AIMC Topic: Algorithms

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Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research
Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill ...

SNAVA-A real-time multi-FPGA multi-model spiking neural network simulation architecture.

Neural networks : the official journal of the International Neural Network Society
Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, large-scale, multi-model SNN computation. This parallel architecture is implemented in ...

Z-Index Parameterization for Volumetric CT Image Reconstruction via 3-D Dictionary Learning.

IEEE transactions on medical imaging
Despite the rapid developments of X-ray cone-beam CT (CBCT), image noise still remains a major issue for the low dose CBCT. To suppress the noise effectively while retain the structures well for low dose CBCT image, in this paper, a sparse constraint...

Querying clinical data in HL7 RIM based relational model with morph-RDB.

Journal of biomedical semantics
BACKGROUND: Semantic interoperability is essential when carrying out post-genomic clinical trials where several institutions collaborate, since researchers and developers need to have an integrated view and access to heterogeneous data sources. One p...

Diabetic retinopathy screening using deep neural network.

Clinical & experimental ophthalmology
IMPORTANCE: There is a burgeoning interest in the use of deep neural network in diabetic retinal screening.

Artificial intelligence in cardiology.

Wiener klinische Wochenschrift
Decision-making is complex in modern medicine and should ideally be based on available data, structured knowledge and proper interpretation in the context of an individual patient. Automated algorithms, also termed artificial intelligence that are ab...

Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.

BMC bioinformatics
BACKGROUND: One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to le...

Using machine learning algorithms to identify genes essential for cell survival.

BMC bioinformatics
BACKGROUND: With the explosion of data comes a proportional opportunity to identify novel knowledge with the potential for application in targeted therapies. In spite of this huge amounts of data, the solutions to treating complex disease is elusive....

CNN-based ranking for biomedical entity normalization.

BMC bioinformatics
BACKGROUND: Most state-of-the-art biomedical entity normalization systems, such as rule-based systems, merely rely on morphological information of entity mentions, but rarely consider their semantic information. In this paper, we introduce a novel co...

ECG data compression using a neural network model based on multi-objective optimization.

PloS one
Electrocardiogram (ECG) data analysis is of great significance to the diagnosis of cardiovascular disease. ECG compression should be processed in real time, and the data should be based on lossless compression and have high predictability. In terms o...