Unknown Entities Research Articles

Agent-Based Trust and Reputation Model in Smart IoT Environments

The Internet of Things (IoT) enables smart devices to connect, share and exchange data with each other through the internet. Since an IoT environment is open and dynamic, IoT participants may need to collaborate with unknown entities with no proven track record. To ensure successful collaboration among these entities, it is important to establish a mechanism that ensures all entities operate in a trustworthy manner. We present a trust and reputation model that can be used to select the best service provider in an IoT environment. Our proposed model, IoT-CADM (Comprehensive Agent-based Decision-making Model for IoT) is an agent-based decentralised trust and reputation model that can be used to select the best service provider for a particular service based on multi-context quality of service. IoT-CADM is developed using a smart multi-agent IoT environment where information about entities is collected and evaluated using a trust and reputation algorithm. We evaluated the performance of the proposed model against some other well-known models in a simulated smart factory supply chain system. Our experimental results showed that the proposed IoT-CADM achieved the best performance.

Abstract PO5-13-07: Prediction of recurrence in triple negative breast cancer patients after receiving neoadjuvanttreatment using plasma metabolomics

Abstract Background Early-stage triple-negative breast cancer (TNBC) is usually treated with neoadjuvant chemotherapy (NADC), but prognosis remains poor compared with other BC subtypes. Unfortunately, less than 30% of BC patients achieve pathological complete response (pCR) to NADC and the overall survival of TNBC when cancer spreads is estimated at 10-13 months. Complete response is associated with improved progression-free and overall survival rates, but more accurate prognostic markers are needed. In this study, we assessed the prognostic value of blood circulating metabolites by using targeted-based metabolomics. Methods Patients received standard neoadjuvant chemotherapy with epirubicin plus cyclophosphamide followed by paclitaxel. Blood samples were obtained upon completion of chemotherapy and before surgery. Pathological response to treatment was recorded according to the Miller & Payne system, and categorized as partial (MP 1, 2, & 3) or complete response (MP 4 & 5). Blood samples were collected in EDTA tubes, centrifuged and plasma was stored at -80°C. After QC standard addition and methanol extraction of proteins, four fractions of the resulting extract were analysed: two by reverse-phase/UPLC-MS/MS methods with positive ion mode electrospray ionization (ESI), one by reverse phase/UPLC-MS/MS with negative ion mode ESI, and one by hydrophilic interaction liquid chromatography/UPLC-MS/MS) by Metabolon using a Thermo Scientific Q-Exactive mass spectrometer with a heated electrospray ionization source and an Orbitrap mass analyser. Using Metabolon's hardware and software, raw data were extracted, identified, QC processed and quantified using the area under the curve (AUC). Identified compounds were compared with library entries of purified standards or recurrent unknown entities. The prognostic capacity of each metabolite was assessed using BRB ArrayTools. Selected metabolites were validated using a targeted approach using compound standards as follows. Plasma samples were re-analysed by UPLC/MS using a Vanquish LC coupled to an Orbitrap Exploris 240 MS (Thermo Fisher Scientific) with positive and negative ionization mode ESI under polarity switching. Identification of compounds were performed by comparison of retention times (against in-house authentic standards), accurate mass (with an accepted deviation of 3ppm), and MS/MS spectra. These analyses were carried out by MS-Omics (Denmark). Results Twenty patients treated in the Medical Oncology Unit of the University Hospital of Jaén were recruited. Median age was 50 (31-76). Twelve patients obtained a pathological complete response. Median follow-up after surgery was 25 months. Seven patients eventually had distant metastases (2 in the lungs, 1 in the brain, 1 in the bones and 3 multi-site). Metabolon untargeted analysis identified and quantified 985 metabolites, 789 after xenobiotics exclusion. Survival analysis identified 16 metabolites related with distant relapse (p< 0.05). The ten metabolites with lower p-value were selected for targeted validation. Ms-Omics pipeline allowed absolute quantification of 5 metabolites, 3 of them showing high correlation (R >0.7) between both measurements (targeted and untargeted). Validation of the prognostic value of the candidate metabolites is ongoing in a larger TNBC cohort. Conclusions Metabolomics is a useful tool for the detection of simply assessable and cost-effective prognostic biomarkers in TNBC. Easily monitoring the presence of minimal residual disease is worth to be settled up in the clinical practice for the most aggressive molecular subtype of breast cancer. In this work we have defined a set of metabolites that could predict distant relapse events in TNBC patients treated with neoadjuvant chemotherapy. Further analyses are being carried out to validate the prognostic value of the proposed candidate metabolites. Citation Format: Pedro Sánchez-Rovira, Rocio Urbano-Cubero, Angelo Gamez Pozo, Carmen González-Olmedo, Alicia Cano-Jimenez, Andrea Zapater- Moros, Leticia Díaz-Beltrán, Lucia Trilla-Fuertes, Mariana Díaz-Almirón, Enrique Espinosa, Pilar Zamora, Juan Angel Fresno Vara. Prediction of recurrence in triple negative breast cancer patients after receiving neoadjuvanttreatment using plasma metabolomics [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-13-07.

Hidden diversity of cestodes in wild African carnivores: I. Non-taeniid cyclophyllideans

Our knowledge of parasites in wildlife remains limited, primarily due to restricted access to samples, especially of parasites from protected species. This present study contributes to the comprehension of the enigmatic world of helminths of African wild mammals and cestode biodiversity by combining both molecular and morphological analysis. Cestode samples were opportunistically collected from 77 individual definitive hosts in South Africa, Namibia and Ethiopia, encompassing 15 different species of wild African carnivores and additionally domestic cats. The analysis revealed 32 different cyclophyllidean species of which 21 (65.6 %) represent previously unknown genetic entities. They belong to the families Mesocestoididae, Hymenolepididae, Dipylidiidae and Taeniidae. Here we cover the non-taeniid cestodes, while the taeniids will be addressed in a separate publication. Three of the non-taeniid species uncovered in this study could be assigned to the genus Mesocestoides and were isolated from servals and domestic cats. The white-tailed mongoose was found to be a suitable host for a species belonging to the Hymenolepididae, which was identified as Pseudandrya cf. mkuzii. Both feline and canine genotypes of Dipylidium caninum were detected in domestic cats, the canine genotype also in an African wolf. In addition to these, a novel species of Dipylidium was discovered in an aardwolf. Lastly, four distinct species of Joyeuxiella were found in this study, revealing a cryptic species complex and emphasizing the need for a taxonomic reassessment of this genus. Despite the limited scope of our study in terms of geography and sample size, the results highlight that biodiversity of cestodes in African wild mammals is grossly under-researched and follow-up studies are urgently required, in particular linking morphology to gene sequences.

Bayesian Inference with Complex Knowledge Graph Evidence

Knowledge Graphs (KGs) provide a widely used format for representing entities and their relationships and have found use in diverse applications including question answering and recommendation. A majority of current research on KG inference has focused on reasoning with atomic facts (triples) and has disregarded the possibility of making complex evidential observations involving logical operators (negation, conjunction, disjunction) and quantifiers (existential, universal). Further, while the application of complex evidence has been explored in KG-based query answering (KGQA) research, in many practical online settings, observations are made sequentially. For example, in KGQA, additional context may be incrementally suggested to narrow down the answer. Or in interactive recommendation, user critiques may be expressed sequentially in order to narrow down a set of preferred items. Both settings are indicative of information filtering or tracking tasks that are reminiscent of belief tracking in Bayesian inference. In fact, in this paper, we precisely cast the problem of belief tracking over unknown KG entities given incremental complex KG evidence as a Bayesian filtering problem. Specifically, we leverage Knowledge-based Model Construction (KBMC) over the logical KG evidence to instantiate a Markov Random Field (MRF) likelihood representation to perform closed-form Bayesian inference with complex KG evidence (BIKG). We experimentally evaluate BIKG in incremental KGQA and interactive recommendation tasks demonstrating that it outperforms non-incremental methodologies and leads to better incorporation of conjunctive evidence vs. existing complex KGQA methods like CQD that leverage fuzzy T-norm operators. Overall, this work demonstrates a novel, efficient, and unified perspective of logic, KGs, and online inference through the lens of closed-form BIKG.

Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism

The inductive relation prediction of knowledge graphs, as an important research topic, aims at predicting the missing relation between unknown entities with many real-world applications. Existing approaches toward this problem mostly use enclosing subgraphs to extract the features of target nodes to make predictions; however, there is a tendency to ignore the neighboring relations outside the enclosing subgraph, thus leading to inaccurate predictions. In addition, they also neglect the rich commonsense information that can help filter out less convincing results. In order to address the above issues, this paper proposes a commonsense-guided inductive relation prediction method with a dual attention mechanism called CNIA. Specifically, in addition to the enclosing subgraph, we added the multi-hop neighboring relations of target nodes, thereby forming a neighbor-enriched subgraph where the initial embeddings are generated. Next, we obtained the subgraph representations with a dual attention (i.e., edge-aware and relation-aware) mechanism, as well as the neighboring relational path embeddings. Then, we concatenated the two embeddings before feeding them into the supervised learning model. A commonsense re-ranking mechanism was introduced to filter the results that conformed to commonsense. Extensive experiments on WN18RR, FB15k-237, and NELL995 showed that CNIA achieves better prediction results when compared to the state-of-the-art models. The results suggested that our proposed model can be considered as an effective and state-of-the-art solution for inductive relation prediction.

Bacterial community structure, adaptations and prevalence of antimicrobial resistance in bacteria from Antarctica: A review

Antarctica serves as an ideal prototype to study past climatic condition as the ice core records dates back to 100,000 years. Microbiological research on ice core has opened a doorway to our understanding on biodiversity and its contribution to the frozen ecosystem. In this review, the ice core bacterial diversity of East and West Antarctica has been documented. Cold-adapted bacteria hold immense potential in biotechnological applications as they possess functional advantages over mesophilic bacteria. Some of the neoteric applications of bioproducts of Antarctic bacteria have been stated in this review. The current climate change due to global warming exerts a humongous effect on the ecosystem of the polar region. The region being susceptible to any fluctuation in temperature, the exacerbated melting of Antarctic glaciated mass contributes to the rise of global sea level, with loss of ice harbouring unknown microbial entities. This review mainly focuses on the bacterial diversity of East and West Antarctic counterparts, the prevalence of antibiotic-resistant genes, the adaption strategies conferred by bacteria thriving in Antarctic habitats and their potential biotechnological applications.

Case Report: Remarkable breakthrough: successful treatment of a rare intracranial mesenchymal, FET::CREB fusion-positive tumor treated with patient-tailored multimodal therapy

BackgroundIntracranial mesenchymal tumors are a rare type of neoplasm (0.3% of all soft tissue tumors) characterized by a fusion of a FET family gene (usually EWSR1, rarely FUS) to CREB family genes (CREB1, ATF1, and CREM) with a slow-growing and favorable prognosis. Mesenchymal tumors are most frequently localized in the subcutaneous tissue (typically in the limbs and hands) of young adults and have rarely been diagnosed in the central nervous system. Surgery is the gold standard treatment; adjuvant radiation therapy and chemotherapy with sarcoma-based regimens have been used in rare cases when complete surgical excision was not recommended. In terms of prognosis, these tumors show a tendency for local relapse. The longest patient outcomes reported in the literature are five years.Case descriptionThis case describes a 27-year-old woman with unconventional extracranial metastatic sites of myxoid intracranial mesenchymal tumor FET::CREB fusion-positive and high expression of PD-1 (40%) and PD-L1 (30%). Based on clinical, molecular, and histological characteristics, she underwent various local and systemic therapies, including surgery, proton beam therapy, the use of immune checkpoint inhibitors, and chemotherapy. These treatments led to a complete remission of the disease after eight years from tumor diagnosis.ConclusionsOur case sheds light on the importance of precision medicine and tailored therapy to explore new treatment opportunities for rare or unknown tumor entities.

Enhanced Temporal Knowledge Graph Completion via Learning High-Order Connectivity and Attribute Information

Temporal knowledge graph completion (TKGC) refers to the prediction and filling in of missing facts on time series, which is essential for many downstream applications. However, many existing TKGC methods suffer from two limitations: (1) they only consider direct relations between entities and fail to express high-order structural dependencies between entities; and (2) they only leverage relation quadruples of temporal knowledge graphs, ignoring attribute information that contains rich semantic information. This makes them vulnerable to sparsity and incompleteness problems. In response, we propose HCAE, a temporal knowledge graph completion model that includes high-order connectivity and attribute information. This consists mainly of a recursive embedding propagation layer and a multi-head attention aggregation layer. The former leverages a recursive mechanism to update entity embeddings and can learn high-order connectivity information between entities in linear complexity time. The latter leverages an attention mechanism to understand the importance of different attributes for entity representation automatically. Combining high-order connectivity and attribute information can lead to more diverse entity representations and help enhance the model’s ability to infer unknown entities. Comparative experiments on three real-world datasets show that the model’s inference accuracy significantly outperforms other benchmark methods, especially regarding knowledge graphs with many unknown entities or relations.

Towards a Rapid-Turnaround Low-Depth Unbiased Metagenomics Sequencing Workflow on the Illumina Platforms.

Unbiased metagenomic sequencing is conceptually well-suited for first-line diagnosis as all known and unknown infectious entities can be detected, but costs, turnaround time and human background reads in complex biofluids, such as plasma, hinder widespread deployment. Separate preparations of DNA and RNA also increases costs. In this study, we developed a rapid unbiased metagenomics next-generation sequencing (mNGS) workflow with a human background depletion method (HostEL) and a combined DNA/RNA library preparation kit (AmpRE) to address this issue. We enriched and detected bacterial and fungal standards spiked in plasma at physiological levels with low-depth sequencing (<1 million reads) for analytical validation. Clinical validation also showed 93% of plasma samples agreed with the clinical diagnostic test results when the diagnostic qPCR had a Ct < 33. The effect of different sequencing times was evaluated with the 19 h iSeq 100 paired end run, a more clinically palatable simulated iSeq 100 truncated run and the rapid 7 h MiniSeq platform. Our results demonstrate the ability to detect both DNA and RNA pathogens with low-depth sequencing and that iSeq 100 and MiniSeq platforms are compatible with unbiased low-depth metagenomics identification with the HostEL and AmpRE workflow.

Web Interface of NER and RE with BERT for Biomedical Text Mining

The BioBERT Named Entity Recognition (NER) model is a high-performance model designed to identify both known and unknown entities. It surpasses previous NER models utilized by text-mining tools, such as tmTool and ezTag, in effectively discovering novel entities. In previous studies, the Biomedical Entity Recognition and Multi-Type Normalization Tool (BERN) employed this model to identify words that represent specific names, discern the type of the word, and implement it on a web page to offer NER service. However, we aimed to offer a web service that includes Relation Extraction (RE), a task determining the relation between entity pairs within a sentence. First, just like BERN, we fine-tuned the BioBERT NER model within the biomedical domain to recognize new entities. We identified two categories: diseases and genes/proteins. Additionally, we fine-tuned the BioBERT RE model to determine the presence or absence of a relation between the identified gene–disease entity pairs. The NER and RE results are displayed on a web page using the Django web framework. NER results are presented in distinct colors, and RE results are visualized as graphs in NetworkX and Cytoscape, allowing users to interact with the graphs.

Who is DNS serving for? A human-software perspective of modeling DNS services

The Domain Name System (DNS) is indispensable for almost all Internet services. It has been extensively studied for applications such as anomaly detection. However, the fundamental question of whether a DNS query from a querent (i.e., an IP address) is triggered by humans or issued by software entities remains unclear. Addressing this question enables us to profile the querent’s behavior from a human-software perspective, facilitating the understanding of “who is DNS serving for?”. In this study, we systematically performed querent-centric DNS modeling. Through in-depth measurements of three real-world DNS datasets of diverse origins, we developed an entropy-based method to distinguish between human and non-human queries and proposed a semi-supervised solution towards a community-level view for detecting and estimating software entities in a network. The solution can not only detect unknown software entities but is also NAT-compatible because it can detect and estimate software entities of multiple hosts NATed behind a single querent. An extensive evaluation demonstrates that our approach provides a new functionality for automatically disclosing the distinction between human and non-human domain names as well as a priori-independent and NAT-compatible functionality of discovering nearly 50% of the software entities and estimating their population using DNS queries.

Improving Knowledge Base Updates with CAIA: A Method Utilizing Capsule Network and Attentive Intratriplet Association Features

The ongoing and effective application of the knowledge base hinges on the dynamic updating of the knowledge base, which, in turn, depends on the accurate verification of triplets. Current methods struggle to manage unknown new entities and relations or have issues with relation characterization. This paper presents a novel approach to enhance the precision and efficiency of triplet verification during the knowledge base update process, utilizing a combination of a capsule network and an innovative feature known as the capsule network and attentive intratriplet association (CAIA). The method initially draws comprehensive triplet features from both structural and textual data. Following this, association attention values between entities and relations are derived and included with the triplet features as association features. A two‐layer capsule network is then used to score the triplets. Experimental results demonstrate that the CAIA method outperforms established baseline methods in three key metrics—H@1, H@3, and mean reciprocal rank (MRR) on FB15k‐237‐OWE+ and DBPedia50k+ datasets—showing its superior accuracy for entities, and the average increase was about 2.8%. It also excels in relation accuracy, showing the best results for the three metrics of mean rank, H@1, and MRR on both datasets, and the average improvement was about 9.1%. Additionally, more optimal parameter values in the current experimental environment are identified by comparing the effects of the different neuron numbers and capsule numbers. Based on the above research, this paper fills the research gap of deep learning in knowledge base updating.

Retracted: Recognition of Unknown Entities in Specific Financial Field Based on ERNIE-Doc-BiLSTM-CRF.

[This retracts the article DOI: 10.1155/2022/3139898.].

Knowledge guided distance supervision for biomedical relation extraction in Chinese electronic medical records

The goal of biomedical relation extraction is to obtain structured information from electronic medical records by identifying relations among clinical entities. By integrating the advantages of unsupervised and semi-supervised learning, the distant supervision approach has achieved significant success for a relation extraction task without a large amount of labeled corpora. However, in many cases, the recognized entities from the Chinese clinical text are not defined in semantic knowledge base, which limits the application of distant supervision for biomedical relation extraction. This work proposes a Knowledge Guided Distance Supervision (KGDS) model for handling the biomedical relation extraction task in Chinese electronic medical records. To handle the unknown entities, entity-type alignment (instead of entity alignment in traditional distant supervision) is employed for extracting coarse-grained relations. Then, by learning the relation embeddings both from semantic knowledge base and electronic medical record dataset as knowledge-enhanced features, this work presents a knowledge-enhanced bootstrapping learning process for fine-grained relation disambiguation. The empirical experiments on the real-world dataset of electronic medical records illustrate that our KGDS model achieves the best performance comparing to other state-of-the-art models, thereby advancing the field of biomedical relation extraction from Chinese electronic medical records.

Recognition of Unknown Entities in Specific Financial Field Based on ERNIE-Doc-BiLSTM-CRF.

The Internet is rich in information related to the financial field. The financial entity information text containing new internet vocabulary has a certain impact on the results of existing recognition algorithms. How to solve the problems of new vocabulary and polysemy is a problem to be solved in the current field. This paper proposes an ERNIE-Doc-BiLSTM-CRF named entity recognition model based on the pretrained language model. Compared with the traditional model, the ERNIE-Doc pretrained language model constructs a unique word vector from the word vector and combines the location coding, which solves polysemy problem well. The intensive skimming mechanism realizes the long text processing well and captures the context information effectively. The experimental results show that the accuracy of this model is 86.72%, the recall rate is 83.39%, and the F1 value is 85.02%, which is 13.36% higher than other models; the recall rate is increased by 13.05%, and the F1 value is increased by 13.21%.

Alpert multi-wavelets for functional inverse problems: direct optimization and deep learning

Computational engineering models often contain unknown entities (e.g. parameters, initial and boundary conditions) that require estimation from other measured observable data. Estimating such unknown entities is challenging when they involve spatio-temporal fields because such functional variables often require an infinite-dimensional representation. We address this problem by transforming an unknown functional field using Alpert wavelet bases and truncating the resulting spectrum. Hence the problem reduces to the estimation of few coefficients that can be performed using common optimization methods. We apply this method on a one-dimensional heat transfer problem where we estimate the heat source field varying in both time and space. The observable data is comprised of temperature measured at several thermocouples in the domain. This latter is composed of either copper or stainless steel. The optimization using our method based on wavelets is able to estimate the heat source with an error between 5% and 7%. We analyze the effect of the domain material and number of thermocouples as well as the sensitivity to the initial guess of the heat source. Finally, we estimate the unknown heat source using a different approach based on deep learning techniques where we consider the input and output of a multi-layer perceptron in wavelet form. We find that this deep learning approach is more accurate than the optimization approach with errors below 4%.

Knowledge graph with machine learning for product design

Knowledge graph is a particular form of graph that represents knowledge through entities and relations. Machine learning, particularly deep learning, can be adopted to construct, interpret, and enrich knowledge graph towards unknown entities and relations. As a knowledge-intensive endeavour, product design can greatly benefit from knowledge graph with machine learning. A structured framework is proposed to develop design-specific knowledge graph, based on which, deep learning is leveraged to learn graph embeddings, make predictions, and support reasoning. The framework effectiveness is validated through a quantitative experiment, where knowledge graph is used to make design-related predictions about smart products in home environment.

A Few-Shot Inductive Link Prediction Model in Knowledge Graphs

Link prediction aims to predict the missing facts in knowledge graphs. Most previous work focuses on the transductive link prediction, which cannot predict unknown entities. However, knowledge graphs are evolving in practical scenarios and new entities are constantly added. A graph neural network based on subgraph structure can effectively make predictions on a knowledge graph composed of unknown entities. Based on this method, we propose a new inductive link prediction model MILP, which uses meta-learning to predict unseen entities on few-shot data. Specifically, MILP divides the training data into four tasks according to the relation types and constructs a subgraph structure of each triplet, and then trains each task sequentially through the meta-learning framework which uses graph neural network to score the triplets. Experiments are carried out on the benchmark inductive link prediction datasets, and the results show that in most cases the proposed model achieves better results than the baseline models, proving the effectiveness of MILP.

Confronting the nameless-faceless: a duoethnography of navigating turnover and early career socialization

PurposeThe purpose of this duoethnography was to share our narratives as a left-behind early career faculty (ECF) and graduate student with minoritized identities and reflect on academic socialization processes. Specifically, when many scholars are raising alarms about the retention and success of faculty with minoritized identities, it is crucial to recognize the dimensions of socialization within the organizational context of academia.Design/methodology/approachThe authors sought an approach that would facilitate the interrogation of the overlap and divergence of the authors’ perspectives. Duoethnography research design was chosen for its focus on self-reflection as well as on the importance of the expression and consideration of those diverging perspectives. The goal was collaboration to generate a discussion that deepens a complex understanding of socialization in and professional commitment to academia.FindingsThe central insight that surfaced from the analysis of our duoethnography data is the enhanced understanding of the “nameless-faceless” dimension of academic socialization. Endeavoring to understand why faculty leave and how those who are left behind make sense of that departure led the authors to examine the unknown entities the authors are responsible to and for so they may better understand their commitment to academia.Research limitations/implicationsThe authors’ findings reveal that the nameless–faceless element is just a generalized behavior adopted in the interest of restricted and individual advantage. Diversity and equity practices are touted as a priority, but frequently, institutions act in ways that establish their own self-interests. The authors argue that we are all the nameless–faceless when they participate in academic norms that work to uphold and perpetuate traditional practices in academia.Practical implicationsThe authors’ findings point to intentional mentoring and integration of responsibility in faculty roles as potential recruitment and retention tools.Originality/valueThe authors extend the importance of collaboration and mentorship in retaining graduate students and EFC to the concept of intertwined professional commitment, or the theory that it is not simply the outcomes that are influenced by the support and cooperation between faculty with minoritized identities but that our professional commitment to academia is strengthened by that collaboration and witnessing each other's purpose and motivation to remain in academia.

Microbiome analysis, the immune response and transplantation in the era of next generation sequencing

The human gastrointestinal tract, skin and mucosal surfaces are inhabited by a complex system of bacteria, viruses, fungi, archaea, protists, and eukaryotic parasites with predominance of bacteria and bacterial viruses (bacteriophages). Collectively these microbes form the microbiota of the microecosystem of humans. Recent advancement in technologies for nucleic acid isolation from various environmental samples, feces and body secretions and advancements in shotgun throughput massive parallel DNA and RNA sequencing along with 16S ribosomal gene sequencing have unraveled the identity of otherwise unknown microbial entities constituting the human microecosystem. The improved transcriptome analysis, technological developments in biochemical analytical methods and availability of complex bioinformatics tools have allowed us to begin to understand the metabolome of the microbiome and the biochemical pathways and potential signal transduction pathways in human cells in response to microbial infections and their products. Also, developments in human whole genome sequencing, targeted gene sequencing of histocompatibility genes and other immune response associated genes by Next Generation Sequencing (NGS) have allowed us to have a better conceptualization of immune responses, and alloimmune responses. These modern technologies have enabled us to dive into the intricate relationship between commensal symbiotic and pathogenic microbiome and immune system. For the most part, the commensal symbiotic microbiota helps to maintain normal immune homeostasis besides providing healthy nutrients, facilitating digestion, and protecting the skin, mucosal and intestinal barriers. However, changes in diets, administration of therapeutic agents like antibiotics, chemotherapeutic agents, immunosuppressants etc. along with certain host factors including human histocompatibility antigens may alter the microbial ecosystem balance by causing changes in microbial constituents, hierarchy of microbial species and even dysbiosis. Such alterations may cause immune dysregulation, breach of barrier protection and lead to immunopathogenesis rather than immune homeostasis. The effects of human microbiome on immunity, health and disease are currently under intense research with cutting edge technologies in molecular biology, biochemistry, and bioinformatics along with tremendous ability to characterize immune response at single cell level. This review will discuss the contemporary status on human microbiome immune system interactions and their potential effects on health, immune homeostasis and allograft transplantation.