Exploring the potential drug–target interactions (DTIs) is a key step in drug discovery and repurposing. In recent years, predicting the probable DTIs through computational methods has gradually become a research hot spot. However, most of the previous studies failed to judiciously take into account the consistency between the chemical properties of drug and its functions. The changes of these relationships may lead to a severely negative effect on the prediction of DTIs. We propose an autoencoder-based method, AEFS, under spatial consistency constraints to predict DTIs. A heterogeneous network is established to integrate the information of drugs, proteins and diseases. The original drug features are projected to an embedding (protein) space by a multi-layer encoder, and further projected into label (disease) space by a decoder. In this process, the clinical information of drugs is introduced to assist the DTI prediction. By maintaining the distribution of drug correlation in the original feature, embedding and label space, AEFS keeps the consistency between chemical properties and functions of drugs. Experimental comparisons indicate that AEFS is more robust for imbalanced data and of significantly superior performance in DTI prediction. Case studies further confirm its ability to mine the latent DTIs. The code of AEFS is available at https://github.com/JackieSun818/AEFS. Supplementary data are available at Bioinformatics online.

Detecting associations between an input gene set and annotated gene sets (e.g., pathways) is an important problem in modern molecular biology. In this paper, we propose two algorithms, termed NetPEA and NetPEA’, for conducting network-based pathway enrichment analysis. Our algorithms consider not only shared genes but also gene–gene interactions. Both algorithms utilize a protein–protein interaction network and a random walk with a restart procedure to identify hidden relationships between an input gene set and pathways, but both use different randomization strategies to evaluate statistical significance and as a result emphasize different pathway properties. Compared to an over representation-based method, our algorithms can identify more statistically significant pathways. Compared to an existing network-based algorithm, EnrichNet, our algorithms have a higher sensitivity in revealing the true causal pathways while at the same time achieving a higher specificity. A literature review of selected results indicates that some of the novel pathways reported by our algorithms are biologically relevant and important. While the evaluations are performed only with KEGG pathways, we believe the algorithms can be valuable for general functional discovery from high-throughput experiments.

For evaluating the classification model of an information system, a proper measure is usually needed to determine if the model is appropriate for dealing with the specific domain task. Though many performance measures have been proposed, few measures were specially defined for multi-class problems, which tend to be more complicated than two-class problems, especially in addressing the issue of class discrimination power. Confusion entropy was proposed for evaluating classifiers in the multi-class case. Nevertheless, it makes no use of the probabilities of samples classified into different classes. In this paper, we propose to calculate confusion entropy based on a probabilistic confusion matrix. Besides inheriting the merit of measuring if a classifier can classify with high accuracy and class discrimination power, probabilistic confusion entropy also tends to measure if samples are classified into true classes and separated from others with high probabilities. Analysis and experimental comparisons show the feasibility of the simply improved measure and demonstrate that the measure does not stand or fall over the classifiers on different datasets in comparison with the compared measures.

Although several traditional Chinese medicine (TCM)-related databases have emerged, they focus on researching single medicinal materials, which is far from sufficient for clinical research and application. In comparison, compound prescriptions are more informative and meaningful in TCM, for they embody the information on the compatibility of TCM besides the relatively isolated information about single medicinal materials. The compatibility information is essential in TCM because it conveys not only what components are involved to treat special diseases but also how to combine these single medical materials. We established a database of Chinese patent medicine and compound prescription (CPMCP). It demonstrates the prescription information of Chinese patent medicines (CPMs) and ancient Chinese medicine prescriptions (CMPs). CPMCP reports their comprehensive and standardized information such as the components, indications and contraindications. It is worth mentioning that we organized relevant experts and spent lots of time manually mapping the functions of compound prescriptions in ancient Chinese to the standardized TCM symptom vocabularies, obtaining a total of 71 414 associations between compound prescriptions and TCM symptoms. In this way, CPMCP established the associations between TCM and modern medicine (MM) according to the associations between TCM symptoms and MM symptoms. In addition, to further exhibit the compatibility mechanism of compound prescriptions, CPMCP summarizes a set of common drug combination principles by analyzing the existing prescriptions. We believe that CPMCP can promote the modernization of TCM and make greater contributions to MM. Database URLhttp://cpmcp.top

The Receiver Operator Characteristic (ROC) curve is well-known in evaluating classification performance in biomedical field. Owing to its superiority in dealing with imbalanced and cost-sensitive data, the ROC curve has been exploited as a popular metric to evaluate and find out disease-related genes (features). The existing ROC-based feature selection approaches are simple and effective in evaluating individual features. However, these approaches may fail to find real target feature subset due to their lack of effective means to reduce the redundancy between features, which is essential in machine learning. In this paper, we propose to assess feature complementarity by a trick of measuring the distances between the misclassified instances and their nearest misses on the dimensions of pairwise features. If a misclassified instance and its nearest miss on one feature dimension are far apart on another feature dimension, the two features are regarded as complementary to each other. Subsequently, we propose a novel filter feature selection approach on the basis of the ROC analysis. The new approach employs an efficient heuristic search strategy to select optimal features with highest complementarities. The experimental results on a broad range of microarray data sets validate that the classifiers built on the feature subset selected by our approach can get the minimal balanced error rate with a small amount of significant features. Compared with other ROC-based feature selection approaches, our new approach can select fewer features and effectively improve the classification performance.

Exploring drug-protein interactions (DPIs) through computational methods can effectively reduce the workload and the cost of DPI identification. Previous works try to predict DPIs by integrating and analyzing the unique features of drugs and proteins. They cannot adequately analyze the consistency between the drug features and the protein features due to their different semantics. However, the consistency of their features, such as the correlation originating from their sharing diseases, may reveal some potential DPIs. Here we propose a deep neural network-based co-coding method (DNNCC for short) to predict novel DPIs. DNNCC projects the original features of drugs and proteins to a common embedding space through a co-coding strategy. In this way, the embedding features of drugs and proteins have the same semantics. Therefore, the prediction module can discover the unknown DPIs by exploring the feature consistency between drugs and proteins. The experimental results indicate that the performance of DNNCC is significantly superior to five state-of-the-art DPI prediction methods under several evaluation metrics. The superiority of integrating and analyzing the common features of drugs and proteins is proved by the ablation experiments. The novel DPIs predicted by DNNCC verify that DNNCC is a powerful prior tool that can effectively discover potential DPIs.