Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2020
Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence can provide information that can identify not only an individual, but ...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2020
The diagnosis of disease often requires analysis of a biopsy. Many diagnoses depend not only on the presence of certain features but on their location within the tissue. Recently, a number of deep learning diagnostic aids have been developed to class...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2020
The integration of multi-modal data, such as histopathological images and genomic data, is essential for understanding cancer heterogeneity and complexity for personalized treatments, as well as for enhancing survival predictions in cancer study. His...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2020
Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of embeddings for medical concepts learned using an extremely large colle...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2020
Computed tomographic (CT) is a fundamental imaging modality to generate cross-sectional views of internal anatomy in a living subject or interrogate material composition of an object, and it has been routinely used in clinical applications and nondes...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2020
Various deep learning models have been developed for different healthcare predictive tasks using Electronic Health Records and have shown promising performance. In these models, medical codes are often aggregated into visit representation without con...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2020
Mental health patients often undergo a variety of treatments before finding an effective one. Improved prediction of treatment response can shorten the duration of trials. A key challenge of applying predictive modeling to this problem is that often ...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2019
Autism spectrum disorder (ASD) is a heritable neurodevelopmental disorder affecting 1 in 59 children. While noncoding genetic variation has been shown to play a major role in many complex disorders, the contribution of these regions to ASD susceptibi...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2019
BACKGROUND: MicroRNAs (miRNAs) are small, non-coding RNA that regulate gene expression through post-transcriptional silencing. Differential expression observed in miRNAs, combined with advancements in deep learning (DL), have the potential to improve...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2019
Cancer is a complex collection of diseases that are to some degree unique to each patient. Precision oncology aims to identify the best drug treatment regime using molecular data on tumor samples. While omics-level data is becoming more widely availa...