AIMC Topic: Algorithms

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Performance of SURF and SIFT Keypoints for the Automated Differentiation of Abnormality in Chest Radiographs.

Studies in health technology and informatics
In this work, automated abnormality detection using keypoint information from Speeded-Up Robust feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors in chest Radiographic (CR) images is investigated and compared. Computerized image...

Patient and Graph Embeddings for Predictive Diagnosis of Drug Iatrogenesis.

Studies in health technology and informatics
In the context of the IA.TROMED project we intend to develop and evaluate original algorithmic methods that will rely on semantic enrichment of embeddings by combining new deep learning algorithms, such as models founded on transformers, and symbolic...

Clustering Results Interpretation of Continuous Variables Using Bayesian Inference.

Studies in health technology and informatics
The present study is devoted to interpretable artificial intelligence in medicine. In our previous work we proposed an approach to clustering results interpretation based on Bayesian Inference. As an application case we used clinical pathways cluster...

Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once.

Mathematical biosciences and engineering : MBE
In this paper, a new model known as YOLO-v5 is initiated to detect defects in PCB. In the past many models and different approaches have been implemented in the quality inspection for detection of defect in PCBs. This algorithm is specifically select...

Double-path parallel convolutional neural network for removing speckle noise in different types of OCT images.

Applied optics
Speckle noises widely exist in optical coherence tomography (OCT) images. We propose an improved double-path parallel convolutional neural network (called DPNet) to reduce speckles. We increase the network width to replace the network depth to extrac...

Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches.

Briefings in bioinformatics
The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tum...

FS-GBDT: identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT.

Briefings in bioinformatics
Cancer is a highly heterogeneous disease caused by dysregulation in different cell types and tissues. However, different cancers may share common mechanisms. It is critical to identify decisive genes involved in the development and progression of can...

DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method.

Briefings in bioinformatics
Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug repositioning. To reduce the experimental cost, a large number of computational approaches have been proposed for this task. The machine learning-based model...

Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework.

Briefings in bioinformatics
DNA N6-methyladenine (6mA) represents important epigenetic modifications, which are responsible for various cellular processes. The accurate identification of 6mA sites is one of the challenging tasks in genome analysis, which leads to an understandi...

ProtFold-DFG: protein fold recognition by combining Directed Fusion Graph and PageRank algorithm.

Briefings in bioinformatics
As one of the most important tasks in protein structure prediction, protein fold recognition has attracted more and more attention. In this regard, some computational predictors have been proposed with the development of machine learning and artifici...