English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The past decade has been witness to an explosive proliferation of data analytics modalities, all seeking to unravel insight into large-scale data sets. Machine learning and AI methodologies now occupy a central role in analyses of data sets that range in nature from genomics, “omics”, clinical, real-world evidence, and demographic data. Despite advances in data analytics/machine learning, access to complex population level clinical and related datasets, translating information into actionable guidance in human health and disease remains a challenge. Interrogative Biology, a systems biology/AI platform generates an unbiased, data-informed network for identifying targets (disease drivers) and biomarkers for disease interception at the point of transition to dysregulation, preceding clinical phenotype. The data topology is enabled by a systematic acquisition and interrogation of longitudinal bio-samples of clinically annotated human matrices (e.g. blood, urine, saliva, tissues) subjected to comprehensive multi-omic (genomic, proteomics, lipidomics and metabolomics) profiling over time. The molecular profiles are integrated with clinical health information using Bayesian artificial intelligence analytics, bAIcis, to generate causal network maps of overall health. Differentials between “health” and “disease” network maps identifies drivers (targets and biomarkers) of disease and are rapidly validated in orthogonal wet-lab disease specific perturbed model systems. Target information imputed into the bAIcis framework can define therapeutic strategies including identification of existing drugs and bio-actives for corrective response. Using a combination of clinic based sampling and dried blood spot analysis for longitudinal dynamic monitoring of markers of health-disease status provides opportunity for proactive clinical management and intervention for corrective response in advance of major deterioration of health status. Taken together, the approach herein allows for health surveillance based on in-depth biological profiling of alterations in the patient narrative to guide treatment modalities and strategies in a longitudinal and dynamic manner to identify, track, intercept, and arrest human disease.

neuro-oncology advances

CSAO-1. Interrogative Biology: Unraveling insights into causal disease drivers by use of a dynamic systems biology and Bayesian AI to identify the intersect of disease and healthy signatures