Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2024
Precision medicine models often perform better for populations of European ancestry due to the over-representation of this group in the genomic datasets and large-scale biobanks from which the models are constructed. As a result, prediction models ma...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2024
Classical machine learning and deep learning models for Computer-Aided Diagnosis (CAD) commonly focus on overall classification performance, treating misclassification errors (false negatives and false positives) equally during training. This uniform...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2024
Advancements in medical imaging and artificial intelligence (AI) have revolutionized the field of cardiac diagnostics, providing accurate and efficient tools for assessing cardiac function. AI diagnostics claims to improve upon the human-to-human var...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2024
The quickly-expanding nature of published medical literature makes it challenging for clinicians and researchers to keep up with and summarize recent, relevant findings in a timely manner. While several closed-source summarization tools based on larg...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2024
Artificial Intelligence (AI) models are substantially enhancing the capability to analyze complex and multi-dimensional datasets. Generative AI and deep learning models have demonstrated significant advancements in extracting knowledge from unstructu...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2023
Vertically partitioned data is distributed data in which information about a patient is distributed across multiple sites. In this study, we propose a novel algorithm (referred to as VdistCox) for the Cox proportional hazards model (Cox model), which...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2023
Accurate prediction of TCR binding affinity to a target antigen is important for development of immunotherapy strategies. Recent computational methods were built on various deep neural networks and used the evolutionary-based distance matrix BLOSUM t...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2023
Several biomedical applications contain multiple treatments from which we want to estimate the causal effect on a given outcome. Most existing Causal Inference methods, however, focus on single treatments. In this work, we propose a neural network th...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2023
Precision medicine requires a deep understanding of complex biomedical and healthcare data, which is being generated at exponential rates and increasingly made available through public biobanks, electronic medical record systems and biomedical databa...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2023
Identifying effective target-disease associations (TDAs) can alleviate the tremendous cost incurred by clinical failures of drug development. Although many machine learning models have been proposed to predict potential novel TDAs rapidly, their cred...