AIMC Topic: Deep Learning

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SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.

International journal of molecular sciences
Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently show...

Deep learning of mammary gland distribution for architectural distortion detection in digital breast tomosynthesis.

Physics in medicine and biology
Computer aided detection (CADe) for breast lesions can provide an important reference for radiologists in breast cancer screening. Architectural distortion (AD) is a type of breast lesion that is difficult to detect. A majority of CADe methods focus ...

Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: Deep convolutional neural networks (CNN) for single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been used to improve the diagnostic accuracy of coronary artery disease (CAD). This study was to design an...

Understanding global changes in fine-mode aerosols during 2008-2017 using statistical methods and deep learning approach.

Environment international
Despite their extremely small size, fine-mode aerosols have significant impacts on the environment, climate, and human health. However, current understandings of global changes in fine-mode aerosols are limited. In this study, we employed newly devel...

A deep learning-integrated micro-CT image analysis pipeline for quantifying rice lodging resistance-related traits.

Plant communications
Lodging is a common problem in rice, reducing its yield and mechanical harvesting efficiency. Rice architecture is a key aspect of its domestication and a major factor that limits its high productivity. The ideal rice culm structure, including major_...

Designing deep learning studies in cancer diagnostics.

Nature reviews. Cancer
The number of publications on deep learning for cancer diagnostics is rapidly increasing, and systems are frequently claimed to perform comparable with or better than clinicians. However, few systems have yet demonstrated real-world medical utility. ...

Bringing Structural Implications and Deep Learning-Based Drug Identification for Mutants.

Journal of chemical information and modeling
Colorectal cancer is considered one of the leading causes of death that is linked with the Kirsten Rat Sarcoma () harboring codons 13 and 61 mutations. The objective for this study is to search for clinically important codon 61 mutations and analyze ...

DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection.

Communications biology
In general, animal behavior can be described as the neuronal-driven sequence of reoccurring postures through time. Most of the available current technologies focus on offline pose estimation with high spatiotemporal resolution. However, to correlate ...

Verifying explainability of a deep learning tissue classifier trained on RNA-seq data.

Scientific reports
For complex machine learning (ML) algorithms to gain widespread acceptance in decision making, we must be able to identify the features driving the predictions. Explainability models allow transparency of ML algorithms, however their reliability with...

Deep convolutional neural networks to predict cardiovascular risk from computed tomography.

Nature communications
Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment....