intCC: An efficient weighted integrative consensus clustering of multimodal data
Pac Symp Biocomput. 2024;29:627-640.ABSTRACTHigh throughput profiling of multiomics data provides a valuable resource to better understand the complex human disease such as cancer and to potentially uncover new subtypes. Integrative clustering has emerged as a powerful unsupervised learning framework for subtype discovery. In this paper, we propose an efficient weighted integrative clustering called intCC by combining ensemble method, consensus clustering and kernel learning integrative clustering. We illustrate that intCC can accurately uncover the latent cluster structures via extensive simulation studies and a case stud...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Can Huang Pei Fen Kuan Source Type: research
Large language models (llms) and chatgpt for biomedicine
Pac Symp Biocomput. 2024;29:641-644.ABSTRACTLarge Language Models (LLMs) are a type of artificial intelligence that has been revolutionizing various fields, including biomedicine. They have the capability to process and analyze large amounts of data, understand natural language, and generate new content, making them highly desirable in many biomedical applications and beyond. In this workshop, we aim to introduce the attendees to an in-depth understanding of the rise of LLMs in biomedicine, and how they are being used to drive innovation and improve outcomes in the field, along with associated challenges and pitfalls.PMID:...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Cecilia Arighi Steven Brenner Zhiyong Lu Source Type: research
Practical Approaches to Enhancing Fairness, Social Responsibility and the Inclusion of Diverse Viewpoints in Biomedicine
Pac Symp Biocomput. 2024;29:645-649.ABSTRACTThe following sections are included:Workshop DescriptionLearning ObjectivesPresenter InformationAbout the Workshop OrganizersPresentationsSpeaker Presentations.PMID:38160313 (Source: Pacific Symposium on Biocomputing)
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Daphne O Martschenko Nicole Martinez-Martin Meghan Halley Source Type: research
Risk prediction: Methods, Challenges, and Opportunities
Pac Symp Biocomput. 2024;29:650-653.ABSTRACTThe following sections are included:Introduction to the workshopWorkshop Presenters.PMID:38160314 (Source: Pacific Symposium on Biocomputing)
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Ruowang Li Rui Duan Lifang He Jason H Moore Source Type: research
Statistical analysis of single-cell protein data
Pac Symp Biocomput. 2024;29:654-660.ABSTRACTImmune modulation is considered a hallmark of cancer initiation and progression, with immune cell density being consistently associated with clinical outcomes of individuals with cancer. Multiplex immunofluorescence (mIF) microscopy combined with automated image analysis is a novel and increasingly used technique that allows for the assessment and visualization of the tumor microenvironment (TME). Recently, application of this new technology to tissue microarrays (TMAs) or whole tissue sections from large cancer studies has been used to characterize different cell populations in ...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Brooke L Fridley Simon Vandekar Inna Chervoneva Julia Wrobel Siyuan Ma Source Type: research
Tools for assembling the cell: Towards the era of cell structural bioinformatics
Pac Symp Biocomput. 2024;29:661-665.ABSTRACTCells consist of large components, such as organelles, that recursively factor into smaller systems, such as condensates and protein complexes, forming a dynamic multi-scale structure of the cell. Recent technological innovations have paved the way for systematic interrogation of subcellular structures, yielding unprecedented insights into their roles and interactions. In this workshop, we discuss progress, challenges, and collaboration to marshal various computational approaches toward assembling an integrated structural map of the human cell.PMID:38160316 (Source: Pacific Sympo...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Mengzhou Hu Xikun Zhang Andrew Latham Andrej Ε ali Trey Ideker Emma Lundberg Source Type: research
A Conversational Agent for Early Detection of Neurotoxic Effects of Medications through Automated Intensive Observation
We present a fully automated AI-based system for intensive monitoring of cognitive symptoms of neurotoxicity that frequently appear as a result of immunotherapy of hematologic malignancies. Early manifestations of these symptoms are evident in the patient's speech in the form of mild aphasia and confusion and can be detected and effectively treated prior to onset of more serious and potentially life-threatening impairment. We have developed the Automated Neural Nursing Assistant (ANNA) system designed to conduct a brief cognitive assessment several times per day over the telephone for 5-14 days following infusion of the im...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Serguei Pakhomov Jacob Solinsky Martin Michalowski Veronika Bachanova Source Type: research
Leveraging 3D Echocardiograms to Evaluate AI Model Performance in Predicting Cardiac Function on Out-of-Distribution Data
The objective of this project is to leverage 3D echos to simulate realistic human variation of image acquisition and better understand the OOD performance of a previously validated AI model. In doing so, we develop tools for interpreting 3D echo data and quantifiably recreating common variation in image acquisition between sonographers. We also developed a technique for finding good standard 2D views in 3D echo volumes. We found the performance of the AI model we evaluated to be as expected when the view is good, but variations in acquisition position degraded AI model performance. Performance on far from ideal views was p...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Grant Duffy Kai Christensen David Ouyang Source Type: research
BrainSTEAM: A Practical Pipeline for Connectome-based fMRI Analysis towards Subject Classification
Pac Symp Biocomput. 2024;29:53-64.ABSTRACTFunctional brain networks represent dynamic and complex interactions among anatomical regions of interest (ROIs), providing crucial clinical insights for neural pattern discovery and disorder diagnosis. In recent years, graph neural networks (GNNs) have proven immense success and effectiveness in analyzing structured network data. However, due to the high complexity of data acquisition, resulting in limited training resources of neuroimaging data, GNNs, like all deep learning models, suffer from overfitting. Moreover, their capability to capture useful neural patterns for downstrea...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Alexis Li Yi Yang Hejie Cui Carl Yang Source Type: research
MaTiLDA: An Integrated Machine Learning and Topological Data Analysis Platform for Brain Network Dynamics
Pac Symp Biocomput. 2024;29:65-80.ABSTRACTTopological data analysis (TDA) combined with machine learning (ML) algorithms is a powerful approach for investigating complex brain interaction patterns in neurological disorders such as epilepsy. However, the use of ML algorithms and TDA for analysis of aberrant brain interactions requires substantial domain knowledge in computing as well as pure mathematics. To lower the threshold for clinical and computational neuroscience researchers to effectively use ML algorithms together with TDA to study neurological disorders, we introduce an integrated web platform called MaTiLDA. MaTi...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Katrina Prantzalos Dipak Upadhyaya Nassim Shafiabadi Guadalupe Fernandez-BacaVaca Nick Gurski Kenneth Yoshimoto Subhashini Sivagnanam Amitava Majumdar Satya S Sahoo Source Type: research
Zoish: A Novel Feature Selection Approach Leveraging Shapley Additive Values for Machine Learning Applications in Healthcare
Pac Symp Biocomput. 2024;29:81-95.ABSTRACTIn the intricate landscape of healthcare analytics, effective feature selection is a prerequisite for generating robust predictive models, especially given the common challenges of sample sizes and potential biases. Zoish uniquely addresses these issues by employing Shapley additive values-an idea rooted in cooperative game theory-to enable both transparent and automated feature selection. Unlike existing tools, Zoish is versatile, designed to seamlessly integrate with an array of machine learning libraries including scikit-learn, XGBoost, CatBoost, and imbalanced-learn.The distinc...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Hossein Javedani Sadaei Salvatore Loguercio Mahdi Shafiei Neyestanak Ali Torkamani Daria Prilutsky Source Type: research
SynTwin: A graph-based approach for predicting clinical outcomes using digital twins derived from synthetic patients
Pac Symp Biocomput. 2024;29:96-107.ABSTRACTThe concept of a digital twin came from the engineering, industrial, and manufacturing domains to create virtual objects or machines that could inform the design and development of real objects. This idea is appealing for precision medicine where digital twins of patients could help inform healthcare decisions. We have developed a methodology for generating and using digital twins for clinical outcome prediction. We introduce a new approach that combines synthetic data and network science to create digital twins (i.e. SynTwin) for precision medicine. First, our approach starts by ...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Jason H Moore Xi Li Jui-Hsuan Chang Nicholas P Tatonetti Dan Theodorescu Yong Chen Folkert W Asselbergs Mythreye Venkatesan Zhiping Paul Wang Source Type: research
Optimizing Computer-Aided Diagnosis with Cost-Aware Deep Learning Models
This study introduces a novel deep learning-based CAD system that incorporates a cost-sensitive parameter into the activation function. By applying our methodologies to two medical imaging datasets, our proposed study shows statistically significant increases of 3.84% and 5.4% in sensitivity while maintaining overall accuracy for Lung Image Database Consortium (LIDC) and Breast Cancer Histological Database (BreakHis), respectively. Our findings underscore the significance of integrating cost-sensitive parameters into future CAD systems to optimize performance and ultimately reduce costs and improve patient outcomes.PMID:38...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Charmi Patel Yiyang Wang Thiruvarangan Ramaraj Roselyne Tchoua Jacob Furst Daniela Raicu Source Type: research
VetLLM: Large Language Model for Predicting Diagnosis from Veterinary Notes
Pac Symp Biocomput. 2024;29:120-133.ABSTRACTLack of diagnosis coding is a barrier to leveraging veterinary notes for medical and public health research. Previous work is limited to develop specialized rule-based or customized supervised learning models to predict diagnosis coding, which is tedious and not easily transferable. In this work, we show that open-source large language models (LLMs) pretrained on general corpus can achieve reasonable performance in a zero-shot setting. Alpaca-7B can achieve a zero-shot F1 of 0.538 on CSU test data and 0.389 on PP test data, two standard benchmarks for coding from veterinary notes...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Yixing Jiang Jeremy A Irvin Andrew Y Ng James Zou Source Type: research
Impact of Measurement Noise on Genetic Association Studies of Cardiac Function
Pac Symp Biocomput. 2024;29:134-147.ABSTRACTRecent research has effectively used quantitative traits from imaging to boost the capabilities of genome-wide association studies (GWAS), providing further understanding of disease biology and various traits. However, it's important to note that phenotyping inherently carries measurement error and noise that could influence subsequent genetic analyses. The study focused on left ventricular ejection fraction (LVEF), a vital yet potentially inaccurate quantitative measurement, to investigate how imprecision in phenotype measurement affects genetic studies. Several methods of acqui...
Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Milos Vukadinovic Gauri Renjith Victoria Yuan Alan Kwan Susan C Cheng Debiao Li Shoa L Clarke David Ouyang Source Type: research
