Biology subjects

Arazkhani, N.

Publications and source records attributed to Arazkhani, N..

3 recordsLinked to original sources

bioRxiv2025

GBM model refinement with literature curation, rule-based NLP, and LLMs

In this work, our goal was twofold: (1) improve an existing glioblastoma multiforme (GBM) executable mechanistic model and (2) evaluate the effectiveness traditional natural language processing (NLP) pipeline and the generative AI approach in the process of model improvement. We used a suite of graph metrics and tools for interaction filtering and classification to collect data and conduct the analysis. Our results suggest that a more comprehensive literature search is necessary to collect enough information through automated paper retrieval and interaction extraction. Additionally, we found that graph metrics present a promising approach for model refinement, as they can provide useful insights and guidance when selecting new information to be added to a mechanistic model.

systems biology↗

bioRxiv2024

Context-driven interaction retrieval and classification for modeling, curation, and reuse

Automated extraction of molecular interactions from scientific literature has outpaced the development of systematic methods for integrating this information with curated models and knowledge graphs. Here we present VIOLIN (Versatile Interaction Organizing to Leverage Information in Networks), a configurable, attribute-aware reconciliation framework that formally compares newly extracted interaction lists against structured baseline graphs. VIOLIN classifies each interaction as a corroboration, contradiction, flagged case, or extension, and supports configurable attribute inclusion strategies and mismatch semantics to adjust reconciliation strictness. We evaluate VIOLIN using interaction lists generated by two traditional NLP systems (REACH, INDRA) and two large language models (GPT-4.1, Llama 3) across multiple literature corpora and structurally distinct baseline graphs. Across all conditions, reconciliation outcomes were stable and interpretable, with extensions dominating and corroboration-contradiction balance reflecting intrinsic structural relationships between baseline graphs and extracted evidence. Sensitivity analyses demonstrate that attribute inclusion and classification scheme selection shift category boundaries predictably. Benchmark evaluations confirm high algorithmic correctness and alignment with expert curation. VIOLIN is publicly available as a Python package and through web-based interface (https://nmzlab.github.io/Tools-UI).

systems biology↗

bioRxiv2024

The BioRECIPE Knowledge Representation Format

AO_SCPLOWBSTRACTC_SCPLOWThe BioRECIPE (Biological system Representation for Evaluation, Curation, Interoperability, Preserving, and Execution) knowledge representation format was introduced to facilitate seamless human-machine interaction while creating, verifying, evaluating, curating, and expanding executable models of intra- and intercellular signaling. This format allows a human user to easily preview and modify any model component, while it is at the same time readable by machines and can be processed by a suite of model development and analysis tools. The BioRECIPE format is compatible with multiple representation formats, natural language processing tools, modeling tools, and databases that are used by the systems biology community. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/579694v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@76a130org.highwire.dtl.DTLVardef@505cb4org.highwire.dtl.DTLVardef@1f64e68org.highwire.dtl.DTLVardef@195bd03_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗