AIGO-DTI: Predicting Drug-Target Interactions Based on Improved Drug Properties Combined with Adaptive Iterative Algorithms.

Artificial intelligence-based methods for predicting drug-target interactions (DTIs) aim to explore reliable drug candidate targets rapidly and cost-effectively to accelerate the drug development process. However, current methods are often limited by the topological regularities of drug molecules, making them difficult to generalize to a broader chemical space. Additionally, the use of similarity to measure DTI network links often introduces noise, leading to false DTI relationships and affecting the prediction accuracy. To address these issues, this study proposes an Adaptive Iterative Graph Optimization (AIGO)-DTI prediction framework. This framework integrates atomic cluster information and enhances molecular features through the design of functional group prompts and graph encoders, optimizing the construction of DTI association networks. Furthermore, the optimization of graph structure is transformed into a node similarity learning problem, utilizing multihead similarity metric functions to iteratively update the network structure to improve the quality of DTI information. Experimental results demonstrate the outstanding performance of AIGO-DTI on multiple public data sets and label reversal data sets. Case studies, molecular docking, and existing research validate its effectiveness and reliability. Overall, the method proposed in this study can construct comprehensive and reliable DTI association network information, providing new graphing and optimization strategies for DTI prediction, which contribute to efficient drug development and reduce target discovery costs.

Research on disease diagnosis based on teacher-student network and Raman spectroscopy.

Diabetic nephropathy is a serious complication of diabetes, and primary Sjögren's syndrome is a disease that poses a major threat to women's health. Therefore, studying these two diseases is of practical significance. In the field of spectral analysis, although common Raman spectral feature selection models can effectively extract features, they have the problem of changing the characteristics of the original data. The teacher-student network combined with Raman spectroscopy can perform feature selection while retaining the original features, and transfer the performance of the complex deep neural network structure to another lightweight network structure model. This study selects five flow learning models as the teacher network, builds a neural network as the student network, uses multi-layer perceptron for classification, and selects the optimal features based on the evaluation indicators accuracy, precision, recall, and F1-score. After five-fold cross-validation, the research results show that in the diagnosis of diabetic nephropathy, the optimal accuracy rate can reach 98.3%, which is 14.02% higher than the existing research; in the diagnosis of primary Sjögren's syndrome, the optimal accuracy rate can be reached 100%, which is 10.48% higher than the existing research. This study proved the feasibility of Raman spectroscopy combined with teacher-student network in the field of disease diagnosis by producing good experimental results in the diagnosis of diabetic nephropathy and primary Sjögren's syndrome.

A method for accurate identification of Uyghur medicinal components based on Raman spectroscopy and multi-label deep learning.

Uyghur medicine is one of the four major ethnic medicines in China and is a component of traditional Chinese medicine. The intrinsic quality of Uyghur medicinal materials will directly affect the clinical efficacy of Uyghur medicinal preparations. However, in recent years, problems such as adulteration of Uyghur medicinal materials and foreign bodies with the same name still exist, so it is necessary to strengthen the quality control of Uyghur medicines to guarantee Uyghur medicinal efficacy. Identifying the components of Uyghur medicines can clarify the types of medicinal materials used, is a crucial step to realizing the quality control of Uyghur medicines, and is also an important step in screening the effective components of Uyghur medicines. Currently, the method of identifying the components of Uyghur medicines relies on manual detection, which has the problems of high toxicity of the unfolding agent, poor stability, high cost, low efficiency, etc. Therefore, this paper proposes a method based on Raman spectroscopy and multi-label deep learning model to construct a model Mix2Com for accurate identification of Uyghur medicine components. The experiments use computer-simulated mixtures as the dataset, introduce the Long Short-Term Memory Model (LSTM) and Attention mechanism to encode the Raman spectral data, use multiple parallel networks for decoding, and ultimately realize the macro parallel prediction of medicine components. The results show that the model is trained to achieve 90.76% accuracy, 99.41% precision, 95.42% recall value and 97.37% F1 score. Compared to the traditional XGBoost model, the method proposed in the experiment improves the accuracy by 49% and the recall value by 18%; compared with the DeepRaman model, the accuracy is improved by 9% and the recall value is improved by 14%. The method proposed in this paper provides a new solution for the accurate identification of Uyghur medicinal components. It helps to improve the quality standard of Uyghur medicinal materials, advance the research on screening of effective chemical components of Uyghur medicines and their action mechanisms, and then promote the modernization and development of Uyghur medicine.

RamanCMP: A Raman spectral classification acceleration method based on lightweight model and model compression techniques.

In recent years, Raman spectroscopy combined with deep learning techniques has been widely used in various fields such as medical, chemical, and geological. However, there is still room for optimization of deep learning techniques and model compression algorithms for processing Raman spectral data. To further optimize deep learning models applied to Raman spectroscopy, in this study time, accuracy, sensitivity, specificity and floating point operations numbers(FLOPs) are used as evaluation metrics to optimize the model, which is named RamanCompact(RamanCMP). The experimental data used in this research are selected from the RRUFF public dataset, which consists of 723 Raman spectroscopy data samples from 10 different mineral categories. In this paper, 1D-EfficientNet adapted to the spectral data as well as 1D-DRSN are proposed to improve the model classification accuracy. To achieve better classification accuracy while optimizing the time parameters, three model compression methods are designed: knowledge distillation using 1D-EfficientNet model as a teacher model to train convolutional neural networks(CNN), proposing a channel conversion method to optimize 1D-DRSN model, and using 1D-DRSN model as a feature extractor in combination with linear discriminant analysis(LDA) model for classification. Compared with the traditional LDA and CNN models, the accuracy of 1D-EfficientNet and 1D-DRSN is improved by more than 20%. The time of the distilled model is reduced by 9680.9s compared with the teacher model 1D-EfficientNet under the condition of losing 2.07% accuracy. The accuracy of the distilled model is improved by 20% compared to the CNN student model while keeping inference efficiency constant. The 1D-DRSN optimized with channel conversion method saves 60% inference time of the original 1D-DRSN model. Feature extraction reduces the inference time of 1D-DRSN model by 93% with 94.48% accuracy. This study innovatively combines lightweight models and model compression algorithms to improve the classification speed of deep learning models in the field of Raman spectroscopy, forming a complete set of analysis methods and laying the foundation for future research.

Identification of Chinese red wine origins based on Raman spectroscopy and deep learning.

In this study, we combined Raman spectroscopy with deep learning for the first time to establish an accurate, simple, and fast method to identify the origin of red wines. We collected Raman spectra from 200 red wine samples of the Cabernet Sauvignon variety from four different origins with a portable Raman spectrometer. The red wine samples, made in 2021, were from the same producer in China. Differences were found by analyzing the Raman spectra of red wine samples. These differences are mainly caused by ethanol, carboxylic acids, and polyphenols. After further analysis, for different origins, the different performances of these substances on the Raman spectrum are related to the climate and geographical conditions of the origin. The Raman spectra were analyzed by principal component analysis (PCA). The data with PCA dimensionality reduction were imported into an artificial neural network (ANN), multifeature fusion convolutional neural network (MCNN), GoogLeNet, and residual neural network (ResNet) to establish red wine origin identification models. The classification results of the model prove that climate, geography, and other conditions can provide support for the classification of red wine origin. The experiments showed that all four models performed well, among which MCNN performed the best with 93.2% classification accuracy, and the area under the curve (AUC) was 0.987. This study provides a new means to classify the origin of red wine and opens up new ideas for identifying origins in the food field.

Colon cancer diagnosis and staging classification based on machine learning and bioinformatics analysis.

Advanced metastasis of colon cancer makes it more difficult to treat colon cancer. Finding the markers of colon cancer (Colon Cancer) can diagnose the stage of cancer in time and improve the prognosis with timely treatment. This paper uses gene expression profiling data from The Cancer Genome Atlas (TCGA) for the diagnosis of colon cancer and its staging. In this study, we first selected the gene modules with the greatest correlation with cancer by Weighted Gene Co-expression Network Analysis (WGCNA), extracted the characteristic genes for differential expression results using the least absolute shrinkage and selection operator algorithm (Lasso) and performed survival analysis, and then combined the genes in the modules with the Lasso-extracted feature genes were combined to diagnose colon cancer versus healthy controls using RF, SVM and decision trees, and colon cancer staging was diagnosed using differentially expressed genes for each stage. Finally, Protein-Protein Interaction Networks (PPI) networks were done for 289 genes to identify clusters of aggregated proteins for survival analysis. Finally, the RF model had the best results in the diagnosis of colon cancer versus control group fold cross-validation with an average accuracy of 99.81%, F1 value reaching 0.9968, accuracy of 99.88%, and recall of 99.5%, and an average accuracy of 91.5%, F1 value reaching 0.7679, accuracy of 86.94%, and recall in the diagnosis of colon cancer stages I, II, III and IV. The recall rate reached 73.04%, and eight genes associated with colon cancer prognosis were identified for GCNT2, GLDN, SULT1B1, UGT2B15, PTGDR2, GPR15, BMP5 and CPT2.

The application of feature engineering in establishing a rapid and robust model for identifying patients with glioma.

The aim of the study is to evaluate the efficacy of the combination of Raman spectroscopy with feature engineering and machine learning algorithms for detecting glioma patients. In this study, we used Raman spectroscopy technology to collect serum spectra of glioma patients and healthy people and used feature engineering-based classification models for prediction. First, to reduce the dimensionality of the data, we used two feature extraction algorithms which are partial least squares (PLS) and principal component analysis (PCA). Then, the principal components were selected using the feature selection methods of four correlation indexes, namely, Relief-F (RF), the Pearson correlation coefficient (PCC), the F-score (FS) and term variance (TV). Finally, back-propagation neural network (BP), linear discriminant analysis (LDA) and support vector machine (SVM) classification models were established. To improve the reliability of the model, we used a fivefold cross validation to measure the prediction performance between different models. In this experiment, 33 classification models were established. Integrating 4 classification criteria, PLS-Relief-F-BP, PLS-F-Score-BP, PLS-LDA and PLS-Relief-F-SVM had better effects, and their accuracy rates reached 97.58%, 96.33%, 97.87% and 96.19%, respectively. The experimental results show that feature engineering can select more representative features, reduce computational time complexity and simplify the model. The classification model established in this experiment can not only increase the robustness of the model and shorten the discrimination time but also realize the rapid, stable and accurate diagnosis of glioma patients, which has high clinical application value.