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An integrated deep learning model for the prediction of pathological complete response to neoadjuvant chemotherapy with serial ultrasonography in breast cancer patients: a multicentre, retrospective study.

Breast cancer research : BCR
BACKGROUND: The biological phenotype of tumours evolves during neoadjuvant chemotherapy (NAC). Accurate prediction of pathological complete response (pCR) to NAC in the early-stage or posttreatment can optimize treatment strategies or improve the bre...

A Deep Learning Approach to Detect Anomalies in an Electric Power Steering System.

Sensors (Basel, Switzerland)
As anomaly detection for electrical power steering (EPS) systems has been centralized using model- and knowledge-based approaches, EPS system have become complex and more sophisticated, thereby requiring enhanced reliability and safety. Since most cu...

RGN: Residue-Based Graph Attention and Convolutional Network for Protein-Protein Interaction Site Prediction.

Journal of chemical information and modeling
The prediction of a protein-protein interaction site (PPI site) plays a very important role in the biochemical process, and lots of computational methods have been proposed in the past. However, the majority of the past methods are time consuming and...

A machine learning method for predicting the probability of MODS using only non-invasive parameters.

Computer methods and programs in biomedicine
OBJECTIVES: Timely and accurate prediction of multiple organ dysfunction syndrome (MODS) is essential for the rescue and treatment of trauma patients However, existing methods are invasive, easily affected by artifacts and can be difficult to perform...

Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences.

BMC bioinformatics
Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to ...

Finger Vein Verification on Different Datasets Based on Deep Learning with Triplet Loss.

Computational and mathematical methods in medicine
In this study, deep learning and triplet loss function methods are used for finger vein verification research, and the model is trained and validated between different kinds of datasets including FV-USM, HKPU, and SDUMLA-HMT datasets. This work gives...

Biological signatures and prediction of an immunosuppressive status-persistent critical illness-among orthopedic trauma patients using machine learning techniques.

Frontiers in immunology
BACKGROUND: Persistent critical illness (PerCI) is an immunosuppressive status. The underlying pathophysiology driving PerCI remains incompletely understood. The objectives of the study were to identify the biological signature of PerCI development, ...

DeepMPM: a mortality risk prediction model using longitudinal EHR data.

BMC bioinformatics
BACKGROUND: Accurate precision approaches have far not been developed for modeling mortality risk in intensive care unit (ICU) patients. Conventional mortality risk prediction methods can hardly extract the information in longitudinal electronic medi...

A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.

BMC bioinformatics
BACKGROUND: Selecting and prioritizing candidate disease genes is necessary before conducting laboratory studies as identifying disease genes from a large number of candidate genes using laboratory methods, is a very costly and time-consuming task. T...

Diagnostic performance of convolutional neural networks for dental sexual dimorphism.

Scientific reports
Convolutional neural networks (CNN) led to important solutions in the field of Computer Vision. More recently, forensic sciences benefited from the resources of artificial intelligence, especially in procedures that normally require operator-dependen...