Peptic Ulcer Disease

Establishment of a SVM classifier to predict recurrence of ovarian cancer.

Gene expression data using retrieved ovarian cancer (OC) samples were used to identify genes of interest and a support vector machine (SVM) classifier was subsequently established to predict the recurrence of OC. Three datasets (GSE17260, GSE44104 and GSE51088) investigating OC gene expression were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) in samples from p...

Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences.

Machine learning based predictions of protein⁻protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The intensive feature engineering in most of these methods makes the prediction task more tedious and trivial. The emerging deep learning technology enabling automatic feature engineering is gaining great success ...

Postoperative bleeding risk prediction for patients undergoing colorectal surgery.

BACKGROUND: There is limited consensus regarding risk factors for postoperative bleeding. The objective of this work was to investigate the capability...

A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy.

BACKGROUND AND AIMS: GI angiectasia (GIA) is the most common small-bowel (SB) vascular lesion, with an inherent risk of bleeding. SB capsule endoscopy...

Deep Endoscopic Visual Measurements.

Robotic endoscopic systems offer a minimally invasive approach to the examination of internal body structures, and their application is rapidly extend...

Presentation and diagnosis of patients with type 3 von Willebrand disease in resources-limited laboratory.

Von Willebrand disease (VWD) is a bleeding disorder that results from decreased von Willebrand factor (VWF) activity <0.30 iu/mL. Therefore, the diagn...

Identification of differentially expressed genes associated with asthma in children based on the bioanalysis of the regulatory network.

Asthma, the most common chronic respiratory tract disease in children, is characterized by allergy, recurring airway obstruction and bronchospasm. The...

Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy.

Colorectal cancer is the fourth leading cause of cancer deaths worldwide and the second leading cause in the United States. The risk of colorectal can...

Protein-Protein Interactions Prediction via Multimodal Deep Polynomial Network and Regularized Extreme Learning Machine.

Predicting the protein-protein interactions (PPIs) has played an important role in many applications. Hence, a novel computational method for PPIs pre...

Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training.

To realize the full potential of deep learning for medical imaging, large annotated datasets are required for training. Such datasets are difficult to...

Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches.

BACKGROUND: Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Prote...

Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification.

This paper proposes a novel methodology for automatic detection and localization of gastrointestinal (GI) anomalies in endoscopic video frame sequence...

Automated classification of celiac disease during upper endoscopy: Status quo and quo vadis.

A large amount of digital image material is routinely captured during esophagogastroduodenoscopies but, for the most part, is not used for confirming ...

Gene expression profiles reveal key genes for early diagnosis and treatment of adamantinomatous craniopharyngioma.

Adamantinomatous craniopharyngioma (ACP) is an aggressive brain tumor that occurs predominantly in the pediatric population. Conventional diagnosis me...

Identification of Clinically Meaningful Plasma Transfusion Subgroups Using Unsupervised Random Forest Clustering.

Statistical techniques such as propensity score matching and instrumental variable are commonly employed to "simulate" randomization and adjust for me...

Annotating activation/inhibition relationships to protein-protein interactions using gene ontology relations.

BACKGROUND: Signaling pathways can be reconstructed by identifying 'effect types' (i.e. activation/inhibition) of protein-protein interactions (PPIs)....

MTGO: PPI Network Analysis Via Topological and Functional Module Identification.

Protein-protein interaction (PPI) networks are viable tools to understand cell functions, disease machinery, and drug design/repositioning. Interpreti...

PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation.

Protein-protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Theref...

A hybrid model based on neural networks for biomedical relation extraction.

Biomedical relation extraction can automatically extract high-quality biomedical relations from biomedical texts, which is a vital step for the mining...

Completing sparse and disconnected protein-protein network by deep learning.

BACKGROUND: Protein-protein interaction (PPI) prediction remains a central task in systems biology to achieve a better and holistic understanding of c...