Biology subjects

Eicher, T. D.

Publications and source records attributed to Eicher, T. D..

4 recordsLinked to original sources

bioRxiv2026

FERRET: Framework to Evaluate Robustness in Regulatory Networks Using Heterogeneous Cell Types

Techniques for evaluating gene regulatory network (GRN) inference methods typically focus on recovering small ground-truth networks or on benchmarking against simulated data. However, both approaches have important limitations and fail to capture the biological variability present in real datasets. FERRET is a framework for benchmarking single-cell GRN inference methods based on a simple biological assumption: independent estimates of the regulatory network from the same cellular state should resemble one another more closely than estimates from distinct cellular states. Rather than relying on incomplete or simulated ground truth, FERRET quantifies network robustness using two complementary metrics: Robustness Area Under the Curve (RAUC), an AUC-like measure of within-cell-type network similarity relative to between-cell-type similarity, and Monotonicity, which assesses the consistency of network similarity across edge-weight cutoffs. FERRET also supports biological validation through pathway enrichment analysis. We validate FERRET using experimentally derived ChIP-seq networks from B lymphocytes and fibroblasts as positive controls and randomly generated networks as negative controls, showing that biologically related networks receive high robustness scores whereas randomly generated networks receive scores consistent with chance. Finally, we apply FERRET to multiple GRN inference methods on real single-cell RNA-sequencing datasets to identify methods that produce the most robust, biologically informative regulatory networks. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/739795v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@da7e4eorg.highwire.dtl.DTLVardef@9a5425org.highwire.dtl.DTLVardef@a6e60org.highwire.dtl.DTLVardef@d466cd_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Genomic, Transcriptomic, and Regulomic Analyses Do Not Support Profound Autism as a Distinct Biological Category

The Lancet Commission on the Future of Care and Clinical Research in Autism proposed the construct of "profound autism" as a recognizable subtype of autism. Supporters argue that this classification is necessary to ensure that autistic persons with severe impairment receive appropriate research attention and policy support, whereas critics contend that the construct lacks scientific validity and may reflect social or political considerations more than biological distinction. To inform this debate, we evaluate whether the proposed "profound autism" category represents a distinct genetic phenotype using multiple molecular data types collected in a large cohort. Across genomic, transcriptomic, and regulatory analyses, we find no evidence supporting "profound autism" as a biologically distinct phenotypic group. Instead, differences emerge primarily in inferred gene regulatory networks distinguishing nonspeaking from speaking autistic children, suggesting potential regulatory mechanisms contributing to speech ability. These findings suggest that future research into severe impairment may be more productive if focused on specific traits--such as speech impairment--rather than attempting to define a distinct biological subtype within the multidimensional phenomenon of autism.

bioinformatics↗

bioRxiv2025

BLOBFISH: Bipartite Limited Subnetworks from Multiple Observations using Breadth-First Search with Constrained Hops

SummaryIn analyzing gene regulatory network models, a common question is how members of a particular set of genes are connected. For example, one might want to explore network relationship between a set of differentially expressed genes, a gene set previously reported in the literature, or elements of one or more pathways. BLOBFISH uses a breadth-first search algorithm adapted to bipartite graphs to identify a compact subnetwork connecting the members of a pre-specified set of genes, providing a regulatory context that can shed light on specific mechanisms involved in a phenotype and its development. We demonstrate the use of BLOBFISH to extract gene regulatory subnetworks reflecting tissue specificity using publicly available data from the Genotype Tissue Expression (GTEx) project.

bioinformatics↗

bioRxiv2024

Finding Salient Multi-Omic Interactomes Relevant to Multiple Biomedical Outcomes using Graph Ensemble Neural Networks

Although multi-omics integration relevant to patient outcome is typically characterized by an analyte interactome, current multi-omic integration methods either (1) model outcome without directly including associations between analytes, (2) model the interactome without directly evaluating the saliency of the model in the context of outcome, or (3) model outcome in a high-dimensional parameter space not suitable for small sample sizes (which are common in multi-omics studies). We introduce Graph Ensemble Neural Network (GENN), a methodology that learns the interactome most predictive of outcome in a low-dimensional parameter space built on complementary attributes for all possible analyte associations (metafeatures). We show that GENN is robust to noise in measurements using a theoretical model, outperforms the predictive performance of existing methods when evaluated on Tegafur drug response in NCI-60 cancer cell line data, and uncovers potentially novel multi-omic mechanisms driving total serum IgE levels in pediatric asthma and patient survival in glioblastomas.

bioinformatics↗