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1.
Artigo em Inglês | MEDLINE | ID: mdl-32053072

RESUMO

Reconstruction of gene regulatory networks (GRN) plays an important role in understanding the complexity, functionality and pathways of biological systems, which could support the design of new drugs for diseases. Because differential equation models are flexible and strong, these models have been utilized to identify biochemical reactions and gene regulatory networks. This paper investigates the differential equation models for reverse engineering gene regulatory networks. We introduce three kinds of differential equation models, including ordinary differential equation (ODE), time-delayed differential equation (TDDE) and stochastic differential equation (SDE). ODE models include linear ODE, nonlinear ODE and S-system model. We also discuss the evolutionary algorithms, which are utilized to search the optimal structures and parameters of differential equation models. This investigation could provide a comprehensive understanding of differential equation models, and lead to the discovery of novel differential equation models.

2.
BMC Bioinformatics ; 20(1): 527, 2019 Oct 28.
Artigo em Inglês | MEDLINE | ID: mdl-31660856

RESUMO

BACKGROUND: Cancer subtype classification attains the great importance for accurate diagnosis and personalized treatment of cancer. Latest developments in high-throughput sequencing technologies have rapidly produced multi-omics data of the same cancer sample. Many computational methods have been proposed to classify cancer subtypes, however most of them generate the model by only employing gene expression data. It has been shown that integration of multi-omics data contributes to cancer subtype classification. RESULTS: A new hierarchical integration deep flexible neural forest framework is proposed to integrate multi-omics data for cancer subtype classification named as HI-DFNForest. Stacked autoencoder (SAE) is used to learn high-level representations in each omics data, then the complex representations are learned by integrating all learned representations into a layer of autoencoder. Final learned data representations (from the stacked autoencoder) are used to classify patients into different cancer subtypes using deep flexible neural forest (DFNForest) model.Cancer subtype classification is verified on BRCA, GBM and OV data sets from TCGA by integrating gene expression, miRNA expression and DNA methylation data. These results demonstrated that integrating multiple omics data improves the accuracy of cancer subtype classification than only using gene expression data and the proposed framework has achieved better performance compared with other conventional methods. CONCLUSION: The new hierarchical integration deep flexible neural forest framework(HI-DFNForest) is an effective method to integrate multi-omics data to classify cancer subtypes.


Assuntos
Neoplasias/genética , Metilação de DNA , Expressão Gênica , Genômica , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , MicroRNAs , Neoplasias/classificação
3.
Comput Methods Programs Biomed ; 176: 69-80, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31200913

RESUMO

BACKGROUND AND OBJECTIVE: Quantitative assessment of subretinal fluid in spectral domain optical coherence tomography (SD-OCT) images is crucial for the diagnosis of central serous chorioretinopathy. For the subretinal fluid segmentation, the traditional methods need to segment retinal layers and then segment subretinal fluid. The layer segmentation has a high influence on subretinal fluid segmentation, so we aim to develop a deep learning model to segment subretinal fluid automatically without layer segmentation. METHODS: In this paper, we propose a novel image-to-image double-branched and area-constraint fully convolutional networks (DA-FCN) for segmenting subretinal fluid in SD-OCT images. Firstly, the dataset is extended by mirroring image, which helps to overcome the over-fitting problem in the training stage. Then, double-branched structures are designed to learn the shallow coarse and deep representations from the SD-OCT images. DA-FCN model is directly trained using the image and corresponding pixel-based ground truth. Finally, we introduce a novel supervision mechanism by jointing the area loss LA with the softmax loss LS to learn more representative features. RESULTS: The testing dataset with 52 SD-OCT volumes from 35 eyes of 35 patients is used for the evaluation of the proposed algorithm based on the cross-validation method. For the three criterions, including the true positive volume fraction, dice similarity coefficient, and positive predicative value, our method can obtain the results of (1) 94.3, 95.3, and 96.4 for dataset 1; (2) 97.3, 95.3, and 93.4 for dataset 2; (3) 93.0, 92.8, and 92.8 for dataset 3; (4) 89.7, 90.1, and 92.6 for dataset 4. CONCLUSION: In this work, we propose a novel fully convolutional network for the automatic segmentation of the subretinal fluid. By constructing the double branched structures and area constraint term, our method shows higher segmentation accuracy without layer segmentation compared with other methods.


Assuntos
Coriorretinopatia Serosa Central/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/métodos , Imagem Tridimensional , Retina/diagnóstico por imagem , Descolamento Retiniano/diagnóstico por imagem , Tomografia de Coerência Óptica , Algoritmos , Humanos , Modelos Lineares , Probabilidade , Reprodutibilidade dos Testes
4.
Technol Health Care ; 27(S1): 185-193, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31045538

RESUMO

BACKGROUND: For a protein to execute its function, ensuring its correct subcellular localization is essential. In addition to biological experiments, bioinformatics is widely used to predict and determine the subcellular localization of proteins. However, single-feature extraction methods cannot effectively handle the huge amount of data and multisite localization of proteins. Thus, we developed a pseudo amino acid composition (PseAAC) method and an entropy density technique to extract feature fusion information from subcellular multisite proteins. OBJECTIVE: Predicting multiplex protein subcellular localization and achieve high prediction accuracy. METHOD: To improve the efficiency of predicting multiplex protein subcellular localization, we used the multi-label k-nearest neighbors algorithm and assigned different weights to various attributes. The method was evaluated using several performance metrics with a dataset consisting of protein sequences with single-site and multisite subcellular localizations. RESULTS: Evaluation experiments showed that the proposed method significantly improves the optimal overall accuracy rate of multiplex protein subcellular localization. CONCLUSION: This method can help to more comprehensively predict protein subcellular localization toward better understanding protein function, thereby bridging the gap between theory and application toward improved identification and monitoring of drug targets.


Assuntos
Aminoácidos/análise , Proteínas/análise , Frações Subcelulares/metabolismo , Biologia Computacional/métodos , Bases de Dados de Proteínas , Entropia , Proteômica
5.
Eur J Med Chem ; 171: 54-65, 2019 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-30909020

RESUMO

An efficient protocol for highly chemoselective introduction of dithiocarbamate groups to nitrogen position of indoles with bis(dialkylaminethiocarbonyl)disulfides was achieved by employing t-BuOK as a promoter. Based on this methodology, twenty nine novel indole-dithiocarbamate compounds were prepared in moderate to excellent yields at room temperature. All compounds were evaluated for their anti-inflammatory activity. Most of the compounds exhibited high potency on inhibiting the releasing of tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6). Four of them were found to suppress in vitro cytokine production in a dose-dependent manner with IC50 values in the nanomolar range. Additionally, 3-methyl-1H-indol-1-yl dimethylcarbamodithioate (3o) effectively ameliorated histopathological changes of lung tissues and attenuated lipopolysaccharides (LPS)-induced acute lung injury (ALI) in vivo. These data suggest that the new indole-dithiocarbamate derivatives could be particularly useful for further pharmaceutical development for the treatment of ALI.


Assuntos
Lesão Pulmonar Aguda/tratamento farmacológico , Anti-Inflamatórios não Esteroides/farmacologia , Indóis/farmacologia , Inflamação/tratamento farmacológico , Tiocarbamatos/farmacologia , Lesão Pulmonar Aguda/induzido quimicamente , Lesão Pulmonar Aguda/metabolismo , Animais , Anti-Inflamatórios não Esteroides/síntese química , Anti-Inflamatórios não Esteroides/química , Relação Dose-Resposta a Droga , Indóis/química , Inflamação/induzido quimicamente , Inflamação/metabolismo , Lipopolissacarídeos/antagonistas & inibidores , Lipopolissacarídeos/farmacologia , Masculino , Camundongos , Camundongos Endogâmicos C57BL , Estrutura Molecular , Relação Estrutura-Atividade , Tiocarbamatos/química
6.
Chemosphere ; 223: 659-667, 2019 May.
Artigo em Inglês | MEDLINE | ID: mdl-30802831

RESUMO

Oxygen evolution complex (OEC) in photosystem II (PSII) is sensitive to environmental stressors. However, oxidative damage mechanism in PSII-OEC is still unclear. Here, we investigated photosynthetic performance of PSII, oxidative stress and antioxidant reaction induced by reactive oxygen species (ROS) in a unicellular green alga Chlorella vulgaris (C. vulgaris) under the stress of cetyltrimethylammonium chloride (CTAC). From the changes of chlorophyll fluorescence parameters and PSII activity, it was proved that the electron transport, which occurred initially at the electron donor side of OEC, was disturbed by CTAC. Moreover, a significant decrease of the oxygen evolution rate in OEC (40.95%) while an increase of ROS (50.50%) was obtained after the exposure to 0.6 mg/L CTAC compared to the control (without CTAC), confirming that more oxygen transferred to ROS under the stress. Furthermore, the increased ROS in chloroplast and the structural destruction in thylakoid membrane were observed, respectively. These results proved that oxidative damage mechanism in PSII-OEC is mainly through the reduction of oxygen evolution and the production of excessive ROS, thus leading to the destruction of OEC performance and chloroplast structure.


Assuntos
Compostos de Bis-Trimetilamônio/toxicidade , Chlorella vulgaris/metabolismo , Estresse Oxidativo/efeitos dos fármacos , Oxigênio/química , Complexo de Proteína do Fotossistema II/química , Transporte de Elétrons , Complexo de Proteína do Fotossistema II/metabolismo , Espécies Reativas de Oxigênio/metabolismo , Tilacoides/ultraestrutura
7.
Comput Biol Med ; 105: 102-111, 2019 02.
Artigo em Inglês | MEDLINE | ID: mdl-30605812

RESUMO

Automatic and reliable segmentation for geographic atrophy in spectral-domain optical coherence tomography (SD-OCT) images is a challenging task. To develop an effective segmentation method, a two-stage deep learning framework based on an auto-encoder is proposed. Firstly, the axial data of cross-section images were used as samples instead of the projection images of SD-OCT images. Next, a two-stage learning model that includes offline-learning and self-learning was designed based on a stacked sparse auto-encoder to obtain deep discriminative representations. Finally, a fusion strategy was used to refine the segmentation results based on the two-stage learning results. The proposed method was evaluated on two datasets consisting of 55 and 56 cubes, respectively. For the first dataset, our method obtained a mean overlap ratio (OR) of 89.85 ±â€¯6.35% and an absolute area difference (AAD) of 4.79 ±â€¯7.16%. For the second dataset, the mean OR and AAD were 84.48 ±â€¯11.98%, 11.09 ±â€¯13.61%, respectively. Compared with the state-of-the-art algorithms, experiments indicate that the proposed algorithm can provide more accurate segmentation results on these two datasets without using retinal layer segmentation.

8.
Sci Rep ; 8(1): 17787, 2018 12 12.
Artigo em Inglês | MEDLINE | ID: mdl-30542062

RESUMO

Inference of gene regulatory network (GRN) is crucial to understand intracellular physiological activity and function of biology. The identification of large-scale GRN has been a difficult and hot topic of system biology in recent years. In order to reduce the computation load for large-scale GRN identification, a parallel algorithm based on restricted gene expression programming (RGEP), namely MPRGEP, is proposed to infer instantaneous and time-delayed regulatory relationships between transcription factors and target genes. In MPRGEP, the structure and parameters of time-delayed S-system (TDSS) model are encoded into one chromosome. An original hybrid optimization approach based on genetic algorithm (GA) and gene expression programming (GEP) is proposed to optimize TDSS model with MapReduce framework. Time-delayed GRNs (TDGRN) with hundreds of genes are utilized to test the performance of MPRGEP. The experiment results reveal that MPRGEP could infer more accurately gene regulatory network than other state-of-art methods, and obtain the convincing speedup.


Assuntos
Biologia Computacional/métodos , Redes Reguladoras de Genes/genética , Algoritmos , Computação em Nuvem , Expressão Gênica/genética , Modelos Biológicos , Fatores de Transcrição/genética
9.
J Cancer ; 9(19): 3620-3625, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30310520

RESUMO

To explore the role of phospholipase D1 (PLD1) mRNA in transition of prostate cancer (PCa) cells to androgen independence, we used Arraystar Human LncRNA Microarray V3.0 to detect and compare the differential expression of PLD1 and its signaling pathway-related gene in standard androgen dependence prostate cancer (ADPC) cell line LNCaP before and after the occurrence of androgen independence prostate cancer (AIPC) transition. In addition, we used the shRNA lentiviral vector to inhibit the PLD1 mRNA expression and observed its effect on LNCaP cell proliferation after AIPC transition by using MTS method. The results showed that the expression level of PLD1 mRNA was increased by 373-fold after AIPC transition (P<0.05); the PI3K/AKT signaling pathway-related gene expression was also elevated (P<0.05); the growth rate of LNCaP cells that had transited to androgen independence was reduced by about 30% when the PLD1 mRNA expression was inhibited by the shRNA lentivirus as compared with the negative control group (P<0.05). All these results suggest that PLD1 mRNA and the related PI3K/AKT signaling pathway may play an important role in AIPC. Down-regulating the expression of PLD1 mRNA could to some extent inhibit the proliferation rate of PCa cells after AIPC transition.

10.
Int J Mol Sci ; 19(10)2018 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-30326663

RESUMO

Gene regulatory network (GRN) inference can understand the growth and development of animals and plants, and reveal the mystery of biology. Many computational approaches have been proposed to infer GRN. However, these inference approaches have hardly met the need of modeling, and the reducing redundancy methods based on individual information theory method have bad universality and stability. To overcome the limitations and shortcomings, this thesis proposes a novel algorithm, named HSCVFNT, to infer gene regulatory network with time-delayed regulations by utilizing a hybrid scoring method and complex-valued flexible neural network (CVFNT). The regulations of each target gene can be obtained by iteratively performing HSCVFNT. For each target gene, the HSCVFNT algorithm utilizes a novel scoring method based on time-delayed mutual information (TDMI), time-delayed maximum information coefficient (TDMIC) and time-delayed correlation coefficient (TDCC), to reduce the redundancy of regulatory relationships and obtain the candidate regulatory factor set. Then, the TDCC method is utilized to create time-delayed gene expression time-series matrix. Finally, a complex-valued flexible neural tree model is proposed to infer the time-delayed regulations of each target gene with the time-delayed time-series matrix. Three real time-series expression datasets from (Save Our Soul) SOS DNA repair system in E. coli and Saccharomyces cerevisiae are utilized to evaluate the performance of the HSCVFNT algorithm. As a result, HSCVFNT obtains outstanding F-scores of 0.923, 0.8 and 0.625 for SOS network and (In vivo Reverse-Engineering and Modeling Assessment) IRMA network inference, respectively, which are 5.5%, 14.3% and 72.2% higher than the best performance of other state-of-the-art GRN inference methods and time-delayed methods.


Assuntos
Algoritmos , Biologia Computacional , Redes Reguladoras de Genes , Teorema de Bayes , Biologia Computacional/métodos , Reparo do DNA , Escherichia coli/genética , Reprodutibilidade dos Testes , Saccharomyces cerevisiae/genética , Sensibilidade e Especificidade
11.
Bioresour Technol ; 250: 10-16, 2018 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-29153645

RESUMO

In this work, the protein coronas of activated sludge proteins on TiO2 nanoparticles (TNPs) and ZnO nanoparticles (ZNPs) were characterized. The proteins with high affinity to TNPs and ZNPs were identified by shotgun proteomics, and their effects of on the distributions of TNPs and ZNPs in activated sludge were concluded. In addition, the effects of protein coronas on the aggregations of TNPs and ZNPs were evaluated. Thirty and nine proteins with high affinities to TNPs and ZNPs were identified, respectively. The proteomics and adsorption isotherms demonstrated that activated sludge had a higher affinity to TNPs than to ZNPs. The aggregation percentages of ZNPs at 35, 53, and 106 mg/L of proteins were 13%, 14%, and 18%, respectively, whereas those of TNPs were 21%, 30%, 41%, respectively. The proteins contributed to ZNPs aggregation by dissolved Zn ion-bridging, whereas the increasing protein concentrations enhanced the TNPs aggregation through macromolecule bridging flocculation.


Assuntos
Proteínas de Bactérias , Nanopartículas , Coroa de Proteína , Esgotos , Óxido de Zinco
12.
Bioengineered ; 9(1): 196-202, 2018 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-28886267

RESUMO

Experimental methods play a crucial role in identifying the subcellular localization of proteins and building high-quality databases. However, more efficient, automated computational methods are required to predict the subcellular localization of proteins on a large scale. Various efficient feature extraction methods have been proposed to predict subcellular localization, but challenges remain. In this paper, three novel feature extraction methods are established to improve multi-site prediction. The first novel feature extraction method utilizes repetitive information via moving windows based on a dipeptide pseudo amino acid composition method (R-Dipeptide). The second novel feature extraction method utilizes the impact of each amino acid residue on its following residues based on pseudo amino acids (I-PseAAC). The third novel feature extraction method provides local information about protein sequences that reflects the strength of the physicochemical properties of residues (PseAAC2). The multi-label k-nearest neighbor algorithm (MLKNN) is used to predict the subcellular localization of multi-site virus proteins. The best overall accuracy values of R-Dipeptide, I-PseAAC, and PseAAC2 when applied to dataset S from Virus-mPloc are 59.92%, 59.13%, and 57.94% respectively.


Assuntos
Algoritmos , Aminoácidos/química , Dipeptídeos/química , Células Eucarióticas/virologia , Proteínas Virais/química , Vírus/química , Sequência de Aminoácidos , Animais , Capsídeo/química , Compartimento Celular , Membrana Celular/química , Membrana Celular/virologia , Núcleo Celular/química , Núcleo Celular/virologia , Biologia Computacional/métodos , Conjuntos de Dados como Assunto , Retículo Endoplasmático/química , Retículo Endoplasmático/virologia , Células Eucarióticas/química , Humanos , Interações Hidrofóbicas e Hidrofílicas
13.
Phys Chem Chem Phys ; 19(48): 32708-32714, 2017 Dec 13.
Artigo em Inglês | MEDLINE | ID: mdl-29199287

RESUMO

Lithium-sulfur (Li-S) batteries have attracted increasing attention due to their high theoretical capacity, being a promising candidate for portable electronics, electric vehicles and large-scale energy storage. The interactions of bilayer structured graphitic C3N4 (bi-C3N4) with S8, lithium polysulfides (LiPSs), 1,3-dioxolane, 1,2-dimethoxyethane and tetrahydrofuran ether-based solvents have been studied using first-principles calculations. It has been found that the (micropore-scale) interlayer of bi-C3N4 shows intimate contact and strong binding with S8 and LiPSs due to the formation of chemical Li-N bonds. The incorporation of soluble LiPSs by the wrinkled layers of bi-C3N4 with 5.5-7.2 Å interlayer pores can suppress the shuttling effect. The interlayer ultramicropores with interlayer distances of <4 Å can accommodate the small Li2S2 and Li2S molecules, and impede the irreversible reaction between the solvents and the LiPSs. The calculated energy gap of bi-C3N4 decreases to be narrow during lithiation. Our results can provide a guideline for promoting the electrochemical performance of microporous g-C3N4/sulfur composites for Li-S batteries.

14.
Sensors (Basel) ; 17(4)2017 Mar 28.
Artigo em Inglês | MEDLINE | ID: mdl-28350336

RESUMO

In this paper, the problem of sensor fault and delay tolerant control problem for a class of networked control systems under external disturbances is investigated. More precisely, the dynamic characteristics of the external disturbance and sensor fault are described as the output of exogenous systems first. The original sensor fault and delay tolerant control problem is reformulated as an equivalence problem with designed available system output and reformed performance index. The feedforward and feedback sensor fault tolerant controller (FFSFTC) can be obtained by utilizing the solutions of Riccati matrix equation and Stein matrix equation. Based on the designed fault diagnoser, the proposed FFSFTC is further reconstructed to compensate for the sensor fault and delayed measurement effects. Finally, numerical examples are provided to illustrate the effectiveness of our proposed FFSFTC with different cases with various types of sensor faults, measurement delays and external disturbances.

15.
IEEE/ACM Trans Comput Biol Bioinform ; 14(5): 1122-1133, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28113983

RESUMO

Accurate classification on protein structural is playing an important role in Bioinformatics. An increase in evidence demonstrates that a variety of classification methods have been employed in such a field. In this research, the features of amino acids composition, secondary structure's feature, and correlation coefficient of amino acid dimers and amino acid triplets have been used. Flexible neutral tree (FNT), a particular tree structure neutral network, has been employed as the classification model in the protein structures' classification framework. Considering different feature groups owing diverse roles in the model, impact factors of different groups have been put forward in this research. In order to evaluate different impact factors, Impact Factors Scaling (IFS) algorithm, which aim at reducing redundant information of the selected features in some degree, have been put forward. To examine the performance of such framework, the 640, 1189, and ASTRAL datasets are employed as the low-homology protein structure benchmark datasets. Experimental results demonstrate that the performance of the proposed method is better than the other methods in the low-homology protein tertiary structures.


Assuntos
Modelos Químicos , Modelos Moleculares , Conformação Proteica , Proteínas/química , Proteínas/ultraestrutura , Análise de Sequência de Proteína/métodos , Simulação por Computador
16.
IEEE Trans Neural Netw Learn Syst ; 28(10): 2255-2267, 2017 10.
Artigo em Inglês | MEDLINE | ID: mdl-27390189

RESUMO

This paper presents a nearest neighbor partitioning method designed to improve the performance of a neural-network classifier. For neural-network classifiers, usually the number, positions, and labels of centroids are fixed in partition space before training. However, that approach limits the search for potential neural networks during optimization; the quality of a neural network classifier is based on how clear the decision boundaries are between classes. Although attempts have been made to generate floating centroids automatically, these methods still tend to generate sphere-like partitions and cannot produce flexible decision boundaries. We propose the use of nearest neighbor classification in conjunction with a neural-network classifier. Instead of being bound by sphere-like boundaries (such as the case with centroid-based methods), the flexibility of nearest neighbors increases the chance of finding potential neural networks that have arbitrarily shaped boundaries in partition space. Experimental results demonstrate that the proposed method exhibits superior performance on accuracy and average f-measure.

17.
Artigo em Inglês | MEDLINE | ID: mdl-27790412

RESUMO

Since the first report of blaNDM-1, 16 blaNDM variants have been identified among Gram-negative bacteria worldwide. Recently, a novel blaNDM variant, blaNDM-13, was identified in the chromosome of an ST101 Escherichia coli isolate from Nepal. Here we first reported plasmid-mediated blaNDM-13 in a carbapenem-resistant E. coli ST5138 clinical isolate associated with hospital-acquired urinary tract infection from China. blaNDM-13 and blaSHV-12 coexisted on the a ~54 Kb self-transferable plasmid. Compared with NDM-1, NDM-13, NDM-3, and NDM-4 had two amino acid substitutions (D95N and M154L), one amino acid substitution (D95N) and one amino acid substitutions (M154L), respectively. Complete plasmid sequencing showed that blaNDM-13-harboring plasmid (pNDM13-DC33) was highly similar to the blaNDM-1-harboring IncX3 plasmid pNDM-HN380, a common blaNDM-harboring vector circulating in China. In accordance with the structure of pNDM-HN380, pNDM13-DC33 consists of a 33-kb backbone encoding plasmid replication (repB), stability partitioning, and transfer (tra, trb, and pil) functions, and a 21-kb antimicrobial resistance region with high GC content between umuD and mpr genes. In conclusion, the present study is the first report of a plasmid-encoded blaNDM-13 and the complete sequence of a blaNDM-13-harboring plasmid (pNDM13-DC33). blaNDM-13 maybe originate from blaNDM-1 located on a pNDM-HN380-like plasmid by sequential mutations.


Assuntos
Infecções por Escherichia coli/microbiologia , Escherichia coli/isolamento & purificação , Plasmídeos/análise , Análise de Sequência de DNA , beta-Lactamases/genética , China , Conjugação Genética , Infecção Hospitalar/microbiologia , Escherichia coli/enzimologia , Escherichia coli/genética , Transferência Genética Horizontal , Genes Bacterianos , Humanos , Infecções Urinárias/microbiologia
18.
Comput Biol Med ; 72: 218-25, 2016 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-27058285

RESUMO

Regulatory interactions among target genes and regulatory factors occur instantaneously or with time-delay. In this paper, we propose a novel approach namely TDSDMI based on time-delayed S-system model (TDSS) model and delayed mutual information (DMI) to infer time-delay gene regulatory network (TDGRN). Firstly DMI is proposed to delete redundant regulator factors for each target gene. Secondly restricted gene expression programming (RGEP) is proposed as a new representation of the TDSS model to identify instantaneous and time-delayed interactions. To verify the effectiveness of the proposed method, TDSDMI is applied to both simulated and real biological datasets. Experimental results reveal that TDSDMI performs better than the recent reconstruction methods.


Assuntos
Redes Reguladoras de Genes , Modelos Teóricos , Humanos
19.
J Bioinform Comput Biol ; 14(4): 1650012, 2016 08.
Artigo em Inglês | MEDLINE | ID: mdl-27093908

RESUMO

This paper demonstrates a new time-delayed mass action model which applies a set of delay differential equations (DDEs) to represent the dynamics of gene regulatory networks (GRNs). The mass action model is a classical model which is often used to describe the kinetics of biochemical processes, so it is fit for GRN modeling. The ability to incorporate time-delayed parameters in this model enables different time delays of interaction between genes. Moreover, an efficient learning method which employs population-based incremental learning (PBIL) algorithm and trigonometric differential evolution (TDE) algorithm TDE is proposed to automatically evolve the structure of the network and infer the optimal parameters from observed time-series gene expression data. Experiments on three well-known motifs of GRN and a real budding yeast cell cycle network show that the proposal can not only successfully infer the network structure and parameters but also has a strong anti-noise ability. Compared with other works, this method also has a great improvement in performances.


Assuntos
Algoritmos , Redes Reguladoras de Genes , Modelos Genéticos , Biologia Computacional/métodos , Expressão Gênica
20.
Artigo em Inglês | MEDLINE | ID: mdl-26452288

RESUMO

Protein sub-cellular localization prediction has attracted much attention in recent years because of its importance for protein function studying and targeted drug discovery, and that makes it to be an important research field in bioinformatics. Traditional experimental methods which ascertain the protein sub-cellular locations are costly and time consuming. In the last two decades, machine learning methods got increasing development, and a large number of machine learning based protein sub-cellular location predictors have been developed. However, most of such predictors can only predict proteins in only one subcellular location. With the development of biology techniques, more and more proteins which have two or even more sub-cellular locations have been found. It is much more significant to study such proteins because they have extremely useful implication for both basic biology and bioinformatics research. In order to improve the accuracy of prediction, much more feature information which can represent the protein sequence should be extracted. In this paper, several feature extraction methods were fused together to extract the feature information, then the multi-label k nearest neighbors (ML-KNN) algorithm was used to predict protein sub-cellular locations. The best overall accuracies we got for dataset s1 in constructing Gpos-mploc is 66.7304 and 59.9206 percent for dataset s2 in constructing Virus-mPLoc.


Assuntos
Aminoácidos/análise , Biologia Computacional/métodos , Espaço Intracelular/química , Proteínas/química , Proteínas/fisiologia , Análise de Sequência de Proteína/métodos , Algoritmos , Sequência de Aminoácidos , Aminoácidos/química , Proteínas de Bactérias/análise , Proteínas de Bactérias/química , Bases de Dados de Proteínas , Bactérias Gram-Positivas
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