AIMC Topic: Deep Learning

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Development and validation of a deep learning model for morphological assessment of myeloproliferative neoplasms using clinical data and digital pathology.

British journal of haematology
The subjectivity of morphological assessment and the overlapping pathological features of different subtypes of myeloproliferative neoplasms (MPNs) make accurate diagnosis challenging. To improve the pathological assessment of MPNs, we developed a di...

Utilizing deep learning-based causal inference to explore vancomycin's impact on continuous kidney replacement therapy necessity in blood culture-positive intensive care unit patients.

Microbiology spectrum
Patients with positive blood cultures in the intensive care unit (ICU) are at high risk for septic acute kidney injury requiring continuous kidney replacement therapy (CKRT), especially when treated with vancomycin. This study developed a machine lea...

Classification of acute myeloid leukemia by pre-trained deep neural networks: A comparison with different activation functions.

Medical engineering & physics
Acute Myeloid Leukemia(AML) is a rapidly progressing cancer affecting blood and bone marrow, marked by the swift proliferation of abnormal myeloid cells. Effective treatment requires precise classification of AML subtypes. Conventional classification...

Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution.

PloS one
Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memo...

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics
Currently, biomedical event extraction has received considerable attention in various fields, including natural language processing, bioinformatics, and computational biomedicine. This has led to the emergence of numerous machine learning and deep le...

LEC-Codec: Learning-Based Genome Data Compression.

IEEE/ACM transactions on computational biology and bioinformatics
In this paper, we propose a Learning-based gEnome Codec (LEC), which is designed for high efficiency and enhanced flexibility. The LEC integrates several advanced technologies, including Group of Bases (GoB) compression, multi-stride coding and bidir...

Enhancing Spatial Domain Identification in Spatially Resolved Transcriptomics Using Graph Convolutional Networks With Adaptively Feature-Spatial Balance and Contrastive Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Recent advancements in spatially transcriptomics (ST) technologies have enabled the comprehensive measurement of gene expression profiles while preserving the spatial information of cells. Combining gene expression profiles and spatial information ha...

A Protein-Context Enhanced Master Slave Framework for Zero-Shot Drug Target Interaction Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Drug Target Interaction (DTI) prediction plays a crucial role in in-silico drug discovery, especially for deep learning (DL) models. Along this line, existing methods usually first extract features from drugs and target proteins, and use drug-target ...

Improving Antifreeze Proteins Prediction With Protein Language Models and Hybrid Feature Extraction Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate identification of antifreeze proteins (AFPs) is crucial in developing biomimetic synthetic anti-icing materials and low-temperature organ preservation materials. Although numerous machine learning-based methods have been proposed for AFPs pr...