FLONE: fully Lorentz network embedding for inferring novel drug targets.

Motivation: To predict drug targets, graph-based machine-learning methods have been widely used to capture the relationships between drug, target and disease entities in drug-disease-target (DDT) networks. However, many methods cannot explicitly consider disease types at inference time and so will predict the same target for a given drug under any disease condition. Meanwhile, DDT networks are usually organized hierarchically carrying interactive relationships between involved entities, but these methods, especially those based on Euclidean embedding cannot fully utilize such topological information, which might lead to sub-optimal results.

Improving therapeutic synergy score predictions with adverse effects using multi-task heterogeneous network learning.

Drug combinations could trigger pharmacological therapeutic effects (TEs) and adverse effects (AEs). Many computational methods have been developed to predict TEs, e.g.

Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videos.

Background: To study the association between dynamic iris change and primary angle-closure disease (PACD) with anterior segment optical coherence tomography (AS-OCT) videos and develop an automated deep learning system for angle-closure screening as well as validate its performance.

Methods: A total of 369 AS-OCT videos (19,940 frames)-159 angle-closure subjects and 210 normal controls (two datasets using different AS-OCT capturing devices)-were included. The correlation between iris changes (pupil constriction) and PACD was analyzed based on dynamic clinical parameters (pupil diameter) under the guidance of a senior ophthalmologist.

Understanding How Fundus Image Quality Degradation Affects CNN-based Diagnosis.

Quality degradation (QD) is common in the fundus images collected from the clinical environment. Although diagnosis models based on convolutional neural networks (CNN) have been extensively used to interpret retinal fundus images, their performances under QD have not been assessed. To understand the effects of QD on the performance of CNN-based diagnosis model, a systematical study is proposed in this paper.

MVD-Net: Semantic Segmentation of Cataract Surgery Using Multi-View Learning.

Semantic segmentation of surgery scenarios is a fundamental task for computer-aided surgery systems. Precise segmentation of surgical instruments and anatomies contributes to capturing accurate spatial information for tracking. However, uneven reflection and class imbalance lead the segmentation in cataract surgery to a challenging task.