Motivation: Highly Active AntiRetroviral Therapies (HAART) can prolong life significantly to people infected by HIV since, although unable to eradicate the virus, they are quite effective in maintaining control of the infection. However, since HAART have several undesirable side effects, it is considered useful to suspend the therapy according to a suitable schedule of Structured Therapeutic Interruptions (STI). In the present article we describe an application of genetic algorithms (GA) aimed at finding the optimal schedule for a HAART simulated with an agent-based model (ABM) of the immune system that reproduces the most significant features of the response of an organism to the HIV-1 infection.
Results: The genetic algorithm helps in finding an optimal therapeutic schedule that maximizes immune restoration, minimizes the viral count and, through appropriate interruptions of the therapy, minimizes the dose of drug administered to the simulated patient. To validate the efficacy of the therapy that the genetic algorithm indicates as optimal, we ran simulations of opportunistic diseases and found that the selected therapy shows the best survival curve among the different simulated control groups.
Availability: A version of the C-ImmSim simulator is available at http://www.iac.cnr.it/~filippo/c-ImmSim.html
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http://dx.doi.org/10.1093/bioinformatics/btm408 | DOI Listing |
Clin Transl Med
January 2025
Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
Precision medicine in less-defined subtype diffuse large B-cell lymphoma (DLBCL) remains a challenge due to the heterogeneous nature of the disease. Programmed cell death (PCD) pathways are crucial in the advancement of lymphoma and serve as significant prognostic markers for individuals afflicted with lymphoid cancers. To identify robust prognostic biomarkers that can guide personalized management for less-defined subtype DLBCL patients, we integrated multi-omics data derived from 339 standard R-CHOP-treated patients diagnosed with less-defined subtype DLBCL from three independent cohorts.
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School of Computer Engineering, Jiangsu Ocean University, Lianyungang, 222005, China.
Background: Cancer classification has consistently been a challenging problem, with the main difficulties being high-dimensional data and the collection of patient samples. Concretely, obtaining patient samples is a costly and resource-intensive process, and imbalances often exist between samples. Moreover, expression data is characterized by high dimensionality, small samples and high noise, which could easily lead to struggles such as dimensionality catastrophe and overfitting.
View Article and Find Full Text PDFBMC Genomics
December 2024
Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, 02-106, Poland.
Low Complexity Regions (LCRs) are segments of proteins with a low diversity of amino acid composition. These regions play important roles in proteins. However, annotations describing these functions are dispersed across databases and scientific literature.
View Article and Find Full Text PDFBMC Genomics
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School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China.
Background: The subcellular localization of mRNA plays a crucial role in gene expression regulation and various cellular processes. However, existing wet lab techniques like RNA-FISH are usually time-consuming, labor-intensive, and limited to specific tissue types. Researchers have developed several computational methods to predict mRNA subcellular localization to address this.
View Article and Find Full Text PDFJ Cell Mol Med
December 2024
Department of Gynecology, School of Medicine, Shanghai First Maternity and Infant Hospital, Tongji University, Shanghai, China.
Ovarian cancer (OvCa) is the most lethal gynaecology malignancies worldwide. Neutrophil extracellular traps (NETs), net-like protein structures produced by activated neutrophils and DNA-histone complexes, have a central role in tumours, though haven't been fully explored in OvCa. We obtained transcriptome data from TCGA-OvCa database (n = 376) as training, ICGC-OvCa database (n = 111) as validation and GTEx database (n = 180) as controls.
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