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Antibiotic resistance continues to be a growing concern for global health, accentuating the need for novel antibiotic discoveries. Traditional methodologies in this field have relied heavily on extensive experimental screening, which is often time-consuming and costly. Contrastly, computer-assisted drug screening offers rapid, cost-effective solutions. In this work, we propose FIAMol-AB, a deep learning model that combines graph neural networks, text convolutional networks and molecular fingerprint techniques. This method also combines an attention mechanism to fuse multiple forms of information within the model. The experiments show that FIAMol-AB may offer potential advantages in antibiotic discovery tasks over some existing methods. We conducted some analysis based on our model's results, which help highlight the potential significance of certain features in the model's predictive performance. Compared to different models, ours demonstrate promising results, indicating potential robustness and versatility. This suggests that by integrating multi-view information and attention mechanisms, FIAMol-AB might better learn complex molecular structures, potentially improving the precision and efficiency of antibiotic discovery. We hope our FIAMol-AB can be used as a useful method in the ongoing fight against antibiotic resistance., Competing Interests: Declaration of competing interest The authors of this article declare no conflicts of interest. The authors have no financial or personal relationships with any individuals or organizations that could influence the research., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Dimensionality reduction is a hot topic in machine learning that can help researchers find key features from complex medical or biological data, which is crucial for biological sequence research, drug development, etc. However, when applied to specific datasets, different dimensionality reduction methods generate different results, which produces instability and makes tuning the parameters a time-consuming task. Exploring high quality features, genes, or attributes from complex data is an important task and challenge. To ensure the efficiency, robustness, and accuracy of experiments, in this work, we developed a dimensionality reduction tool MRMD3.0 based on the ensemble strategy of link analysis. It is mainly divided into two steps: first, the ensemble method is used to integrate different feature ranking algorithms to calculate feature importance, and then the forward feature search strategy combined with cross-validation is used to explore the proper feature combination. Compared with the previously developed version, MRMD3.0 has added more link-based ensemble algorithms, including PageRank, HITS, LeaderRank, and TrustRank. At the same time, more feature ranking algorithms have been added, and their effect and calculation speed have been greatly improved. In addition, the newest version provides an interface used by each feature ranking method and five kinds of charts to help users analyze features. Finally, we also provide an online webserver to help researchers analyze the data. Availability and implementation Webserver: http://lab.malab.cn/soft/MRMDv3/home.html. GitHub: https://github.com/heshida01/MRMD3.0., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Alzheimer's disease (AD) is a severe neurodegenerative disease with slow course of onset and deterioration with time. With the speedup of global aging, AD has become a disease that seriously threatens the physical health of the elderly; therefore, the effective prevention and treatments of AD is an extremely important area of study for researchers and clinicians. Rapid technological developments have promoted the analysis of various kinds of complex data sets using machine learning methods. The common machine learning algorithms, such as Lasso, SVM and Random Forest, are very important in AD research. To help accelerate AD-related research, we review some recent research progress on Alzheimer's disease, including database, image analysis, gene expression, etc., which can provide AD researchers with more comprehensive research methods., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The rising commercial use and large-scale production of engineered nanoparticles (NPs) may lead to unintended exposure to humans. The central nervous system (CNS) is a potential susceptible target of the inhaled NPs, but so far the amount of studies on this aspect is limited. Here, we focus on the potential neurological lesion in the brain induced by the intranasally instilled titanium dioxide (TiO₂) particles in rutile phase and of various sizes and surface coatings. Female mice were intranasally instilled with four different types of TiO₂ particles (i.e. two types of hydrophobic particles in micro- and nano-sized without coating and two types of water-soluble hydrophilic nano-sized particles with silica surface coating) every other day for 30 days. Inductively coupled plasma mass spectrometry (ICP-MS) were used to determine the titanium contents in the sub-brain regions. Then, the pathological examination of brain tissues and measurements of the monoamine neurotransmitter levels in the sub-brain regions were performed. We found significant up-regulation of Ti contents in the cerebral cortex and striatum after intranasal instillation of hydrophilic TiO₂ NPs. Moreover, TiO₂ NPs exposure, in particular the hydrophilic NPs, caused obvious morphological changes of neurons in the cerebral cortex and significant disturbance of the monoamine neurotransmitter levels in the sub-brain regions studied. Thus, our results indicate that the surface modification of the NPs plays an important role on their effects on the brain. In addition, the difference in neurotoxicity of the two types of hydrophilic NPs may be induced by the shape differences of the materials. The present results suggest that physicochemical properties like size, shape and surface modification of the nanomaterials should be considered when evaluating their neurological effects., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)