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Background. Cervical cancer is the most common gynecological malignancy, and its incidence has tended to be younger in recent years. Through the analysis of high-throughput expression data, the identification of key genes in cancer and healthy individuals as predictors of cervical cancer is of great significance for the early detection and early treatment of cervical cancer. Method. Granular computing is a concept and computing paradigm to deal with problems through information granulation, and the process of granulation can be realized by means of clustering. Based on this, this paper proposes an AB method to obtain representative elements in a multiattribute data system. First, the evaluation index FHEI of the clustering structure is introduced, and Algorithm 1 is designed to obtain the optimal clustering structure of each attribute of the data system and use it as the base cluster. Secondly, based on the clustering ensemble technology of granular computing, Algorithm 2 is designed with the help of the concept of information entropy. The algorithm takes the base cluster as the input to obtain the optimal ensemble clustering structure. Finally, using the nearest center principle, the representative elements of each class in the optimal ensemble clustering structure are obtained. Results. In this paper, the differentially expressed genes (DEGs) are screened out by using the gene expression data of cervical cancer, and the scores of the four interaction relationships among the DEGs are used as a multiattribute data system and input into the AB method. The five representative elements obtained are RTTN, SAMD10, ZNF207, WAC, and METTL14, which are the predictors of cervical cancer. The classification accuracy of these predictors is as high as 98.82%. This paper also conducts a comparative study between the AB method and other classical methods on six independent gene expression datasets. The results show that the number of predictors obtained by the AB method is small but has a high classification accuracy in the classification of patient samples. [ABSTRACT FROM AUTHOR]

To screen differentially expressed genes quickly and efficiently in breast cancer, two gene microarray datasets of breast cancer, GSE15852 and GSE45255, were downloaded from GEO. By combining the Logistic Regression and Random Forest algorithm, this paper proposed a novel method named LR-RF to select differentially expressed genes of breast cancer on microarray data by the Bonferroni test of FWER error measure. Comparing with Logistic Regression and Random Forest, our study shows that LR-FR has a great facility in selecting differentially expressed genes. The average prediction accuracy of the proposed LR-RF from replicating random test 10 times surprisingly reaches ${{93.11}}$ percent with variance as low as ${{0.00045}}$ . The prediction accuracy rate reaches a maximum 95.57 percent when threshold value $\alpha = 0.2$ in the random forest algorithm process of ranking genes’ importance score, and the differentially expressed genes are relatively few in number. In addition, through analyzing the gene interaction networks, most of the top 20 genes we selected were found to involve in the development of breast cancer. All of these results demonstrate the reliability and efficiency of LR-RF. It is anticipated that LR-RF would provide new knowledge and method for biologists, medical scientists, and cognitive computing researchers to identify disease-related genes of breast cancer. [ABSTRACT FROM AUTHOR]

Exploring the genetic structure of influenza viruses attracts the attention in the field of molecular ecology and medical genetics, whose epidemics cause morbidity and mortality worldwide. The rapid variations in RNA strand and changes of protein structure of the virus result in low-accuracy subtyping identification and make it difficult to develop effective drugs and vaccine. This paper constructs the evolutionary structure of avian influenza virus system considering both hemagglutinin and neuraminidase protein fragments. An optimization model was established to determine the rational granularity of the virus system for exploring the intrinsic relationship among the subtypes based on the fuzzy hierarchical evaluation index. Thus, an algorithm was presented to extract the rational structure. Furthermore, to reduce the systematic and computational complexity, the granular signatures of virus system were identified based on the coarse-grained idea and then its performance was evaluated through a designed classifier. The results showed that the obtained virus signatures could approximate and reflect the whole avian influenza virus system, indicating that the proposed method could identify the effective virus signatures. Once a new molecular virus is detected, it is efficient to identify the homologous virus hierarchically. [ABSTRACT FROM AUTHOR]

Based on the five climatic factors such as monthly mean temperature, monthly mean maximum temperature, monthly average lowest temperature, the average monthly net radiation and the monthly mean precipitation in the breeding distribution area of the typical migratory species such as red-crowned crane in Northeast China from 1981 to 1990, this paper studied the characteristics analysis on the related climate data by using the theory and methods of interval statistics, hierarchical clustering based on normalization distance and clustering fusion technology. The results show that the most appropriate time in breeding habitat was May to September. The conclusion coincided with the biological characteristics and ecological characteristics of red-crowned crane in Northeast China. Extracting climatic factors from climate data was critical to the predictive distribution of species. The used climatic factors in the past mostly depended on the biological and ecological characteristics of species and the correlation analysis for climatic factors in the distribution area of species. Instead, this method only depended on climate data in the distribution area of the predictive species entirely, and it obtained data mining and data processing. This set of research methods possess generality and will provide basis for the research about the impact of future climate change on the predictive distribution of the migratory species in Northeast China. [ABSTRACT FROM AUTHOR]

In this paper, based on granular space, some hierarchical clustering problems and analysis for fuzzy proximity relation are developed by using rigorous mathematical descriptions, and four results are obtained. First, the granular representation of a fuzzy proximity relation is studied, an algorithm is obtained to compute the granular space (or hierarchical clustering structure) and the min-transitive closure derived from the same fuzzy proximity relation on a finite universe, and the consistent clustering properties are discussed. Second, a consistency index that is based on granular space is developed, and a global optimizing mathematical model is established to obtain the optimal clustering of a fuzzy proximity relation. Furthermore, for given two fuzzy proximity relations on different granulations of the same universe, a collaborative clustering method is studied by the intersection operation, and this method can provide a better hierarchical clustering structure. Finally, the theory for hierarchical clustering analysis of fuzzy proximity relations on granular space is established. For given two fuzzy proximity relations on the same universe, two sufficient conditions (Theorems 6.1 and 6.2) of isomorphism are given, and the necessary condition (Theorem 6.3) and the sufficient condition (Theorem 6.4) for \varepsilon-similarity are obtained. We also illustrate the effectiveness of these theories and methods with some practical examples. These results may help form a comprehensive theory and methodology for related potential application and will help us to obtain a deeper understanding of the essence of hierarchical clustering procedures. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the research on granular space theory and structural clusters of metric based on granular space is introduced, and a comprehensive of theoretical and analyzing methodologies is developed. The granular space theory is established based on normalized equicrural metric, and the consistent cluster characteristics of ordered granular spaces are derived. Details are shared for some related subject, such as the granular representation of structural cluster from normalized metric, the optimal cluster determination, the fusion of structural cluster, and the structural clustering analysis of metric space based on granular space, etc. Theories and methodologies are established for structural clustering based on metric space, and developed a series of mathematical models and formal description tools for the research on its potential applications. Direct and geometric interpretations for structural clustering analysis are provided to assist deeper understanding the essence of clustering (or classifying) procedure. [ABSTRACT FROM AUTHOR]

Simple Summary: Following changing cellular signals, various genes adjust their activities and initiate transcripts with the right rates. The precision of such a transcriptional response has a fundamental role in the survival and development of lives. Quite unexpectedly, gene transcription has been uncovered to occur in sporadic bursts, rather than in a continuous manner. This has raised a provoking issue of how the bursting transmits regulatory signals, and it remains controversial whether the burst size, frequency, or both, take the role of signal transmission. Here, this study showed that only the burst frequency was subject to modulation by activators that carry the regulatory signals. A higher activator concentration led to a larger frequency, whereas the size remains unchanged. When very high, the burst cluster emerged, which may be mistaken as a large burst. This work thus supports the conclusion that transcription regulation is in a "digital" way. Gene transcription has been uncovered to occur in sporadic bursts. However, due to technical difficulties in differentiating individual transcription initiation events, it remains debated as to whether the burst size, frequency, or both are subject to modulation by transcriptional activators. Here, to bypass technical constraints, we addressed this issue by introducing two independent theoretical methods including analytical research based on the classic two-model and information entropy research based on the architecture of transcription apparatus. Both methods connect the signaling mechanism of transcriptional bursting to the characteristics of transcriptional uncertainty (i.e., the differences in transcriptional levels of the same genes that are equally activated). By comparing the theoretical predictions with abundant experimental data collected from published papers, the results exclusively support frequency modulation. To further validate this conclusion, we showed that the data that appeared to support size modulation essentially supported frequency modulation taking into account the existence of burst clusters. This work provides a unified scheme that reconciles the debate on burst signaling. [ABSTRACT FROM AUTHOR]

Breast cancer is highly heterogeneous. The subtypes defined using immunohistochemistry markers and gene expression profilings (GEP) are related but not equivalent, with inter-connections under investigated. Our previous study revealed a set of differentially expressed genes (diff-genes), containing 1015 mRNAs and 69 miRNAs, which characterize the immunohistochemistry-defined breast tumor subtypes at the GEP level. However, they may convey redundant information due to the large amount of genes included. By reducing the dimension of the diff-genes, we identified 119 mRNAs and 20 miRNAs best explaining breast tumor heterogeneity with the most succinct number of genes found using hierarchical clustering and nearest-to-center principle. The final signature panel contains 119 mRNAs, whose superiority over diff-genes was replicated in two independent public datasets. The comparison of our signature with two pioneering signatures, the Sorlie's signature and PAM50, suggests a novel marker, FOXA1, in breast cancer classification. Subtype-specific feature genes are reported to characterize each immunohistochemistry-defined subgroup. Pathway and network analysis reveal the critical roles of Notch signalings in [ER+|PR+]HER2− and cell cycle in [ER+|PR+]HER2+ tumors. Our study reveals the primary differences among the four immunohistochemistry-defined breast tumors at the mRNA and miRNA levels, and proposes a novel signature for breast tumor subtyping given GEP data. [ABSTRACT FROM AUTHOR]