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Found 14 papers, 12 papers with code
An Open-Source Knowledge Graph Ecosystem for the Life Sciences
Translational research requires data at multiple scales of biological organization.
Democratising Knowledge Representation with BioCypher
Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods.
Experimental design for causal query estimation in partially observed biomolecular networks
Therefore, designing an experiment based on a well-chosen subset of network components can increase estimation accuracy, and reduce experimental and computational costs.
A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs
The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics.
An Open Challenge for Inductive Link Prediction on Knowledge Graphs
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 tasks that imply training on one graph and performing inference over a new graph with unseen entities.
Ranked #1 on
Inductive Link Prediction
on ILPC22-Small
ChemicalX: A Deep Learning Library for Drug Pair Scoring
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.
Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
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 key challenges such as target identification.
A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective
We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources.
Do-calculus enables estimation of causal effects in partially observed biomolecular pathways
This has limited the use of LVMs for causal inference in biomolecular pathways.
Leveraging Structured Biological Knowledge for Counterfactual Inference: a Case Study of Viral Pathogenesis
This manuscript proposes a general approach for querying a causal biological knowledge graph, and converting the qualitative result into a quantitative structural causal model that can learn from data to answer the question.
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs.
Ranked #1 on
Link Prediction
on WN18
(training time (s) metric)
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.
The role of metadata in reproducible computational research
Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results.
Digital Libraries Software Engineering
The KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and Transferability
Therefore, we present the KEEN Universe, an ecosystem for knowledge graph embeddings that we have developed with a strong focus on reproducibility and transferability.
