AIMC Topic: Models, Biological

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Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC.

Journal of theoretical biology
The prediction of subcellular localization of an apoptosis protein is still a challenging task, and existing methods mainly based on protein primary sequences. In this study, we propose a novel model called MACC-PSSM by integrating Moran autocorrelat...

Extracting Biomedical Events with Parallel Multi-Pooling Convolutional Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Biomedical event extraction is important for medical research and disease prevention, which has attracted much attention in recent years. Traditionally, most of the state-of-the-art systems have been based on shallow machine learning methods, which r...

Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology.

Computers in biology and medicine
Hospital readmission is one of the critical metrics used for measuring the performance of hospitals. The HITECH Act imposes penalties when patients are readmitted to hospitals if they are diagnosed with one of the six conditions mentioned in the Act....

Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease.

PloS one
Prognostic modelling is important in clinical practice and epidemiology for patient management and research. Electronic health records (EHR) provide large quantities of data for such models, but conventional epidemiological approaches require signifi...

Mapping the Potential Global Codling Moth (Cydia pomonella L.) Distribution Based on a Machine Learning Method.

Scientific reports
The spread of invasive species may pose great threats to the economy and ecology of a region. The codling moth (Cydia pomonella L.) is one of the 100 worst invasive alien species in the world and is the most destructive apple pest. The economic losse...

Disease Specific Ontology of Adverse Events: Ontology extension and adaptation for Chronic Kidney Disease.

Computers in biology and medicine
BACKGROUND: Adverse Event (AE) ontology can be used to support interoperability and computer-assisted reasoning of AEs. Despite significant progress in developing biomedical ontologies, they are facing the obstacle of adoption partly because those on...

A deep convolutional neural network approach for astrocyte detection.

Scientific reports
Astrocytes are involved in various brain pathologies including trauma, stroke, neurodegenerative disorders such as Alzheimer's and Parkinson's diseases, or chronic pain. Determining cell density in a complex tissue environment in microscopy images an...

A Biologically Inspired Approach for Robot Depth Estimation.

Computational intelligence and neuroscience
Aimed at building autonomous service robots, reasoning, perception, and action should be properly integrated. In this paper, the depth cue has been analysed as an early stage given its importance for robotic tasks. So, from neuroscience findings, a h...

The Fuzziness of the Molecular World and Its Perspectives.

Molecules (Basel, Switzerland)
Scientists want to comprehend and control complex systems. Their success depends on the ability to face also the challenges of the corresponding computational complexity. A promising research line is artificial intelligence (AI). In AI, fuzzy logic p...

Artificial Fish Swarm Optimization Based Method to Identify Essential Proteins.

IEEE/ACM transactions on computational biology and bioinformatics
It is well known that essential proteins play an extremely important role in controlling cellular activities in living organisms. Identifying essential proteins from protein protein interaction (PPI) networks is conducive to the understanding of cell...