AIMC Topic: Humans

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A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences.

Cell systems
Engineering gene and protein sequences with defined functional properties is a major goal of synthetic biology. Deep neural network models, together with gradient ascent-style optimization, show promise for sequence design. The generated sequences ca...

A machine learning-based prognostic predictor for stage III colon cancer.

Scientific reports
Limited biomarkers have been identified as prognostic predictors for stage III colon cancer. To combat this shortfall, we developed a computer-aided approach which combing convolutional neural network with machine classifier to predict the prognosis ...

Identifying the relative importance of predictors of survival in out of hospital cardiac arrest: a machine learning study.

Scandinavian journal of trauma, resuscitation and emergency medicine
INTRODUCTION: Studies examining the factors linked to survival after out of hospital cardiac arrest (OHCA) have either aimed to describe the characteristics and outcomes of OHCA in different parts of the world, or focused on certain factors and wheth...

Neonatal EEG sleep stage classification based on deep learning and HMM.

Journal of neural engineering
OBJECTIVE: Automatic sleep stage scoring is of great importance for investigating sleep architecture during infancy. In this work, we introduce a novel multichannel approach based on deep learning networks and hidden Markov models (HMM) to improve th...

Epigenome-based splicing prediction using a recurrent neural network.

PLoS computational biology
Alternative RNA splicing provides an important means to expand metazoan transcriptome diversity. Contrary to what was accepted previously, splicing is now thought to predominantly take place during transcription. Motivated by emerging data showing th...

Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults.

PloS one
BACKGROUND: Falls in the elderly are a major public health concern because of their high incidence, the involvement of many risk factors, the considerable post-fall morbidity and mortality, and the health-related and social costs. Given that many fal...

Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study.

PloS one
BACKGROUND: Machine learning (ML) is able to extract patterns and develop algorithms to construct data-driven models. We use ML models to gain insight into the relative importance of variables to predict obstructive coronary artery disease (CAD) usin...

Development of robust artificial neural networks for prediction of 5-year survival in bladder cancer.

Urologic oncology
PURPOSE: When exploring survival outcomes for patients with bladder cancer, most studies rely on conventional statistical methods such as proportional hazards models. Given the successful application of machine learning to handle big data in many dis...

Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI.

Clinical research in cardiology : official journal of the German Cardiac Society
BACKGROUND: Currently, patient selection in TAVI is based upon a multidisciplinary heart team assessment of patient comorbidities and surgical risk stratification. In an era of increasing need for precision medicine and quickly expanding TAVI indicat...

Multi-Institutional Assessment and Crowdsourcing Evaluation of Deep Learning for Automated Classification of Breast Density.

Journal of the American College of Radiology : JACR
OBJECTIVE: We developed deep learning algorithms to automatically assess BI-RADS breast density.