Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network.

After several waves of COVID-19 led to a massive loss of human life worldwide due to the changes in its variants and the vast explosion. Several researchers proposed neural network-based drug discovery techniques to fight against the pandemic; utilizing neural networks has limitations (Exponential time complexity, Non-Convergence, Mode Collapse, and Diminished Gradient). To overcome those difficulties, this paper proposed a hybrid architecture that will help to repurpose the most appropriate medicines for the treatment of COVID-19. A brief investigation of the sequences has been made to discover the gene density and noncoding proportion through the next gene sequencing. The paper tracks the exceptional locales in the virus DNA sequence as a Drug Target Region (DTR). Then the variable DNA neighborhood search is applied to this DTR to obtain the DNA interaction network to show how the genes are correlated. A drug database has been obtained based on the ontological property of the genomes with advanced D3Similarity so that all the chemical components of the drug database have been identified. Other methods obtained hydroxychloroquine as an effective drug which was rejected by WHO. However, The experimental results show that Remdesivir and Dexamethasone are the most effective drugs, with 97.41 and 97.93%, respectively.

PPIGCF: A Protein-Protein Interaction-Based Gene Correlation Filter for Optimal Gene Selection.

Biological data at the omics level are highly complex, requiring powerful computational approaches to identifying significant intrinsic characteristics to further search for informative markers involved in the studied phenotype. In this paper, we propose a novel dimension reduction technique, protein-protein interaction-based gene correlation filtration (PPIGCF), which builds on gene ontology (GO) and protein-protein interaction (PPI) structures to analyze microarray gene expression data. PPIGCF first extracts the gene symbols with their expression from the experimental dataset, and then, classifies them based on GO biological process (BP) and cellular component (CC) annotations. Every classification group inherits all the information on its CCs, corresponding to the BPs, to establish a PPI network. Then, the gene correlation filter (regarding gene rank and the proposed correlation coefficient) is computed on every network and eradicates a few weakly correlated genes connected with their corresponding networks. PPIGCF finds the information content (IC) of the other genes related to the PPI network and takes only the genes with the highest IC values. The satisfactory results of PPIGCF are used to prioritize significant genes. We performed a comparison with current methods to demonstrate our technique's efficiency. From the experiment, it can be concluded that PPIGCF needs fewer genes to reach reasonable accuracy (~99%) for cancer classification. This paper reduces the computational complexity and enhances the time complexity of biomarker discovery from datasets.

3PNMF-MKL: A non-negative matrix factorization-based multiple kernel learning method for multi-modal data integration and its application to gene signature detection.

In this current era, biomedical big data handling is a challenging task. Interestingly, the integration of multi-modal data, followed by significant feature mining (gene signature detection), becomes a daunting task. Remembering this, here, we proposed a novel framework, namely, three-factor penalized, non-negative matrix factorization-based multiple kernel learning with soft margin hinge loss (3PNMF-MKL) for multi-modal data integration, followed by gene signature detection. In brief, limma, employing the empirical Bayes statistics, was initially applied to each individual molecular profile, and the statistically significant features were extracted, which was followed by the three-factor penalized non-negative matrix factorization method used for data/matrix fusion using the reduced feature sets. Multiple kernel learning models with soft margin hinge loss had been deployed to estimate average accuracy scores and the area under the curve (AUC). Gene modules had been identified by the consecutive analysis of average linkage clustering and dynamic tree cut. The best module containing the highest correlation was considered the potential gene signature. We utilized an acute myeloid leukemia cancer dataset from The Cancer Genome Atlas (TCGA) repository containing five molecular profiles. Our algorithm generated a 50-gene signature that achieved a high classification AUC score (viz., 0.827). We explored the functions of signature genes using pathway and Gene Ontology (GO) databases. Our method outperformed the state-of-the-art methods in terms of computing AUC. Furthermore, we included some comparative studies with other related methods to enhance the acceptability of our method. Finally, it can be notified that our algorithm can be applied to any multi-modal dataset for data integration, followed by gene module discovery.

Fractal Dimension-Based Infection Detection in Chest X-ray Images.

The current ongoing trend of dimension detection of medical images is one of the challenging areas which facilitates several improvements in accurate measuring of clinical imaging based on fractal dimension detection methodologies. For medical diagnosis of any infection, detection of dimension is one of the major challenges due to the fractal shape of the medical object. Significantly improved outcome indicates that the performance of fractal dimension detection techniques is better than that of other state-of-the-art methods to extract diagnostically significant information from clinical image. Among the fractal dimension detection methodologies, fractal geometry has developed an efficient tool in medical image investigation. In this paper, a novel methodology of fractal dimension detection of medical images is proposed based on the concept of box counting technique to evaluate the fractal dimension. The proposed method has been evaluated and compared to other state-of-the-art approaches, and the results of the proposed algorithm graphically justify the mathematical derivation of the box counting approach in terms of Hurst exponent.

CoWarriorNet: A Novel Deep-Learning Framework for CoVID-19 Detection from Chest X-Ray Images.

Even after scavenging the existence of mankind for the past year, the wrath of CoVID-19 is yet to die down. Countries like India are still getting haunted by the devastating conundrum, with coronavirus ripping through its citizens in the concurrent second wave. The surge of cases has prompted rapid intervention, with medical authorities pushing it to the limit to curve a roadblock to its aggressive growth. But, even after effortless work, human intervention remains slow and insufficient. Furthermore, relevant testing methodologies have shown weakness while detecting threats, with the recent growth of post-Covid complexities, thereby leaving a painful mark. This as such created a major requirement for technological advancements, which can cater to the mass. The growth of computational prowess in the past decade made the field of Deep Learning a major contributor in curving out algorithms to solve this. Adding to the excellent foundation of Deep Learning, this paper, proposes a novel CoWarriorNet model for rapid detection of CoVID-19, via chest X-ray images, which adds in an extra layer of precision and confirmation in the detection of cases in both pre-Covid and post-Covid conditions. The proposed classification model curves out an excellent accuracy of 97.8%, with the major eye-candy being the sensitivity rate of 0.99 when detecting CoVID-19 cases. This model introduces a new concept of Alpha Trimmed Average Pooling, which along with the novel architecture adds a subtle touch to its high efficiency, thereby giving a much-needed solution to the medical experts. The two-mouthed architecture provides the added benefit of a confidence score, deducing human aid in case of discrepancy. Supplementary Information: The online version contains supplementary material available at 10.1007/s00354-021-00143-1.

Elucidating the molecular interaction of Zebrafish (Danio rerio) peptidoglycan recognition protein 2 with diaminopimelic acid and lysine type peptidoglycans using in silico approaches.

Peptidoglycan recognition proteins (PGRPs) belong to the family of pattern recognition receptor, represent the major constituent of innate immunity. Although PGRPs are structurally conserved through evolution, their involvement in innate immunity is different in vertebrates and invertebrates. They are highly specific towards recognition of ligands and can hydrolyze bacterial peptidoglycans (PGNs). Zebrafish PGRPs (zPGRPs) have both peptidoglycans lytic amidase activity and broad-spectrum bactericidal activity, but far less is known about how these receptors recognize these microbial ligands. Such studies are hindered due to lack of structural and functional configuration of zPGRPs. Therefore, in this study, we predicted the three-dimensional structure of zPGRP2 through theoretical modeling, investigated the conformational and dynamic properties through molecular dynamics simulations. Molecular docking study revealed the microbial ligands, that is, muramyl pentapeptide-DAP , muramyl pentapeptide-LYS, muramyl tripeptide-DAP, muramyl tripeptide-Lys, muramyl tetrapeptide-DAP, muramyl tetrapeptide-LYS and tracheal cytotoxin interacts with the conserved amino acids of the ligand recognition site comprised of ß1, α2, α4, ß4 and loops connecting ß1 - α2, α2 - ß2, ß3 - ß4 and α4 - α5. Conserved His31, His32, Ala34, Ile35, Pro36, Lys38, Asp60, Trp61, Trp63, Ala89, His90, Asp106, His143 and Arg144 are predicted to essential for binding and provides stability to these zPGRP-PGN complexes. Our study provides basic molecular information for further research on the immune mechanisms of PGRP's in Zebrafish. The plasticity of the zPGRP's binding site revealed by these microbial ligands suggests an intrinsic capacity of the innate immune system to rapidly evolve specificities to meet new microbial challenges in the future.Communicated by Ramaswamy H. Sarma.