AIMC Topic: Deep Learning

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Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2023
Cell type identification from single-cell transcriptomic data is a common goal of single-cell RNA sequencing (scRNAseq) data analysis. Deep neural networks have been employed to identify cell types from scRNAseq data with high performance. However, i...
Deep Learning Neural Networks, Computer Supervised Machine Learning Transcriptome
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A Deep Learning Framework for Predicting Protein Functions With Co-Occurrence of GO Terms.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2023
The understanding of protein functions is critical to many biological problems such as the development of new drugs and new crops. To reduce the huge gap between the increase of protein sequences and annotations of protein functions, many methods hav...
Deep Learning Proteins Amino Acid Sequence Gene Ontology
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AttentionDTA: Drug-Target Binding Affinity Prediction by Sequence-Based Deep Learning With Attention Mechanism.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2023
The identification of drug-target relations (DTRs) is substantial in drug development. A large number of methods treat DTRs as drug-target interactions (DTIs), a binary classification problem. The main drawback of these methods are the lack of reliab...
Amino Acid Sequence Binding Sites Deep Learning Drug Development
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How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2023
The binding of DNA sequences to cell type-specific transcription factors is essential for regulating gene expression in all organisms. Many variants occurring in these binding regions play crucial roles in human disease by disrupting the cis-regulati...
Binding Sites Chromatin Immunoprecipitation Sequencing Virulence Transcription Factors Humans Nucleotides DNA Deep Learning
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PhenoBERT: A Combined Deep Learning Method for Automated Recognition of Human Phenotype Ontology.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2023
Automated recognition of Human Phenotype Ontology (HPO) terms from clinical texts is of significant interest to the field of clinical data mining. In this study, we develop a combined deep learning method named PhenoBERT for this purpose. PhenoBERT u...
Humans Phenotype Data Mining Deep Learning Family Benchmarking
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Attention Deficit Hyperactivity Disorder Classification Based on Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2023
Attention Deficit Hyperactivity Disorder (ADHD) is a type of mental health disorder that can be seen from children to adults and affects patients' normal life. Accurate diagnosis of ADHD as early as possible is very important for the treatment of pat...
Brain Mapping Attention Deficit Disorder with Hyperactivity Adult Deep Learning Child Magnetic Resonance Imaging Humans Brain
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A Multi-Attention Approach for Person Re-Identification Using Deep Learning.

Sensors (Basel, Switzerland) Apr 2, 2023
Person re-identification (Re-ID) is a method for identifying the same individual via several non-interfering cameras. Person Re-ID has been felicitously applied to an assortment of computer vision applications. Due to the emergence of deep learning a...
Phenotype Deep Learning Humans Algorithms
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A Deep-Learning-Computed Cancer Score for the Identification of Human Hepatocellular Carcinoma Area Based on a Six-Colour Multiplex Immunofluorescence Panel.

Cells Apr 2, 2023
Liver cancer is one of the most frequently diagnosed and fatal cancers worldwide, with hepatocellular carcinoma (HCC) being the most common primary liver cancer. Hundreds of studies involving thousands of patients have now been analysed across differ...
Color Fluorescent Antibody Technique Liver Neoplasms Carcinoma, Hepatocellular Humans Deep Learning
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A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging.

Diagnostic and interventional imaging Apr 2, 2023
PURPOSE: The purpose of this study was to develop and evaluate a deep learning model to detect bone marrow edema (BME) in sacroiliac joints and predict the MRI Assessment of SpondyloArthritis International Society (ASAS) definition of active sacroili...
Female Adult Humans Prospective Studies Spondylarthritis Edema Sacroiliac Joint Middle Aged Bone Marrow Diseases Magnetic Resonance Imaging Deep Learning Sacroiliitis Back Pain
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Report on the AAPM deep-learning spectral CT Grand Challenge.

Medical physics Apr 1, 2023
BACKGROUND: This Special Report summarizes the 2022 AAPM Grand Challenge on Deep-Learning spectral Computed Tomography (DL-spectral CT) image reconstruction.
Algorithms Tomography, X-Ray Computed Humans Phantoms, Imaging Deep Learning X-Rays Image Processing, Computer-Assisted
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