Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing最新文献

{"title":"Session Introduction: Precision Medicine: Multi-modal and multi-scale methods to promote mechanistic understanding of disease.","authors":"Hannah Carter, Steven Brenner, Yana Bromberg","doi":"10.1142/9789819807024_0027","DOIUrl":"10.1142/9789819807024_0027","url":null,"abstract":"<p><p>Precision medicine focuses on developing treatments and preventative strategies tailored to an individual's genomic profile, lifestyle, and environmental context. The Precision Medicine sessions at the Pacific Symposium on Biocomputing (PSB) have consistently spotlighted progress in this domain. Our 2025 manuscript collection features algorithmic innovations that integrate data across scales and diverse data modalities, presenting novel techniques to derive clinically relevant insights from molecular datasets. These studies highlight recent advances in technology and analytics and their application toward realizing the potential of precision medicine to enhance human health outcomes and extend lifespan.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"377-381"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142818858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Privacy-Preserving Cancer Classification with Convolutional Neural Networks.","authors":"Aurora A F Colombo, Luca Colombo, Alessandro Falcetta, Manuel Roveri","doi":"10.1142/9789819807024_0040","DOIUrl":"10.1142/9789819807024_0040","url":null,"abstract":"<p><p>Precision medicine significantly enhances patients prognosis, offering personalized treatments. Particularly for metastatic cancer, incorporating primary tumor location into the diagnostic process greatly improves survival rates. However, traditional methods rely on human expertise, requiring substantial time and financial resources. To address this challenge, Machine Learning (ML) and Deep Learning (DL) have proven particularly effective. Yet, their application to medical data, especially genomic data, must consider and encompass privacy due to the highly sensitive nature of data. In this paper, we propose OGHE, a convolutional neural network-based approach for privacy-preserving cancer classification designed to exploit spatial patterns in genomic data, while maintaining confidentiality by means of Homomorphic Encryption (HE). This encryption scheme allows the processing directly on encrypted data, guaranteeing its confidentiality during the entire computation. The design of OGHE is specific for privacy-preserving applications, taking into account HE limitations from the outset, and introducing an efficient packing mechanism to minimize the computational overhead introduced by HE. Additionally, OGHE relies on a novel feature selection method, VarScout, designed to extract the most significant features through clustering and occurrence analysis, while preserving inherent spatial patterns. Coupled with VarScout, OGHE has been compared with existing privacy-preserving solutions for encrypted cancer classification on the iDash 2020 dataset, demonstrating their effectiveness in providing accurate privacy-preserving cancer classification, and reducing latency thanks to our packing mechanism. The code is released to the scientific community.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"565-579"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uterine fibroids show evidence of shared genetic architecture with blood pressure traits.","authors":"Alexis T Akerele, Jacqueline A Piekos, Jeewoo Kim, Nikhil K Khankari, Jacklyn N Hellwege, Todd L Edwards, Digna R Velez Edwards","doi":"10.1142/9789819807024_0021","DOIUrl":"10.1142/9789819807024_0021","url":null,"abstract":"<p><p>Uterine leiomyomata (fibroids, UFs) are common, benign tumors in females, having an estimated prevalence of up to 80%. They are fibrous masses growing within the myometrium leading to chronic symptoms like dysmenorrhea, abnormal uterine bleeding, anemia, severe pelvic pain, and infertility. Hypertension (HTN) is a common risk factor for UFs, though less prevalent in premenopausal individuals. While observational studies have indicated strong associations between UFs and HTN, the biological mechanisms linking the two conditions remain unclear. Understanding the relationship between HTN and UFs is crucial because UFs and HTN lead to substantial comorbidities adversely impacting female health. Identifying the common underlying biological mechanisms can improve treatment strategies for both conditions. To clarify the genetic and causal relationships between UFs and BP, we conducted a bidirectional, two-sample Mendelian randomization (MR) analysis and evaluated the genetic correlations across BP traits and UFs. We used data from a multi-ancestry genome-wide association study (GWAS) meta-analysis of UFs (44,205 cases and 356,552 controls), and data from a cross-ancestry GWAS meta-analysis of BP phenotypes (diastolic BP [DBP], systolic BP [SBP], and pulse pressure [PP], N=447,758). We evaluated genetic correlation of BP phenotypes and UFs with linkage disequilibrium score regression (LDSC). LDSC results indicated a positive genetic correlation between DBP and UFs (Rg=0.132, p<5.0x10-5), and SBP and UFs (Rg=0.063, p<2.5x10-2). MR using UFs as the exposure and BP traits as outcomes indicated a relationship where UFs increases DBP (odds ratio [OR]=1.20, p<2.7x10-3). Having BP traits as exposures and UFs as the outcome showed that DBP and SBP increase risk for UFs (OR =1.04, p<2.2x10-3; OR=1.00, p<4.0x10-2; respectively). Our results provide evidence of shared genetic architecture and pleiotropy between HTN and UFs, suggesting common biological pathways driving their etiologies. Based on these findings, DBP appears to be a stronger risk factor for UFs compared to SBP and PP.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"281-295"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11649017/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Drug Impact on Laboratory Test Results in Hospital Settings.","authors":"Victorine P Muse, Amalie D Haue, Cristina L Rodríguez, Alejandro A Orozco, Jorge H Biel, Søren Brunak","doi":"10.1142/9789819807024_0026","DOIUrl":"10.1142/9789819807024_0026","url":null,"abstract":"<p><p>Patients experiencing adverse drug events (ADE) from polypharmaceutical regimens present a huge challenge to modern healthcare. While computational efforts may reduce the incidence of these ADEs, current strategies are typically non-generalizable for standard healthcare systems. To address this, we carried out a retrospective study aimed at developing a statistical approach to detect and quantify potential ADEs. The data foundation comprised of almost 2 million patients from two health regions in Denmark and their drug and laboratory data during the years 2011 to 2016. We developed a series of multistate Cox models to compute hazard ratios for changes in laboratory test results before and after drug exposure. By linking the results to data from a drug-drug interaction database, we found that the models showed potential for applications for medical safety agencies and improved efficiency for drug approval pipelines.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"360-376"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"All Together Now: Data Work to Advance Privacy, Science, and Health in the Age of Synthetic Data.","authors":"Lindsay Fernández-Rhodes, Jennifer K Wagner","doi":"10.1142/9789819807024_0049","DOIUrl":"10.1142/9789819807024_0049","url":null,"abstract":"<p><p>There is a disconnect between data practices in biomedicine and public understanding of those data practices, and this disconnect is expanding rapidly every day (with the emergence of synthetic data and digital twins and more widely adopted Artificial Intelligence (AI)/Machine Learning tools). Transparency alone is insufficient to bridge this gap. Concurrently, there is an increasingly complex landscape of laws, regulations, and institutional/ programmatic policies to navigate when engaged in biocomputing and digital health research, which makes it increasingly difficult for those wanting to \"get it right\" or \"do the right thing.\" Mandatory data protection obligations vary widely, sometimes focused on the type of data (and nuanced definition and scope parameters), the actor/entity involved, or the residency of the data subjects. Additional challenges come from attempts to celebrate biocomputing discoveries and digital health innovations, which frequently transform fair and accurate communications into exaggerated hype (e.g., to secure financial investment in future projects or lead to more favorable tenure and promotion decisions). Trust in scientists and scientific expertise can be quickly eroded if, for example, synthetic data is perceived by the public as \"fake data\" or if digital twins are perceived as \"imaginary\" patients. Researchers appear increasingly aware of the scientific and moral imperative to strengthen their work and facilitate its sustainability through increased diversity and community engagement. Moreover, there is a growing appreciation for the \"data work\" necessary to have scientific data become meaningful, actionable information, knowledge, and wisdom-not only for scientists but also for the individuals from whom those data were derived or to whom those data relate. Equity in the process of biocomputing and equity in the distribution of benefits and burdens of biocomputing both demand ongoing development, implementation, and refinement of embedded Ethical, Legal and Social Implications (ELSI) research practices. This workshop is intended to nurture interdisciplinary discussion of these issues and to highlight the skills and competencies all too often considered \"soft skills\" peripheral to other skills prioritized in traditional training and professional development programs. Data scientists attending this workshop will become better equipped to embed ELSI practices into their research.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"690-695"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Species Modeling Identifies Gene Signatures in Type 2 Diabetes Mouse Models Predictive of Inflammatory and Estrogen Signaling Pathways Associated with Alzheimer's Disease Outcomes in Humans.","authors":"Brendan K Ball, Elizabeth A Proctor, Douglas K Brubaker","doi":"10.1142/9789819807024_0031","DOIUrl":"10.1142/9789819807024_0031","url":null,"abstract":"<p><p>Alzheimer's disease (AD), the predominant form of dementia, is influenced by several risk factors, including type 2 diabetes (T2D), a metabolic disorder characterized by the dysregulation of blood sugar levels. Despite mouse and human studies reporting this connection between T2D and AD, the mechanism by which T2D contributes to AD pathobiology is not well understood. A challenge in understanding mechanistic links between these conditions is that evidence between mouse and human experimental models must be synthesized, but translating between these systems is difficult due to evolutionary distance, physiological differences, and human heterogeneity. To address this, we employed a computational framework called translatable components regression (TransComp-R) to overcome discrepancies between pre-clinical and clinical studies using omics data. Here, we developed a novel extension of TransComp-R for multi-disease modeling to analyze transcriptomic data from brain samples of mouse models of AD, T2D, and simultaneous occurrence of both disease (ADxT2D) and postmortem human brain data to identify enriched pathways predictive of human AD status. Our TransComp-R model identified inflammatory and estrogen signaling pathways encoded by mouse principal components derived from models of T2D and ADxT2D, but not AD alone, predicted with human AD outcomes. The same mouse PCs predictive of human AD outcomes were able to capture sex-dependent differences in human AD biology, including significant effects unique to female patients, despite the TransComp-R being derived from data from only male mice. We demonstrated that our approach identifies biological pathways of interest at the intersection of the complex etiologies of AD and T2D which may guide future studies into pathogenesis and therapeutic development for patients with T2D-associated AD.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"426-440"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Granularity of the Illnesses-Related Changes in Regional Homogeneity in Major Depressive Disorder using the UKBB Data.","authors":"Yewen Huang, Syed Ibrar Hussain, Demetrio Labate, Robert Azencott, Paul Thompson, Bhim Adhikari, Peter Kochunov","doi":"10.1142/9789819807024_0046","DOIUrl":"10.1142/9789819807024_0046","url":null,"abstract":"<p><p>Illness related brain effects of neuropsychiatric disorders are not regionally uniform, with some regions showing large pathological effects while others are relatively spared. Presently, Big Data meta-analytic studies tabulate these effects using structural and/or functional brain atlases that are based on the anatomical boundaries, landmarks and connectivity patterns in healthy brains. These patterns are then translated to individual level predictors using approaches such as Regional Vulnerability Index (RVI), which quantifies the agreement between individual brain patterns and the canonical pattern found in the illness. However, the atlases from healthy brains are unlikely to align with deficit pattern expressed in specific disorders such as Major Depressive Disorder (MDD), thus reducing the statistical power for individualized predictions. Here, we evaluated a novel approach, where disorder specific templates are constructed using the Kullback-Leibler (KL) distance to balance granularity, signal-to-noise ratio and the contrast between regional effect sizes to maximize translatability of the population-wide illness pattern at the level of the individual. We used regional homogeneity (ReHo) maps extracted from resting state functional MRI for N = 2, 289 MDD sample (mean age ± s.d.: 63.2 ± 7.2 years) and N = 6104 control subjects (mean age ± s.d.: 62.9 ± 7.2 years) who were free of MDD and any other mental condition. The cortical effects of MDD were analyzed on the 3D spherical surfaces representing cerebral hemispheres. KL-distance was used to organize the cortical surface into 28 regions of interest based on effect sizes, connectivity and signal-to-noise ratio. The RVI values calculated using this novel approach showed significantly higher effect size of the illness than these calculated using standard Desikan brain atlas.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"647-663"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining.","authors":"Gokul Srinivasan, Matthew J Davis, Matthew R LeBoeuf, Michael Fatemi, Zarif L Azher, Yunrui Lu, Alos B Diallo, Marietta K Saldias Montivero, Fred W Kolling, Laurent Perrard, Lucas A Salas, Brock C Christensen, Thomas J Palys, Margaret R Karagas, Scott M Palisoul, Gregory J Tsongalis, Louis J Vaickus, Sarah M Preum, Joshua J Levy","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The advent of spatial transcriptomics technologies has heralded a renaissance in research to advance our understanding of the spatial cellular and transcriptional heterogeneity within tissues. Spatial transcriptomics allows investigation of the interplay between cells, molecular pathways, and the surrounding tissue architecture and can help elucidate developmental trajectories, disease pathogenesis, and various niches in the tumor microenvironment. Photoaging is the histological and molecular skin damage resulting from chronic/acute sun exposure and is a major risk factor for skin cancer. Spatial transcriptomics technologies hold promise for improving the reliability of evaluating photoaging and developing new therapeutics. Challenges to current methods include limited focus on dermal elastosis variations and reliance on self-reported measures, which can introduce subjectivity and inconsistency. Spatial transcriptomics offers an opportunity to assess photoaging objectively and reproducibly in studies of carcinogenesis and discern the effectiveness of therapies that intervene in photoaging and preventing cancer. Evaluation of distinct histological architectures using highly-multiplexed spatial technologies can identify specific cell lineages that have been understudied due to their location beyond the depth of UV penetration. However, the cost and interpatient variability using state-of-the-art assays such as the 10x Genomics Spatial Transcriptomics assays limits the scope and scale of large-scale molecular epidemiologic studies. Here, we investigate the inference of spatial transcriptomics information from routine hematoxylin and eosin-stained (H&E) tissue slides. We employed the Visium CytAssist spatial transcriptomics assay to analyze over 18,000 genes at a 50-micron resolution for four patients from a cohort of 261 skin specimens collected adjacent to surgical resection sites for basal cell and squamous cell keratinocyte tumors. The spatial transcriptomics data was co-registered with 40x resolution whole slide imaging (WSI) information. We developed machine learning models that achieved a macro-averaged median AUC and F1 score of 0.80 and 0.61 and Spearman coefficient of 0.60 in inferring transcriptomic profiles across the slides, and accurately captured biological pathways across various tissue architectures.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"29 ","pages":"477-491"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10813837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139075197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EVALUATING THE RELATIONSHIPS BETWEEN GENETIC ANCESTRY AND THE CLINICAL PHENOME.","authors":"Jacqueline A Piekos, Jeewoo Kim, Jacob M Keaton, Jacklyn N Hellwege, Todd L Edwards, Digna R Velez Edwards","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>There is a desire in research to move away from the concept of race as a clinical factor because it is a societal construct used as an imprecise proxy for geographic ancestry. In this study, we leverage the biobank from Vanderbilt University Medical Center, BioVU, to investigate relationships between genetic ancestry proportion and the clinical phenome. For all samples in BioVU, we calculated six ancestry proportions based on 1000 Genomes references: eastern African (EAFR), western African (WAFR), northern European (NEUR), southern European (SEUR), eastern Asian (EAS), and southern Asian (SAS). From PheWAS, we found phecode categories significantly enriched neoplasms for EAFR, WAFR, and SEUR, and pregnancy complication in SEUR, NEUR, SAS, and EAS (p < 0.003). We then selected phenotypes hypertension (HTN) and atrial fibrillation (AFib) to further investigate the relationships between these phenotypes and EAFR, WAFR, SEUR, and NEUR using logistic regression modeling and non-linear restricted cubic spline modeling (RCS). For EAS and SAS, we chose renal failure (RF) for further modeling. The relationships between HTN and AFib and the ancestries EAFR, WAFR, and SEUR were best fit by the linear model (beta p < 1x10-4 for all) while the relationships with NEUR were best fit with RCS (HTN ANOVA p = 0.001, AFib ANOVA p < 1x10-4). For RF, the relationship with SAS was best fit with a linear model (beta p < 1x10-4) while RCS model was a better fit for EAS (ANOVA p < 1x10-4). In this study, we identify relationships between genetic ancestry and phenotypes that are best fit with non-linear modeling techniques. The assumption of linearity for regression modeling is integral for proper fitting of a model and there is no knowing a priori to modeling if the relationship is truly linear.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"29 ","pages":"389-403"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10802858/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139075245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subject Harmonization of Digital Biomarkers: Improved Detection of Mild Cognitive Impairment from Language Markers.","authors":"Bao Hoang, Yijiang Pang, Hiroko H Dodge, Jiayu Zhou","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Mild cognitive impairment (MCI) represents the early stage of dementia including Alzheimer's disease (AD) and is a crucial stage for therapeutic interventions and treatment. Early detection of MCI offers opportunities for early intervention and significantly benefits cohort enrichment for clinical trials. Imaging and in vivo markers in plasma and cerebrospinal fluid biomarkers have high detection performance, yet their prohibitive costs and intrusiveness demand more affordable and accessible alternatives. The recent advances in digital biomarkers, especially language markers, have shown great potential, where variables informative to MCI are derived from linguistic and/or speech and later used for predictive modeling. A major challenge in modeling language markers comes from the variability of how each person speaks. As the cohort size for language studies is usually small due to extensive data collection efforts, the variability among persons makes language markers hard to generalize to unseen subjects. In this paper, we propose a novel subject harmonization tool to address the issue of distributional differences in language markers across subjects, thus enhancing the generalization performance of machine learning models. Our empirical results show that machine learning models built on our harmonized features have improved prediction performance on unseen data. The source code and experiment scripts are available at https://github.com/illidanlab/subject_harmonization.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"29 ","pages":"187-200"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11017207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139075250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}