Efficient Classification of Hot Spots and Hub Protein Interfaces by Recursive Feature Elimination and Gradient Boosting

Toxicity prediction and classification of Gunqile-7 with small sample based on transfer learning method

Drug-induced diseases are the most important component of iatrogenic disease. It is the duty of doctors to provide a reasonable and safe dose of medication. Gunqile-7 is a Mongolian medicine with analgesic and anti-inflammatory effects. As a foreign substance in the body, even with reasonable medication, it may produce varying degrees of adverse reactions or toxic side effects. Since the cost of collecting Gunqile-7 for pharmacological animal trials is high and the data sample is small, this paper employs transfer learning and data augmentation methods to study the toxicity of Gunqile-7. More specifically, to reduce the necessary number of training samples, the data augmentation approach is employed to extend the data set. Then, the transfer learning method and one-dimensional convolutional neural network are utilized to train the network. In addition, we use the support vector machine-recursive feature elimination method for feature selection to reduce features that have adverse effects on model predictions. Furthermore, due to the important role of the pre-trained model of transfer learning, we select a quantitative toxicity prediction model as the pre-trained model, which is consistent with the purpose of this paper. Lastly, the experimental results demonstrate the efficiency of the proposed method. Our method can improve accuracy by up to 9 percentage points compared to the method without transfer learning on a small sample set.

A novel hypoglycemia alarm framework for type 2 diabetes with high glycemic variability

In patients with type 2 diabetes (T2D), accurate prediction of hypoglycemic events is crucial for maintaining glycemic control and reducing their frequency. However, individuals with high blood glucose variability experience significant fluctuations over time, posing a challenge for early warning models that rely on static features. This article proposes a novel hypoglycemia early alarm framework based on dynamic feature selection. The framework incorporates domain knowledge and introduces multi-scale blood glucose features, including predicted values, essential for early warnings. To address the complexity of the feature matrix, a dynamic feature selection mechanism (Relief-SVM-RFE) is designed to effectively eliminate redundancy. Furthermore, the framework employs online updates for the random forest model, enhancing the learning of more relevant features. The effectiveness of the framework was evaluated using a clinical dataset. For T2D patients with a high coefficient of variation (CV), the framework achieved a sensitivity of 81.15% and specificity of 98.14%, accurately predicting most hypoglycemic events. Notably, the proposed method outperformed other existing approaches. These results indicate the feasibility of anticipating hypoglycemic events in T2D patients with high CV using this innovative framework.

MEM-FET: Essential protein prediction using membership feature and machine learning approach

Proteins are played key roles in different functionalities in our daily life. All functional roles of a protein are a bit enhanced in interaction compared to individuals. Identification of essential proteins of an organism is a time consume and costly task during observation in the wet lab. The results of observation in wet lab always ensure high reliability and accuracy in the biological ground. Essential protein prediction using computational approaches is an alternative choice in research. It proves its significance rapidly in day-to-day life as well as reduces the experimental cost of wet lab effectively. Existing computational methods were implemented using Protein interaction networks (PPIN), Sequence, Gene Expression Dataset (GED), Gene Ontology (GO), Orthologous groups, and Subcellular localized datasets. Machine learning has diverse categories of features that enable to model and predict essential macromolecules of understudied organisms. A novel methodology MEM-FET (membership feature) is predicted based on features, that is, edge clustering coefficient, Average clustering coefficient, subcellular localization, and Gene Ontology within a compartment of common neighbors. The accuracy (ACC) values of the predicted true positive (TP) essential proteins are 0.79, 0.74, 0.78, and 0.71 for YHQ, YMIPS, YDIP, and YMBD datasets. An enriched set of essential proteins are also predicted using the MEM-FET algorithm. Ensemble ML also validated the proposed model with an accuracy of 60%. It has been predicted that MEM-FET algorithms outperform other existing algorithms with an ACC value of 80% for the yeast dataset.

Maximum margin and global criterion based-recursive feature selection

In this research paper, we aim to investigate and address the limitations of recursive feature elimination (RFE) and its variants in high-dimensional feature selection tasks. We identify two main challenges associated with these methods. Firstly, the feature ranking criterion utilized in these approaches is inconsistent with the maximum-margin theory. Secondly, the computation of the criterion is performed locally, lacking the ability to measure the importance of features globally. To overcome these challenges, we propose a novel feature ranking criterion called Maximum Margin and Global (MMG) criterion. This criterion utilizes the classification margin to determine the importance of features and computes it globally, enabling a more accurate assessment of feature importance. Moreover, we introduce an optimal feature subset evaluation algorithm that leverages the MMG criterion to determine the best subset of features. To enhance the efficiency of the proposed algorithms, we provide two alpha seeding strategies that significantly reduce computational costs while maintaining high accuracy. These strategies offer a practical means to expedite the feature selection process. Through extensive experiments conducted on ten benchmark datasets, we demonstrate that our proposed algorithms outperform current state-of-the-art methods. Additionally, the alpha seeding strategies yield significant speedups, further enhancing the efficiency of the feature selection process.

A Unified Multi-Class Feature Selection Framework for Microarray Data

In feature selection research, simultaneous multi-class feature selection technologies are popular because they simultaneously select informative features for all classes. Recursive feature elimination (RFE) methods are state-of-the-art binary feature selection algorithms. However, extending existing RFE algorithms to multi-class tasks may increase the computational cost and lead to performance degradation. With this motivation, we introduce a unified multi-class feature selection (UFS) framework for randomization-based neural networks to address these challenges. First, we propose a new multi-class feature ranking criterion using the output weights of neural networks. The heuristic underlying this criterion is that "the importance of a feature should be related to the magnitude of the output weights of a neural network". Subsequently, the UFS framework utilizes the original features to construct a training model based on a randomization-based neural network, ranks these features by the criterion of the norm of the output weights, and recursively removes a feature with the lowest ranking score. Extensive experiments on 15 real-world datasets suggest that our proposed framework outperforms state-of-the-art algorithms. The code of UFS is available at https://github.com/SVMrelated/UFS.git.

Identifying LncRNA-Encoded Short Peptides Using Optimized Hybrid Features and Ensemble Learning

Long non-coding RNA (lncRNA) contains short open reading frames (sORFs), and sORFs-encoded short peptides (SEPs) have become the focus of scientific studies due to their crucial role in life activities. The identification of SEPs is vital to further understanding their regulatory function. Bioinformatics methods can quickly identify SEPs to provide credible candidate sequences for verifying SEPs by biological experimenrts. However, there is a lack of methods for identifying SEPs directly. In this study, a machine learning method to identify SEPs of plant lncRNA (ISPL) is proposed. Hybrid features including sequence features and physicochemical features are extracted manually or adaptively to construct different modal features. In order to keep the stability of feature selection, the non-linear correction applied in Max-Relevance-Max-Distance (nocRD) feature selection method is proposed, which integrates multiple feature ranking results and uses the iterative random forest for different modal features dimensionality reduction. Classification models with different modal features are constructed, and their outputs are combined for ensemble classification. The experimental results show that the accuracy of ISPL is 89.86% percent on the independent test set, which will have important implications for further studies of functional genomic.

An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things

Internet of Things (IoT) is the fastest growing technology that has applications in various domains such as healthcare, transportation. It interconnects trillions of smart devices through the Internet. A secure network is the basic necessity of the Internet of Things. Due to the increasing rate of interconnected and remotely accessible smart devices, more and more cybersecurity issues are being witnessed among cyber-physical systems. A perfect intrusion detection system (IDS) can probably identify various cybersecurity issues and their sources. In this article, using various telemetry datasets of different Internet of Things scenarios, we exhibit that external users can access the IoT devices and infer the victim user's activity by sniffing the network traffic. Further, the article presents the performance of various bagging and boosting ensemble decision tree techniques of machine learning in the design of an efficient IDS. Most of the previous IDSs just focused on good accuracy and ignored the execution speed that must be improved to optimize the performance of an ID model. Most of the earlier pieces of research focused on binary classification. This study attempts to evaluate the performance of various ensemble machine learning multiclass classification algorithms by deploying on openly available "TON-IoT" datasets of IoT and Industrial IoT (IIoT) sensors.

Noise-immune Extreme Ensemble Learning for Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus

Early diagnosis is currently the most effective way of saving the life of patients with neuropsychiatric systemic lupus erythematosus (NPSLE). However, it is rather difficult to detect this terrible disease at the early stage, due to the subtle and elusive symptomatic signals. Recent studies show that the ¹H-MRS (proton magnetic resonance spectroscopy) imaging technique can capture more information reflecting the early appearance of this disease than conventional magnetic resonance imaging techniques. 1H-MRS data, however, also presents more noises that can bring serious diagnosis bias. We hence proposed a noise-immune extreme ensemble learning technique for effectively leveraging 1H-MRS data for advancing the early diagnosis of NPSLE. Our main results are that 1) by developing generalized maximum correntropy criterion in the kernel extreme learning setting, many types of non-Gaussian noises can be distinguished, and 2) weighted recursive feature elimination, using maximal information coefficient to weight feature's importance, helps to further alleviate the bad impact of noises on the diagnosis performance. The proposed method is assessed on a publicly available dataset with 97.5% accuracy, 95.8% sensitivity, and 99.9% specificity, which well demonstrates its efficacy.

Drug-target interaction prediction via multiple classification strategies

BACKGROUND

Computational prediction of the interaction between drugs and protein targets is very important for the new drug discovery, as the experimental determination of drug-target interaction (DTI) is expensive and time-consuming. However, different protein targets are with very different numbers of interactions. Specifically, most interactions focus on only a few targets. As a result, targets with larger numbers of interactions could own enough positive samples for predicting their interactions but the positive samples for targets with smaller numbers of interactions could be not enough. Only using a classification strategy may not be able to deal with the above two cases at the same time. To overcome the above problem, in this paper, a drug-target interaction prediction method based on multiple classification strategies (MCSDTI) is proposed. In MCSDTI, targets are firstly divided into two parts according to the number of interactions of the targets, where one part contains targets with smaller numbers of interactions (TWSNI) and another part contains targets with larger numbers of interactions (TWLNI). And then different classification strategies are respectively designed for TWSNI and TWLNI to predict the interaction. Furthermore, TWSNI and TWLNI are evaluated independently, which can overcome the problem that result could be mainly determined by targets with large numbers of interactions when all targets are evaluated together.

RESULTS

We propose a new drug-target interaction (MCSDTI) prediction method, which uses multiple classification strategies. MCSDTI is tested on five DTI datasets, such as nuclear receptors (NR), ion channels (IC), G protein coupled receptors (GPCR), enzymes (E), and drug bank (DB). Experiments show that the AUCs of our method are respectively 3.31%, 1.27%, 2.02%, 2.02% and 1.04% higher than that of the second best methods on NR, IC, GPCR and E for TWLNI; And AUCs of our method are respectively 1.00%, 3.20% and 2.70% higher than the second best methods on NR, IC, and E for TWSNI.

CONCLUSION

MCSDTI is a competitive method compared to the previous methods for all target parts on most datasets, which administrates that different classification strategies for different target parts is an effective way to improve the effectiveness of DTI prediction.

Identification of hot regions in hub protein-protein interactions by clustering and PPRA optimization

Background

Protein–protein interactions (PPIs) are the core of protein function, which provide an effective means to understand the function at cell level. Identification of PPIs is the crucial foundation of predicting drug-target interactions. Although traditional biological experiments of identifying PPIs are becoming available, these experiments remain to be extremely time-consuming and expensive. Therefore, various computational models have been introduced to identify PPIs. In protein-protein interaction network (PPIN), Hub protein, as a highly connected node, can coordinate PPIs and play biological functions. Detecting hot regions on Hub protein interaction interfaces is an issue worthy of discussing.

Methods

Two clustering methods, LCSD and RCNOIK are used to detect the hot regions on Hub protein interaction interfaces in this paper. In order to improve the efficiency of K-means clustering algorithm, the best k value is selected by calculating the distance square sum and the average silhouette coefficients. Then, the optimization of residue coordination number strategy is used to calculate the average coordination number. In addition, the pair potentials and relative ASA (PPRA) strategy is also used to optimize the predicted results.

Results

DataHub dataset and PartyHub dataset were used to train two clustering models respectively. Experiments show that LCSD and RCNOIK have the same coverage with Hub protein datasets, and RCNOIK is slightly higher than LCSD in Precision. The predicted hot regions are closer to the standard hot regions.

Conclusions

This paper optimizes two clustering methods based on PPRA strategy. Compared our methods for hot regions prediction against the well-known approaches, our improved methods have the higher reliability and are effective for predicting hot regions on Hub protein interaction interfaces.