In Silico Identification of Anticancer Peptides with Stacking Heterogeneous Ensemble Learning Model and Sequence Information

Credit card fraud detection (CCFD) is important for protecting the cardholder’s property and the reputation of banks. Class imbalance in credit card transaction data is a primary factor affecting the classification performance of current detection models. However, prior approaches are aimed at improving the prediction accuracy of the minority class samples (fraudulent transactions), but this usually leads to a significant drop in the model’s predictive performance for the majority class samples (legal transactions), which greatly increases the investigation cost for banks. In this paper, we propose a heterogeneous ensemble learning model based on data distribution (HELMDD) to deal with imbalanced data in CCFD. We validate the effectiveness of HELMDD on two real credit card datasets. The experimental results demonstrate that compared with current state-of-the-art models, HELMDD has the best comprehensive performance. HELMDD not only achieves good recall rates for both the minority class and the majority class but also increases the savings rate for banks to 0.8623 and 0.6696, respectively.

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BioForte Technology for in Silico Identification of Candidates for a New Microbiome-based Therapeutics and Diagnostics

Case Medical Research ◽

10.31525/ct1-nct04136470 ◽

2019 ◽

Author(s):

Keyword(s):

In Silico ◽

In Silico Identification

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In silico identification, molecular cloning and verification of a novel pig gene homologous to humanBCL10of innate immunity and its preliminary expression profiles in pigs

Hereditas (Beijing) ◽

10.3724/sp.j.1005.2008.00747 ◽

2008 ◽

Vol 30 (6) ◽

pp. 747-754 ◽

Cited By ~ 1

Author(s):

Jian-Feng HE

Keyword(s):

Innate Immunity ◽

Molecular Cloning ◽

In Silico ◽

Expression Profiles ◽

In Silico Identification

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In Silico Identification of Novel Orthosteric Inhibitors of Sphingosine Kinase 1 (SK1)

Current Protein and Peptide Science ◽

10.2174/1389203718666161108092842 ◽

2018 ◽

Vol 19 (5) ◽

pp. 430-444

Author(s):

Ozge Bayraktar ◽

Elif Ozkirimli ◽

Kutlu O. Ulgen

Keyword(s):

In Silico ◽

Sphingosine Kinase ◽

Sphingosine Kinase 1 ◽

In Silico Identification

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In Silico Identification and Molecular Characterization of Extracellular Cathepsin L Proteases from Giardia duodenalis

Current Proteomics ◽

10.2174/1570164617666191016170628 ◽

2020 ◽

Vol 17 (4) ◽

pp. 342-351

Author(s):

Sergio A. Durán-Pérez ◽

José G. Rendón-Maldonado ◽

Lucio de Jesús Hernandez-Diaz ◽

Annete I. Apodaca-Medina ◽

Maribel Jiménez-Edeza ◽

...

Keyword(s):

In Silico ◽

Cathepsin L ◽

Three Dimensional ◽

Giardia Duodenalis ◽

Evolutionary Genetics ◽

Extracellular Proteins ◽

In Silico Identification ◽

Dimensional Models ◽

Three Dimensional Models

Background: The protozoan Giardia duodenalis, which causes giardiasis, is an intestinal parasite that commonly affects humans, mainly pre-school children. Although there are asymptomatic cases, the main clinical features are chronic and acute diarrhea, nausea, abdominal pain, and malabsorption syndrome. Little is currently known about the virulence of the parasite, but some cases of chronic gastrointestinal alterations post-infection have been reported even when the infection was asymptomatic, suggesting that the cathepsin L proteases of the parasite may be involved in the damage at the level of the gastrointestinal mucosa. Objective: The aim of this study was the in silico identification and characterization of extracellular cathepsin L proteases in the proteome of G. duodenalis. Methods: The NP_001903 sequence of cathepsin L protease from Homo sapienswas searched against the Giardia duodenalisproteome. The subcellular localization of Giardia duodenaliscathepsin L proteases was performed in the DeepLoc-1.0 server. The construction of a phylogenetic tree of the extracellular proteins was carried out using the Molecular Evolutionary Genetics Analysis software (MEGA X). The Robetta server was used for the construction of the three-dimensional models. The search for possible inhibitors of the extracellular cathepsin L proteases of Giardia duodenaliswas performed by entering the three-dimensional structures in the FINDSITEcomb drug discovery tool. Results: Based on the amino acid sequence of cathepsin L from Homo sapiens, 8 protein sequences were identified that have in their modular structure the Pept_C1A domain characteristic of cathepsins and two of these proteins (XP_001704423 and XP_001704424) are located extracellularly. Threedimensional models were designed for both extracellular proteins and several inhibitory ligands with a score greater than 0.9 were identified. In vitrostudies are required to corroborate if these two extracellular proteins play a role in the virulence of Giardia duodenalisand to discover ligands that may be useful as therapeutic targets that interfere in the mechanism of pathogenesis generated by the parasite. Conclusion: In silicoanalysis identified two proteins in the Giardia duodenalisprotein repertoire whose characteristics allowed them to be classified as cathepsin L proteases, which may be secreted into the extracellular medium to act as virulence factors. Three-dimensional models of both proteins allowed the identification of inhibitory ligands with a high score. The results suggest that administration of those compounds might be used to block the endopeptidase activity of the extracellular cathepsin L proteases, interfering with the mechanisms of pathogenesis of the protozoan parasite Giardia duodenalis.

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