TPpred-LE: therapeutic peptide function prediction based on label embedding.

Background: Therapeutic peptides play an essential role in human physiology, treatment paradigms and bio-pharmacy. Several computational methods have been developed to identify the functions of therapeutic peptides based on binary classification and multi-label classification. However, these methods fail to explicitly exploit the relationship information among different functions, preventing the further improvement of the prediction performance.

sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure.

Motivation: Antimicrobial peptides (AMPs) are essential components of therapeutic peptides for innate immunity. Researchers have developed several computational methods to predict the potential AMPs from many candidate peptides. With the development of artificial intelligent techniques, the protein structures can be accurately predicted, which are useful for protein sequence and function analysis.

PreTP-Stack: Prediction of Therapeutic Peptides Based on the Stacked Ensemble Learing.

Therapeutic peptide prediction is critical for drug development and therapeutic therapy. Researchers have developed several computational methods to identify different therapeutic peptide types. However, most computational methods focus on identifying the specific type of therapeutic peptides and fail to accurately predict all types of therapeutic peptides.

AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning.

Antimicrobial peptides (AMPs) are important for the human immune system and are currently applied in clinical trials. AMPs have been received much attention for accurate recognition. Recently, several computational methods for identifying AMPs have been proposed.

TPpred-ATMV: therapeutic peptide prediction by adaptive multi-view tensor learning model.

Motivation: Therapeutic peptide prediction is important for the discovery of efficient therapeutic peptides and drug development. Researchers have developed several computational methods to identify different therapeutic peptide types. However, these computational methods focus on identifying some specific types of therapeutic peptides, failing to predict the comprehensive types of therapeutic peptides.

PreTP-EL: prediction of therapeutic peptides based on ensemble learning.

Therapeutic peptides are important for understanding the correlation between peptides and their therapeutic diagnostic potential. The therapeutic peptides can be further divided into different types based on therapeutic function sharing different characteristics. Although some computational approaches have been proposed to predict different types of therapeutic peptides, they failed to accurately predict all types of therapeutic peptides.

A Privacy-Preserving Cross-Domain Healthcare Wearables Recommendation Algorithm Based on Domain-Dependent and Domain-Independent Feature Fusion.

Recently, recommender systems are applied to provide personalized recomendation for healthcare wearables. However, due to the sparsity problem, traditional recommendation algorithms are difficult to achieve desired performance. Considering that consumers often buy and rate other types of items on E-commerce platforms, we can leverage significant information in the auxiliary domains to improve the recommendation performance of healthcare wearables, which can be regarded as cross-domain recommendation.

Evaluating the Effects of Non-Isomorphic Rotation on 3D Manipulation Tasks in Mixed Reality Simulation.

As a hyper-natural interaction technique in 3D user interfaces, non-isomorphic rotation has been considered an effective approach for rotation tasks, where a static or dynamic control-display gain can be applied to amplify or attenuate a rotation. However, it is not clear whether non-isomorphic rotation can benefit 6-degree-of-freedom (6-DOF) manipulation tasks in AR and VR. In this article, we extended the usability studies of non-isomorphic rotation from rotation-only tasks to 6-DOF manipulation tasks and analyzed the collected data using a 2-component model.