Optimization of metabolomic data processing using NOREVA

Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models

“…The predictive power of extracted models can be increased if different MEMs are analysed in a given context to guide the selection of a method that yields models with the largest significance (e.g., models preserving the separation between the observed experimental groups) [ 39 ]. Furthermore, the selection of pre-processing and omics data integration steps also has a significant effect on obtained results and the use of various tools, approaches and datasets significantly increase the accuracy, precision and robustness of analyses [ [40] , [41] , [42] , [43] ]. Setting up workflows combining the integration of different datasets and tools enabling systematic assessment of omics data is one of the main goals of systems medicine [ 44 , 45 ].…”

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

“…In future, the proposed SDGBO can also be applied to other problems, including but not limited to kayak cycle phase segmentation [116], engineering optimization problems [117, 118], service ecosystem [119, 120], location‐based services [121, 122], microgrid planning [123], energy storage planning and scheduling [124], information retrieval services [125, 126], time series analysis [127], urban road planning [128], fault detection [129], human motion capture [130], gene signature identification [131], metabolomic data processing [132, 133], and drug target discovery [134, 135].…”

“…Compared with others, the simulation data obtained by the new optimization achieved the minimal root mean square error and were extremely close to the measured datasheet in solar cells. Using Thin-film ST40, the proposed method identified unknown parameters for SDM as well as DDM under light intensities of 200 W/m 2 , 400 W/m 2 , 600 W/m 2 , 800 W/m 2 and 1000 W/m 2 at the temperature of 25 In future, the proposed SDGBO can also be applied to other problems, including but not limited to kayak cycle phase segmentation [116], engineering optimization problems [117,118], service ecosystem [119,120], location-based services [121,122], microgrid planning [123], energy storage planning and scheduling [124], information retrieval services [125,126], time series analysis [127], urban road planning [128], fault detection [129], human motion capture [130], gene signature identification [131], metabolomic data processing [132,133], and drug target discovery [134,135].…”

GC-MS profiling of Bauhinia variegata major phytoconstituents with computational identification of potential lead inhibitors of SARS-CoV-2 Mpro

“…In recent years FTIR, GC-MS, and LC-MS have frequently used techniques for the identification of functional groups and secondary metabolites of respective medicinal plant extracts. Some recent databases like MMEASE, NOREVA and ANPELA are also being used to identify the compound from the natural resources [ [34] , [35] , [36] , [37] , [38] ]. A gas chromatography along with a mass spectrometry (GC-MS) system was used to conduct the analysis of B. variegata aqueous as well as methanol leaves extract (model; QP2010 Ultra, Shimadzu Corporation, Kyoto, Japan).…”