AIMC Topic: Databases, Factual

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Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval.

Database : the journal of biological databases and curation
The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, ma...

YummyData: providing high-quality open life science data.

Database : the journal of biological databases and curation
Many life science datasets are now available via Linked Data technologies, meaning that they are represented in a common format (the Resource Description Framework), and are accessible via standard APIs (SPARQL endpoints). While this is an important ...

Identification of errors in the IEDB using ontologies.

Database : the journal of biological databases and curation
The Immune Epitope Database (IEDB) is a free online resource that has manually curated over 18 500 references from the scientific literature. Our database presents experimental data relating to the recognition of immune epitopes by the adaptive immun...

FAIR principles and the IEDB: short-term improvements and a long-term vision of OBO-foundry mediated machine-actionable interoperability.

Database : the journal of biological databases and curation
The Immune Epitope Database (IEDB), at www.iedb.org, has the mission to make published experimental data relating to the recognition of immune epitopes easily available to the scientific public. By presenting curated data in a searchable database, we...

Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models.

Neuroinformatics
Pattern recognition models have been increasingly applied to neuroimaging data over the last two decades. These applications have ranged from cognitive neuroscience to clinical problems. A common limitation of these approaches is that they do not inc...

Deep Integrative Analysis for Survival Prediction.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Survival prediction is very important in medical treatment. However, recent leading research is challenged by two factors: 1) the datasets usually come with multi-modality; and 2) sample sizes are relatively small. To solve the above challenges, we d...

Improving the explainability of Random Forest classifier - user centered approach.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Machine Learning (ML) methods are now influencing major decisions about patient care, new medical methods, drug development and their use and importance are rapidly increasing in all areas. However, these ML methods are inherently complex and often d...

Data-driven advice for applying machine learning to bioinformatics problems.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classific...

Mapping Patient Trajectories using Longitudinal Extraction and Deep Learning in the MIMIC-III Critical Care Database.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Electronic Health Records (EHRs) contain a wealth of patient data useful to biomedical researchers. At present, both the extraction of data and methods for analyses are frequently designed to work with a single snapshot of a patient's record. Health ...