Cheng, J. J. | Search bioRxiv

bioRxiv2019

GSDB: a database of 3D chromosome and genome structures reconstructed from Hi-C data

Advances in the study of chromosome conformation capture (3C) technologies, such as Hi-C technique - capable of capturing chromosomal interactions in a genome-wide scale - have led to the development of three-dimensional (3D) chromosome and genome structure reconstruction methods from Hi-C data. The 3D genome structure is important because it plays a role in a variety of important biological activities such as DNA replication, gene regulation, genome interaction, and gene expression. In recent years, numerous Hi-C datasets have been generated, and likewise, a number of genome structure construction algorithms have been developed. However, until now, there has been no freely available repository for 3D chromosome structures. In this work, we outline the construction of a novel Genome Structure Database (GSDB) to create a comprehensive repository that contains 3D structures for Hi-C datasets constructed by a variety of 3D structure reconstruction tools. GSDB contains over 50,000 structures constructed by 12 state-of-the-art chromosome and genome structure prediction methods for publicly used Hi-C datasets with varying resolution. The database is useful for the community to study the function of genome from a 3D perspective. GSDB is accessible at http://sysbio.rnet.missouri.edu/3dgenome/GSDB

bioinformatics↗

bioRxiv2018

Data mining and classification of polycystic ovaries in pelvic ultrasound reports

ObjectivesTo develop and evaluate the performance of a rules-based classifier and a gradient boosted tree model for automatic feature extraction and classification of polycystic ovary morphology (PCOM) in pelvic ultrasounds\n\nMethodsPelvic ultrasound reports from patients at Boston Medical Center between October 1, 2003 and December 12, 2016 were included for analysis, which resulted in 39,093 ultrasound reports from 25,535 unique women. Following the 2003 Rotterdam Consensus Criteria for polycystic ovary syndrome, 2000 randomly selected ultrasounds were manually labeled for PCOM status as present, absent, or unidentifiable. Half of the labeled data was used as a training set, and the other half was used as a test set.\n\nResultsOn the test set of 1000 random US reports, the accuracy of rules-based classifier (RBC) was 97.6% (95% CI: 96.5%, 98.5%) and 96.1% (94.7%, 97.2%) for the gradient boosted tree model (GBT). Both models were more adept at identifying non-PCOM ultrasounds than either unidentifiable or PCOM ultrasounds. The two classifiers estimated prevalence of PCOM within our populations ultrasounds to be about 44%, unidentifiable 32%, and PCOM 24%.\n\nConclusionsAlthough accuracy measured on the test set and inter-rater agreement between the two classifiers (Cohens Kappa = 0.988) was high, a major limitation of our approach is that it uses the ultrasound report text as a proxy and does not directly count follicles from the ultrasound images themselves.

epidemiology↗