The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug-target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.

Understanding how biomolecules interact is a major task of systems biology. To model protein-nucleic acid interactions, it is important to identify the DNA or RNA-binding residues in proteins. Protein sequence features, including the biochemical property of amino acids and evolutionary information in terms of position-specific scoring matrix (PSSM), have been used for DNA or RNA-binding site prediction. However, PSSM is rather designed for PSI-BLAST searches, and it may not contain all the evolutionary information for modelling DNA or RNA-binding sites in protein sequences. In the present study, several new descriptors of evolutionary information have been developed and evaluated for sequence-based prediction of DNA and RNA-binding residues using support vector machines (SVMs). The new descriptors were shown to improve classifier performance. Interestingly, the best classifiers were obtained by combining the new descriptors and PSSM, suggesting that they captured different aspects of evolutionary information for DNA and RNA-binding site prediction. The SVM classifiers achieved 77.3% sensitivity and 79.3% specificity for prediction of DNA-binding residues, and 71.6% sensitivity and 78.7% specificity for RNA-binding site prediction. Predictions at this level of accuracy may provide useful information for modelling protein-nucleic acid interactions in systems biology studies. We have thus developed a web-based tool called BindN+ (
 http://bioinfo.ggc.org/bindn+/
 ) to make the SVM classifiers accessible to the research community.

BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features

Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response

Drug-induced hepatotoxicity may cause acute and chronic liver disease, leading to great concern for patient safety. It is also one of the main reasons for drug withdrawal from the market. Toxicogenomics data has been widely used in hepatotoxicity prediction. In our study, we proposed a multi-dose computational model to predict the drug-induced hepatotoxicity based on gene expression and toxicity data. The dose/concentration information after drug treatment is fully utilized in our study based on the dose-response curve, thus a more informative representative of the dose-response relationship is considered. We also proposed a new feature selection method, named MEMO, which is also one important aspect of our multi-dose model in our study, to deal with the high-dimensional toxicogenomics data. We validated the proposed model using the TG-GATEs, which is a large database recording toxicogenomics data from multiple views. The experimental results show that the drug-induced hepatotoxicity can be predicted with high accuracy and efficiency using the proposed predictive model.

Developing a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics Data

Identification of membrane protein types via multivariate information fusion with Hilbert–Schmidt Independence Criterion

The prediction of drug-target interactions (DTIs) via computational technology plays a crucial role in reducing the experimental cost. A variety of state-of-the-art methods have been proposed to improve the accuracy of DTI predictions. In this paper, we propose a kind of drug-target interactions predictor adopting multi-scale discrete wavelet transform and network features (named as DAWN) in order to solve the DTIs prediction problem. We encode the drug molecule by a substructure fingerprint with a dictionary of substructure patterns. Simultaneously, we apply the discrete wavelet transform (DWT) to extract features from target sequences. Then, we concatenate and normalize the target, drug, and network features to construct feature vectors. The prediction model is obtained by feeding these feature vectors into the support vector machine (SVM) classifier. Extensive experimental results show that the prediction ability of DAWN has a compatibility among other DTI prediction schemes. The prediction areas under the precision-recall curves (AUPRs) of four datasets are 0 . 895 (Enzyme), 0 . 921 (Ion Channel), 0 . 786 (guanosine-binding protein coupled receptor, GPCR), and 0 . 603 (Nuclear Receptor), respectively.

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An Ameliorated Prediction of Drug–Target Interactions Based on Multi-Scale Discrete Wavelet Transform and Network Features

Long non-coding RNA, also known as lncRNA, is a series of single-stranded polynucleotides (no less than 200 nucleotides each), consisting of non-protein coding transcripts. LncRNA plays a crucial role in regulating gene expression, during the transcriptional, post-transcriptional, and epigenetic processes. This is achieved by lncRNA interacts with the corresponding RNA-binding proteins. It has been drawn to a lot of attention that the reduction of the excessive laboratory cost and the increase in speed and accuracy gains benefits from the employment of computational intelligence in lncRNA-protein interaction (LPI) identification. Although numerous pertinent in silico studies of LPI prediction have been proposed, there is still room for enhancing the accuracy of the existing LPI prediction methods. In this paper, we have proposed a novel method for identifying LPI with kernel target alignment based on semi-supervised link prediction (LPI-KTASLP), which adopts multivariate information to predict lncRNAs-proteins interactions. To integrate the heterogeneous kernels, kernel target alignment has been applied to deal with kernel fusion. We have calculated the low-rank approximation matrices of lncRNA and protein, where eigendecomposition is used to reduce computing pressure. The prediction model has been obtained by producing the ultimate LPI prediction matrix. Experimental results show that the prediction ability of the LPI-KTASLP algorithm has surpassed many other LPI prediction schemes. Our method of lncRNA-protein interaction prediction has been evaluated on a standard benchmark dataset of LPIs. We have observed that the highest AUPR of 0.6148 is obtained by our proposed model (LPI-KTASLP). This is superior to the integrated LPLNP (AUPR: 0.4584), the RWR (AUPR: 0.2827), the CF (AUPR: 0.2357), the LPIHN (AUPR: 0.2299), and the LPBNI (AUPR: 0.3302). It is very encouraging that most of the LPI predictions have been confirmed to be close to relevant concentrations.

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LPI-KTASLP: Prediction of LncRNA-Protein Interaction by Semi-Supervised Link Learning With Multivariate Information

DNA-protein interactions appear as pivotal roles in diverse biological procedures and are paramount for cell metabolism, while identifying them with computational means is a kind of prudent scenario in depleting in vitro and in vivo experimental charging. A variety of state-of-the-art investigations have been elucidated to improve the accuracy of the DNA-protein binding sites prediction. Nevertheless, structure-based approaches are limited under the condition without 3D information, and the predictive validity is still refinable. In this essay, we address a kind of competitive method called Multi-scale Local Average Blocks (MLAB) algorithm to solve this issue. Different from structure-based routes, MLAB exploits a strategy that not only extracts local evolutionary information from primary sequences, but also using predicts solvent accessibility. Moreover, the construction about predictors of DNA-protein binding sites wields an ensemble weighted sparse representation model with random under-sampling. To evaluate the performance of MLAB, we conduct comprehensive experiments of DNA-protein binding sites prediction. MLAB gives M C C of 0.392 , 0.315 , 0.439 and 0.245 on PDNA-543, PDNA-41, PDNA-316 and PDNA-52 datasets, respectively. It shows that MLAB gains advantages by comparing with other outstanding methods. M C C for our method is increased by at least 0.053 , 0.015 and 0.064 on PDNA-543, PDNA-41 and PDNA-316 datasets, respectively.

Identification of DNA–protein Binding Sites through Multi-Scale Local Average Blocks on Sequence Information

Long non-coding RNAs (lncRNAs) constitute a large class of transcribed RNA molecules. They have a characteristic length of more than 200 nucleotides which do not encode proteins. They play an important role in regulating gene expression by interacting with the homologous RNA-binding proteins. Due to the laborious and time-consuming nature of wet experimental methods, more researchers should pay great attention to computational approaches for the prediction of lncRNA-protein interaction (LPI). An in-depth literature review in the state-of-the-art in silico investigations, leads to the conclusion that there is still room for improving the accuracy and velocity. This paper propose a novel method for identifying LPI by employing Kernel Ridge Regression, based on Fast Kernel Learning (LPI-FKLKRR). This approach, uses four distinct similarity measures for lncRNA and protein space, respectively. It is remarkable, that we extract Gene Ontology (GO) with proteins, in order to improve the quality of information in protein space. The process of heterogeneous kernels integration, applies Fast Kernel Learning (FastKL) to deal with weight optimization. The extrapolation model is obtained by gaining the ultimate prediction associations, after using Kernel Ridge Regression (KRR). Experimental outcomes show that the ability of modeling with LPI-FKLKRR has extraordinary performance compared with LPI prediction schemes. On benchmark dataset, it has been observed that the best Area Under Precision Recall Curve (AUPR) of 0.6950 is obtained by our proposed model LPI-FKLKRR, which outperforms the integrated LPLNP (AUPR: 0.4584), RWR (AUPR: 0.2827), CF (AUPR: 0.2357), LPIHN (AUPR: 0.2299), and LPBNI (AUPR: 0.3302). Also, combined with the experimental results of a case study on a novel dataset, it is anticipated that LPI-FKLKRR will be a useful tool for LPI prediction.

/pdf/multivariate-information-fusion-with-fast-kernel-learning-to-a196a8sx8u.pdf

Cong Shen

Papers

An Ameliorated Prediction of Drug–Target Interactions Based on Multi-Scale Discrete Wavelet Transform and Network Features

LPI-KTASLP: Prediction of LncRNA-Protein Interaction by Semi-Supervised Link Learning With Multivariate Information

Identification of DNA–protein Binding Sites through Multi-Scale Local Average Blocks on Sequence Information

Multivariate Information Fusion With Fast Kernel Learning to Kernel Ridge Regression in Predicting LncRNA-Protein Interactions.

BAT-Net: An enhanced RNA Secondary Structure prediction via bidirectional GRU-based network with attention mechanism