1,721,179 research outputs found
The long story of SWIM methodology: from grapevine to personalised medicine
Full text linkSWIM is a recently developed network-based tool that fulfils the criteria of the new quickly emerging field of Network Medicine in finding disease-associated genes, called switch genes. The phenotype-specific applications of SWIM are broad and include the identification of switch genes in grapevine berry maturation as well as in complex diseases, including but not limited to human cancers. Here, a brief summary of the promising results obtained by applying SWIM in different biological contexts is presented
SAveRUNNER: an R-based tool for drug repurposing
Full text linkBackground: Currently, no proven effective drugs for the novel coronavirus disease COVID-19 exist and despite widespread vaccination campaigns, we are far short from herd immunity. The number of people who are still vulnerable to the virus is too high to hamper new outbreaks, leading a compelling need to find new therapeutic options devoted to combat SARS-CoV-2 infection. Drug repurposing represents an effective drug discovery strategy from existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. Results: We developed a network-based tool for drug repurposing provided as a freely available R-code, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), with the aim to offer a promising framework to efficiently detect putative novel indications for currently marketed drugs against diseases of interest. SAveRUNNER predicts drug–disease associations by quantifying the interplay between the drug targets and the disease-associated proteins in the human interactome through the computation of a novel network-based similarity measure, which prioritizes associations between drugs and diseases located in the same network neighborhoods. Conclusions: The algorithm was successfully applied to predict off-label drugs to be repositioned against the new human coronavirus (2019-nCoV/SARS-CoV-2), and it achieved a high accuracy in the identification of well-known drug indications, thus revealing itself as a powerful tool to rapidly detect potential novel medical indications for various drugs that are worth of further investigation. SAveRUNNER source code is freely available at https://github.com/giuliafiscon/SAveRUNNER.git, along with a comprehensive user guide
SWIMmeR: An R-based software to unveiling crucial nodes in complex biological networks
No full textWe present SWIMmeR, an open-source version of its predecessor SWIM (SWitchMiner) that is a network-based tool for mining key (switch) genes that are associated with intriguing patterns of molecular coabundance and may play a crucial role in phenotypic transitions in various biological settings. SWIM was originally written in MATLABVR , a proprietary programming language that requires the purchase of a license to install, manipulate, operate and run the software. Over the last years, SWIM has sparked a widespread interest within the scientific community thanks to the promising results obtained through its application in a broad range of phenotype-specific scenarios, spanning from complex diseases to grapevine berry maturation. This success has created the call for it to be distributed in a freely accessible, open-source, runtime environment, such as R, aimed at a general audience of non-expert users that cannot afford the leading proprietary solution. SWIMmeR is provided as a comprehensive collection of R functions and it also includes several additional features that make it less intensive in terms of computer time and more efficient in terms of usability and further implementation and extension
Emerging Role of the Fat Free Mass Preservation during Weight Loss Therapy through a Novel Advanced Bio-Impedance Device (BIA-ACC)
No full textNetwork-based analysis to uncover drug-induced adverse side-effects
No full textDespite the drug approval process consists of
extremely rigorous clinical and preclinical studies, not all side
effects are identified before its marketing, posing a significant
risk to public health. Furthermore, considering the huge use of
economic and human resources, in-silico predictive approaches
for the identification of side effects are essential. In this study,
we introduce a new method based on random walk with restart
algorithm to delineate previously unidentified links between
drugs and side effects, and we apply it on the drug-induced
Asthma and long QT syndrome. We identified the genes
potentially involved in the development of the analyzed side
effect by comparing side-effect-related drugs with drugs not
known to induce side effects. Analyzing the sets of genes most
likely influenced by the perturbation of each individual drug, we
observed that, on average, side-effect-related drugs perturb a
higher percentage of genes involved in the development of side
effects compared to side-effect-unrelated drugs. Based on this
finding, we developed a classifier to explore all possible
unknown associations between drugs and side effects. This
method can be extended to the analysis of other side effects as
well
Integro-differential approach for modeling the COVID-19 dynamics - Impact of confinement measures in Italy
Full text linkThe COVID-19 pandemic has overwhelmed the life and security of most of the world countries, and especially of the Western countries, without similar experiences in the recent past. In a first phase, the response of health systems and governments was disorganized, but then incisive, also driven by the fear of a new and dramatic phenomenon. In the second phase, several governments, including Italy, accepted the doctrine of “coexistence with the virus” by putting into practice a series of containment measures aimed at limiting the dramatic sanitary consequences while not jeopardizing the economic and social stability of the country. Here, we present a new mathematical approach to modeling the COVID-19 dynamics that accounts for typical evolution parameters (i.e., virus variants, vaccinations, containment measurements). Reproducing the COVID-19 epidemic spread is an extremely challenging task due to the low reliability of the available data, the lack of recurrent patterns, and the considerable amount and variability of the involved parameters. However, the adoption of fairly uniform criteria among the Italian regions enabled to test and optimize the model in various conditions leading to robust and interesting results. Although the regional variability is quite large and difficult to predict, we have retrospectively obtained reliable indications on which measures were the most appropriate to limit the transmissibility coefficients within detectable ranges for all the regions. To complicate matters further, the rapid spread of the English variant has upset contexts where the propagation of contagion was close to equilibrium conditions, decreeing success or failure of a certain measure. Finally, we assessed the effectiveness of the zone assignment criteria, highlighting how the reactivity of the measures plays a fundamental role in limiting the spread of the infection and thus the total number of deaths, the most important factor in assessing the success of epidemic management
A network-based algorithm for identifying drug repurposing opportunities for complex diseases
Full text linkSAveRUNNER is a recently developed network-based tool to efficiently identify novel medical indications for currently approved drugs (known as drug repurposing strategy). Up to now, SAveRUNNER has been gainfully applied to unveil repurposable solutions for several diseases, including viral infection (e.g., SARS, COVID-19, HIV, Malaria, Ebola), breast cancer, progressive disorders of central nervous system (e.g., Amyotrophic Lateral Sclerosis, Multiple Sclerosis), and other neurodegenerative diseases (e.g., Alzheimer's Disease). Here, SAveRUNNER algorithm and its main applications are described
Circular RNA mediated gene regulation in human breast cancer: A bioinformatics analysis
Full text linkCircular RNAs (circRNAs) are a new acknowledged class of RNAs that has been shown to play a major role in several biological functions both in physiological and pathological conditions, operating as critical part of regulatory processes, like competing endogenous RNA (ceRNA) networks. The ceRNA hypothesis is a recently discovered molecular mechanism that adds a new key layer of post-transcriptional regulation, whereby various types of RNAs can reciprocally influence each other’s expression competing for binding the same pool of microRNAs, even affecting disease development. In this study, we build a network of circRNA-miRNA-mRNA interactions in human breast cancer, called CERNOMA, that is a bipartite graph with one class of nodes corresponding to differentially expressed miRNAs (DEMs) and the other one corresponding to differentially expressed circRNAs (DEC) and mRNAs (DEGs). A link between a DEC (or DEG) and DEM is placed if it is predicted to be a target of the DEM and shows an opposite expression level trend with respect to the DEM. Within the CERNOMA, we highlighted an interesting deregulated circRNA-miRNA-mRNA triplet, including the up-regulated hsa_circRNA_102908 (BRCA1 associated RING domain 1), the down-regulated miR-410-3p, and the up-regulated ESM1, whose overexpression has been already shown to promote tumor dissemination and metastasis in breast cancer
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