 cites W3039222472 @default.

• next