arXiv subjects

Mengman Wei

Publications and source records attributed to Mengman Wei.

5 recordsLinked to original sources

arXiv2026

DynoSys: A Dynamic Systems Framework for Multimodal Integration of Genetic, Environmental, and Neurobiological Signals

Understanding the development of adolescent behavioral and mental health outcomes requires integrating genetic predisposition, environmental exposures, and neurobiological processes over time. Here, we present a unified quantitative framework that models the human body as a dynamic system, where genetic factors form the foundational state, environmental exposures act as time-varying inputs, the brain might serve as a mediation processor, and behavioral phenotypes emerge as system outputs. Using longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study, we construct harmonized multi-domain representations across six phenotypes: externalizing behavior, internalizing behavior, and four substance use initiation outcomes (alcohol, nicotine, cannabis, and any substance use). We integrate polygenic risk scores (PRS), multi-domain environmental features, and multimodal neuroimaging representations derived through stability selection and dimensionality reduction. Our framework supports both continuous longitudinal modeling and survival-based event modeling through a unified data structure. We further develop interpretable domain-level representations using principal components, weighted risk scores, and cluster-based summaries. These representations enable downstream modeling using survival analysis, state-space models, and machine learning approaches. This work establishes a scalable and interpretable framework for studying how genetic and environmental factors interact over time to shape behavioral outcomes, providing a foundation for identifying modifiable risk factors and informing early intervention strategies.

q-bio.OT↗

arXiv2026

Time-Varying Environmental and Polygenic Predictors of Substance Use Initiation in Youth: A Survival and Causal Modeling Study in the ABCD Cohort

Early substance use initiation is associated with later substance use disorders, but the relative contributions of environmental, behavioral, and genetic factors during adolescence remain unclear. We analyzed 2,366 participants of European genetic ancestry from the Adolescent Brain Cognitive Development Study. Four outcomes were examined through four years of follow-up: initiation of alcohol, nicotine, cannabis, and any substance use. Time-varying Cox models screened predictors, followed by LASSO-selected multivariable Cox models adjusted for age, sex, ancestry principal components, and study site. Marginal structural models evaluated selected predictors. Environmental and behavioral factors were the strongest and most consistent predictors, including youth rule-breaking, impulsivity, parental alcohol-related problems, financial adversity, family context, phone use, negative life events, peer or romantic experiences, sleep, caffeine exposure, and parental monitoring. Polygenic risk scores for alcohol, cannabis, nicotine, and substance use disorder showed weaker and less consistent associations. Although some appeared in screening models, none remained robust after correction, multivariable selection, or causal modeling. In marginal structural models, youth rule-breaking was consistently associated with all four outcomes. Other outcome-specific signals included sensation seeking, lack of planning, parental alcohol-related problems, phone use, sleep duration, and parental monitoring. Early substance use initiation was more strongly associated with dynamic environmental, behavioral, family, and psychosocial factors than with common-variant genetic liability. These findings highlight modifiable developmental pathways and support prevention strategies focused on proximal behavioral and environmental risk contexts.

q-bio.QM↗

arXiv2026

A Joint Survival Modeling and Therapy Knowledge Graph Framework to Characterize Opioid Use Disorder Trajectories

Motivation: Opioid use disorder (OUD) often arises after prescription opioid exposure and follows transitions among onset, remission, and relapse. Linked EHR-survey resources such as the All of Us Research Program enable stage-specific risk modeling and connection to intervention options. Results: We built a multi-stage framework to model time-to-onset, time-to-remission, and time-to-relapse after remission using All of Us EHR and survey data. For each participant we derived longitudinal predictors from clinical conditions and survey concepts, including recent (1/3/12-month) event counts, cumulative exposures, and time since last event. We fit regularized Cox models for each transition and aggregated selection frequencies and hazard ratios to identify a compact set of high-confidence predictors. Pain, mental health, and polysubstance use contributed across stages: chronic pain syndromes, tobacco/nicotine dependence, anxiety and depressive disorders, and cannabis dependence prominently predicted onset and relapse, whereas tobacco dependence during remission and other remission-coded conditions were strongly associated with transition to remission. To support therapeutic prioritization, we constructed a therapy knowledge graph integrating genetic targets, biological pathways, and published evidence to map identified risk factors to candidate treatments in recent OUD studies and clinical guidelines.

q-bio.QM↗

arXiv2025

Hillclimb-Causal Inference: A Data-Driven Approach to Identify Causal Pathways Among Parental Behaviors, Genetic Risk, and Externalizing Behaviors in Children

Motivation: Externalizing behaviors in children, such as aggression, hyperactivity, and defiance, are influenced by complex interplays between genetic predispositions and environmental factors, particularly parental behaviors. Unraveling these intricate causal relationships can benefit from the use of robust data-driven methods. Methods: We developed a method called Hillclimb-Causal Inference, a causal discovery approach that integrates the Hill Climb Search algorithm with a customized Linear Gaussian Bayesian Information Criterion (BIC). This method was applied to data from the Adolescent Brain Cognitive Development (ABCD) Study, which included parental behavior assessments, children's genotypes, and externalizing behavior measures. We performed dimensionality reduction to address multicollinearity among parental behaviors and assessed children's genetic risk for externalizing disorders using polygenic risk scores (PRS), which were computed based on GWAS summary statistics from independent cohorts. Once the causal pathways were identified, we employed structural equation modeling (SEM) to quantify the relationships within the model. Results: We identified prominent causal pathways linking parental behaviors to children's externalizing outcomes. Parental alcohol misuse and broader behavioral issues exhibited notably stronger direct effects (0.33 and 0.20, respectively) compared to children's polygenic risk scores (0.07). Moreover, when considering both direct and indirect paths, parental substance misuse (alcohol, drug, and tobacco) collectively resulted in a total effect exceeding 1.1 on externalizing behaviors. Bootstrap and sensitivity analyses further validated the robustness of these findings.

q-bio.QM↗

arXiv2023

In Silico Tools in PROTACs design

PROTACs, as a highly promising new. therapeutic paradigm, have attracted widespread attention from the academic and pharmaceutical communities in recent years. To date, the design and validation of PROTACs molecule's druggability primarily rely on experimental approaches, making the development process of this kind of drug molecule time-consuming. Computer-aided tools for PROTACs design may offer a potential solution to expedite the design process and enhance its efficiency. This mini review briefly summarizes the in silico tools for PROTACs drug molecule design reported recently.

q-bio.BM↗