A Biologically-Based Computational Approach to Drug Repurposing for Anthrax Infection

Abstract Background Membranous glomerulonephritis (MGN) is a common kidney disease. Despite many evidences support that many immune and inflammation-related genes could serve as effective biomarkers and treatment targets for MGN patients, the potential associations among MGN-, immune- and inflammation-related genes have not been sufficiently understood. Methods Here, a global immune-, inflammation- and MGN-associated triplets (IIMATs) network is constructed and analyzed. An integrated and computational approach is developed to identify dysregulated IIMATs for MGN patients based on expression and interaction data. Results 45 dysregulated IIMATs are identified in MGN by above method. Dysregulated patterns of these dysregulated IIMATs are complex and various. We identify four core clusters from dysregulated IIMATs network and some of these clusters could distinguish MGN and normal samples. Specially, some anti-cancer drugs including Tamoxifen, Bosutinib, Ponatinib and Nintedanib could become candidate drugs for MGN based on drug repurposing strategy follow IIMATs. Functional analysis shows these dysregulated IIMATs are associated with some key functions and chemokine signaling pathway. Conclusions The present study explored the associations among immune, inflammation and MGN. Some effective candidate drugs for MGN were identified based on immune and inflammation. Overall, these comprehensive results provide novel insights into the mechanisms and treatment of MGN.

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Multi-BIRW: a network-based algorithm for drug repurposing

10.1101/2020.09.04.281451 ◽

2020 ◽

Author(s):

Luigi Ferraro

Keyword(s):

Heterogeneous Network ◽

Lower Cost ◽

Drug Repurposing ◽

Similarity Score ◽

Computational Approach ◽

Toxicity Tests ◽

Disease Similarity ◽

Different Types ◽

Disease Associations ◽

Speed Up

AbstractThis paper is based on Computational Biology with an eye towards medicine. In fact our aim is to identify new indications for existing drugs with a computational approach using Systems Biology, in order to speed up the market entry, avoiding some phases of drug development (for example toxicity tests) and with a lower cost.The method that we will discuss exploits the concept of Random Walk in a heterogeneous network, formed by a drug-drug and disease-disease similarity and known drug-disease associations. This is done by combining different types of data in order to connect more effectively two drugs or two diseases with a similarity score. At the end we integrate the known associations between drugs and diseases with the purpose of finding similarity values of new couples.

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Application of Modified Bloch Waves to Image Analysis of Extended Linear Defects

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010005202x ◽

1974 ◽

Vol 32 ◽

pp. 402-403

Author(s):

S. Nakahara ◽

D. M. Maher

Keyword(s):

Image Analysis ◽

Computational Approach ◽

Dislocation Loops ◽

Plane Wave Expansion ◽

Dynamical Equations ◽

Bloch Waves ◽

Linear Defects ◽

Imperfect Crystal ◽

Localized Defects ◽

Defective Crystals

Since Head first demonstrated the advantages of computer displayed theoretical intensities from defective crystals, computer display techniques have become important in image analysis. However the computational methods employed resort largely to numerical integration of the dynamical equations of electron diffraction. As a consequence, the interpretation of the results in terms of the defect displacement field and diffracting variables is difficult to follow in detail. In contrast to this type of computational approach which is based on a plane-wave expansion of the excited waves within the crystal (i.e. Darwin representation ), Wilkens assumed scattering of modified Bloch waves by an imperfect crystal. For localized defects, the wave amplitudes can be described analytically and this formulation has been used successfully to predict the black-white symmetry of images arising from small dislocation loops.

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