No abstract

Motivation Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of knowledge graphs is lacking. Results Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of knowledge graphs. Features include a simple, modular extract-transform-load (ETL) pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate knowledge graphs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. Availability and Implementation https://kghub.org Supplementary information Supplementary data are available at Bioinformatics online.

Objective: To lay the foundation for automated knowledge-based brachytherapy treatment planning using 3D dose estimations, we describe an optimization framework to convert brachytherapy dose distributions directly into dwell times (DTs). 
Approach: A dose rate kernel d ̇(r,θ,φ) was produced by exporting 3D dose for one dwell position from the treatment planning system and normalizing by DT. By translating and rotating this kernel to each dwell position, scaling by DT and summing over all dwell positions, dose was computed (Dcalc). We used a Python-coded COBYLA optimizer to iteratively determine the DTs that minimize the mean squared error between Dcalc and reference dose Dref, computed using voxels with Dref 80-120% of prescription. As validation of the optimization, we showed that the optimizer replicates clinical plans when Dref=clinical dose in 40 patients treated with tandem-and-ovoid (T&O) or tandem-and-ring (T&R) and 0-3 needles. Then we demonstrated automated planning in 10 T&O using Dref=dose predicted from a convolutional neural network developed in past work. Validation and automated plans were compared to clinical plans using mean absolute differences (MAD=1/N ∑n=1 nabs(x-x')) over all voxels (x=Dose, N=#voxels) and DTs (x=DT, N=#dwell positions), mean differences (MD) in organ D2cc and high-risk CTV D90 over all patients (where positive indicates higher clinical dose), and mean Dice similarity coefficients (DSC) for 100% isodose contours. 
Main Results: Validation plans agreed well with clinical plans (MADdose=1.1%, MADDT=4s or 0.8% of total plan time, D2cc MD=-0.2-0.2% and D90 MD=-0.6%, DSC=0.99). For automated plans, MADdose=6.5% and MADDT=10.3s (2.1%). The slightly higher clinical metrics in automated plans (D2cc MD=-3.8--1.3% and D90 MD=-5.1%) were due to higher neural network dose predictions. The overall shape of the automated dose distributions were similar to clinical doses (DSC=0.91). 
Significance: Automated planning with 3D dose predictions could provide significant time savings and standardize treatment planning across practitioners, regardless of experience.

No abstract