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Network inference or reconstruction algorithms play an integral role in successfully analyzing and identifying causal relationships between omics hits for detecting dysregulated and altered signaling components in various contexts, encompassing disease states and drug perturbations. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. pyPARAGON enhances accuracy and minimizes the inclusion of nonspecific interactions in signaling networks by utilizing network rather than relying on pairwise connections among proteins. Through comprehensive evaluations on benchmark signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome and demonstrating tumor-specific signaling pathways. Overall, pyPARAGON is a novel tool for analyzing and integrating multi-omic data in the context of signaling networks. pyPARAGON is available at https://github.com/netlab-ku/pyPARAGON. [ABSTRACT FROM AUTHOR]

The intent of the present investigation is to conduct; experimentally, the effects of nanofluid of water/magnetite and flow channel arrangement on photovoltaic thermal (PVT) systems. Three different configurations of the collector in PVT were researched in various nanofluid's flow rate of M ˙ = 20.0 − 80.0 (k g / h r) , and nano concentration of φ = 0.0 − 2.0 %. The studied arrangements of flow channels in the collector are a sheet-and-plain serpentine tube (PVT-0S), a sheet-and-finned serpentine tube with four fins (PVT-4S) and a sheet-and-finned serpentine tube with eight fins (PVT-8S). The electrical efficiency is compared between the cited three cases and the PV panel without cooling system. The maximum energetic, exergetic and electrical performances were found at the flow rate of M ˙ = 80 (k g / h r) and nano concentration of φ = 2.0 %. It was observed that the PVT-8S system provides the most efficient configuration since the overall energy efficiency (η) in the PVT-8S system are 5.87 % , and 15.59 % higher than the PVT-4S, and PVT-0S systems. Further, the PVT-8S system demonstrated the maximum value of exergy efficiency (Γ = 14.51 %) compared to other studied systems. It was also shown that the electrical efficiency (Λ) augments significantly with the adding the cooling systems to the PV panel. The increase in the electrical efficiency was 12.06 % , 10.87 % , and 8.40 % for the PVT-8S, PVT-4S, and PVT-0S systems; respectively. • The maximum energetic performance is obtained at the flow rate, and volume fraction of 80 (kg/hr) and 2.0%. • The highest overall energy and exergy efficiencies were achieved for the PVT-8S system. • PVT-8S, PVT-4S, and PVT-0S have 12.06%, 10.87%, and 8.40% higher electrical efficiency than PV panel. [ABSTRACT FROM AUTHOR]

In line with the advances in high-throughput technologies, multiple omic datasets have accumulated to study biological systems and diseases coherently. No single omics data type is capable of fully representing cellular activity. The complexity of the biological processes arises from the interactions between omic entities such as genes, proteins, and metabolites. Therefore, multi-omic data integration is crucial but challenging. The impact of the molecular alterations in multi-omic data is not local in the neighborhood of the altered gene or protein; rather, the impact diffuses in the network and changes the functionality of multiple signaling pathways and regulation of the gene expression. Additionally, multi-omic data is high-dimensional and has background noise. Several integrative approaches have been developed to accurately interpret the multi-omic datasets, including machine learning, network-based methods, and their combination. In this review, we overview the most recent integrative approaches and tools with a focus on network-based methods. We then discuss these approaches according to their specific applications, from disease-network and biomarker identification to patient stratification, drug discovery, and repurposing.