Machine learning algorithms give sub-optimal performance in the presence of class-imbalanced dataset. Mammalian target of rapamycin (mTOR) is one of the serine/threonine protein kinase, and plays an integral role in autophagy pathway. Autophagy is a cellular pathway for recycling of macromolecules (proteins, lipids, and organelles), which enables eukaryotic cells to adapt metabolism to survive during adverse growth conditions. Targeting mTOR through therapeutic interventions of autophagy pathway establishes mTOR a promising pharmacological target for autophagy modulation in cancer. The bioactivity dataset of mTOR in ChEMBL, a compound bioactivity database maintained by European Bioinformatics Institute, shows disproportionate distribution of active and inactive classes. The predictive models based on this skewed dataset are biased towards prediction of majority class. Hence, we have used Synthetic Minority Over-sampling TEchnique to deal with class-imbalance problem in bioactivity datasets. We have built and evaluated predictive models based on four commonly used classifiers using both class-imbalanced and class-balanced bioactivity datasets, and compared their performance based on various metrics like accuracy, sensitivity, specificity, F1-measure, and AUC . We observe that the classification models based on balanced dataset generally outperform those that are based on class-imbalanced dataset, irrespective of the classifiers used for classification task. We conclude that predictive models trained over class-balanced dataset can be used for screening large compound bioactivity datasets to predict mTOR inhibitors-like compounds.
Autophagy (in Greek: self-eating) is the cellular process for delivery of heterogenic intracellular material to lysosomal digestion. Protein kinases are integral to the autophagy process, and when dysregulated or mutated cause several human diseases. Atg1, the first autophagy-related protein identified is a serine/threonine protein kinases (STPKs). mTOR (mammalian Target of Rapamycin), AMPK (AMPactivated protein kinase), Akt, MAPK (mitogen-activated protein kinase) and PKC (protein kinase C) are other STPKs which regulate various components/steps of autophagy, and are often deregulated in cancer. MAPK have three subfamilies -ERKs, p38, and JNKs. JNKs (c-Jun N-terminal Kinases) have three isoforms in mammals -JNK1, JNK2, and JNK3, each with distinct cellular locations and functions. JNK1 plays role in starvation induced activation of autophagy, and the context-specific role of autophagy in tumorigenesis establish JNK1 a challenging anticancer drug target. Since JNKs are closely related to other members of MAPK family (p38, MAP kinase and the ERKs), it is difficult to design JNK-selective inhibitors. Designing JNK isoform-selective inhibitors are even more challenging as the ATP-binding sites among all JNKs are highly conserved. Although limited informations are available to explore computational approaches to predict JNK1 inhibitors, it seems diificult to find literature exploring machine learning techniques to predict JNKs inhibitors. This study aims to apply machine learning to predict JNK1 inhibitors regulating autophagy in cancer using Random Forest (RF). Here, RF algorithm is used for two purposes-to select and rank the molecular descriptors calculated using PaDEL descriptor software and as clasifier. The descriptors are prioritized by calculating Variable Importance Measures (VIMs) using functions based on mean square error (IncMSE) and node purity (IncNodePurity) of RF. The classification models based on a set of 22 prioritized descriptors shows accuracy 86.36%, precision 88.27% and AUC (Area Under ROC curve) 0.8914. We conclude that machine learning-based compound classification using Random Forest is one of the ligand-based approach that can be opted for virtual screening of large compound library of JNK1 bioactives. Out of the three isoforms of JNKs (cJun N-terminal Kinases) in human (each with distinct cellular locations and functions), JNK1 plays role in starvation induced activation of autophagy. The role of JNK1 in autophagy modulation and dual role of autophagy in tumor cells makes JNK1 a promising anticancer drug target. Since JNKs are closely related to other members of MAPK (Mitogen-Activated Protein Kinases) family, it is difficult to design JNK selective inhibitors. Designing JNK isoformselective inhibitors are even more challenging as the ATP binding sites among all JNKs are highly conserved.Random forest classifier usually outperforms several other machine learning algorithms for classification and prediction tasks in diverse areas of research. In this work, we have used Random Forest algorit...
COVID-19 is a sickness brought about by coronavirus responsible for causing simple to extreme complications in people. COVID-19 first case was seen in Wuhan, Hubei Province, China, on December, 2019. The World Health Organization (WHO) to pronounce it as a worldwide pandemic on March 11, 2020, as the pandemic has spread quickly all through the world. Regardless of extensive endeavors made to contain the infection, the infection has proceeded with its pervasiveness in numerous nations with changing levels of clinical signs. Henceforth, in this report, we discuss the various strategies, for example, serological and nucleic acid-based procedures which are accessible for the determination and successful counteraction of coronavirus. With expanding the rate of coronavirus cases, the precise and early identification of the COVID-19 is the need of great importance for viable avoidance with treatment and just as to check its spread. Reverse transcriptase-real time polymerase chain reaction (PCR) measures are viewed as the highest quality level for the early identification of infection. This diagnostic technique is being utilized worldwide with recommendations from WHO and Center for Disease Control and Prevention. Reverse transcriptase real-time quantitative PCR (RT-qPCR) is being done compulsory before any medical procedures and major surgeries for early detection, prevention, and management in due time course. Rapid antigen test is also a screening test used widely in hospitals for screening of COVID-19 and before the admission in hospitals. Other nucleic acid amplification test widely done for the detection of COVID-19 are RT-qPCR, next-generation sequencing, clustered regularly interspaced short pallindromic repeats, reverse transcription -loop-mediated isothermal amplification, droplet digital PCR. Some immunological tests are lateral flow, ELISA.
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