FIAMol-AB: A feature fusion and attention-based deep learning method for enhanced antibiotic discovery.

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.

MRMD3.0: A Python Tool and Webserver for Dimensionality Reduction and Data Visualization via an Ensemble Strategy.

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.

Analysis of Drug Repositioning and Prediction Techniques: A Concise Review.

High costs and risks are common issues in traditional drug research and development. Usually, it takes a long time to research and develop a drug, the effects of which are limited to relatively few targets. At present, studies are aiming to identify unknown new uses for existing drugs. Drug repositioning enables drugs to be quickly launched into clinical practice at a low cost because they have undergone clinical safety testing during the development process, which can greatly reduce costs and the risks of failed development. In addition to existing drugs with known indications, drugs that were shelved because of clinical trial failure can also be options for repositioning. In fact, many widely used drugs are identified via drug repositioning at present. This article reviews some popular research areas in the field of drug repositioning and briefly introduces the advantages and disadvantages of these methods, aiming to provide useful insights into future development in this field.

Review of bioinformatics in Azheimer's Disease Research.

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.

HSM6AP: a high-precision predictor for the Homo sapiens N6-methyladenosine (m6 A) based on multiple weights and feature stitching.

Recent studies have shown that RNA methylation modification can affect RNA transcription, metabolism, splicing and stability. In addition, RNA methylation modification has been associated with cancer, obesity and other diseases. Based on information about human genome and machine learning, this paper discusses the effect of the fusion sequence and gene-level feature extraction on the accuracy of methylation site recognition. The significant limitation of existing computing tools was exposed by discovered of new features. (1) Most prediction models are based solely on sequence features and use SVM or random forest as classification methods. (2) Limited by the number of samples, the model may not achieve good performance. In order to establish a better prediction model for methylation sites, we must set specific weighting strategies for training samples and find more powerful and informative feature matrices to establish a comprehensive model. In this paper, we present HSM6AP, a high-precision predictor for the Homo sapiens N6-methyladenosine (m6A) based on multiple weights and feature stitching. Compared with existing methods, HSM6AP samples were creatively weighted during training, and a wide range of features were explored. Max-Relevance-Max-Distance (MRMD) is employed for feature selection, and the feature matrix is generated by fusing a single feature. The extreme gradient boosting (XGBoost), an integrated machine learning algorithm based on decision tree, is used for model training and improves model performance through parameter adjustment. Two rigorous independent data sets demonstrated the superiority of HSM6AP in identifying methylation sites. HSM6AP is an advanced predictor that can be directly employed by users (especially non-professional users) to predict methylation sites. Users can access our related tools and data sets at the following website: http://lab.malab.cn/~lijing/HSM6AP.html The codes of our tool can be publicly accessible at https://github.com/lijingtju/HSm6AP.git.

SubLocEP: a novel ensemble predictor of subcellular localization of eukaryotic mRNA based on machine learning.

MOTIVATION: mRNA location corresponds to the location of protein translation and contributes to precise spatial and temporal management of the protein function. However, current assignment of subcellular localization of eukaryotic mRNA reveals important limitations: (1) turning multiple classifications into multiple dichotomies makes the training process tedious; (2) the majority of the models trained by classical algorithm are based on the extraction of single sequence information; (3) the existing state-of-the-art models have not reached an ideal level in terms of prediction and generalization ability. To achieve better assignment of subcellular localization of eukaryotic mRNA, a better and more comprehensive model must be developed. RESULTS: In this paper, SubLocEP is proposed as a two-layer integrated prediction model for accurate prediction of the location of sequence samples. Unlike the existing models based on limited features, SubLocEP comprehensively considers additional feature attributes and is combined with LightGBM to generated single feature classifiers. The initial integration model (single-layer model) is generated according to the categories of a feature. Subsequently, two single-layer integration models are weighted (sequence-based: physicochemical properties = 3:2) to produce the final two-layer model. The performance of SubLocEP on independent datasets is sufficient to indicate that SubLocEP is an accurate and stable prediction model with strong generalization ability. Additionally, an online tool has been developed that contains experimental data and can maximize the user convenience for estimation of subcellular localization of eukaryotic mRNA.

Optimization of combinations of ginsenoside-Rg1, ginsenoside-Rb1, evodiamine and rutaecarpine for effective therapy of mouse migraine.

Wuzhuyu decoction (WZYD) is a classic traditional Chinese medicine (TCM) formula. It has been extensively used for treating migraine for thousands of years in TCM. Four potential active ingredients from WZYD, ginsenoside-Rg1 (Rg1), ginsenoside-Rb1 (Rb1), evodiamine (Ev) and rutaecarpine (Ru), were found to have positive correlations with pharmacodynamic indicators involving mouse migraine in our previous study. To find a better therapeutic effect on migraine, this research was carried out to optimize the combinations of Rg1, Rb1, Ev and Ru using the uniform design method. The results showed that Rb1 and Ev played key roles in improving the therapeutic effect on mouse migraine by strongly ameliorating pharmacodynamic indicators associated with migraine. They significantly increased the contents of 5-hydroxytryptamine, noradrenaline and dopamine in brain tissues, and reduced the content of nitric oxide in brain tissues and the activities of nitric oxide synthase in both brain tissues and blood serum. The optimal concentrations of Rb1 and Ev were 1057.4 mg/L and 312.5 mg/L, respectively. Rg1 and Ru contributed less to the overall desirability, suggesting that they had reverse effects on some pharmacodynamic indicators of this type of migraine. The verification test demonstrated by the immunohistochemical method that the optimal combination inhibited the expression of c-fos and c-jun in periaqueductal gray of mice, and strongly ameliorated pharmacodynamic indicators. These results suggested that the therapeutic effect of the optimal combination of the four ingredients was strong, and the optimal results were proven to be reliable and accurate.

[Impact of prior cerebral infarction on in-hospital mortality in patients with acute myocardial infarction].

OBJECTIVE: To investigate the impact of prior cerebral infarction (PCI) on in-hospital mortality in patients with Acute Myocardial Infarction (AMI). METHODS: A retrospective analysis of documents of a total of 3572 consecutive patients with AMI admitted to Xuanwu Hospital of Capital Medical University from 2002 Jan. 1 to 2009 Dec. 31 were performed. RESULTS: There were 564 patients (15.8%) with PCI. Compared with the group of without PCI, the group with PCI were substantially older [(69.4 ± 9.9) vs (64.2 ± 12.9) years, P = 0.000], and had a higher prevalence of hypertensive disease, diabetes mellitus, prior myocardial infarction (MI) and non-ST-segment elevation myocardial infarction (NSTEMI) (respectively, 71.0% vs 57.3%; 41.0% vs 25.7%, 12.9% vs 9.5%; 14.9% vs 10.7%, P < 0.01), and a higher in-hospital mortality (16.5% vs 10.0%, P = 0.000). Univariate analysis demonstrated that in-hospital mortality associated with age, gender, extensive anterior MI, anterior MI, diabetes mellitus, prior cerebral infarction, prior myocardial infarction, coronary angiography and percutaneous coronary intervention. Logistic regression analysis found that risk factors were age, extensive anterior MI, anterior MI, diabetes mellitus and prior cerebral infarction, and protective factors were coronary angiography and percutaneous coronary intervention. PCI was independently associated with in-hospital mortality, OR 1.368, 95%CI 1.047 - 1.787, P = 0.022. CONCLUSION: In patients with acute myocardial infarction, the presence of PCI increases the risk of worse in-hospital outcome.

Rutile TiO2 particles exert size and surface coating dependent retention and lesions on the murine brain.

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.