Biology subjects

Butler, R. R.

Publications and source records attributed to Butler, R. R..

4 recordsLinked to original sources

bioRxiv2026

Unlocking Sensitive Data with SPHERE in the Age of AI

Sensitive human data underpin discoveries across medicine, biology and the social sciences, yet privacy regulation often prevents sharing them with collaborators or artificial intelligence (AI) systems. We introduce SPHERE, a model-free method that makes sensitive datasets directly usable by AI and shareable for open science as a synthetic twin, while the original records never leave the local environment. Across 33 datasets spanning five scientific domains, SPHERE protects individual privacy against adversarial re-identification attacks while preserving the data's statistical structure: means, variances and correlations are reproduced exactly, effect size and P value in linear statistical analysis is numerically identical, nonlinear machine-learning utility is retained, and each twin is generated in seconds on a laptop. Frontier AI agents running on the twin reach the same scientific conclusions as on the original records. Analyses of the twin reproduce genome- and proteome-wide results at UK Biobank scale and recover the findings of landmark studies across three independent cohorts and consortia. The approach also extends to deep-learning embeddings across language, vision and time-series, with minimal utility loss. We make the Stanford Alzheimer's Disease Research Center cohort openly available for the first time, as a SPHERE twin spanning nine modalities that any registered researcher can analyze without an approval process. We release SPHERE with certification of each twin's privacy and fidelity, and an AI agent that autonomously executes research tasks on sensitive data without ever accessing it. Sensitive datasets that are currently closed to research could thus become routine inputs to open science and AI to enable key discoveries.

bioinformatics↗

bioRxiv2025

Uncovering Heterogeneous Effects via Localized Feature Selection

Identifying features that interact to trigger disease, while accounting for heterogeneity across diverse populations, is essential for the development of precision and targeted medicine. Despite the availability of vast and complex health-related datasets, most existing works focus on identifying disease-associated features at the population level or within a few subpopulations, often overlooking individual-level heterogeneity within these groups. To address this limitation, we propose a novel framework that utilizes localized test statistics to identify disease-associated features tailored to individual profiles. Our method leverages the recently developed knockoffs methodology to control the noise level of the selection set so that the results are replicable. Moreover, it allows for the discovery of hidden heterogeneous effects within the data, as demonstrated in an application to single-cell RNA sequencing data for Alzheimers disease. By aggregating localized feature selection results, our framework also enables powerful population-level feature selection. Our framework provides a powerful tool for exploratory studies of precision medicine, offering the potential to generate novel hypotheses for confirmatory biological experiments.

bioinformatics↗

bioRxiv2020

Transcriptomic signatures of sex-specific nicotine sensitization and imprinting of self-administration in rats inform GWAS findings on human addiction phenotypes

Rodents are frequently used to model drug addiction, yet their genetic relevance to human addictive behaviors especially the mounting genome-wide association study (GWAS) findings is poorly understood. Considering a possible gateway drug role of nicotine (NIC), we modeled NIC addiction, specifically NIC sensitization (SST) and self-administration (SA), in F1 progeny of inbred Envigo rats (F344/BN) and conducted integrative genomics analyses. We unexpectedly observed male-specific NIC SST and a parental effect of SA only present in paternal F344 crosses. Transcriptional profiling in the ventral tegmental area (VTA) and nucleus accumbens (NAc) core and shell further revealed sex and brain region-specific transcriptomic signatures of SST and SA. We found that genes associated with SST and SA were enriched for those related to synaptic processes, myelin sheath, and tobacco use disorder or chemdependency. Interestingly, SST-associated genes were often downregulated in male VTA but upregulated in female VTA, and strongly enriched for smoking GWAS risk variants, possibly explaining the male-specific SST. For SA, we found widespread region-specific allelic imbalance of expression (AIE), of which genes showing AIE bias towards paternal F344 alleles in NAc core were strongly enriched for SA-associated genes and for GWAS risk variants of smoking initiation, likely contributing to the parental effect of SA. The transcriptional signatures of sex-specific nicotine SST and SA suggest a mechanistic link between genes underlying these processes and human nicotine addiction, providing a resource for understanding the biology underlying the GWAS findings on human smoking and other addictive phenotypes.

neuroscience↗

bioRxiv2018

PhenotypeXpression: sub-classification of disease states using public gene expression data and literature

The success of personalized medicine relies on proper disease classification and subtyping. Differential gene expression among disease subtypes can have a significant impact on treatment effect. This complicates the role of clinicians seeking more tailored diagnoses in cases where granular disease subtypes are not well defined. PhenotypeXpression (PhenoX) is a tool for rapid disease subtyping using publicly available gene expression data and literature. PhenoX aggregates and clusters gene expression data to determine potential disease subtypes, and develops a phenotypic profile for each subtype using term co-occurrences in published literature. Although the availability of public gene expression data is limited, we are able to observe clearly defined subtypes for several conditions.

bioinformatics↗