AIMC Topic: Machine Learning

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A Machine Learning Approach to Characterize the Modulation of the Hippocampal Rhythms Via Optogenetic Stimulation of the Medial Septum.

International journal of neural systems
The medial septum (MS) is a potential target for modulating hippocampal activity. However, given the multiple cell types involved, the changes in hippocampal neural activity induced by MS stimulation have not yet been fully characterized. We combined...

Development of machine learning algorithms for prediction of mortality in spinal epidural abscess.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: In-hospital and short-term mortality in patients with spinal epidural abscess (SEA) remains unacceptably high despite diagnostic and therapeutic advancements. Forecasting this potentially avoidable consequence at the time of admis...

Multi-perspective neural architecture for recommendation system.

Neural networks : the official journal of the International Neural Network Society
Currently, there starts a research trend to leverage neural architecture for recommendation systems. Though several deep recommender models are proposed, most methods are too simple to characterize users' complex preference. In this paper, for a fine...

HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.

Journal of biomedical informatics
BACKGROUND: In precision medicine, deep phenotyping is defined as the precise and comprehensive analysis of phenotypic abnormalities, aiming to acquire a better understanding of the natural history of a disease and its genotype-phenotype associations...

Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease.

Genome research
A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here, we introduce a unified population-genetic and machine-learning model, called inear llele-pecific election nferenc ...

Comparison of machine learning models for seizure prediction in hospitalized patients.

Annals of clinical and translational neurology
OBJECTIVE: To compare machine learning methods for predicting inpatient seizures risk and determine the feasibility of 1-h screening EEG to identify low-risk patients (<5% seizures risk in 48 h).

A Multimodal Authentication for Biometric Recognition System using Intelligent Hybrid Fusion Techniques.

Journal of medical systems
Biometric Recognition and Authentication is used in many applications for the secured identification of the persons. Several Researches has been carried out to strengthen the security algorithms through which the identification can be done in secured...

Trader as a new optimization algorithm predicts drug-target interactions efficiently.

Scientific reports
Several machine learning approaches have been proposed for predicting new benefits of the existing drugs. Although these methods have introduced new usage(s) of some medications, efficient methods can lead to more accurate predictions. To this end, w...

Block Forests: random forests for blocks of clinical and omics covariate data.

BMC bioinformatics
BACKGROUND: In the last years more and more multi-omics data are becoming available, that is, data featuring measurements of several types of omics data for each patient. Using multi-omics data as covariate data in outcome prediction is both promisin...