A corpus of GA4GH phenopackets: Case-level phenotyping for genomic diagnostics and discovery
Not Available
Danis, Daniel↗
Engineering topics
Hansen, Peter
Publications and source records attributed to Hansen, Peter.
Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning
Motivation: Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many biomedical applications, partial knowledge exists about positive edges that represent relationships between pairs of entities, but little to no knowledge is available about negative edges that represent the explicit lack of a relationship between two nodes. For this reason, classification procedures are forced to assume that the vast majority of unlabeled edges are negative. Existing approaches to sampling negative edges for training and evaluating classifiers do so by uniformly sampling pairs of nodes. Results: We show here that this sampling strategy typically leads to sets of positive and negative examples with imbalanced node degree distributions. Using representative heterogeneous biomedical knowledge graph and random walk-based graph machine learning, we show that this strategy substantially impacts classification performance. If users of graph machine-learning models apply the models to prioritize examples that are drawn from approximately the same distribution as the positive examples are, then performance of models as estimated in the validation phase may be artificially inflated. We present a degree-aware node sampling approach that mitigates this effect and is simple to implement. Availability and implementation: Our code and data are publicly available at https://github.com/monarch-initiative/negativeExampleSelection.
59 BASIC BIOLOGICAL SCIENCES↗
An expectation–maximization framework for comprehensive prediction of isoform-specific functions
Advances in RNA sequencing technologies have achieved an unprecedented accuracy in the quantification of mRNA isoforms, but our knowledge of isoform-specific functions has lagged behind. There is a need to understand the functional consequences of differential splicing, which could be supported by the generation of accurate and comprehensive isoform-specific gene ontology annotations. We present isoform interpretation, a method that uses expectation–maximization to infer isoform-specific functions based on the relationship between sequence and functional isoform similarity. We predicted isoform-specific functional annotations for 85 617 isoforms of 17 900 protein-coding human genes spanning a range of 17 430 distinct gene ontology terms. Comparison with a gold-standard corpus of manually annotated human isoform functions showed that isoform interpretation significantly outperforms state-of-the-art competing methods. We provide experimental evidence that functionally related isoforms predicted by isoform interpretation show a higher degree of domain sharing and expression correlation than functionally related genes. We also show that isoform sequence similarity correlates better with inferred isoform function than with gene-level function.
59 BASIC BIOLOGICAL SCIENCES↗
Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer
Abstract Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.
59 BASIC BIOLOGICAL SCIENCES↗
Highlighting the usefulness of string theory by Peter Hansen
No abstract prepared.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Dispersion compensation for attosecond electron pulses
We propose a device to compensate for the dispersion of attosecond electron pulses. The device uses only static electric and magnetic fields and therefore does not require synchronization to the pulsed electron source. Analogous to the well-known optical dispersion compensator, an electron dispersion compensator separates paths by energy in space. Magnetic fields are used as the dispersing element, while a Wien filter is used for compensation of the electron arrival times. We analyze a device with a size of centimeters, which can be applied to ultrafast electron diffraction and microscopy, and fundamental studies.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗