Functional relationship networks, which reveal the collaborative roles between genes, have significantly accelerated our understanding of gene functions and phenotypic relevance. However, establishing such networks for alternatively spliced isoforms remains a difficult, unaddressed problem due to the lack of systematic functional annotations at the isoform level, which renders most supervised learning methods difficult to be applied to isoforms. Here we describe a novel multiple instance learning-based probabilistic approach that integrates large-scale, heterogeneous genomic datasets, including RNA-seq, exon array, protein docking and pseudo-amino acid composition, for modeling a global functional relationship network at the isoform level in the mouse. Using this approach, we formulate a gene pair as a set of isoform pairs of potentially different properties. Through simulation and cross-validation studies, we showed the superior accuracy of our algorithm in revealing the isoform-level functional relationships. The local networks reveal functional diversity of the isoforms of the same gene, as demonstrated by both large-scale analyses and experimental and literature evidence for the disparate functions revealed for the isoforms of Ptbp1 and Anxa6 by our network. Our work can assist the understanding of the diversity of functions achieved by alternative splicing of a limited set of genes in mammalian genomes, and may shift the current gene-centered network prediction paradigm to the isoform level. Author summaryProteins carry out their functions through interacting with each other. Such interactions can be achieved through direct physical interactions, genetic interactions, or co-regulation. To summarize these interactions, researches have established functional relationship networks, in which each gene is represented as a node and the connections between the nodes represent how likely two genes work in the same biological process. Currently, these networks are established at the gene level only, while each gene, in mammalian systems, can be alternatively spliced into multiple isoforms that may have drastically different interaction partners. This information can be mined through integrating data that provide isoform-level information, such as RNA-seq and protein docking scores predicted from amino acid sequences. In this study, we developed a novel algorithm to integrate such data for predicting isoform-level functional relationship networks, which allows us to investigate the collaborative roles between genes at a high resolution.
Purpose: Traditionally, the assessment of X‐ray tube output and detector positioning accuracy of on‐board imagers (OBI) has been performed manually and subjectively with rulers and dosimeters, and typically takes hours to complete. In this study, we have designed a compact modular computational platform to automatically analyze OBI images acquired with in‐house designed phantoms as an efficient and robust surrogate. Methods: The platform was developed as an integrated and automated image analysis‐based platform using MATLAB for easy modification and maintenance. Given a set of images acquired with the in‐house designed phantoms, the X‐ray output accuracy was examined via cross‐validation of the uniqueness and integration minimization of important image quality assessment metrics, while machine geometric and positioning accuracy were validated by utilizing pattern‐recognition based image analysis techniques. Results: The platform input was a set of images of an in‐house designed phantom. The total processing time is about 1–2 minutes. Based on the data acquired from three Varian Truebeam machines over the course of 3 months, the designed test validation strategy achieved higher accuracy than traditional methods. The kVp output accuracy can be verified within +/−2 kVp, the exposure accuracy within 2%, and exposure linearity with a coefficient of variation (CV) of 0.1. Sub‐millimeter position accuracy was achieved for the lateral and longitudinal positioning tests, while vertical positioning accuracy within +/−2 mm was achieved. Conclusion: This new platform delivers to the radiotherapy field an automated, efficient, and stable image analysis‐based procedure, for the first time, acting as a surrogate for traditional tests for LINAC OBI systems. It has great potential to facilitate OBI quality assurance (QA) with the assistance of advanced image processing techniques. In addition, it provides flexible integration of additional tests for expediting other OBI quality assurance tests, such as 2D/3D image quality, making completely automated QA possible. Research Funding from Varian Medical Systems Inc. . Dr. Sasa Mutic receives compensation for providing patient safety training services from Varian Medical Systems, the sponsor of this study.
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