Topic · Health, Toxicology and Mutagenesis · Environmental Science
OpenAlex topic; a paper is listed when OpenAlex scores it at least 0.5 for this topic.
7 papers
Jiadong Yan, Jiexin Chen, Bin Wan, Zhen-Qi Liu, Liang He, Paule Joanne Toussaint, Alan Charles Evans, Sherif Karama
The paper identifies distinct brain maturation pathways linking environmental variation to cognitive development in adolescents using longitudinal data.
bioRxiv · Jun 19, 2026 · PreprintNeuroscience
Thoma Itoh, Yohei Kondo, Tomoya Nakayama, Ai Shinomiya, Kazuhiro Aoki, Takashi Yoshimura, Honda Naoki
The paper introduces a machine learning framework, ISI, to infer how organisms prioritize environmental signals from time-series data, applied to medaka fish gonadal development under natural conditions, revealing dynamic signal importance and gene expression correlations.
bioRxiv · Mar 31, 2025 · PreprintBioinformatics
Aditya Sriram, Rebecca Bohn, Kate F. Kernan, Joseph A. Carcillo, Soyeon Kim, Hyun Jung Park
DeepEXPOKE uses deep learning with knockoff features to distinguish genetic and non-genetic exposure risks in sepsis and coronary heart disease, identifying metabolites and asthma as risk factors.
medRxiv · Oct 15, 2024 · PreprintGenetic & Genomic Medicine
Bin Wang, Changxin Lan, Guohuan Zhang, Mengyuan Ren, Yanqiu Feng, Ning Gao, Weinan Lin, Bahabake Jiangtulu, Zhijian Liu, Xuqiang Shao, Shu Su, Yuting Wang, Han Wang, Fanrong Zhao, Bo Peng, , Mingliang Fang
ExposomeX is a platform that standardizes exposomic analysis to explore the 'Exposure-Biology-Disease' nexus through six modules and validated with multi-omics studies.
bioRxiv · Nov 23, 2022 · PreprintBioinformatics
Peng Gao, Xiaotao Shen, Xinyue Zhang, Chao Jiang, Sai Zhang, Xin Zhou, Sophia Miryam Schüssler‐Fiorenza Rose, Michael P. Snyder
This study integrates longitudinal personal exposome and multi-omics data to show dynamic interactions between environmental exposures and internal biomolecules, highlighting key agrochemicals and fungi.
Genome Research · Jun 1, 2022Peer-reviewedSystems Biology
Francesco Marabita, Tojo James, Anu Karhu, Heidi Virtanen, K Kettunen, Hans C. Stenlund, Fredrik Boulund, Cecilia Hellström, Maja Neiman, Robert Mills, Teemu Perheentupa, Hannele M. Laivuori, Pyry Helkkula, Myles Byrne, Ilkka Jokinen, , Olli Kallioniemi
The paper integrates multiomics and digital health data from 96 individuals over 16 months to identify independent molecular factors linked to health outcomes and lifestyle changes.
Cell Systems · Dec 1, 2021Peer-reviewedSystems Biology
The paper introduces a linear mixed model integrating genomic and exposomic data to improve phenotypic prediction for complex traits, showing increased variance explanation and prediction accuracy.
Scientific Reports · Nov 2, 2021Peer-reviewedGenetics