A key unmet need in the management of hemophilia A (HA) is the lack of clinically validated markers that are associated with the development of neutralizing antibodies to Factor VIII (FVIII) (commonly referred to as inhibitors). This study aimed to identify relevant biomarkers for FVIII inhibition using Machine Learning (ML) and Explainable AI (XAI) using the My Life Our Future (MLOF) research repository. The dataset includes biologically relevant variables such as age, race, sex, ethnicity, and the variants in the gene. In addition, we previously carried out Human Leukocyte Antigen Class II (HLA-II) typing on samples obtained from the MLOF repository. Using this information, we derived other patient-specific biologically and genetically important variables. These included identifying the number of foreign FVIII derived peptides, based on the alignment of the endogenous FVIII and infused drug sequences, and the foreign-peptide HLA-II molecule binding affinity calculated using NetMHCIIpan. The data were processed and trained with multiple ML classification models to identify the top performing models. The top performing model was then chosen to apply XAI via SHAP, (SHapley Additive exPlanations) to identify the variables critical for the prediction of FVIII inhibitor development in a hemophilia A patient. Using XAI we provide a robust and ranked identification of variables that could be predictive for developing inhibitors to FVIII drugs in hemophilia A patients. These variables could be validated as biomarkers and used in making clinical decisions and during drug development. The top five variables for predicting inhibitor development based on SHAP values are: (i) the baseline activity of the FVIII protein, (ii) mean affinity of all foreign peptides for HLA DRB 3, 4, & 5 alleles, (iii) mean affinity of all foreign peptides for HLA DRB1 alleles), (iv) the minimum affinity among all foreign peptides for HLA DRB1 alleles, and (v) mutation type.
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http://dx.doi.org/10.1016/j.heliyon.2023.e16331 | DOI Listing |
Comput Biol Chem
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College of Artificial Intelligence, Tianjin University of Science and Technology, No. 9, 13th Street, Tianjin Economic-Technological Development Area, Tianjin, 300457, China. Electronic address:
The enzyme turnover number (k) is crucial for understanding enzyme kinetics and optimizing biotechnological processes. However, experimentally measured k values are limited due to the high cost and labor intensity of wet-lab measurements, necessitating robust computational methods. To address this issue, we propose PreTKcat, a framework that integrates pre-trained representation learning and machine learning to predict k values.
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Department of Arts and Humanities, School of Education, Universidad Pedagógica y Tecnológica de Colombia, Tunja, Colombia.
In Colombia, LGBTIQ+ identities in the educational field are often considered sensitive and perceived as taboo, which may contribute to their underrepresentation in research. In the English as a foreign language (EFL) field, limited attention has been given to the perspectives and experiences of LGBTIQ+ teachers in schools. As a result, the perceptions and realities faced by this group of stakeholders have been overlooked, creating a gap in research.
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Centre for translational Medicine and Parasitology, Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Protective immunity to malaria depends on acquisition of parasite-specific antibodies, with Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) being one of the most important target antigens. The effector functions of PfEMP1-specific IgG include inhibition of infected erythrocyte (IE) sequestration and opsonization of IEs for cell-mediated destruction. IgG glycosylation modulates antibody functionality, with increased affinity to FcγRIIIa for IgG lacking fucose in the Fc region (Fc-afucosylation).
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State Key Laboratory of Wheat Improvement, Shandong Agricultural University, No. 61, Daizong Road, Taian 271018, China.
Moths use pheromones to ensure intraspecific communication. Nevertheless, few studies are focused on both intra- and intersexual communication based on pheromone recognition. Pheromone-binding proteins (PBPs) are generally believed pivotal for male moths in recognizing female pheromones.
View Article and Find Full Text PDFbioRxiv
November 2024
Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN, United States.
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