E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Special Protein or RNA Molecules Computational Identification 2019"

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: 28 October 2019.

Special Issue Editor

Guest Editor
Prof. Dr. Quan Zou

Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
E-Mail
Interests: bioinformatics; molecular computing; sequence alignment; systems biology

Special Issue Information

Dear colleagues,

The discovery of new molecules remains an important and challenging task. For some special proteins or RNA molecules, it is difficult, time-consuming, and costly to detect new ones. These special proteins include cytokines, enzymes, cell-penetrating peptides, anticancer peptides, cancerlectins, G protein-coupled receptors, etc. Some noncoding RNAs are also required to be annotated in the sequencing data, such as microRNA, snoRNA, snRNA, circle RNA, tRNA, etc. Researchers have often employed computer programs to list some candidates, and validated the candidates using molecular experiments. The “computer program” used is a key issue, which could cut wet experiment costs. High false positive software would lead to high costs in the validation process.

In addition to proteins, we encourage authors to pay attention to noncoding RNA molecules. MicroRNA and other noncoding RNA detections are still openly challenging for bioinformatic researchers. A perfect performance could remove the cost of Northern Blot or rtPCR. RNA function and the RNA–disease relationship are also interesting and welcome. Some network methods, including random walk and matrix factorization, have been employed in the RNA–disease relationship prediction. However, they are not robust. Sometimes, state-of-the-art methods would be invalid upon updating the datasets. I hope to see more novel and robust methods and golden benchmark datasets in the new Special Issue.

Prof. Dr. Quan Zou
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bioinformatics
  • Machine learning
  • Feature selection
  • Protein classification
  • PseAAC features
  • Anticancer peptides
  • Cell-penetrating peptides
  • Oncogene
  • DNA/RNA binding proteins
  • MHC binding peptide
  • Noncoding RNA
  • MicroRNA
  • RNA–disease relationship
  • Network

Published Papers (5 papers)

View options order results:
result details:
Displaying articles 1-5
Export citation of selected articles as:

Research

Open AccessArticle
In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method
Int. J. Mol. Sci. 2019, 20(17), 4106; https://doi.org/10.3390/ijms20174106 (registering DOI)
Received: 25 May 2019 / Revised: 20 August 2019 / Accepted: 20 August 2019 / Published: 22 August 2019
PDF Full-text (1229 KB) | Supplementary Files
Abstract
Drug-induced liver injury (DILI) is a major factor in the development of drugs and the safety of drugs. If the DILI cannot be effectively predicted during the development of the drug, it will cause the drug to be withdrawn from markets. Therefore, DILI [...] Read more.
Drug-induced liver injury (DILI) is a major factor in the development of drugs and the safety of drugs. If the DILI cannot be effectively predicted during the development of the drug, it will cause the drug to be withdrawn from markets. Therefore, DILI is crucial at the early stages of drug research. This work presents a 2-class ensemble classifier model for predicting DILI, with 2D molecular descriptors and fingerprints on a dataset of 450 compounds. The purpose of our study is to investigate which are the key molecular fingerprints that may cause DILI risk, and then to obtain a reliable ensemble model to predict DILI risk with these key factors. Experimental results suggested that 8 molecular fingerprints are very critical for predicting DILI, and also obtained the best ratio of molecular fingerprints to molecular descriptors. The result of the 5-fold cross-validation of the ensemble vote classifier method obtain an accuracy of 77.25%, and the accuracy of the test set was 81.67%. This model could be used for drug‐induced liver injury prediction. Full article
(This article belongs to the Special Issue Special Protein or RNA Molecules Computational Identification 2019)
Open AccessArticle
Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks
Int. J. Mol. Sci. 2019, 20(15), 3648; https://doi.org/10.3390/ijms20153648
Received: 11 June 2019 / Revised: 17 July 2019 / Accepted: 18 July 2019 / Published: 25 July 2019
PDF Full-text (2374 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on integrating similarities and associating information contained in heterogeneous miRNA-disease networks. However, these methods establish only shallow prediction models that fail to capture complex relationships among [...] Read more.
Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on integrating similarities and associating information contained in heterogeneous miRNA-disease networks. However, these methods establish only shallow prediction models that fail to capture complex relationships among miRNA similarities, disease similarities, and miRNA-disease associations. We propose a prediction method on the basis of network representation learning and convolutional neural networks to predict disease miRNAs, called CNNMDA. CNNMDA deeply integrates the similarity information of miRNAs and diseases, miRNA-disease associations, and representations of miRNAs and diseases in low-dimensional feature space. The new framework based on deep learning was built to learn the original and global representation of a miRNA-disease pair. First, diverse biological premises about miRNAs and diseases were combined to construct the embedding layer in the left part of the framework, from a biological perspective. Second, the various connection edges in the miRNA-disease network, such as similarity and association connections, were dependent on each other. Therefore, it was necessary to learn the low-dimensional representations of the miRNA and disease nodes based on the entire network. The right part of the framework learnt the low-dimensional representation of each miRNA and disease node based on non-negative matrix factorization, and these representations were used to establish the corresponding embedding layer. Finally, the left and right embedding layers went through convolutional modules to deeply learn the complex and non-linear relationships among the similarities and associations between miRNAs and diseases. Experimental results based on cross validation indicated that CNNMDA yields superior performance compared to several state-of-the-art methods. Furthermore, case studies on lung, breast, and pancreatic neoplasms demonstrated the powerful ability of CNNMDA to discover potential disease miRNAs. Full article
(This article belongs to the Special Issue Special Protein or RNA Molecules Computational Identification 2019)
Figures

Figure 1

Open AccessArticle
An Ensemble Classifier to Predict Protein–Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model
Int. J. Mol. Sci. 2019, 20(14), 3511; https://doi.org/10.3390/ijms20143511
Received: 2 June 2019 / Revised: 4 July 2019 / Accepted: 15 July 2019 / Published: 17 July 2019
PDF Full-text (1272 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Protein plays a critical role in the regulation of biological cell functions. Among them, whether proteins interact with each other has become a fundamental problem, because proteins usually perform their functions by interacting with other proteins. Although a large amount of protein–protein interactions [...] Read more.
Protein plays a critical role in the regulation of biological cell functions. Among them, whether proteins interact with each other has become a fundamental problem, because proteins usually perform their functions by interacting with other proteins. Although a large amount of protein–protein interactions (PPIs) data has been produced by high-throughput biotechnology, the disadvantage of biological experimental technique is time-consuming and costly. Thus, computational methods for predicting protein interactions have become a research hot spot. In this research, we propose an efficient computational method that combines Rotation Forest (RF) classifier with Local Binary Pattern (LBP) feature extraction method to predict PPIs from the perspective of Position-Specific Scoring Matrix (PSSM). The proposed method has achieved superior performance in predicting Yeast, Human, and H. pylori datasets with average accuracies of 92.12%, 96.21%, and 86.59%, respectively. In addition, we also evaluated the performance of the proposed method on the four independent datasets of C. elegans, H. pylori, H. sapiens, and M. musculus datasets. These obtained experimental results fully prove that our model has good feasibility and robustness in predicting PPIs. Full article
(This article belongs to the Special Issue Special Protein or RNA Molecules Computational Identification 2019)
Figures

Figure 1

Open AccessArticle
MPLs-Pred: Predicting Membrane Protein-Ligand Binding Sites Using Hybrid Sequence-Based Features and Ligand-Specific Models
Int. J. Mol. Sci. 2019, 20(13), 3120; https://doi.org/10.3390/ijms20133120
Received: 8 May 2019 / Revised: 23 June 2019 / Accepted: 23 June 2019 / Published: 26 June 2019
PDF Full-text (2847 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Membrane proteins (MPs) are involved in many essential biomolecule mechanisms as a pivotal factor in enabling the small molecule and signal transport between the two sides of the biological membrane; this is the reason that a large portion of modern medicinal drugs target [...] Read more.
Membrane proteins (MPs) are involved in many essential biomolecule mechanisms as a pivotal factor in enabling the small molecule and signal transport between the two sides of the biological membrane; this is the reason that a large portion of modern medicinal drugs target MPs. Therefore, accurately identifying the membrane protein-ligand binding sites (MPLs) will significantly improve drug discovery. In this paper, we propose a sequence-based MPLs predictor called MPLs-Pred, where evolutionary profiles, topology structure, physicochemical properties, and primary sequence segment descriptors are combined as features applied to a random forest classifier, and an under-sampling scheme is used to enhance the classification capability with imbalanced samples. Additional ligand-specific models were taken into consideration in refining the prediction. The corresponding experimental results based on our method achieved an appreciable performance, with 0.63 MCC (Matthews correlation coefficient) as the overall prediction precision, and those values were 0.604, 0.7, and 0.692, respectively, for the three main types of ligands: drugs, metal ions, and biomacromolecules. MPLs-Pred is freely accessible at http://icdtools.nenu.edu.cn/. Full article
(This article belongs to the Special Issue Special Protein or RNA Molecules Computational Identification 2019)
Figures

Graphical abstract

Open AccessArticle
mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides
Int. J. Mol. Sci. 2019, 20(8), 1964; https://doi.org/10.3390/ijms20081964
Received: 15 March 2019 / Revised: 8 April 2019 / Accepted: 18 April 2019 / Published: 22 April 2019
Cited by 5 | PDF Full-text (2973 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for [...] Read more.
Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resultant predicted probabilities of ACPs were further utilized as feature vectors. (ii) The predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model called mACPpred. Cross-validation analysis showed that the proposed predictor performs significantly better than individual feature encodings. Furthermore, mACPpred significantly outperformed the existing methods compared in this study when objectively evaluated on an independent dataset. Full article
(This article belongs to the Special Issue Special Protein or RNA Molecules Computational Identification 2019)
Figures

Figure 1

Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top