Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing最新文献_第4页

Abstention and Threshold Identification for Uncertainty Management in Clinical Decision Tools: A Case Study using Human-In-The-Loop Pediatric Autism Classifiers. 临床决策工具中不确定性管理的弃权和阈值识别：使用人在环儿童自闭症分类器的案例研究。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2026-01-01 DOI: 10.1142/9789819824755_0009

Aiden Ko, Aaron Kline, Kaitlyn Dunlap, SaiMourya Surabhi, Parnian Azizian, Peter Y Washington, Dennis P Wall

{"title":"Abstention and Threshold Identification for Uncertainty Management in Clinical Decision Tools: A Case Study using Human-In-The-Loop Pediatric Autism Classifiers.","authors":"Aiden Ko, Aaron Kline, Kaitlyn Dunlap, SaiMourya Surabhi, Parnian Azizian, Peter Y Washington, Dennis P Wall","doi":"10.1142/9789819824755_0009","DOIUrl":"10.1142/9789819824755_0009","url":null,"abstract":"Uncertainty quantification remains an underdeveloped aspect of AI-based clinical decision tools. As AI systems become increasingly prevalent in healthcare, it is essential not only to measure uncertainty but also to manage it in ways that support clinical decision-making. In this study, we investigate abstention as a practical mechanism for managing uncertainty in diagnostic classifiers. To stress-test this approach, we deliberately evaluate abstention performance on a purposefully noisy dataset of pediatric autism video assessments comprising heterogeneous video sources and a diverse range of human raters. We apply abstention strategies to existing autism classifiers trained on diagnostic assessment data, comparing baseline performance to a range of thresholding configurations that trade off retained sample coverage against key clinical metrics. We compare performance gains from prioritizing sensitivity or specificity to targeting a balanced increase in Youden's J to demonstrate a wide variety of use cases that abstention can enable. This work demonstrates a concrete use case of introducing abstention into the output range of clinical decision models, enabling both uncertainty quantification and management in diagnostic classifiers.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"31 ","pages":"114-129"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13363080/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310238","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}

引用次数: 0

BioLM-NET: an interpretable deep learning model combining prior biological knowledge and contextual LLM gene embeddings on multi-omics data to predict disease. BioLM-NET：一个可解释的深度学习模型，结合了先前的生物学知识和上下文LLM基因嵌入的多组学数据，以预测疾病。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2026-01-01 DOI: 10.1142/9789819824755_0047

Jubair Ibn Malik Rifat, Thasina Tabashum, Md Marufi Rahman, Md Farhad Mokter, Sarthak Engala, Serdar Bozdag

{"title":"BioLM-NET: an interpretable deep learning model combining prior biological knowledge and contextual LLM gene embeddings on multi-omics data to predict disease.","authors":"Jubair Ibn Malik Rifat, Thasina Tabashum, Md Marufi Rahman, Md Farhad Mokter, Sarthak Engala, Serdar Bozdag","doi":"10.1142/9789819824755_0047","DOIUrl":"10.1142/9789819824755_0047","url":null,"abstract":"Biologically informed deep neural networks, which connect input layer to hidden layers based on genepathway relationship have gained popularity in recent years. However, most existing methods do not incorporate protein-protein interactions (PPI) and protein-DNA interactions (PDI) in their designs. In this study, we introduce BioLM-NET, a deep learning-based framework that fuses single cell or bulk gene expression data and DNA methylation data with prior biological knowledge including Protein- Protein Interactions (PPI), Protein-DNA Interactions (PDI). BioLM-NET also aggregates latent representation of omics signals at pathway-level through an attention-based pathway layer where a pretrained large language model (LLM) was incorporated to generate context-specific gene embeddings. We evaluated BioLM-NET on single cell colorectal cancer data from scTrioseq2 platform to predict primary and metastatic cancer cells, on TCGA-BRCA, TCGA-GBM, TCGA-COAD to predict cancer subtypes and ROSMAP data to predict Alzheimer's disease patient. Our results showed that BioLMNET outperformed baseline and state-of-the-art (SOTA) methods, P-NET and PASNet with statistical significance on scTrioseq2 data, TCGA-COAD and ROSMAP data and ties with SVM and Dense neural network on TCGA-BRCA data. Our ablation studies demonstrated the importance of incorporating PPI, PDI data and attention-based pathway layer. We also interpret our models and found out that our important input features are significantly enriched in GO terms and KEGG pathways and can serve as potential biomarkers or therapeutic targets for the corresponding disease.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"31 ","pages":"644-663"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12952666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310771","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}

引用次数: 0

WATCH-SS: Developing a Trustworthy and Explainable Modular Framework for Detecting Cognitive Impairment from Spontaneous Speech. 开发一个可信赖和可解释的模块框架来检测自发语言的认知障碍。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2026-01-01 DOI: 10.1142/9789819824755_0024

Sydney Pugh, Matthew Hill, Sy Hwang, Rachel Wu, Kuk Jang, Stacy Iannone, Karen O'Connor, Kyra O'Brien, Eric Eaton, Kevin Johnson

{"title":"WATCH-SS: Developing a Trustworthy and Explainable Modular Framework for Detecting Cognitive Impairment from Spontaneous Speech.","authors":"Sydney Pugh, Matthew Hill, Sy Hwang, Rachel Wu, Kuk Jang, Stacy Iannone, Karen O'Connor, Kyra O'Brien, Eric Eaton, Kevin Johnson","doi":"10.1142/9789819824755_0024","DOIUrl":"10.1142/9789819824755_0024","url":null,"abstract":"Early detection of cognitive impairment (CI) is critical for timely intervention in Alzheimer's disease and AD-related dementias. To address this, we propose the Warning Assessment and Alerting Tool for Cognitive Health from Spontaneous Speech (WATCH-SS), a modular and explainable three-stage framework for detecting CI from a patient's speech sample. The framework uses detectors for five linguistic and acoustic indicators of CI, aggregates their outputs into a set of clinically interpretable summary features, and uses a predictive model for CI classification. We consider multiple approaches to implementing these detectors that range from simple, computationally efficient methods suitable for real-time analysis to strong, resource-intensive methods, better for high accuracy offline analysis. On the DementiaBank ADReSS dataset, WATCH-SS achieved strong predictive performance (AUC = 80% on the test set). This work demonstrates that a modular, feature-based approach can achieve strong performance while providing a transparent diagnostic profile, representing a significant step towards a trustworthy and clinically-usable screening tool for primary care.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"31 ","pages":"338-353"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12952668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310690","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}

引用次数: 0

Using Large Language Models to Audit Model Healthcare Biases. 使用大型语言模型审计模型医疗保健偏差。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2026-01-01 DOI: 10.1142/9789819824755_0038

Zara N Ansari, Aaron Fanous, Jesutofunmi A Omiye, Ank Agarwal, Roxana Daneshjou

{"title":"Using Large Language Models to Audit Model Healthcare Biases.","authors":"Zara N Ansari, Aaron Fanous, Jesutofunmi A Omiye, Ank Agarwal, Roxana Daneshjou","doi":"10.1142/9789819824755_0038","DOIUrl":"10.1142/9789819824755_0038","url":null,"abstract":"Large language models (LLMs) can potentially mitigate pain points in healthcare tasks such as decision support, text summarization, and question-answering. However, LLMs exhibit bias related to race, gender identity, sexual orientation, and other demographics, posing a major concern. Although human review helps reduce bias, the sheer data volume renders thorough evaluation impractical and onerous at scale. This motivates the use of LLMs in auditing models for bias. This study uses the Stanford Healthcare red-teaming dataset, which contains prompts, outputs, and expert-level bias labels, to examine how model size and prompting techniques affect bias detection with GPT-3.5-turbo, GPT-4o, llama3.3, and o1-mini. Our results show that the best model for bias detection depends on the chosen metric. Smaller, cost-effective models like o1-mini outperformed GPT-4o in precision and F1 scores, with up to 53.11% higher precision and 10.32% higher F1. This suggests that smaller models may be preferable when precision or F1 is a priority. Additionally, self-critiquing capabilities in larger models do not significantly improve bias detection over smaller models (χ2, p = 0.597). Moreover, the use of prompting techniques, particularly Thread of Thought, significantly enhanced bias detection across all models, (χ2, p < 0.001). Our findings suggest that depending on the metric of concern for the auditor, smaller models can offer a costeffective alternative to larger models.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"31 ","pages":"524-537"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310719","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}

引用次数: 0

ReXVQA: A Large-scale Visual Question Answering Benchmark for Generalist Chest X-ray Understanding. ReXVQA：用于通才胸部x射线理解的大规模视觉问答基准。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2026-01-01 DOI: 10.1142/9789819824755_0018

Ankit Pal, Jung-Oh Lee, Xiaoman Zhang, Malaikannan Sankarasubbu, Seunghyeon Roh, Won Jung Kim, Meesun Lee, Pranav Rajpurkar

{"title":"ReXVQA: A Large-scale Visual Question Answering Benchmark for Generalist Chest X-ray Understanding.","authors":"Ankit Pal, Jung-Oh Lee, Xiaoman Zhang, Malaikannan Sankarasubbu, Seunghyeon Roh, Won Jung Kim, Meesun Lee, Pranav Rajpurkar","doi":"10.1142/9789819824755_0018","DOIUrl":"10.1142/9789819824755_0018","url":null,"abstract":"We present ReXVQA, the largest and most comprehensive benchmark for visual question answering (VQA) in chest radiology, comprising 694,841 questions paired with 160,000 chest X-rays studies across training, validation, and test sets. Unlike prior efforts that rely heavily on template based queries, ReXVQA introduces a diverse and clinically authentic task suite reflecting five core radiological reasoning skills: presence assessment, location analysis, negation detection, differential diagnosis, and geometric reasoning. We evaluate eight state-of-the-art multimodal large language models, including MedGemma-4Bit, Qwen2.5-VL, Janus-Pro-7B, and Eagle2-9B. The best-performing model (MedGemma) achieves 83.24% overall accuracy. To bridge the gap between AI performance and clinical expertise, we conducted a comprehensive human reader study involving 3 senior radiology residents on 200 randomly sampled cases. Our evaluation demonstrates that MedGemma achieved superior performance (83.84% accuracy) compared to human readers (best radiology resident: 77.27%), representing a significant milestone where AI performance exceeds human evaluation on chest X-ray interpretation. The reader study reveals distinct performance patterns between AI models and radiology residents, with strong inter-reader agreement among the human readers while showing more variable agreement patterns between human readers and AI models. ReXVQA establishes a new standard for evaluating generalist radiological AI systems, offering public leaderboards, fine-grained evaluation splits, structured explanations, and category-level breakdowns. This benchmark lays the foundation for next-generation AI systems capable of mimicking expert-level clinical reasoning beyond narrow pathology classification.Dataset: https://hf.co/datasets/rajpurkarlab/ReXVQA; Supplementary Material: https://github.com/monk1337/PSB-Conference-2025/blob/main/Appendix.pdf.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"31 ","pages":"251-264"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310919","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}

引用次数: 0

Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models. 量化临床护理中的意外：利用基础模型检测电子健康记录中的高信息量事件。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2026-01-01 DOI: 10.1142/9789819824755_0013

Michael C Burkhart, Bashar Ramadan, Luke Solo, William F Parker, Brett K Beaulieu-Jones

引用次数: 0

ED-Explain: Personalized Video Instructions for Patients Discharged from the Emergency Department. ED-Explain：为急诊科出院患者提供个性化视频指导。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2026-01-01 DOI: 10.1142/9789819824755_0020

Luyang Luo, Emma Chen, Xiaoman Zhang, Julian Nicolas Acosta, Boyang Tom Jin, Fatma Gunturkun, Christian Rose, Carl Preiksaitis, Brian Suffoletto, Pranav Rajpurkar, David A Kim

{"title":"ED-Explain: Personalized Video Instructions for Patients Discharged from the Emergency Department.","authors":"Luyang Luo, Emma Chen, Xiaoman Zhang, Julian Nicolas Acosta, Boyang Tom Jin, Fatma Gunturkun, Christian Rose, Carl Preiksaitis, Brian Suffoletto, Pranav Rajpurkar, David A Kim","doi":"10.1142/9789819824755_0020","DOIUrl":"10.1142/9789819824755_0020","url":null,"abstract":"This paper presents ED-Explain, an integrated, AI-driven system that transforms emergency department (ED) discharge instructions and electronic health records into personalized video presentations featuring a virtual healthcare provider. By leveraging multimodal ED data, large language models, and video generation, we aimed to produce accessible discharge instructions tailored to patients' ED visits. Four board-certified Emergency Medicine physicians reviewed 39 pairs of original and ED-Explain-produced discharge instructions. AI video summaries received significantly higher (p<0.001) average ratings (1-5) of completeness (4.1 vs. 3.1), correctness (3.9 vs. 3.5) and patient accessibility (3.4 vs. 2.9). Physicians expressed reservations about 13.3% of ED-Explain's discharge instructions for patient viewing, and only 4.6% of ED-Explain's instructions were found inappropriate for use with patients. Physician feedback suggests that AI-enhanced video discharge summaries have potential to improve communication of discharge information to ED patients, though patient-centered evaluation is needed. This work contributes to the growing field of AIassisted healthcare communication and offers insights into the potential for AI to improve physician-patient communication and patient self-efficacy.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"31 ","pages":"280-293"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12952664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310734","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}

引用次数: 0

Scoring Physician Risk Communication in Prostate Cancer Using Large Language Models. 使用大型语言模型对前列腺癌医生风险沟通进行评分。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2026-01-01 DOI: 10.1142/9789819824755_0006

Guillermo Lopez-Garcia, Dongfang Xu, Michael Luu, Renning Zheng, Timothy J Daskivich, Graciela Gonzalez-Hernandez

{"title":"Scoring Physician Risk Communication in Prostate Cancer Using Large Language Models.","authors":"Guillermo Lopez-Garcia, Dongfang Xu, Michael Luu, Renning Zheng, Timothy J Daskivich, Graciela Gonzalez-Hernandez","doi":"10.1142/9789819824755_0006","DOIUrl":"10.1142/9789819824755_0006","url":null,"abstract":"Effective risk communication is essential to shared decision-making in prostate cancer care. However, the quality of physician communication of key concepts varies widely in real-world consultations. Manual evaluation of communication is labor-intensive and not scalable. We present a structured, rubric-based framework that uses large language models (LLMs) to automatically score the quality of risk communication in prostate cancer consultations. Using transcripts from 20 clinical visits, we curated and annotated 487 physician-spoken sentences that referenced five key concepts for shared decision-making: cancer prognosis, life expectancy, and three treatment side effects (erectile dysfunction, incontinence, and irritative urinary symptoms). Each sentence was assigned a score from 0 to 5 based on the precision and patient-specificity of communicated risk, using a validated scoring rubric. We modeled this task as five multiclass classification problems and evaluated both finetuned transformer baselines and GPT-4o with rubric-based and chain-of-thought (CoT) prompting. Our best performing approach, which combined rubric-based CoT prompting with few-shot learning, achieved micro averaged F1 scores between 85.0 and 92.0 across domains, outperforming supervised baselines and matching inter-annotator agreement. These findings establish a scalable foundation for AI-driven evaluation of physician-patient communication in oncology and beyond.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"31 ","pages":"71-84"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13598975/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310901","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}

引用次数: 0

Barriers to Designing Inclusive Ecological Momentary Assessment and Wearable Data Collection Protocols for AI-Driven Substance Use Monitoring in Hawai'i. 在夏威夷为人工智能驱动的物质使用监测设计包容性生态瞬时评估和可穿戴数据收集协议的障碍。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2026-01-01 DOI: 10.1142/9789819824755_0041

Yinan Sun, Aditi Jaiswal, Ali Kargarandehkordi, Christopher Slade, Roberto M Benzo, Kristina T Phillips, Peter Washington

{"title":"Barriers to Designing Inclusive Ecological Momentary Assessment and Wearable Data Collection Protocols for AI-Driven Substance Use Monitoring in Hawai'i.","authors":"Yinan Sun, Aditi Jaiswal, Ali Kargarandehkordi, Christopher Slade, Roberto M Benzo, Kristina T Phillips, Peter Washington","doi":"10.1142/9789819824755_0041","DOIUrl":"10.1142/9789819824755_0041","url":null,"abstract":"Ecological momentary assessment (EMA) and wearable sensors offer unprecedented opportunities to capture the dynamics of substance use through real-time, high-resolution behavioral and physiological data. These data streams are increasingly used to train AI/ML models for digital phenotyping and predictive intervention, raising critical questions about fairness, bias, and inclusivity in model development. However, the adoption of these technologies, or the lack thereof, among diverse and historically marginalized groups raises questions and challenges of equity, cultural relevance, and participant trust. In this study, we conducted a four-week observational study with adults in Hawai.i where we combined continuous Fitbit monitoring and daily EMA surveys to document substance use patterns and cravings. Through semi-structured interviews and grounded theory analysis, we identified six primary barriers to study participation and adherence: (1) disruptions to daily routines, (2) physical and psychosocial discomfort associated with wearing the Fitbit device, (3) concerns about aesthetic compatibility and professional appearance, (4) phonerelated issues, (5) challenges related to substance use and cravings, and (6) socially sensitive contexts. We also highlight participant-identified facilitators, such as the value of participant-driven scheduling, motivational feedback, and contextually adaptive protocols. Drawing on these collective findings, we propose a set of design guidelines aimed at advancing the inclusivity, engagement, and fairness of wearable-based EMA research.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"31 ","pages":"566-579"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310754","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}

引用次数: 0

SeizureFormer: A Multi-Scale Transformer for Seizure Risk Forecasting from RNS-Derived Biomarkers. 癫痫发作前：一个多尺度变压器癫痫发作风险预测从rns衍生的生物标志物。

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Pub Date : 2026-01-01 DOI: 10.1142/9789819824755_0007

Tianning Feng, Juntong Ni, Wei Jin, Ezequiel Gleichgerrcht

引用次数: 0