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A hierarchical deep learning based approach for multi-functional enzyme classification.

Protein science : a publication of the Protein Society
Enzymes are critical proteins in every organism. They speed up essential chemical reactions, help fight diseases, and have a wide use in the pharmaceutical and manufacturing industries. Wet lab experiments to figure out an enzyme's function are time ...

Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1.

PloS one
OBJECTIVES: Antidepressants are first-line treatments for major depressive disorder (MDD), but 40-60% of patients will not respond, hence, predicting response would be a major clinical advance. Machine learning algorithms hold promise to predict trea...

COVID-19 deep classification network based on convolution and deconvolution local enhancement.

Computers in biology and medicine
Computer Tomography (CT) detection can effectively overcome the problems of traditional detection of Corona Virus Disease 2019 (COVID-19), such as lagging detection results and wrong diagnosis results, which lead to the increase of disease infection ...

Deep learning for classification of pediatric chest radiographs by WHO's standardized methodology.

PloS one
BACKGROUND: The World Health Organization (WHO)-defined radiological pneumonia is a preferred endpoint in pneumococcal vaccine efficacy and effectiveness studies in children. Automating the WHO methodology may support more widespread application of t...

Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study.

Scientific reports
There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospectively inc...

Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images.

Nature biomedical engineering
In machine learning for image-based medical diagnostics, supervised convolutional neural networks are typically trained with large and expertly annotated datasets obtained using high-resolution imaging systems. Moreover, the network's performance can...

COVID-19 detection using federated machine learning.

PloS one
The current COVID-19 pandemic threatens human life, health, and productivity. AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates usin...

Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs.

Nature communications
The large majority of variants identified by GWAS are non-coding, motivating detailed characterization of the function of non-coding variants. Experimental methods to assess variants' effect on gene expressions in native chromatin context via direct ...

Lung Lesion Localization of COVID-19 From Chest CT Image: A Novel Weakly Supervised Learning Method.

IEEE journal of biomedical and health informatics
Chest computed tomography (CT) image data is necessary for early diagnosis, treatment, and prognosis of Coronavirus Disease 2019 (COVID-19). Artificial intelligence has been tried to help clinicians in improving the diagnostic accuracy and working ef...