Search

Many major diseases, including various types of cancer, are increasingly threatening human health. However, the mechanisms of the dynamic processes underlying these diseases remain ambiguous. From the holistic perspective of systems science, complex biological networks can reveal biological phenomena. Changes among networks in different states influence the direction of living organisms. The identification of the kernel differential subgraph (KDS) that leads to drastic changes is critical. The existing studies contribute to the identification of a KDS in networks with the same nodes; however, networks in different states involve the disappearance of some nodes or the appearance of some new nodes. In this paper, we propose a new topology-based KDS (TKDS) method to explore the core module from gene regulatory networks with different nodes in this process. For the common nodes, the TKDS method considers the differential value (D-value) of the topological change. For the different nodes, TKDS identifies the most similar gene pairs and computes the D-value. Hence, TKDS discovers the essential KDS, which considers the relationships between the same nodes as well as different nodes. After applying this method to non-small cell lung cancer (NSCLC), we identified 30 genes that are most likely related to NSCLC and extracted the KDSs in both the cancer and normal states. Two significance functional modules were revealed, and gene ontology (GO) analyses and literature mining indicated that the KDSs are essential to the processes in NSCLC. In addition, compared with existing methods, TKDS provides a unique perspective in identifying particular genes and KDSs related to NSCLC. Moreover, TKDS has the potential to predict other critical disease-related genes and modules.

It is challenging to obtain reliable and optimal mapping between networks for alignment algorithms when both nodal and topological structures are taken into consideration due to the underlying NP-hard problem. Here, we introduce an adaptive hybrid algorithm that combines the classical Hungarian algorithm and the Greedy algorithm (HGA) for the global alignment of biomolecular networks. With this hybrid algorithm, every pair of nodes with one in each network is first aligned based on node information (e.g., their sequence attributes) and then followed by an adaptive and convergent iteration procedure for aligning the topological connections in the networks. For four well-studied protein interaction networks, i.e., C.elegans, yeast, D.melanogaster, and human, applications of HGA lead to improved alignments in acceptable running time. The mapping between yeast and human PINs obtained by the new algorithm has the largest value of common gene ontology (GO) terms compared to those obtained by other existing algorithms, while it still has lower Mean normalized entropy (MNE) and good performances on several other measures. Overall, the adaptive HGA is effective and capable of providing good mappings between aligned networks in which the biological properties of both the nodes and the connections are important.

Backgrounds: Recent explosion of biological data brings a great challenge for the traditional clustering algorithms. With increasing scale of data sets, much larger memory and longer runtime are required for the cluster identification problems. The affinity propagation algorithm outperforms many other classical clustering algorithms and is widely applied into the biological researches. However, the time and space complexity become a great bottleneck when handling the large-scale data sets. Moreover, the similarity matrix, whose constructing procedure takes long runtime, is required before running the affinity propagation algorithm, since the algorithm clusters data sets based on the similarities between data pairs., Methods: Two types of parallel architectures are proposed in this paper to accelerate the similarity matrix constructing procedure and the affinity propagation algorithm. The memory-shared architecture is used to construct the similarity matrix, and the distributed system is taken for the affinity propagation algorithm, because of its large memory size and great computing capacity. An appropriate way of data partition and reduction is designed in our method, in order to minimize the global communication cost among processes., Result: A speedup of 100 is gained with 128 cores. The runtime is reduced from serval hours to a few seconds, which indicates that parallel algorithm is capable of handling large-scale data sets effectively. The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies.

Backgrounds: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence) alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly., Methods: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction., Results: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective prediction of long length proteins could be possible by the CNNcon.

Detection of protein families in large scale database is a difficult but important biological problem. Computational clustering methods can effectively address the problem. Although there exist many clustering algorithms, most of them are just based on the threshold. Their computational performances are affected by the weight distribution greatly, and they are only valid for some special networks. A new network clustering algorithm, Markov Finding and Clustering (MFC), is proposed to cluster the proteins into their functionally specific families accurately in this paper. The MFC algorithm makes an improvement in the random walk process and reduces the affection of the noise on the clustering result. It has a good performance on these networks which are not well addressed by existing algorithms sensitive to the noise. Finally, experiments on the protein sequence datasets demonstrate that the algorithm is effective in the detection of protein families and has a better performance than the current algorithms.

Glioblastoma (GBM) is a fast-growing type of malignant primary brain tumor. To explore the mechanisms in GBM, complex biological networks are used to reveal crucial changes among different biological states, which reflect on the development of living organisms. It is critical to discover the kernel differential subgraph (KDS) that leads to drastic changes. However, identifying the KDS is similar to the Steiner Tree problem that is an NP-hard problem. In this paper, we developed a criterion to explore the KDS (CKDS), which considered the connectivity and scale of KDS, the topological difference of nodes and function relevance between genes in the KDS. The CKDS algorithm was applied to simulated datasets and three single-cell RNA sequencing (scRNA-seq) datasets including GBM, fetal human cortical neurons (FHCN) and neural differentiation. Then we performed the network topology and functional enrichment analyses on the extracted KDSs. Compared with the state-of-art methods, the CKDS algorithm outperformed on simulated datasets to discover the KDSs. In the GBM and FHCN, seventeen genes (one biomarker, nine regulatory genes, one driver genes, six therapeutic targets) and KEGG pathways in KDSs were strongly supported by literature mining that they were highly interrelated with GBM. Moreover, focused on GBM, there were fifteen genes (including ten regulatory genes, three driver genes, one biomarkers, one therapeutic target) and KEGG pathways found in the KDS of neural differentiation process from activated neural stem cells (aNSC) to neural progenitor cells (NPC), while few genes and no pathway were found in the period from NPC to astrocytes (Ast). These experiments indicated that the process from aNSC to NPC is a key differentiation period affecting the development of GBM. Therefore, the CKDS algorithm provides a unique perspective in identifying cell-type-specific genes and KDSs.

The major histocompatibility complex class II (MHC-II) molecule plays a crucial role in immunology. Computational prediction of MHC-II binding peptides can help researchers understand the mechanism of immune systems and design vaccines. Most of the prediction algorithms for MHC-II to date have made large efforts in human leukocyte antigen (HLA, the name of MHC in Human) molecules encoded in the DR locus. However, HLA DQ molecules are equally important and have only been made less progress because it is more difficult to handle them experimentally. In this study, we propose an artificial neural network-based approach called MHC2NNZ to predict peptides binding to HLA DQ molecules. Unlike previous artificial neural network-based methods, MHC2NNZ not only considers sequence similarity features but also captures the chemical and physical properties, and a novel method incorporating these properties is proposed to represent peptide flanking regions (PFR). Furthermore, MHC2NNZ improves the prediction accuracy by combining with amino acid preference at more specific positions of the peptides binding core. By evaluating on 3549 peptides binding to six most frequent HLA DQ molecules, MHC2NNZ is demonstrated to outperform other state-of-the-art MHC-II prediction methods. [ABSTRACT FROM AUTHOR]

Rapid progress in research and development of wireless networking and communication technologies has created different types of wireless systems (e.g., Bluetooth, IEEE 802.11, UMTS, and satellite networks). These systems are envisioned to coordinate with each other to provide ubiquitous high-data-rate services to mobile users. In this article a novel architecture, Architecture for Ubiquitous Mobile Communications (AMC), is introduced that integrates these heterogeneous wireless systems. AMC eliminates the need for direct service level agreements among service providers by using a third party, a network interoperating agent. Instead of deploying a totally new infrastructure, AMC extends the existing infra-structure to integrate heterogeneous wireless systems. It uses IP as the interconnection protocol. By using IP as the gluing protocol, transparency to the heterogeneities of the individual systems is achieved in AMC. Third-party-based authentication and billing algorithms are designed for AMC. New mobility management protocols are also developed to support seamless roaming between different wireless systems. [ABSTRACT FROM AUTHOR]

Cluster analysis plays an important role in identifying the natural structure of the target dataset. It has been widely used in many fields, such as pattern recognition, machine learning, image segmentation, document clustering and so on. There are many different methods to conduct cluster analysis. Namely, most real datasets are non-spherical and have complex shapes. Although these methods are widely used to deal with clustering tasks, they are susceptible to noise and arbitrary shapes. Thus, we propose a novel clustering algorithm (called RNN-NSDC) in this paper, which is based on the natural reverse nearest neighbor structure. Firstly, we apply the reverse nearest neighbors in the algorithm to extract core objects. Secondly, our algorithm uses the neighbor structure information of core objects to cluster. And excluding noise effects, core sets can well represent the structure of clusters. Therefore, the RNN-NSDC can obtain the optimal cluster numbers for the datasets which contain clusters of outliers and arbitrary shapes. To verify the efficiency and accuracy of the RNN-NSDC, synthetic datasets and real datasets are used for experiments. The results indicate the superiority of the RNN-NSDC compared with K-means, DBSCAN, DPC, SNNDPC, DCore and NaNLORE. • The criterion of extracting the core objects is simple and efficient. • There is no need to set parameters in RNN-NSDC. • RNN-NSDC can be applied to complex patterns with extremely large variations in density. • RNN-NSDC is robust to outliers and noises. [ABSTRACT FROM AUTHOR]