Ekene Francis Okagbue, Ujunwa Perpetua Ezeachikulo, Ilokanulo Samuel Nchekwubemchukwu, Ilodibe Emeka Chidiebere, Obisoanya Kosiso, Cheick Amadou Tidiane Ouattaraa, Esther Onyinye Nwigwe
The effects of Covid-19 pandemic on the education system in Nigeria: The role of competency-based education
International journal of educational research open
application machine learning ml omics research growing exponential rate owing increasing availability large amounts data model training specifically metabolomics ml enabled prediction tandem mass spectrometry retention time data recently due advent ion mobility new ml models introduced collision crosssection ccs prediction trained different relatively small data sets covering thousands small molecules hampers systematic comparison compared four existing mlbased ccs prediction models capacity predict ccs values using recently introduced metlinccs data set also compared simple linear models ml models used fingerprints regressors analyzed role structural diversity data ml models trained explored practical application models metabolite annotation using ccs values results showed limited capability existing models achieve necessary accuracy adopted routine metabolomics analysis showed particular molecule accuracy could improved models trained large number structurally similar counterparts therefore suggest current annotation capabilities significantly altered models trained heterogeneous data sets composed large homogeneous hubs structurally similar molecules predicted