Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1604.05189

Understanding the Systems Biology of Pathogen Virulence Using Semantic Methodologies

David Rhee·Kevin Shieh·Julie Sullivan·Gos Micklem·Kami Kim·Aaron Golden
View original source ↗Read source document ↗Export metadata ↓

Abstract

Systems biology approaches to the integrative study of cells, organs and organisms offer the best means of understanding in a holistic manner the diversity of molecular assays that can be now be implemented in a high throughput manner. Such assays can sample the genome, epigenome, proteome, metabolome and microbiome contemporaneously, allowing us for the first time to perform a complete analysis of physiological activity. The central problem remains empowering the scientific community to actually implement such an integration, across seemingly diverse data types and measurements. One promising solution is to apply semantic techniques on a self-consistent and implicitly correct ontological representation of these data types. In this paper we describe how we have applied one such solution, based around the InterMine data warehouse platform which uses as its basis the Sequence Ontology, to facilitate a systems biology analysis of virulence in the apicomplexan pathogen $Toxoplasma~gondii$, a common parasite that infects up to half the worlds population, with acute pathogenic risks for immuno-compromised individuals or pregnant mothers. Our solution, which we named `toxoMine', has provided both a platform for our collaborators to perform such integrative analyses and also opportunities for such cyberinfrastructure to be further developed, particularly to take advantage of possible semantic similarities of value to knowledge discovery in the Omics enterprise. We discuss these opportunities in the context of further enhancing the capabilities of this powerful integrative platform.

Explore related subjects

q-bio.QM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

David Rhee, Kevin Shieh, Julie Sullivan, Gos Micklem, Kami Kim, Aaron Golden. 2016-04-18. Understanding the Systems Biology of Pathogen Virulence Using Semantic Methodologies. https://doi.org/10.1109/icsc.2016.33

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Triplication: an important component of the modern scientific method

A scientific-study protocol (defined) is designed to deliver results from which inductive inference is allowed. In the nineteenth century, triplication was introduced into the plant sciences and Fisher's p<0.05 rule (1925) incorporated into triple-result protocols designed to counter random/systematic errors which contribute to real-world variability. Aims were to: (1) classify replication protocols; (2) assess their prevalence in plant-science studies (published during one twenty-first-century year; for defined variable construct); (3) explore triplication rationale. Methods: a plant-sciences protocol-prevalence report was produced; experimental/associational-study proportions analyzed; and real-world-data proxies used to show confidence-interval-width patterns with increasing replicate number. Results: 25% plant-science studies analyzed showed triplication, including 11% triple-result protocols (including greater replicate numbers: 48%;17%, respectively). Theoretical considerations indicated that even if systematic errors predominate, (previously-known) square-root rules sometimes apply, contributing to triplication importance (exemplified by real-world-data proxies). Conclusions: Triplication was extensively applied in studies analysed. There are strong methodological reasons why triplication, rather than duplication/quadruplication, is the appropriate standard: triple-result protocols: (a) effectively reduce false positives to acceptable levels; (b) give qualitatively-different information (shape) from duplication; (c) have a large efficiency advantage (concerning confidence-interval widths) over quadruplication. Plant-science methodological standards remained high in the twenty-first century (at least in 2017), despite immense publication pressures.

q-bio.QM↗
arXiv2026

Representation learning of human cortical folding to reveal long lasting neurodevelopmental signatures

The human brain folds in utero, primarily during late gestation. Shortly after birth, cortical folding patterns are established and remain stable thereafter, making them promising early neurodevelopmental markers. Yet it is unclear whether the representations given by current neuroimaging foundation models capture cortical folding variability. Here, we introduce Champollion, a self-supervised learning framework that learns interpretable local representations of cortical folding from structural MRI. Optimized on representative folding-related tasks, Champollion accurately captures known folding patterns across cortical regions and external datasets. In a comprehensive benchmark, it consistently outperforms neuroimaging and general-purpose foundation models. Furthermore, Champollion reveals richer genetic associations than conventional morphometric descriptors and identifies localized folding signatures associated with incomplete hippocampal inversion, prematurity, and maternal smoking. These results establish cortical folding as a rich and largely untapped source of neurodevelopmental information, and Champollion provides a unified framework for discovering, localizing and interpreting long lasting cortical folding signatures.

q-bio.QM↗
arXiv2026

ADMET-EvO: a self-evolving scientific agent for sustained research across heterogeneous tasks

Scientific agents can move beyond automated model building by using accumulated evidence to revise both their questions and experimental strategies. The challenge is sustaining this adaptation across heterogeneous tasks without overfitting decisions to internal validation. Absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction provides a demanding setting across diverse assays, datasets and chemical domains. We therefore developed ADMET-EvO, an evidence-gated agent that formalizes endpoints, generates falsifiable hypotheses and tests interventions across data, feature and model axes. It carries supported, rejected and inconclusive outcomes forward to guide each new cycle. Across the 22-task Therapeutics Data Commons (TDC) ADMET benchmark, ADMET-EvO achieved the highest task-normalized score of 96.77. Evidence-guided selection reduced cumulative fitting time by 72.2% within a predefined non-inferiority margin. It also formalized 43 toxicity-related tasks and constructed endpoint-specific predictors. Together, these results show how ADMET-EvO can accumulate evidence, revise its strategy and expand its research scope over time.

q-bio.QM↗