TG-CDDPM: text-guided antimicrobial peptides generation based on conditional denoising diffusion probabilistic model.

Antimicrobial peptides (AMPs) have emerged as a promising substitution to antibiotics thanks to their boarder range of activities, less likelihood of drug resistance, and low toxicity. Traditional biochemical methods for AMP discovery are costly and inefficient. Deep generative models, including the long-short term memory model, variational autoencoder model, and generative adversarial model, have been widely introduced to expedite AMP discovery.

The synergy effect of multi-country policy actions announced in reaction to global risk: A network structure perspective.

The policy actions of countries reflect adaptive responses of local components within the system to the dynamic global risk landscape. These responses can generate interactions and synergy effects on alleviating the evolution of global risks. Adopting a network perspective, the study proposes a theoretical framework that connects three structural characteristics of policy synergy, namely, synergy scale, alignment intensity, and timing synchronization.

MDTL-ACP: Anticancer Peptides Prediction Based on Multi-Domain Transfer Learning.

Anticancer peptides (ACPs) have emerged as one of the most promising therapeutic agents for cancer treatment. They are bioactive peptides featuring broad-spectrum activity and low drug-resistance. The discovery of ACPs via traditional biochemical methods is laborious and costly.

Decoupling evolution of economic activity and carbon transfer in China: A multi-level analysis from network perspective.

Economic activities among multiple regions are always accompanied by carbon transfers. Analyzing coupling characteristics of economic activities and carbon transfer linkages based on the supply-demand relationships, can further reveal the networked structures of the multiregional interactions and common development trend of various industries, shedding light on carbon emission governance and high-quality development. This study advances novel coupling network models at the regional and industrial levels, and empirically analyzes the coupling characteristics in China based on the input-output data in 2012, 2015, and 2017.

MIX-TPI: a flexible prediction framework for TCR-pMHC interactions based on multimodal representations.

Motivation: The interactions between T-cell receptors (TCR) and peptide-major histocompatibility complex (pMHC) are essential for the adaptive immune system. However, identifying these interactions can be challenging due to the limited availability of experimental data, sequence data heterogeneity, and high experimental validation costs.

Results: To address this issue, we develop a novel computational framework, named MIX-TPI, to predict TCR-pMHC interactions using amino acid sequences and physicochemical properties.

Competitive Decomposition-Based Multiobjective Architecture Search for the Dendritic Neural Model.

The dendritic neural model (DNM) is computationally faster than other machine-learning techniques, because its architecture can be implemented by using logic circuits and its calculations can be performed entirely in binary form. To further improve the computational speed, a straightforward approach is to generate a more concise architecture for the DNM. Actually, the architecture search is a large-scale multiobjective optimization problem (LSMOP), where a large number of parameters need to be set with the aim of optimizing accuracy and structural complexity simultaneously.

Noninvasive Cuffless Blood Pressure Estimation With Dendritic Neural Regression.

Blood pressure (BP) is one of the most important indicators of health. BP that is too high or too low causes varying degrees of diseases, such as renal impairment, cerebrovascular incidents, and cardiovascular diseases. Since traditional cuff-based BP measurement techniques have the drawbacks of patient discomfort and the impossibility of continuous BP monitoring, noninvasive cuffless continuous BP measurement has become a popular topic.

Transmission trend of the COVID-19 pandemic predicted by dendritic neural regression.

In 2020, a novel coronavirus disease became a global problem. The disease was called COVID-19, as the first patient was diagnosed in December 2019. The disease spread around the world quickly due to its powerful viral ability.

Accuracy Versus Simplification in an Approximate Logic Neural Model.

An approximate logic neural model (ALNM) is a novel single-neuron model with plastic dendritic morphology. During the training process, the model can eliminate unnecessary synapses and useless branches of dendrites. It will produce a specific dendritic structure for a particular task.

Neurons with Multiplicative Interactions of Nonlinear Synapses.

Neurons are the fundamental units of the brain and nervous system. Developing a good modeling of human neurons is very important not only to neurobiology but also to computer science and many other fields. The McCulloch and Pitts neuron model is the most widely used neuron model, but has long been criticized as being oversimplified in view of properties of real neuron and the computations they perform.

A Pruning Neural Network Model in Credit Classification Analysis.

Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs) have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN) and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets.