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

Biobanks hold immense potential for genomic research, but fragmented data and incompatible tools slow progress. This workshop equipped participants with Nextflow, a powerful workflow language to streamline bioinformatic analyses across biobanks. We taught participants to write code in their preferred language and demonstrated how Nextflow handles the complexities, ensuring consistent, reproducible results across different platforms. This interactive session was ideal for beginner-to-intermediate researchers who want to (1) Leverage biobank data for genomic discoveries, (2) Build portable and scalable analysis pipelines, (3) Ensure reproducibility in their findings, (4) Gain hands-on experience through presentations, demonstrations, tutorials, and discussions with bioinformatics experts.

Malnutrition poses risks regarding cognitive, behavioral, and physical well-being. The aim of this study was to investigate the prevalent health issues associated with malnutrition by utilizing electronic health records (EHR) data. The IBM Watson Health, Explorys platform was used to access the EHR data. Two cohorts were created by two queries; patients with a history of malnutrition (n=5180) and patients without a history of malnutrition diagnosis (n= 413890). The log odds ratio and χ2 statistic were used to identify the statistically significant differences between these two cohorts. We found that there were 35 terms that were more common among the cohort with the malnutrition diagnosis. These terms were categorized under developmental anomalies, infectious agents, respiratory system issues, digestive system issues, pregnancy/prenatal problems, mental, behavioral, or neurodevelopmental disorders, diseases of the ear or mastoid process, diseases of the visual system, and chromosomal anomalies. The management of malnutrition in children is a complex problem that can be addressed with a multifactorial approach. Based on the key themes emerging from among the commonly prevalent terms identified in our study, infection prevention, education in appropriate nutritional solutions for digestive health issues, supportive services to address neurodevelopmental needs, and quality prenatal healthcare would constitute beneficial prevention efforts. Improving our understanding of malnutrition is necessary to develop new interventions for prevention and treatment.

Heart failure (HF) is one of the most common, complex, heterogeneous diseases in the world, with over 1-3% of the global population living with the condition. Progression of HF can be tracked via MRI measures of structural and functional changes to the heart, namely left ventricle (LV), including ejection fraction, mass, end-diastolic volume, and LV end-systolic volume. Moreover, while genome-wide association studies (GWAS) have been a useful tool to identify candidate variants involved in HF risk, they lack crucial tissue-specific and mechanistic information which can be gained from incorporating additional data modalities. This study addresses this gap by incorporating transcriptome-wide and proteome-wide association studies (TWAS and PWAS) to gain insights into genetically-regulated changes in gene expression and protein abundance in precursors to HF measured using MRI-derived cardiac measures as well as full-stage all-cause HF. We identified several gene and protein overlaps between LV ejection fraction and end-systolic volume measures. Many of the overlaps identified in MRI-derived measurements through TWAS and PWAS appear to be shared with all-cause HF. We implicate many putative pathways relevant in HF associated with these genes and proteins via gene-set enrichment and protein-protein interaction network approaches. The results of this study (1) highlight the benefit of using multi-omics to better understand genetics and (2) provide novel insights as to how changes in heart structure and function may relate to HF.

Genome-wide association studies (GWAS) are an important tool for the study of complex disease genetics. Decisions regarding the quality control (QC) procedures employed as part of a GWAS can have important implications on the results and their biological interpretation. Many GWAS have been conducted predominantly in cohorts of European ancestry, but many initiatives aim to increase the representation of diverse ancestries in genetic studies. The question of how these data should be combined and the consequences that genetic variation across ancestry groups might have on GWAS results warrants further investigation. In this study, we focus on several commonly used methods for combining genetic data across diverse ancestry groups and the impact these decisions have on the outcome of GWAS summary statistics. We ran GWAS on two binary phenotypes using ancestry-specific, multi-ancestry mega-analysis, and meta-analysis approaches. We found that while multi-ancestry mega-analysis and meta-analysis approaches can aid in identifying signals shared across ancestries, they can diminish the signal of ancestry-specific associations and modify their effect sizes. These results demonstrate the potential impact on downstream post-GWAS analyses and follow-up studies. Decisions regarding how the genetic data are combined has the potential to mask important findings that might serve individuals of ancestries that have been historically underrepresented in genetic studies. New methods that consider ancestry-specific variants in conjunction with the shared variants need to be developed.

This PSB 2025 session is focused on opportunities, challenges and solutions for translating Big Data Imaging Genomic findings toward powering decision making in personalized medicine and guiding individual clinical decisions. It combines many of the scientific directions that are of interest to PSB members including Big Data analyses, pattern recognition, machine learning and AI, electronic health records and others.

This study investigates the potential of using synthetic text to augment training data for Natural Language Processing (NLP) models, specifically within the context of peer support tools. We surveyed 22 participants-13 professional peer supporters and 9 AI-proficient individuals-tasked with distinguishing between AI-generated and human-written sentences. Using signal detection theory and confidence-based metrics, we evaluated the accuracy and confidence levels of both groups. The results show no significant differences in rater agreement between the two groups (p = 0.116), with overall classification accuracy falling below chance levels (mean accuracy = 43.10%, p < 0.001). Both groups exhibited a tendency to misclassify low-fidelity sentences as AI-generated, with peer supporters showing a significant bias (p = 0.007). Further analysis revealed a significant negative correlation between errors and confidence among AI-proficient raters (r = -0.429, p < 0.001), suggesting that as their confidence increased, their error rates decreased. Our findings support the feasibility of using synthetic text to mimic human communication, with important implications for improving the fidelity of peer support interventions through NLP model development.

Given the complexity and multifactorial nature of Alzheimer's disease, investigating potential drug-gene targets is imperative for developing effective therapies and advancing our understanding of the underlying mechanisms driving the disease. We present an explainable ML model that integrates the role and impact of gene interactions to drive the genomic variant feature selection. The model leverages both the Alzheimer's knowledge base and the Drug-Gene interaction database (DGIdb) to identify a list of biologically plausible novel gene-drug targets for further investigation. Model validation is performed on an ethnically diverse study sample obtained from the Alzheimer's Disease Sequencing Project (ADSP), a multi-ancestry multi-cohort genomic study. To mitigate population stratification and spurious associations from ML analysis, we implemented novel data curation methods. The study outcomes include a set of possible gene targets for further functional follow-up and drug repurposing.

Autism Spectrum Disorder (ASD) encompasses a range of developmental disabilities marked by differences in social functioning, cognition, and behavior. Both genetic and environmental factors are known to contribute to ASD, yet the exact etiological factors remain unclear. Developing integrative models to explore the effects of gene expression on behavioral and cognitive traits attributed to ASD can uncover environmental and genetic interactions. A notable aspect of ASD research is the sex-wise diagnostic disparity: males are diagnosed more frequently than females, which suggests potential sex-specific biological influences. Investigating neuronal microstructure, particularly axonal conduction velocity offers insights into the neural basis of ASD. Developing robust models that evaluate the vast multidimensional datasets generated from genetic and microstructural processing poses significant challenges. Traditional feature selection techniques have limitations; thus, this research aims to integrate principal component analysis (PCA) with supervised machine learning algorithms to navigate the complex data space. By leveraging various neuroimaging techniques and transcriptomics data analysis methods, this methodology builds on traditional implementations of PCA to better contextualize the complex genetic and phenotypic heterogeneity linked to sex differences in ASD and pave the way for tailored interventions.

Over the past decade, wearable technology has dramatically changed how patients manage chronic diseases. The widespread availability of on-body sensors, such as heart rate monitors and continuous glucose monitoring (CGM) sensors, has allowed patients to have real-time data about their health. Most of these data are readily available on patients' smartphone applications, where patients can view their current and retrospective data. For patients with diabetes, CGM has transformed how their disease is managed. Many sensor devices interface with smartphones to display charts, metrics, and alerts. However, these metrics and plots may be challenging for some patients to interpret. In this work, we explore how large language models (LLMs) can be used to answer questions about CGM data. We produce an open-source benchmark of time-series question-answering tasks for CGM data in diabetes management. We evaluate different LLM frameworks to provide a performance benchmark. Lastly, we highlight the need for more research on how to optimize LLM frameworks to best handle questions about wearable data. Our benchmark is publicly available for future use and development. While this benchmark is specifically designed for diabetes care, our model implementation and several of the statistical tasks can be extended to other wearable device domains.

Multimodal models have become increasingly important as they surpass single-modality approaches on diverse tasks ranging from question-answering to disease diagnosis. Despite the importance of multimodal learning, existing efforts focus on vision-language applications, where the number of modalities rarely exceeds four (images, text, audio, video). However, data in healthcare domain, may include many more modalities like X-rays, PET scans, MRIs, genetic screening, genomic data, and clinical notes, creating a need for both efficient and accurate data integration. Many state-of-the-art multimodal models rely on cross-attention or self-attention for effective data integration, which do not scale well for applications with more than two modalities. The complexity per layer of computing attention in either paradigm is, at best, quadratic with respect to the number of modalities, posing a computational bottleneck that impedes broad adoption. To address this, we propose a new attention mechanism, One-Versus-Others (OvO) attention, that scales linearly with the number of modalities, thus offering a significant reduction in computational complexity compared to existing multimodal attention methods. Using three clinical datasets with multiple diverse modalities, we show that our method decreases computation costs while maintaining or increasing performance compared to popular integration techniques. Across all clinical datasets, OvO reduced the number of required floating point operations (FLOPs) by at least 91.98%, demonstrating its significant impact on efficiency and enabling multi-modal predictions in healthcare.