AIMC Topic: Deep Learning

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Current methods and challenges for deep learning in drug discovery.

Drug discovery today. Technologies
Driven by rapid advances in computer hardware and publicly available datasets over the past decade, deep learning has achieved tremendous success in the transformation of many computational disciplines. These novel technologies have had considerable ...

Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review.

Nutrients
The prevalence of childhood and adolescence overweight an obesity is raising at an alarming rate in many countries. This poses a serious threat to the current and near-future health systems, given the association of these conditions with different co...

Efficacy of deep learning methods for predicting under-five mortality in 34 low-income and middle-income countries.

BMJ open
OBJECTIVES: To explore the efficacy of machine learning (ML) techniques in predicting under-five mortality (U5M) in low-income and middle-income countries (LMICs) and to identify significant predictors of U5M.

Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Currently, it is challenging to detect acute ischemic stroke (AIS)-related changes on computed tomography (CT) images. Therefore, we aimed to develop and evaluate an automatic AIS detection system involving a two-stage deep ...

Deep learning method for localization and segmentation of abdominal CT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Computed Tomography (CT) imaging is widely used for studying body composition, i.e., the proportion of muscle and fat tissues with applications in areas such as nutrition or chemotherapy dose design. In particular, axial CT slices from the 3rd lumbar...

A robust and interpretable end-to-end deep learning model for cytometry data.

Proceedings of the National Academy of Sciences of the United States of America
Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Existing approaches in interpreting and using cytometry measurements include manual or automated ...

Comparative Analysis on Machine Learning and Deep Learning to Predict Post-Induction Hypotension.

Sensors (Basel, Switzerland)
Hypotensive events in the initial stage of anesthesia can cause serious complications in the patients after surgery, which could be fatal. In this study, we intended to predict hypotension after tracheal intubation using machine learning and deep lea...

Deep Learning Modeling of Androgen Receptor Responses to Prostate Cancer Therapies.

International journal of molecular sciences
Gain-of-function mutations in human androgen receptor (AR) are among the major causes of drug resistance in prostate cancer (PCa). Identifying mutations that cause resistant phenotype is of critical importance for guiding treatment protocols, as well...