A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval
By Jonas Pfeiffer, Samuel Broscheit, Rainer Gemulla, Mathias Göschl. 2018
12 citations recorded by the source corpus.
In this study, we investigate learning-to-rank and query refinement approaches for information retrieval in the pharmacogenomic domain. The goal is to improve the information retrieval process of biomedical curators, who manually build knowledge bases for personalized medicine. We study how to exploit the relationships between genes, variants, drugs, diseases and outcomes as features for document ranking and query refinement. For a supervised approach, we are faced with a small amount of annotated data and a large amount of unannotated data. Therefore, we explore ways to use a neural document auto-encoder in a semi-supervised approach. We show that a combination of established algorithms, feature-engineering and a neural auto-encoder model yield promising results in this setting.
Subfields: Artificial Intelligence, Molecular Biology, Information Retrieval & Search, Computational Biology & Bioinformatics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Manzil Zaheer
- Rajarshi Das
- Andrew McCallum
- Shehzaad Dhuliawala
- Hannaneh Hajishirzi
- William W. Cohen
- Ruslan Salakhutdinov
- Percy Liang
In Molecular Biology
In Information Retrieval & Search
- Manzil Zaheer
- Rajarshi Das
- Andrew McCallum
- Shehzaad Dhuliawala
- Hannaneh Hajishirzi
- Le Song
- Yuyu Zhang
- Arun Ramamurthy
In Computational Biology & Bioinformatics
Citations in the corpus
- Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering (2019, methodology): “Instead of using such automatic relevance feedback, Nogueira & Cho (2017) train a query reformulation model to maximize the recall of a IR system using RL and Pfeiffer et al. (2018) showed that query refinement is effective for IR in bio-medical domain.”