AIMC Topic: Algorithms

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Identifying drug-target interactions based on graph convolutional network and deep neural network.

Briefings in bioinformatics
Identification of new drug-target interactions (DTIs) is an important but a time-consuming and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers have sought to identify DTIs using computational approaches. Howev...

Application of deep learning methods in biological networks.

Briefings in bioinformatics
The increase in biological data and the formation of various biomolecule interaction databases enable us to obtain diverse biological networks. These biological networks provide a wealth of raw materials for further understanding of biological system...

Integrating multi-network topology for gene function prediction using deep neural networks.

Briefings in bioinformatics
MOTIVATION: The emergence of abundant biological networks, which benefit from the development of advanced high-throughput techniques, contributes to describing and modeling complex internal interactions among biological entities such as genes and pro...

GPS-Palm: a deep learning-based graphic presentation system for the prediction of S-palmitoylation sites in proteins.

Briefings in bioinformatics
As an important reversible lipid modification, S-palmitoylation mainly occurs at specific cysteine residues in proteins, participates in regulating various biological processes and is associated with human diseases. Besides experimental assays, compu...

Regression on imperfect class labels derived by unsupervised clustering.

Briefings in bioinformatics
Outcome regressed on class labels identified by unsupervised clustering is custom in many applications. However, it is common to ignore the misclassification of class labels caused by the learning algorithm, which potentially leads to serious bias of...

Biological applications of knowledge graph embedding models.

Briefings in bioinformatics
Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e. biological knowledge graphs, are then processed using graph exploratory approaches to perform different types of analytical and ...

A novel machine learning approach (svmSomatic) to distinguish somatic and germline mutations using next-generation sequencing data.

Zoological research
Somatic mutations are a large category of genetic variations, which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants (SNVs) could facilitate downstream analysis of tumorigenesis. Many computational methods have...

Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper.

Journal of the American Medical Informatics Association : JAMIA
The development and implementation of clinical decision support (CDS) that trains itself and adapts its algorithms based on new data-here referred to as Adaptive CDS-present unique challenges and considerations. Although Adaptive CDS represents an ex...

Trust and medical AI: the challenges we face and the expertise needed to overcome them.

Journal of the American Medical Informatics Association : JAMIA
Artificial intelligence (AI) is increasingly of tremendous interest in the medical field. How-ever, failures of medical AI could have serious consequences for both clinical outcomes and the patient experience. These consequences could erode public tr...

Building longitudinal medication dose data using medication information extracted from clinical notes in electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To develop an algorithm for building longitudinal medication dose datasets using information extracted from clinical notes in electronic health records (EHRs).