Research Insights
The Research Insights area highlights recent advances in science and medicine as well as global laboratory research. Our readers gain tremendous benefit from research discoveries in an indirect manner.
Myosina is a Druggable Target in the Widespread Protozoan Parasite Toxoplasma Gondii
Toxoplasma gondiiis a widespread apicomplexan parasite that can cause severe disease in its human hosts
CRISPR-Analytics (CRISPR-A): A platform for precise analytics and simulations for gene editing
Gene editing characterization with currently available tools does not always give precise relative proportions among the different types of gene edits present in an edited bulk of cells
New Workflow Predicts Drug Targets Against Sars-Cov-2 via Metabolic Changes in Infected Cells
COVID is one of the deadliest respiratory diseases and its emergence caught the pharmaceutical industry off guard While vaccines have been rapidly developed treatment options for infected people remain scarce and COVID poses a substantial global threat This study presents a novel workflow to predict robust druggable targets against emerging RNA vir...
A Systematic Evaluation of Deep Learning Methods for the Prediction of Drug Synergy in Cancer
One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance A common strategy to overcome resistance is the use of combination therapies However the space of possibilities is huge and efficient search strategies are required Machine Learning ML can be a useful tool for the discovery of novel
Spatial and Temporal Correlations in Human Cortex Are Inherently Linked and Predicted by Functional Hierarchy, Vigilance State as Well as Antiepileptic Drug Load
The ability of neural circuits to integrate information over time and across different cortical areas is believed an essential ingredient for information processing in the brain Temporal and spatial correlations in cortex dynamics have independently been shown to capture these integration properties in taskdependent ways A fundamental question rema...
scAmpi—A versatile pipeline for single-cell RNA-seq analysis from basics to clinics
Singlecell RNA sequencing scRNAseq has emerged as a powerful technique to decipher tissue composition at the singlecell level and to inform on disease mechanisms tumor heterogeneity and the state of the immune microenvironment Although multiple methods for the computational analysis of scRNAseq data exist their application in a clinical setting dem...
Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI)
Adverse event pathogenesis is often a complex process which compromises multiple events ranging from the molecular to the phenotypic level In toxicology Adverse Outcome Pathways AOPs aim to formalize this as temporal sequences of events in which event relationships should be supported by causal evidence according to the tailored BradfordHill criter...
Explainable detection of adverse drug reaction with imbalanced data distribution
Analysis of healthrelated texts can be used to detect adverse drug reactions ADR The greatest challenge for ADR detection lies in imbalanced data distributions where words related to ADR symptoms are often minority classes As a result trained models tend to converge to a point that strongly biases towards the majority class and then ignores the min...
Genome-wide cross-cancer analysis illustrates the critical role of bimodal miRNA in patient survival and drug responses to PI3K inhibitors
Heterogeneity of cancer means many tumorigenic genes are only aberrantly expressed in a subset of patients and thus follow a bimodal distribution having two modes of expression within a single population Traditional statistical techniques that compare sample means between cancer patients and healthy controls fail to detect bimodally expressed genes...
Mutational analysis of SARS-CoV-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape
SARSCoV variants with enhanced transmissibility represent a serious threat to global health Here we report machine learning models that can predict the impact of receptorbinding domain RBD mutations on receptor ACE affinity which is linked to infectivity and escape from human serum antibodies which is linked to viral neutralization
