Information Fusion in the Immune System

Biologically-inspired methods such as evolutionary algorithms and neural networks are proving useful in the field of information fusion. Artificial Immune Systems (AISs) are a biologically-inspired approach which take inspiration from the biological immune system. Interestingly, recent research has show how AISs which use multi-level information sources as input data can be used to build effective algorithms for real time computer intrusion detection. This research is based on biological information fusion mechanisms used by the human immune system and as such might be of interest to the information fusion community. The aim of this paper is to present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISsComment: 10 pages, 6 tables, 6 figures, Information Fusio

Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system, providing the initial detection of pathogenic invaders. Research into this family of cells has revealed that they perform information fusion which directs immune responses. We have derived a Dendritic Cell Algorithm based on the functionality of these cells, by modelling the biological signals and differentiation pathways to build a control mechanism for an artificial immune system. We present algorithmic details in addition to experimental results, when the algorithm was applied to anomaly detection for the detection of port scans. The results show the Dendritic Cell Algorithm is sucessful at detecting port scans.Comment: 21 pages, 17 figures, Information Fusio

Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system, providing the initial detection of pathogenic invaders. Research into this family of cells has revealed that they perform information fusion which directs immune responses. We have derived a Dendritic Cell Algorithm based on the functionality of these cells, by modelling the biological signals and differentiation pathways to build a control mechanism for an artificial immune system. We present algorithmic details in addition to experimental results, when the algorithm was applied to anomaly detection for the detection of port scans. The results show the Dendritic Cell Algorithm is successful at detecting port scans

An Application Case Study on Multi-sensor Data fusion System for Intelligent Process Monitoring

AbstractMulti-sensor data fusion is a technology to enable combining information from several sources in order to form a unified picture. Focusing on the indirect method, an attempt was made to build up a multi-sensor data fusion system to monitor the condition of grinding wheels with force signals and the acoustic emission (AE) signals. An artificial immune algorithm based multi-signals processing method was presented in this paper. The intelligent monitoring system is capable of incremental supervised learning of grinding conditions and quickly pattern recognition, and can continually improve the monitoring precision. The application case indicates that the accuracy of condition identification is about 87%, and able to meet the industrial need on the whole

INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

Non-Canonical Thinking for Targeting ALK-Fusion Onco-Proteins in Lung Cancer.

Anaplastic lymphoma kinase (ALK) gene rearrangements have been identified in lung cancer at 3-7% frequency, thus representing an important subset of genetic lesions that drive oncogenesis in this disease. Despite the availability of multiple FDA-approved small molecule inhibitors targeting ALK fusion proteins, drug resistance to ALK kinase inhibitors is a common problem in clinic. Thus, there is an unmet need to deepen the current understanding of genomic characteristics of ALK rearrangements and to develop novel therapeutic strategies that can overcome ALK inhibitor resistance. In this review, we present the genomic landscape of ALK fusions in the context of co-occurring mutations with other cancer-related genes, pointing to the central role of genetic epistasis (gene-gene interactions) in ALK-driven advanced-stage lung cancer. We discuss the possibility of targeting druggable domains within ALK fusion partners in addition to available strategies inhibiting the ALK kinase domain directly. Finally, we examine the potential of targeting ALK fusion-specific neoantigens in combination with other treatments, a strategy that could open a new avenue for the improved treatment of ALK positive lung cancer patients

Pathogen Response Genes Mediate Caenorhabditis elegans Innate Immunity

Innate immunity is crucial in the response and defense against pathogens for invertebrates and vertebrates alike. The soil nematode Caenorhabditis elegans is a useful model to study the eukaryotic innate immune response to microbial pathogenesis. Prior research indicates that the protein receptor FSHR-1 plays an important role in the innate recognition of intestinal infection due to pathogen consumption. Determining what genes are controlled by FSHR-1 may uncover an unknown pathway that could increase not only the comprehension of the C. elegans immune system but also innate immunity generally. To characterize the function of FSHR-1, four candidate pathogen response genes that appear to be regulated by FSHR-1 were evaluated in worms infected with Pseudomonas aeruginosa. Although intestine specific RNA interference of these four genes did not show immunity phenotypes, quantitative PCR suggests that FSHR-1 regulates the basal and/or infection-induced expression of three of the four genes. To explore this FSHR-1-dependent transcriptional induction, fluorescent transgenic reporters were constructed for the three candidate FSHR-1 target genes. The spatial expression of one putative pathogen response gene was characterized in transgenic worms under both control and pathogenic conditions. RNA interference was performed to assess the FSHR-1 dependency of this expression pattern

A hybrid system for fault detection and sensor fusion based on fuzzy clustering and artificial immune systems

In this study, an efficient new hybrid approach for multiple sensors data fusion and fault detection is presented, addressing the problem with possible multiple faults, which is based on conventional fuzzy soft clustering and artificial immune system (AIS). The proposed hybrid system approach consists of three main phases. In the first phase signal separation is performed using the Fuzzy C-Means (FCM) algorithm. Subsequently a single (fused) signal based on the information provided from the sensor signals is generated by the fusion engine. The information provided from the previous two phases is used for fault detection in the third phase based on the Artificial Immune System (AIS) negative selection mechanism. The simulations and experiments for multiple sensor systems have confirmed the strength of the new approach for online fusing and fault detection. The hybrid system gives a fault tolerance by handling different problems such as noisy sensor signals and multiple faulty sensors. This makes the new hybrid approach attractive for solving such fusion problems and fault detection during real time operations. This hybrid system is extended for early fault detection in complex mechanical systems based on a set of extracted features; these features characterize the collected sensors data. The hybrid system is able to detect the onset of fault conditions which can lead to critical damage or failure. This early detection of failure signs can provide more effective information for any maintenance actions or corrective procedure decisions