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Special Issue "Molecular Computing and Bioinformatics"

A special issue of Molecules (ISSN 1420-3049).

Deadline for manuscript submissions: 1 March 2019

Special Issue Editors

Guest Editor
Prof. Dr. Xiangxiang Zeng

Department of Computer Science, Xiamen University, Xiamen 361005, China
Website | E-Mail
Interests: molecular computing; membrane computing; neural computing; systems biology
Guest Editor
Prof. Dr. Alfonso Rodríguez-Patón

Department of Artificial Intelligence, Universidad Politcnica de Madrid, Madrid 28660, Spain
Website | E-Mail
Interests: DNA computing; molecular computing; synthetic biology
Guest Editor
Prof. Dr. Quan Zou

School of Computer Science and Technology, Tianjin University, Tianjin 300350, China
Website | E-Mail
Interests: bioinformatics; molecular computing; sequence alignment; systems biology

Special Issue Information

Dear Colleagues,

Molecular Computing and Bioinformatics are two important interdisciplinary sciences on molecules and computers. Molecular Computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is the potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of—and to do it cheaply. Molecules are only a few nanometers in size, making possible chips containing billions—even trillions—of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary, bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular Computing and Bioinformatics pays attention to the same object, have close relationships, but work towards different orientations.

The Guest Editors look forward to collecting a set of recent advances in the related topics, to provide a platform for researchers, and bridge computer researchers, bioengineers and molecular biologists.

Prof. Dr. Xiangxiang Zeng
Prof. Dr. Alfonso Rodríguez-Patón
Prof. Dr. Quan Zou
Guest Editors

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. Molecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). 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

  • DNA Computing
  • neural computing
  • self-assembling and self-organizing systems
  • super-turing computation
  • cellular automata
  • evolutionary computation
  • swarm intelligence
  • ant algorithms
  • artificial immune systems
  • artificial life
  • membrane computing
  • amorphous computing
  • computational systems biology
  • computational neuroscience
  • synthetic biology
  • cellular (in-vivo) computing
  • protein disorder region
  • systems biology
  • protein inter-residue contacts prediction

Published Papers (16 papers)

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Research

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Open AccessArticle 8-Bit Adder and Subtractor with Domain Label Based on DNA Strand Displacement
Molecules 2018, 23(11), 2989; https://doi.org/10.3390/molecules23112989
Received: 16 October 2018 / Revised: 10 November 2018 / Accepted: 13 November 2018 / Published: 15 November 2018
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Abstract
DNA strand displacement, which plays a fundamental role in DNA computing, has been widely applied to many biological computing problems, including biological logic circuits. However, there are many biological cascade logic circuits with domain labels based on DNA strand displacement that have not
[...] Read more.
DNA strand displacement, which plays a fundamental role in DNA computing, has been widely applied to many biological computing problems, including biological logic circuits. However, there are many biological cascade logic circuits with domain labels based on DNA strand displacement that have not yet been designed. Thus, in this paper, cascade 8-bit adder/subtractor with a domain label is designed based on DNA strand displacement; domain t and domain f represent signal 1 and signal 0, respectively, instead of domain t and domain f are applied to representing signal 1 and signal 0 respectively instead of high concentration and low concentration high concentration and low concentration. Basic logic gates, an amplification gate, a fan-out gate and a reporter gate are correspondingly reconstructed as domain label gates. The simulation results of Visual DSD show the feasibility and accuracy of the logic calculation model of the adder/subtractor designed in this paper. It is a useful exploration that may expand the application of the molecular logic circuit. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Open AccessArticle Inferring microRNA-Environmental Factor Interactions Based on Multiple Biological Information Fusion
Molecules 2018, 23(10), 2439; https://doi.org/10.3390/molecules23102439
Received: 15 August 2018 / Revised: 14 September 2018 / Accepted: 18 September 2018 / Published: 24 September 2018
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Abstract
Accumulated studies have shown that environmental factors (EFs) can regulate the expression of microRNA (miRNA) which is closely associated with several diseases. Therefore, identifying miRNA-EF associations can facilitate the study of diseases. Recently, several computational methods have been proposed to explore miRNA-EF interactions.
[...] Read more.
Accumulated studies have shown that environmental factors (EFs) can regulate the expression of microRNA (miRNA) which is closely associated with several diseases. Therefore, identifying miRNA-EF associations can facilitate the study of diseases. Recently, several computational methods have been proposed to explore miRNA-EF interactions. In this paper, a novel computational method, MEI-BRWMLL, is proposed to uncover the relationship between miRNA and EF. The similarities of miRNA-miRNA are calculated by using miRNA sequence, miRNA-EF interaction, and the similarities of EF-EF are calculated based on the anatomical therapeutic chemical information, chemical structure and miRNA-EF interaction. The similarity network fusion is used to fuse the similarity between miRNA and the similarity between EF, respectively. Further, the multiple-label learning and bi-random walk are employed to identify the association between miRNA and EF. The experimental results show that our method outperforms the state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Open AccessArticle Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures
Molecules 2018, 23(8), 2055; https://doi.org/10.3390/molecules23082055
Received: 25 June 2018 / Revised: 2 August 2018 / Accepted: 7 August 2018 / Published: 16 August 2018
PDF Full-text (8019 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Gene fusion structure is a class of common somatic mutational events in cancer genomes, which are often formed by chromosomal mutations. Identifying the driver gene(s) in a fusion structure is important for many downstream analyses and it contributes to clinical practices. Existing computational
[...] Read more.
Gene fusion structure is a class of common somatic mutational events in cancer genomes, which are often formed by chromosomal mutations. Identifying the driver gene(s) in a fusion structure is important for many downstream analyses and it contributes to clinical practices. Existing computational approaches have prioritized the importance of oncogenes by incorporating prior knowledge from gene networks. However, different methods sometimes suffer different weaknesses when handling gene fusion data due to multiple issues such as fusion gene representation, network integration, and the effectiveness of the evaluation algorithms. In this paper, Synstable Fusion (SYN), an algorithm for computationally evaluating the fusion genes, is proposed. This algorithm uses network-based strategy by incorporating gene networks as prior information, but estimates the driver genes according to the destructiveness hypothesis. This hypothesis balances the two popular evaluation strategies in the existing studies, thereby providing more comprehensive results. A machine learning framework is introduced to integrate multiple networks and further solve the conflicting results from different networks. In addition, a synchronous stability model is established to reduce the computational complexity of the evaluation algorithm. To evaluate the proposed algorithm, we conduct a series of experiments on both artificial and real datasets. The results demonstrate that the proposed algorithm performs well on different configurations and is robust when altering the internal parameter settings. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Open AccessArticle Putative Iron Acquisition Systems in Stenotrophomonas maltophilia
Molecules 2018, 23(8), 2048; https://doi.org/10.3390/molecules23082048
Received: 6 June 2018 / Revised: 22 July 2018 / Accepted: 23 July 2018 / Published: 16 August 2018
Cited by 1 | PDF Full-text (1550 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Iron has been shown to regulate biofilm formation, oxidative stress response and several pathogenic mechanisms in Stenotrophomonas maltophilia. Thus, the present study is aimed at identifying various iron acquisition systems and iron sources utilized during iron starvation in S. maltophilia. The
[...] Read more.
Iron has been shown to regulate biofilm formation, oxidative stress response and several pathogenic mechanisms in Stenotrophomonas maltophilia. Thus, the present study is aimed at identifying various iron acquisition systems and iron sources utilized during iron starvation in S. maltophilia. The annotations of the complete genome of strains K279a, R551-3, D457 and JV3 through Rapid Annotations using Subsystems Technology (RAST) revealed two putative subsystems to be involved in iron acquisition: the iron siderophore sensor and receptor system and the heme, hemin uptake and utilization systems/hemin transport system. Screening for these acquisition systems in S. maltophilia showed the presence of all tested functional genes in clinical isolates, but only a few in environmental isolates. NanoString nCounter Elements technology, applied to determine the expression pattern of the genes under iron-depleted condition, showed significant expression for FeSR (6.15-fold), HmuT (12.21-fold), Hup (5.46-fold), ETFb (2.28-fold), TonB (2.03-fold) and Fur (3.30-fold). The isolates, when further screened for the production and chemical nature of siderophores using CAS agar diffusion (CASAD) and Arnows’s colorimetric assay, revealed S. maltophilia to produce catechol-type siderophore. Siderophore production was also tested through liquid CAS assay and was found to be greater in the clinical isolate (30.8%) compared to environmental isolates (4%). Both clinical and environmental isolates utilized hemoglobin, hemin, transferrin and lactoferrin as iron sources. All data put together indicates that S. maltophilia utilizes siderophore-mediated and heme-mediated systems for iron acquisition during iron starvation. These data need to be further confirmed through several knockout studies. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Open AccessArticle Correcting Errors in Image Encryption Based on DNA Coding
Molecules 2018, 23(8), 1878; https://doi.org/10.3390/molecules23081878
Received: 25 June 2018 / Revised: 24 July 2018 / Accepted: 24 July 2018 / Published: 27 July 2018
Cited by 2 | PDF Full-text (2354 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
As a primary method, image encryption is widely used to protect the security of image information. In recent years, image encryption pays attention to the combination with DNA computing. In this work, we propose a novel method to correct errors in image encryption,
[...] Read more.
As a primary method, image encryption is widely used to protect the security of image information. In recent years, image encryption pays attention to the combination with DNA computing. In this work, we propose a novel method to correct errors in image encryption, which results from the uncertainty of DNA computing. DNA coding is the key step for DNA computing that could decrease the similarity of DNA sequences in DNA computing as well as correct errors from the process of image encryption and decryption. The experimental results show our method could be used to correct errors in image encryption based on DNA coding. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Open AccessArticle Theoretical Prediction of the Complex P-Glycoprotein Substrate Efflux Based on the Novel Hierarchical Support Vector Regression Scheme
Molecules 2018, 23(7), 1820; https://doi.org/10.3390/molecules23071820
Received: 7 July 2018 / Revised: 19 July 2018 / Accepted: 20 July 2018 / Published: 22 July 2018
PDF Full-text (3724 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood–brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range
[...] Read more.
P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood–brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range of substrates. Nevertheless, the mechanism of P-gp substrate efflux is rather complex since it can take place through active transport and passive permeability in addition to multiple P-gp substrate binding sites. A nonlinear quantitative structure–activity relationship (QSAR) model was developed in this study using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to explore the perplexing relationships between descriptors and efflux ratio. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 50, r2 = 0.96, qCV2 = 0.94, RMSE = 0.10, s = 0.10) and test set (n = 13, q2 = 0.80–0.87, RMSE = 0.21, s = 0.22). When subjected to a variety of statistical validations, the developed HSVR model consistently met the most stringent criteria. A mock test also asserted the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Open AccessArticle The Cartesian Product and Join Graphs on Edge-Version Atom-Bond Connectivity and Geometric Arithmetic Indices
Molecules 2018, 23(7), 1731; https://doi.org/10.3390/molecules23071731
Received: 25 May 2018 / Revised: 27 June 2018 / Accepted: 6 July 2018 / Published: 16 July 2018
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Abstract
The Cartesian product and join are two classical operations in graphs. Let dL(G)(e) be the degree of a vertex e in line graph L(G) of a graph G. The edge versions of
[...] Read more.
The Cartesian product and join are two classical operations in graphs. Let dL(G)(e) be the degree of a vertex e in line graph L(G) of a graph G. The edge versions of atom-bond connectivity (ABCe) and geometric arithmetic (GAe) indices of G are defined as efE(L(G))dL(G)(e)+dL(G)(f)2dL(G)(e)×dL(G)(f) and efE(L(G))2dL(G)(e)×dL(G)(f)dL(G)(e)+dL(G)(f), respectively. In this paper, ABCe and GAe indices for certain Cartesian product graphs (such as PnPm, PnCm and PnSm) are obtained. In addition, ABCe and GAe indices of certain join graphs (such as Cm+Pn+Sr, Pm+Pn+Pr, Cm+Cn+Cr and Sm+Sn+Sr) are deduced. Our results enrich and revise some known results. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Open AccessArticle Causal Discovery Combining K2 with Brain Storm Optimization Algorithm
Molecules 2018, 23(7), 1729; https://doi.org/10.3390/molecules23071729
Received: 12 May 2018 / Revised: 7 July 2018 / Accepted: 9 July 2018 / Published: 16 July 2018
PDF Full-text (1676 KB) | HTML Full-text | XML Full-text
Abstract
Exploring and detecting the causal relations among variables have shown huge practical values in recent years, with numerous opportunities for scientific discovery, and have been commonly seen as the core of data science. Among all possible causal discovery methods, causal discovery based on
[...] Read more.
Exploring and detecting the causal relations among variables have shown huge practical values in recent years, with numerous opportunities for scientific discovery, and have been commonly seen as the core of data science. Among all possible causal discovery methods, causal discovery based on a constraint approach could recover the causal structures from passive observational data in general cases, and had shown extensive prospects in numerous real world applications. However, when the graph was sufficiently large, it did not work well. To alleviate this problem, an improved causal structure learning algorithm named brain storm optimization (BSO), is presented in this paper, combining K2 with brain storm optimization (K2-BSO). Here BSO is used to search optimal topological order of nodes instead of graph space. This paper assumes that dataset is generated by conforming to a causal diagram in which each variable is generated from its parent based on a causal mechanism. We designed an elaborate distance function for clustering step in BSO according to the mechanism of K2. The graph space therefore was reduced to a smaller topological order space and the order space can be further reduced by an efficient clustering method. The experimental results on various real-world datasets showed our methods outperformed the traditional search and score methods and the state-of-the-art genetic algorithm-based methods. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Open AccessArticle Scoring Amino Acid Mutations to Predict Avian-to-Human Transmission of Avian Influenza Viruses
Molecules 2018, 23(7), 1584; https://doi.org/10.3390/molecules23071584
Received: 17 May 2018 / Revised: 13 June 2018 / Accepted: 19 June 2018 / Published: 29 June 2018
Cited by 1 | PDF Full-text (1762 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Avian influenza virus (AIV) can directly cross species barriers and infect humans with high fatality. Using machine learning methods, the present paper scores the amino acid mutations and predicts interspecies transmission. Initially, 183 signature positions in 11 viral proteins were screened by the
[...] Read more.
Avian influenza virus (AIV) can directly cross species barriers and infect humans with high fatality. Using machine learning methods, the present paper scores the amino acid mutations and predicts interspecies transmission. Initially, 183 signature positions in 11 viral proteins were screened by the scores of five amino acid factors and their random forest rankings. The most important amino acid factor (Factor 3) and the minimal range of signature positions (50 amino acid residues) were explored by a supporting vector machine (the highest-performing classifier among four tested classifiers). Based on these results, the avian-to-human transmission of AIVs was analyzed and a prediction model was constructed for virology applications. The distributions of human-origin AIVs suggested that three molecular patterns of interspecies transmission emerge in nature. The novel findings of this paper provide important clues for future epidemic surveillance. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Open AccessArticle Detection of Protein Complexes Based on Penalized Matrix Decomposition in a Sparse Protein–Protein Interaction Network
Molecules 2018, 23(6), 1460; https://doi.org/10.3390/molecules23061460
Received: 21 May 2018 / Revised: 11 June 2018 / Accepted: 12 June 2018 / Published: 15 June 2018
PDF Full-text (800 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease
[...] Read more.
High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease rapidly when applied to sparse protein–protein interaction (PPI) networks. In this study, based on penalized matrix decomposition (PMD), a novel method of penalized matrix decomposition for the identification of protein complexes (i.e., PMDpc) was developed to detect protein complexes in the human protein interaction network. This method mainly consists of three steps. First, the adjacent matrix of the protein interaction network is normalized. Second, the normalized matrix is decomposed into three factor matrices. The PMDpc method can detect protein complexes in sparse PPI networks by imposing appropriate constraints on factor matrices. Finally, the results of our method are compared with those of other methods in human PPI network. Experimental results show that our method can not only outperform classical algorithms, such as CFinder, ClusterONE, RRW, HC-PIN, and PCE-FR, but can also achieve an ideal overall performance in terms of a composite score consisting of F-measure, accuracy (ACC), and the maximum matching ratio (MMR). Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Open AccessArticle Discovering Structural Motifs in miRNA Precursors from the Viridiplantae Kingdom
Molecules 2018, 23(6), 1367; https://doi.org/10.3390/molecules23061367
Received: 29 April 2018 / Revised: 1 June 2018 / Accepted: 4 June 2018 / Published: 6 June 2018
PDF Full-text (1654 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A small non-coding molecule of microRNA (19–24 nt) controls almost every biological process, including cellular and physiological, of various organisms’ lives. The amount of microRNA (miRNA) produced within an organism is highly correlated to the organism’s key processes, and determines whether the system
[...] Read more.
A small non-coding molecule of microRNA (19–24 nt) controls almost every biological process, including cellular and physiological, of various organisms’ lives. The amount of microRNA (miRNA) produced within an organism is highly correlated to the organism’s key processes, and determines whether the system works properly or not. A crucial factor in plant biogenesis of miRNA is the Dicer Like 1 (DCL1) enzyme. Its responsibility is to perform the cleavages in the miRNA maturation process. Despite everything we already know about the last phase of plant miRNA creation, recognition of miRNA by DCL1 in pre-miRNA structures of plants remains an enigma. Herein, we present a bioinformatic procedure we have followed to discover structure patterns that could guide DCL1 to perform a cleavage in front of or behind an miRNA:miRNA* duplex. The patterns in the closest vicinity of microRNA are searched, within pre-miRNA sequences, as well as secondary and tertiary structures. The dataset consists of structures of plant pre-miRNA from the Viridiplantae kingdom. The results confirm our previous observations based on Arabidopsis thaliana precursor analysis. Hereby, our hypothesis was tested on pre-miRNAs, collected from the miRBase database to show secondary structure patterns of small symmetric internal loops 1-1 and 2-2 at a 1–10 nt distance from the miRNA:miRNA* duplex. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Open AccessArticle Small Universal Bacteria and Plasmid Computing Systems
Molecules 2018, 23(6), 1307; https://doi.org/10.3390/molecules23061307
Received: 25 April 2018 / Revised: 18 May 2018 / Accepted: 21 May 2018 / Published: 29 May 2018
Cited by 2 | PDF Full-text (398 KB) | HTML Full-text | XML Full-text
Abstract
Bacterial computing is a known candidate in natural computing, the aim being to construct “bacterial computers” for solving complex problems. In this paper, a new kind of bacterial computing system, named the bacteria and plasmid computing system (BP system), is proposed. We investigate
[...] Read more.
Bacterial computing is a known candidate in natural computing, the aim being to construct “bacterial computers” for solving complex problems. In this paper, a new kind of bacterial computing system, named the bacteria and plasmid computing system (BP system), is proposed. We investigate the computational power of BP systems with finite numbers of bacteria and plasmids. Specifically, it is obtained in a constructive way that a BP system with 2 bacteria and 34 plasmids is Turing universal. The results provide a theoretical cornerstone to construct powerful bacterial computers and demonstrate a concept of paradigms using a “reasonable” number of bacteria and plasmids for such devices. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Open AccessArticle ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers
Molecules 2018, 23(5), 1028; https://doi.org/10.3390/molecules23051028
Received: 9 April 2018 / Revised: 22 April 2018 / Accepted: 25 April 2018 / Published: 27 April 2018
PDF Full-text (4270 KB) | HTML Full-text | XML Full-text
Abstract
A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing efficiency of text mining. In
[...] Read more.
A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing efficiency of text mining. In this paper, we address this challenge by introducing parallel processing on a supercomputer. We developed paraBTM, a runnable framework that enables parallel text mining on the Tianhe-2 supercomputer. It employs a low-cost yet effective load balancing strategy to maximize the efficiency of parallel processing. We evaluated the performance of paraBTM on several datasets, utilizing three types of named entity recognition tasks as demonstration. Results show that, in most cases, the processing efficiency can be greatly improved with parallel processing, and the proposed load balancing strategy is simple and effective. In addition, our framework can be readily applied to other tasks of biomedical text mining besides NER. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Open AccessArticle To Decipher the Mycoplasma hominis Proteins Targeting into the Endoplasmic Reticulum and Their Implications in Prostate Cancer Etiology Using Next-Generation Sequencing Data
Molecules 2018, 23(5), 994; https://doi.org/10.3390/molecules23050994
Received: 7 March 2018 / Revised: 16 April 2018 / Accepted: 18 April 2018 / Published: 24 April 2018
PDF Full-text (7063 KB) | HTML Full-text | XML Full-text
Abstract
Cancer was initially considered a genetic disease. However, recent studies have revealed the connection between bacterial infections and growth of different types of cancer. The enteroinvasive strain of Mycoplasma hominis alters the normal behavior of host cells that may result in the growth
[...] Read more.
Cancer was initially considered a genetic disease. However, recent studies have revealed the connection between bacterial infections and growth of different types of cancer. The enteroinvasive strain of Mycoplasma hominis alters the normal behavior of host cells that may result in the growth of prostate cancer. The role of M. hominis in the growth and development of prostate cancer still remains unclear. The infection may regulate several factors that influence prostate cancer growth in susceptible individuals. The aim of this study was to predict M. hominis proteins targeted into the endoplasmic reticulum (ER) of the host cell, and their potential role in the induction of prostate cancer. From the whole proteome of M. hominis, 19 proteins were predicted to be targeted into the ER of host cells. The results of our study predict that several proteins of M. hominis may be targeted to the host cell ER, and possibly alter the normal pattern of protein folding. These predicted proteins can modify the normal function of the host cell. Thus, the intercellular infection of M. hominis in host cells may serve as a potential factor in prostate cancer etiology. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Review

Jump to: Research

Open AccessReview Application of Molecular Methods in the Identification of Ingredients in Chinese Herbal Medicines
Molecules 2018, 23(10), 2728; https://doi.org/10.3390/molecules23102728
Received: 9 September 2018 / Revised: 19 October 2018 / Accepted: 20 October 2018 / Published: 22 October 2018
PDF Full-text (537 KB) | HTML Full-text | XML Full-text
Abstract
There are several kinds of Chinese herbal medicines originating from diverse sources. However, the rapid taxonomic identification of large quantities of Chinese herbal medicines is difficult using traditional methods, and the process of identification itself is prone to error. Therefore, the traditional methods
[...] Read more.
There are several kinds of Chinese herbal medicines originating from diverse sources. However, the rapid taxonomic identification of large quantities of Chinese herbal medicines is difficult using traditional methods, and the process of identification itself is prone to error. Therefore, the traditional methods of Chinese herbal medicine identification must meet higher standards of accuracy. With the rapid development of bioinformatics, methods relying on bioinformatics strategies offer advantages with respect to the speed and accuracy of the identification of Chinese herbal medicine ingredients. This article reviews the applicability and limitations of biochip and DNA barcoding technology in the identification of Chinese herbal medicines. Furthermore, the future development of the two technologies of interest is discussed. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Open AccessReview Machine Learning for Drug-Target Interaction Prediction
Molecules 2018, 23(9), 2208; https://doi.org/10.3390/molecules23092208
Received: 5 August 2018 / Revised: 27 August 2018 / Accepted: 27 August 2018 / Published: 31 August 2018
PDF Full-text (507 KB) | HTML Full-text | XML Full-text
Abstract
Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve
[...] Read more.
Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this review, our goal is to focus on machine learning approaches and provide a comprehensive overview. First, we summarize a brief list of databases frequently used in drug discovery. Next, we adopt a hierarchical classification scheme and introduce several representative methods of each category, especially the recent state-of-the-art methods. In addition, we compare the advantages and limitations of methods in each category. Lastly, we discuss the remaining challenges and future outlook of machine learning in DTI prediction. This article may provide a reference and tutorial insights on machine learning-based DTI prediction for future researchers. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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