{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:40:44Z","timestamp":1783611644961,"version":"3.55.0"},"reference-count":35,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T00:00:00Z","timestamp":1639699200000},"content-version":"vor","delay-in-days":350,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"GRRC program of Gyeonggi province","award":["GRRC-Gachon2020 (B04)"],"award-info":[{"award-number":["GRRC-Gachon2020 (B04)"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Beta\u2010lactamase (<jats:italic>\u03b2<\/jats:italic>\u2010lactamase) produced by different bacteria confers resistance against <jats:italic>\u03b2<\/jats:italic>\u2010lactam\u2010containing drugs. The gene encoding <jats:italic>\u03b2<\/jats:italic>\u2010lactamase is plasmid\u2010borne and can easily be transferred from one bacterium to another during conjugation. By such transformations, the recipient also acquires resistance against the drugs of the <jats:italic>\u03b2<\/jats:italic>\u2010lactam family. <jats:italic>\u03b2<\/jats:italic>\u2010Lactam antibiotics play a vital significance in clinical treatment of disastrous diseases like soft tissue infections, gonorrhoea, skin infections, urinary tract infections, and bronchitis. Herein, we report a prediction classifier named as <jats:italic>\u03b2<\/jats:italic>Lact\u2010Pred for the identification of <jats:italic>\u03b2<\/jats:italic>\u2010lactamase proteins. The computational model uses the primary amino acid sequence structure as its input. Various metrics are derived from the primary structure to form a feature vector. Experimentally determined data of positive and negative beta\u2010lactamases are collected and transformed into feature vectors. An operating algorithm based on the artificial neural network is used by integrating the position relative features and sequence statistical moments in PseAAC for training the neural networks. The results for the proposed computational model were validated by employing numerous types of approach, i.e., self\u2010consistency testing, jackknife testing, cross\u2010validation, and independent testing. The overall accuracy of the predictor for self\u2010consistency, jackknife testing, cross\u2010validation, and independent testing presents 99.76%, 96.07%, 94.20%, and 91.65%, respectively, for the proposed model. Stupendous experimental results demonstrated that the proposed predictor \u201c<jats:italic>\u03b2<\/jats:italic>Lact\u2010Pred\u201d has surpassed results from the existing methods.<\/jats:p>","DOI":"10.1155\/2021\/8974265","type":"journal-article","created":{"date-parts":[[2021,12,18]],"date-time":"2021-12-18T02:20:35Z","timestamp":1639794035000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["<i>\u03b2<\/i>Lact\u2010Pred: A Predictor Developed for Identification of Beta\u2010Lactamases Using Statistical Moments and PseAAC via 5\u2010Step Rule"],"prefix":"10.1155","volume":"2021","author":[{"given":"Muhammad Adeel","family":"Ashraf","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaser 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