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17 pages, 1411 KiB

Open AccessArticle

A Parallel Computing Approach to Gene Expression and Phenotype Correlation for Identifying Retinitis Pigmentosa Modifiers in Drosophila

by Chawin Metah, Amal Khalifa and Rebecca Palu

Computation 2023, 11(6), 118; https://doi.org/10.3390/computation11060118 - 14 Jun 2023

Viewed by 1041

Abstract

As a genetic eye disorder, retinitis pigmentosa (RP) has been a focus of researchers to find a diagnosis through either genome-wide association (GWA) or RNAseq analysis. In fact, GWA and RNAseq are considered two complementary approaches to gaining a more comprehensive understanding of [...] Read more.

As a genetic eye disorder, retinitis pigmentosa (RP) has been a focus of researchers to find a diagnosis through either genome-wide association (GWA) or RNAseq analysis. In fact, GWA and RNAseq are considered two complementary approaches to gaining a more comprehensive understanding of the genetics of different diseases. However, RNAseq analysis can provide information about the specific mechanisms underlying the disease and the potential targets for therapy. This research proposes a new approach to differential gene expression (DGE) analysis, which is the heart of the core-analysis phase in any RNAseq study. Based on the Drosophila Genetic Reference Panel (DGRP), the gene expression dataset is computationally analyzed in light of eye-size phenotypes. We utilized the foreach and the doParallel R packages to run the code on a multicore machine to reduce the running time of the original algorithm, which exhibited an exponential time complexity. Experimental results showed an outstanding performance, reducing the running time by 95% while using 32 processes. In addition, more candidate modifier genes for RP were identified by increasing the scope of the analysis and considering more datasets that represent different phenotype models. Full article

(This article belongs to the Special Issue Computational Biology and High-Performance Computing)

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19 pages, 9366 KiB

Open AccessArticle

Computational Methods in the Drug Delivery of Carbon Nanocarriers onto Several Compounds in Sarraceniaceae Medicinal Plant as Monkeypox Therapy

by Fatemeh Mollaamin

Computation 2023, 11(4), 84; https://doi.org/10.3390/computation11040084 - 20 Apr 2023

Cited by 5 | Viewed by 1211

Abstract

In this article, monkeypox is studied as a zoonotic poxvirus disease which can occur in humans and other animals due to substitution of the amino acid serine with methionine. We investigate the (+)-catechin, betulinic acid, ursolic acid, quercetin-3-O-galactoside, luteolin-7-O-glucoside, and myricetin in Sarracenia [...] Read more.

In this article, monkeypox is studied as a zoonotic poxvirus disease which can occur in humans and other animals due to substitution of the amino acid serine with methionine. We investigate the (+)-catechin, betulinic acid, ursolic acid, quercetin-3-O-galactoside, luteolin-7-O-glucoside, and myricetin in Sarracenia purpurea drugs from Sarraceniaceae family for treating monkeypox disease. This is performed via adsorption onto the surface of (6,6) armchair single-walled carbon nanotube (SWCNT) at the B3LYP/6-311+G (2d,p) level of theory in a water medium as the drug delivery method at 300 K. Sarracenia purpurea has attracted much attention for use in the clinical treatment of monkeypox disease due to the adsorption of its effective compounds of (+)-catechin, betulinic acid, ursolic acid, quercetin-3-O-galactoside, luteolin-7-O-glucoside, and myricetin onto the surface of (6,6) armchair SWCNT, a process which introduces an efficient drug delivery system though NMR, IR and UV-VIS data analysis to the optimized structure. In addition to the lowering of the energy gap (∆E = E _LUMO − E_HOMO), HOMO–LUMO energy has illustrated the charge transfer interactions taking place within (+)-catechin, betulinic acid, ursolic acid, quercetin-3-O-galactoside, luteolin-7-O-glucoside, and myricetin. The atomic charges have provided the proper perception of molecular theory and the energies of fundamental molecular orbitals. Full article

(This article belongs to the Special Issue Computational Biology and High-Performance Computing)

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19 pages, 2056 KiB

Open AccessStudy Protocol

Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction

by Luana Conte, Emanuele Rizzo, Tiziana Grassi, Francesco Bagordo, Elisabetta De Matteis and Giorgio De Nunzio

Computation 2024, 12(3), 47; https://doi.org/10.3390/computation12030047 - 3 Mar 2024

Viewed by 1332

Abstract

Pedigree charts remain essential in oncological genetic counseling for identifying individuals with an increased risk of developing hereditary tumors. However, this valuable data source often remains confined to paper files, going unused. We propose a computer-aided detection/diagnosis system, based on machine learning and [...] Read more.

Pedigree charts remain essential in oncological genetic counseling for identifying individuals with an increased risk of developing hereditary tumors. However, this valuable data source often remains confined to paper files, going unused. We propose a computer-aided detection/diagnosis system, based on machine learning and deep learning techniques, capable of the following: (1) assisting genetic oncologists in digitizing paper-based pedigree charts, and in generating new digital ones, and (2) automatically predicting the genetic predisposition risk directly from these digital pedigree charts. To the best of our knowledge, there are no similar studies in the current literature, and consequently, no utilization of software based on artificial intelligence on pedigree charts has been made public yet. By incorporating medical images and other data from omics sciences, there is also a fertile ground for training additional artificial intelligence systems, broadening the software predictive capabilities. We plan to bridge the gap between scientific advancements and practical implementation by modernizing and enhancing existing oncological genetic counseling services. This would mark the pioneering development of an AI-based application designed to enhance various aspects of genetic counseling, leading to improved patient care and advancements in the field of oncogenetics. Full article

(This article belongs to the Special Issue Computational Biology and High-Performance Computing)

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