143 citations · 615 across the 13 of their papers we have counts for
14 papers
An Open-Source Knowledge Graph Ecosystem for the Life Sciences
Tiffany J. Callahan, Ignacio J. Tripodi, Adrianne L. Stefanski +29
Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these…
Democratising Knowledge Representation with BioCypher
Sebastian Lobentanzer, Patrick Aloy, Jan Baumbach +26
Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate h…
Ontology Development Kit: a toolkit for building, maintaining, and standardising biomedical ontologies
Nicolas Matentzoglu, Damien Goutte-Gattat, Shawn Zheng Kai Tan +24
Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous quality control checking, and dep…
A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs
Charles Tapley Hoyt, Max Berrendorf, Mikhail Galkin +2
The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics. Here, we review existing rank-based m…
An Open Challenge for Inductive Link Prediction on Knowledge Graphs
Mikhail Galkin, Max Berrendorf, Charles Tapley Hoyt
An emerging trend in representation learning over knowledge graphs (KGs) moves beyond transductive link prediction tasks over a fixed set of known entities in favor of inductive ta…
ChemicalX: A Deep Learning Library for Drug Pair Scoring
Benedek Rozemberczki, Charles Tapley Hoyt, Anna Gogleva +9
In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task. The prim…
A Simple Standard for Sharing Ontological Mappings (SSSOM)
Nicolas Matentzoglu, James P. Balhoff, Susan M. Bello +41
Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representatio…
Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
Stephen Bonner, Ian P Barrett, Cheng Ye +4
Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE) models have recently begun to be explored in the context of drug discovery and have the potential to assist in…
A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective
Stephen Bonner, Ian P Barrett, Cheng Ye +5
Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of…
Leveraging Structured Biological Knowledge for Counterfactual Inference: a Case Study of Viral Pathogenesis
Jeremy Zucker, Kaushal Paneri, Sara Mohammad-Taheri +8
Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, c…
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt +4
Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs. While each of them…
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework
Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt +6
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult. In order to as…
The role of metadata in reproducible computational research
Jeremy Leipzig, Daniel Nüst, Charles Tapley Hoyt +3
Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In additio…
The KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and Transferability
Mehdi Ali, Hajira Jabeen, Charles Tapley Hoyt +1
There is an emerging trend of embedding knowledge graphs (KGs) in continuous vector spaces in order to use those for machine learning tasks. Recently, many knowledge graph embeddin…