Cancer is a well-known killer of human beings, which has led to countless deaths and misery. Anticancer peptides open a promising perspective for cancer treatment, and they have various attractive advantages. Conventional wet experiments are expensive and inefficient for finding and identifying novel anticancer peptides. There is an urgent need to develop a novel computational method to predict novel anticancer peptides. In this study, we propose a deep learning long short-term memory (LSTM) neural network model, ACP-DL, to effectively predict novel anticancer peptides. More specifically, to fully exploit peptide sequence information, we developed an efficient feature representation approach by integrating binary profile feature and k-mer sparse matrix of the reduced amino acid alphabet. Then we implemented a deep LSTM model to automatically learn how to identify anticancer peptides and non-anticancer peptides. To our knowledge, this is the first time that the deep LSTM model has been applied to predict anticancer peptides. It was demonstrated by cross-validation experiments that the proposed ACP-DL remarkably outperformed other comparison methods with high accuracy and satisfied specificity on benchmark datasets. In addition, we also contributed two new anticancer peptides benchmark datasets, ACP740 and ACP240, in this work. The source code and datasets are available at https://github.com/haichengyi/ACP-DL.

Background: The key to modern drug discovery is to find, identify and prepare drug molecular targets. However, due to the influence of throughput, precision and cost, traditional experimental methods are difficult to be widely used to infer these potential Drug-Target Interactions (DTIs). Therefore, it is urgent to develop effective computational methods to validate the interaction between drugs and target.Methods: We developed a deep learning-based model for DTIs prediction. The proteins evolutionary features are extracted via Position Specific Scoring Matrix (PSSM) and Legendre Moment (LM) and associated with drugs molecular substructure fingerprints to form feature vectors of drug-target pairs. Then we utilized the Sparse Principal Component Analysis (SPCA) to compress the features of drugs and proteins into a uniform vector space. Lastly, the deep long short-term memory (DeepLSTM) was constructed for carrying out prediction.Results: A significant improvement in DTIs prediction performance can be observed on experimental results, with AUC of 0.9951, 0.9705, 0.9951, 0.9206, respectively, on four classes important drug-target datasets. Further experiments preliminary proves that the proposed characterization scheme has great advantage on feature expression and recognition. We also have shown that the proposed method can work well with small dataset.Conclusion: The results demonstration that the proposed approach has a great advantage over state-of-the-art drug-target predictor. To the best of our knowledge, this study first tests the potential of deep learning method with memory and Turing completeness in DTIs prediction.

The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research.

Long non-coding RNA (lncRNA) play critical roles in the occurrence and development of various diseases. The determination of the lncRNA-disease associations thus would contribute to provide new insights into the pathogenesis of the disease, the diagnosis, and the gene treatments. Considering that traditional experimental approaches are difficult to detect potential human lncRNA-disease associations from the vast amount of biological data, developing computational method could be of significant value. In this paper, we proposed a novel computational method named LDASR to identify associations between lncRNA and disease by analyzing known lncRNA-disease associations. First, the feature vectors of the lncRNA-disease pairs were obtained by integrating lncRNA Gaussian interaction profile kernel similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. Second, autoencoder neural network was employed to reduce the feature dimension and get the optimal feature subspace from the original feature set. Finally, Rotating Forest was used to carry out prediction of lncRNA-disease association. The proposed method achieves an excellent preference with 0.9502 AUC in leave-one-out cross-validations (LOOCV) and 0.9428 AUC in 5-fold cross-validation, which significantly outperformed previous methods. Moreover, two kinds of case studies on identifying lncRNAs associated with colorectal cancer and glioma further proves the capability of LDASR in identifying novel lncRNA-disease associations. The promising experimental results show that the LDASR can be an excellent addition to the biomedical research in the future.

Identifying Drug-Target Interactions (DTIs) is a critical step in studying pathogenesis and drug development. Due to the fact that conventional experimental methods usually suffer from high costs and low efficiency, various computational methods have been proposed to detect potential DTIs by extracting features from the biological information of drugs and their target proteins. Though effective, most of them fall short of considering the topological structure of the DTI network, which provides a global view to discover novel DTIs. In this paper, a network-based computational method, namely LG-DTI, is proposed to accurately predict DTIs over a heterogeneous information network. For drugs and target proteins, LG-DTI first learns not only their local representations from drug molecular structures and protein sequences, but also their global representations by using a semi-supervised heterogeneous network embedding method. These two kinds of representations consist of the final representations of drugs and target proteins, which are then incorporated into a Random Forest classifier to complete the task of DTI prediction. The performance of LG-DTI has been evaluated on two independent datasets and also compared with several state-of-the-art methods. Experimental results show the superior performance of LG-DTI. Moreover, our case study indicates that LG-DTI can be a valuable tool for identifying novel DTIs.

The long noncoding RNAs (lncRNAs) are ubiquitous in organisms and play crucial role in a variety of biological processes and complex diseases. Emerging evidences suggest that lncRNAs interact with corresponding proteins to perform their regulatory functions. Therefore, identifying interacting lncRNA-protein pairs is the first step in understanding the function and mechanism of lncRNA. Since it is time-consuming and expensive to determine lncRNA-protein interactions by high-throughput experiments, more robust and accurate computational methods need to be developed. In this study, we developed a new sequence distributed representation learning based method for potential lncRNA-Protein Interactions Prediction, named LPI-Pred, which is inspired by the similarity between natural language and biological sequences. More specifically, lncRNA and protein sequences were divided into k-mer segmentation, which can be regard as "word" in natural language processing. Then, we trained out the RNA2vec and Pro2vec model using word2vec and human genome-wide lncRNA and protein sequences to mine distribution representation of RNA and protein. Then, the dimension of complex features is reduced by using feature selection based on Gini information impurity measure. Finally, these discriminative features are used to train a Random Forest classifier to predict lncRNA-protein interactions. Five-fold cross-validation was adopted to evaluate the performance of LPI-Pred on three benchmark datasets, including RPI369, RPI488 and RPI2241. The results demonstrate that LPI-Pred can be a useful tool to provide reliable guidance for biological research.