AIMC Topic: Deep Learning

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Utilizing Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women.

IEEE/ACM transactions on computational biology and bioinformatics
Genome-Wide Association Studies (GWAS) are used to identify statistically significant genetic variants in case-control studies. The main objective is to find single nucleotide polymorphisms (SNPs) that influence a particular phenotype (i.e., disease ...

Integrating Language Model and Reading Control Gate in BLSTM-CRF for Biomedical Named Entity Recognition.

IEEE/ACM transactions on computational biology and bioinformatics
Biomedical named entity recognition (Bio-NER) is an important preliminary step for many biomedical text mining tasks. The current mainstream methods for NER are based on the neural networks to avoid the complex hand-designed features derived from var...

An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA.

IEEE transactions on medical imaging
One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecul...

An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Automated Gleason grading is an important preliminary step for quantitative histopathological feature extraction. Different from the traditional task of classifying small pre-selected homogeneous regions, semantic segmentation provides pixel-wise Gle...

A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series.

Sensors (Basel, Switzerland)
Data-driven methods with multi-sensor time series data are the most promising approaches for monitoring machine health. Extracting fault-sensitive features from multi-sensor time series is a daunting task for both traditional data-driven methods and ...

Sign backpropagation: An on-chip learning algorithm for analog RRAM neuromorphic computing systems.

Neural networks : the official journal of the International Neural Network Society
Currently, powerful deep learning models usually require significant resources in the form of processors and memory, which leads to very high energy consumption. The emerging resistive random access memory (RRAM) has shown great potential for constru...

Probe Efficient Feature Representation of Gapped K-mer Frequency Vectors from Sequences Using Deep Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Gapped k-mers frequency vectors (gkm-fv) has been presented for extracting sequence features. Coupled with support vector machine (gkm-SVM), gkm-fvs have been used to achieve effective sequence-based predictions. However, the huge computation of a la...

Can Deep Learning Improve Genomic Prediction of Complex Human Traits?

Genetics
The genetic analysis of complex traits does not escape the current excitement around artificial intelligence, including a renewed interest in "deep learning" (DL) techniques such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNN...

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....

A hierarchical multimodal system for motion analysis in patients with epilepsy.

Epilepsy & behavior : E&B
During seizures, a myriad of clinical manifestations may occur. The analysis of these signs, known as seizure semiology, gives clues to the underlying cerebral networks involved. When patients with drug-resistant epilepsy are monitored to assess thei...