Hierarchical Relationships Research Articles

Detecting global and local hierarchical structures in cell-cell communication using CrossChat

Cell-cell communication (CCC) occurs across different biological scales, ranging from interactions between large groups of cells to interactions between individual cells, forming a hierarchical structure. Globally, CCC may exist between clusters or only subgroups of a cluster with varying size, while locally, a group of cells as sender or receiver may exhibit distinct signaling properties. Current existing methods infer CCC from single-cell RNA-seq or Spatial Transcriptomics only between predefined cell groups, neglecting the existing hierarchical structure within CCC that are determined by signaling molecules, in particular, ligands and receptors. Here, we develop CrossChat, a novel computational framework designed to infer and analyze the hierarchical cell-cell communication structures using two complementary approaches: a global hierarchical structure using a multi-resolution clustering method, and multiple local hierarchical structures using a tree detection method. This framework provides a comprehensive approach to understand the hierarchical relationships within CCC that govern complex tissue functions. By applying our method to two nonspatial scRNA-seq datasets sampled from COVID-19 patients and mouse embryonic skin, and two spatial transcriptomics datasets generated from Stereo-seq of mouse embryo and 10x Visium of mouse wounded skin, we showcase CrossChat’s functionalities for analyzing both global and local hierarchical structures within cell-cell communication.

Disentangled similarity graph attention heterogeneous biological memory network for predicting disease-associated miRNAs

BackgroundThe association between MicroRNAs (miRNAs) and diseases is crucial in treating and exploring many diseases or cancers. Although wet-lab methods for predicting miRNA-disease associations (MDAs) are effective, they are often expensive and time-consuming. Significant advancements have been made using Graph Neural Network-based methods (GNN-MDAs) to address these challenges. However, these methods still face limitations, such as not considering nodes’ deep-level similarity associations and hierarchical learning patterns. Additionally, current models do not retain the memory of previously learned heterogeneous historical information about miRNAs or diseases, only focusing on parameter learning without the capability to remember heterogeneous associations.ResultsThis study introduces the K-means disentangled high-level biological similarity to utilize potential hierarchical relationships fully and proposes a Graph Attention Heterogeneous Biological Memory Network architecture (DiGAMN) with memory capabilities. Extensive experiments were conducted across four datasets, comparing the DiGAMN model and its disentangling method against ten state-of-the-art non-disentangled methods and six traditional GNNs. DiGAMN excelled, achieving AUC scores of 96.35%, 96.10%, 96.01%, and 95.89% on the Data1 to Data4 datasets, respectively, surpassing all other models. These results confirm the superior performance of DiGAMN and its disentangling method. Additionally, various ablation studies were conducted to validate the contributions of different modules within the framework, and’s encoding statuses and memory units of DiGAMN were visualized to explore the utility and functionality of its modules. Case studies confirmed the effectiveness of DiGAMN’s predictions, identifying several new disease-associated miRNAs.ConclusionsDiGAMN introduces the use of a disentangled biological similarity approach for the first time and successfully constructs a Disentangled Graph Attention Heterogeneous Biological Memory Network model. This network can learn disentangled representations of similarity information and effectively store the potential biological entanglement information of miRNAs and diseases. By integrating disentangled similarity information with a heterogeneous attention memory network, DiGAMN enhances the model’s ability to capture and utilize complex underlying biological data, significantly outperforming many existing models. The concepts used in this method also provide new perspectives for predicting miRNAs associated with diseases.

Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling.

Advancements in artificial intelligence (AI)-driven predictive modeling in dentistry are outpacing the clinical translation of research findings. Predictive modeling uses statistical methods to anticipate norms related to TMJ dynamics, complementing imaging modalities like cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI). Deep learning, a subset of AI, helps quantify and analyze complex hierarchical relationships in occlusion and TMJ function. This narrative review explores the application of predictive modeling and deep learning to identify clinical trends and associations related to occlusion and TMJ function. Debates persist regarding best practices for managing occlusal factors in temporomandibular joint (TMJ) function analysis while interpreting and quantifying findings related to the TMJ and occlusion and mitigating biases remain challenging. Data generated from noninvasive chairside tools such as jaw trackers, video tracking, and 3D scanners with virtual articulators offer unique insights by predicting variations in dynamic jaw movement, TMJ, and occlusion. The predictions help us understand the highly individualized norms surrounding TMJ function that are often required to address temporomandibular disorders (TMDs) in general practice. Normal TMJ function, occlusion, and the appropriate management of TMDs are complex and continue to attract ongoing debate. This review examines how predictive modeling and artificial intelligence aid in understanding occlusion and TMJ function and provides insights into complex dental conditions such as TMDs that may improve diagnosis and treatment outcomes with noninvasive techniques.

Vulnerability Evolution Mechanism of High-Speed Railway Construction System in the Goaf Site of Qinshui Coalfield

The main reason for serious consequences caused by disasters is “insufficient emergency preparedness and obvious system vulnerability”. To ensure the safe operation and maintenance of the high-speed railway in goaf sites and achieve effective hierarchical management, the vulnerability theory was introduced, based on the system approach, to explore the characteristics of hierarchical evolution. Firstly, based on the analysis of the internal and external disturbances of the high-speed railway construction system with underlying goaf areas, the vulnerability characteristic elements were defined, and a conceptual vulnerability model was proposed. Secondly, based on the literature research, accident cases, and standards and specifications, 25 factors affecting vulnerability were identified and a two-dimensional cross matrix was used to establish a vulnerability evaluation index system. Then, ISM was used to build hierarchical relationships among indicators, analyze the vulnerability characteristics of direct factors, indirect factors and essential factors, and a vulnerability evolution analysis framework was proposed. Finally, the Taiyuan–Jiaozuo high-speed railway project was selected for hierarchical and progressive vulnerability management. The study analyzed the coupling effect and evolution mechanism of various elements of the safety management system under external disturbances, identified the weak links of the high-speed railway system in the goaf site, and provided a decision-making basis for continuous dynamic optimization and emergency response technology, which complied with the strategic policy of sustainable development.

‘No longer what you used to be’: Renegotiating relations between de facto states and their patrons

The dependence of de facto states/breakaway-territories on external patrons is well-established, but we lack detailed knowledge of the dynamics of these hierarchical relationships, including opportunities for client agency. Through a comparative study of two patron–client relationships that had reached a crisis point, this article examines how the weaker client responds when at odds with its patron. Drawing on original data, including archival sources and interviews, it provides a bottom-up analysis of the relationship between Serbia and Republika Srpska after the signing of the Dayton Peace Agreement and between Armenia and Nagorno Karabakh immediately after the 2020 war. The out-of-equilibrium behaviour presents an opportunity for analysing the underlying mechanisms of patron–client relationships. In both cases, the crisis resulted in deep mistrust of the patron, but the de facto states were unable to sever links. They were constrained by a lack of reliable alternative supporters, continued patron dependence and powerful ethno-nationalist narratives. Non-recognition added to the client’s weakness, yet we still find clear examples of agency: external supporters were courted, and patrons were pressured through obstruction or appeals to its domestic forces. What resulted was a renegotiated, less hierarchical relationship. The article adds to our understanding of patron–client relationships, including those involving non-sovereign entities where an effective patronage monopoly still exists. It contributes to the existing literature with typologies of patron–client crises and client strategies for reclaiming autonomy, and with detailed empirical evidence of client agency and constraining factors, and by demonstrating the potential leverage of some of the weakest international actors.

Improving the depth and complexity of stakeholder deliberation using images: Experimental evidence from Indonesia

In this study, we tested interventions to improve the depth and complexity of deliberation processes with rural aquaculture farmers in Indonesia facing collective action problems in governing water distribution. The field experiment was conducted in four aquaculture villages in Lombok, Indonesia, where farmers were actively involved in the co-management PITAP program to maintain irrigation canals. The intervention was a version of the Social-Ecological Systems Framework (SESF) translated into images that depicted the variables of the framework in the context of the case study. We hypothesized that the connected image-based SESF deliberation tool could facilitate more in-depth discussions on the complexities of social and ecological issues compared to baseline (no images) and control groups (images with no framework relationships). To gauge the farmers' intent to care for irrigation canals post-PITAP collectively, we employed the Reasoned Action Approach (RAA) from environmental psychology. Our experimental study revealed that employing the SESF as an image-based deliberation tool did not measurably improve the depth and complexity of the discussions within the context of the traditional aquaculture communities in Indonesia. Nevertheless, the data derived from the content of discussions indicates that employing the image-based tool results in different but important outcomes among the experimental groups with implications for a better understanding of context and culture where deliberation processes occur. We further reflected on our findings on the hierarchical societal relationships in the context of rural communities in Indonesia that influence the deliberation style and reception.

Towards anarchist abolitionist futures for violence prevention: Beyond ‘counterterrorism’

Towards anarchist abolitionist futures for violence prevention: Beyond ‘counterterrorism’

Gene expression meta-analysis identifies novel pathways of the avian influenza virus disease

Humans and other animals are both susceptible to avian influenza virus. The avian influenza (AI) pandemic could be brought on by the appearance of a new, radical AI virus capable of spreading disease and maintaining prolonged human-to-human transmissions. The possibility of an AI pandemic makes it important for public health. Despite efforts to identify a linkage between them, the hierarchical relationship between all the factors that influence the pathophysiology of this disease, the shared biological pathways, and the exact identities of its important triggers are yet unknown. To find shared gene expression profiles and overlapping biological processes, an integrated gene expression meta-analysis was carried out for three independent microarray data of the avian influenza virus. This study found 1284 common differentially expressed genes (DEGs), of which 73 were overexpressed and 119 were under-expressed, analyzed using various packages in the R tool. The extensive biological, functional enrichment and pathway analysis was performed using the EnrichR tool and identified the defence response to the symbiont (GO:0140546), Interferon Alpha/Beta Signaling (R-HSA-909733), and spliceosome as the most enriched terms of biological process and pathways respectively. In a network meta-analysis, ISG15 and RELA were pinpointed as the top hub genes for over and under-expression, respectively. This meta-analysis technique for avian influenza infection highlights important gene profiles and their linked pathways. These findings highlight the value of using meta-analysis to detect novel gene markers that may offer key insight into disease pathogenesis and perhaps pave the way for creating more effective therapeutic approaches.

Exploring pre-service teachers’ professional experiences through the lens of boundary crossing: An Australian case study

This qualitative case study uses a boundary crossing lens to explore pre-service teachers’ professional experience through their own perspectives and those of their mentor teachers. A constant comparative method was used to analyse multiple sources of data, including in-depth interviews, field observations, feedback sessions, and reflection meetings. Data analysis revealed three major themes and nine subthemes. These themes illustrated the way the pre-service teachers experienced boundary crossing throughout the professional experience, including (1) navigating collaborative and hierarchical relationships, (2) integrating converging and diverging epistemologies, and (3) engaging in identity work. The study details the subthemes by giving voice to converging and opposing perspectives and reveals the complexities inherent in boundary crossing. Implications for leveraging the professional learning of pre-service teachers echo previous studies and herald the need for considering multiplicity and hybridity in any bridge-construction endeavours across universities, schools and the spaces in between.

Relation Constrained Capsule Graph Neural Networks for Non-Rigid Shape Correspondence

Non-rigid 3D shape correspondence aims to establish dense correspondences between two non-rigidly deformed 3D shapes. However, the variability and symmetry of non-rigid shapes usually lead to mismatches due to shape deformation, topological changes, or data with severe noise. To finding an accurate correspondence between 3D dynamic shapes for the local deformation complexity, this article proposes a Relation Constrained Capsule Graph Network (RC-CGNet), which combines global and local features by encouraging the relation constraints between the embedding feature space and the input shape space based on the functional maps framework. Specifically, we design a Diffusion Graph Attention Network (DGANet) to segment the surface into parts with correct edge boundary between two regions. The Minimum Spanning Tree (MST) of geodesic curves among the singularities obtained from the segmented parts is added as relation constraints, which can compute isometric correspondences in both direct and symmetric directions. Besides that, the relation-and-attention constrained neural networks are designed to learn the shape correspondence via attention-aware CapsNet and functional maps under relation constraints. To improve the convergence speed and matching accuracy, we propose an optimized residual network structure based on the Nesterov Accelerated Gradient (NAG) to extract local features, and use graph convolution structure to extract global features. Moreover, a lightweight Gated Attention Module (GAM) is designed to fuse global and local features to obtain a richer feature representation. Since the capsule network has better spatial reasoning ability than the traditional convolutional neural network, our novel network architecture is a dual-route capsule network based on Routing Attention Fusion Block (RAFB), filtering low-discriminative capsules from a holistic view by exploiting geometric hierarchical relationships of semantic parts. Experiments on open datasets show that our method has excellent accuracy and wide adaptability.

Different Maps, Same Territory: Dialogical Self Theory and its Predecessors

Dialogical Self Theory has not emerged in a historical vacuum. Its forerunners, whether the Eastern Atman, the tripartite Platonic self with its clear hierarchical relations, the Cartesian “ghost in the machine,” the Freudian ego-id hydraulic model or the Jungian topography of ego and unconscious, represent the theoretical grounding from which the dialogical self has emerged and self-articulated as much as have William James, George Herbert Mead and Mikel Bakhtin. Most of them, including DST, center on developmental models, and see the self as intrinsically oriented toward a more integrated sense of itself that Jung called “wholeness,” although their internal politics differ. Plato is an authoritarian, for whom the rational dimension must rule the spirited and the appetitive; otherwise, disorder and injustice prevail. Freud’s battle cry “where id was, ego shall be” is continually compromised by his realization that “ego is not master in its own house”; and Jung’s notion of wholeness involves a process of compensatory integration of conscious and unconscious contents of the personality that demands intensive shadow work. DST borrows from them all in one guise or another, but, in contrast with its predecessors, places its emphasis on intersubjectivity, polyphony and boundary-work, and as such is positioned for dialogical relations, not only between the I positions within its boundaries, but with the I positions of subjects who are grounded in other social, cultural and political systems. As such, the acronym DST might also stand for Democratic Self Theory, reflecting its historical emergence at a moment of rapidly increasing globalization of the information environment, and the resulting confrontation between disparate ideological traditions and forms of life that were hitherto invisible to each other. This paper represents a preliminary meditation on the infrastructure of DST within the context of the larger theoretical ground from which it emerges. It aims, not to seek exact correspondences or contradictions with other theories, but to act as a “bridging theory” between psychodynamic, constructivist, and humanist theories, thereby exploring the family resemblances that ground it, and acting to deconstruct the boundaries between it and other conceptual landscapes.

A corpus-assisted discourse analysis of socio-cultural values in Chinese and English social stories for autistic individuals

Social Stories are utilised to educate autistic individuals about appropriate social behaviours and politeness in social contexts. Based on the Theory of Mind (ToM), Social Stories are associated with neurotypicality which suggests human brains function in a similar way. Employing corpus-assisted discourse analysis, the study examines and compares the cultural differences between Chinese Social Stories (CSS) and English Social Stories (ESS) through analysing keywords, frequency of lexical items and collocation of the first-person pronoun. The results reveal that CSS emphasise the Confucian values of politeness, filial piety and hierarchical human relationship, while ESS recognise individual preferences, emotions and the value of fairness. The results enrich the knowledge of the discoursal and rhetorical features of Social Stories in different languages and provide theoretical implications for understanding the concepts of normality, politeness and neurotypicality. This article concludes by arguing that society should adopt the neurodiversity approach towards autistic individuals.

«А НЕНАВИДЕТЬ У МЕНЯ ЕСТЬ КОГО»: ЗЛОСТЬ, ГНЕВ И ИХ (НЕ)ВЫРАЖЕНИЕ В МОСКОВСКОЙ УНИВЕРСИТЕТСКОЙ СРЕДЕ 30–40-Х ГОДОВ ХХ ВЕКА

The article draws on memoirs, questionnaires, letters and diaries of students of the Faculty of History at Lomonosov Moscow State University in the second half of the 1930s and the first half of the 1940s to examine the practices of expressing anger, rage and similar emotions in this milieu. Representations of such feelings are rare in the ego-documents examined, but still allow us to draw a tentative conclusion about the existence of norms that provided in hierarchical relations for greater freedom to express these feelings ‘top down’ or ‘horizontally’ than ‘bottom-up’. When an expression of anger or rage was felt by the authors to be undesirable, they resorted to three ways of legitimizing it: providing a justification, placing oneself in a morally stronger and more empowered position, or redirecting one’s anger to a legitimate object.

Beyond postcolonial heteronomy: Kurdish question, decolonisation, and the relational time of democratic confederalism

Abdullah Öcalan’s theory of democratic confederalism, emerging from the Kurdish anti-colonial struggle against the states of Turkey, Iran, Iraq and Syria, diverges from a postcolonial framework by centring on social ecology. This paper argues that Öcalan’s approach signifies a rejection of the ‘heteronomy’ of postcolonial theory’s ‘dualist imaginary’, which reduces the interactive agencies shaping societies’ transformation to those implicated in the West–Rest binary. This binarism perpetuates a ‘ruptured’ understanding of non-Western political histories and a dualist notion of ‘unevenness’ that either neglects ‘inter-subaltern’ hierarchical relations – including colonial ones – or distorts their historical and agential roots. Öcalan’s theory, addressing the Kurdish condition as a constellation of fragmented ‘inter-subaltern’ colonies, implies a rejection of heteronomy both analytically and normatively. Analytically, it highlights the simultaneous transformation of state institutions and statelessness through intersocietal interactions, illuminating how the ‘unevenness of possibilities’ influences interactive transformations. Normatively, it aims to address the ‘uneven possibilities’ for ‘interactive’ political ‘doing’ and ‘alterations’, empowering suppressed ‘otherness’ through ‘interactive autonomy’. These proposals promote a relational and interactive understanding of time, framed around the core notion of ‘time as possibility’, which is crucial for advancing a relational international ontology and a decolonising perspective beyond the obstacle of heteronomy.

Event type induction using latent variables with hierarchical relationship analysis

The conventional approach to event extraction requires predefined event types and their corresponding annotations to train event extractors. However, these prerequisites are often difficult to satisfy in real-world applications. To automatically induct event types, most work has been devoted to clustering event triggers, where a cluster of event triggers is represented as an event type. Some works use trigger semantics, while others use co-occurrence relationships to cluster triggers. However, the clustering results of event triggers obtained by the above work are not sufficiently detailed in describing event types, making it difficult to accurately determine the corresponding event types manually. This paper proposes an open-domain event type induction framework that automatically discovers a set of event types from a given corpus. Unlike previous work on event trigger clustering, this paper takes into consideration the hierarchical relationship of event types to partition the event trigger clusters into event mains and subtypes. The framework employs a latent variable-based neural generation module and a semantic-based clustering module, the former of which obtains event trigger clusters representing the main types of events by jointly projecting the co-occurrence and semantic information of event triggers into a latent space for event type latent variable mining, and the latter of which further divides these event trigger clusters into event subtypes based on semantic information. Finally, experiment results show that, compared with the benchmark model, the ETGen-Clus can improve event type quality scores of 6.23% and 3.11% on the two datasets, respectively.

The ‘bad’ pasts of ‘good’ norms: explaining the opium ban through the postcolonial lens

The global proscription of the opium trade and its non-medical consumption took almost a century to evolve. Within International Relations, opium’s ban is explained as part of the normative order that culminated in the global anti-drugs prohibition regime of the 1900s. The conventional view presents the history of opium’s ban as a ‘bad’ imperial and local practice discontinued through the activism of ‘good’ transnational actors. However, seen through the postcolonial lens, this simplistic narrative is upended. By centering hierarchical power relations, the postcolonial lens demands a reflective interrogation into ‘global’ standards to expose parochial subjectivities that are otherwise couched in the universalistic language of modernity, rationality and progress. Though the ban on opium challenged an imperial practice, this article shows that it did so with the help of discourses that were themselves imperialistic. An analysis of the underlying evangelical, racial and scientific discourses illustrate how it was in the context of a specific worldview that opium came to be vilified. It also explains how the anti-opium views of the newly decolonized countries like India and China were suffused with the ethical visions of the anti-colonial and nation-building movements. A granular interrogation into opium’s history is necessary as we try and work our way toward a more nuanced and textured understanding of the international normative order while eschewing a one-dimensional, unproblematic, and simplified understanding of norms in IR.

Hierarchical multi-label classification model for science and technology news based on heterogeneous graph semantic enhancement

In the context of high-quality economic development, technological innovation has emerged as a fundamental driver of socio-economic progress. The consequent proliferation of science and technology news, which acts as a vital medium for disseminating technological advancements and policy changes, has attracted considerable attention from technology management agencies and innovation organizations. Nevertheless, online science and technology news has historically exhibited characteristics such as limited scale, disorderliness, and multi-dimensionality, which is extremely inconvenient for users of deep application. While single-label classification techniques can effectively categorize textual information, they face challenges in leading science and technology news classification due to a lack of a hierarchical knowledge framework and insufficient capacity to reveal knowledge integration features. This study proposes a hierarchical multi-label classification model for science and technology news, enhanced by heterogeneous graph semantics. The model captures multi-dimensional themes and hierarchical structural features within science and technology news through a hierarchical transmission module. It integrates graph convolutional networks to extract node information and hierarchical relationships from heterogeneous graphs, while also incorporating prior knowledge from domain knowledge graphs to address data scarcity. This approach enhances the understanding and classification capabilities of the semantics of science and technology news. Experimental results demonstrate that the model achieves precision, recall, and F1 scores of 84.21%, 88.89%, and 86.49%, respectively, significantly surpassing baseline models. This research presents an innovative solution for hierarchical multi-label classification tasks, demonstrating significant application potential in addressing data scarcity and complex thematic classification challenges.

HUMAN EXPLOITATION IN ARAVIND ADIGA’S NOVEL THE WHITE TIGER

Aravind Adiga’s The White Tiger explores the stark social disparities between the wealthy and impoverished in India, illustrating how systemic poverty perpetuates marginalization. The protagonist, Balram Halwai, narrates his journey from a lower-caste background to becoming a self-made, successful individual in India. Balram's story is not just one of personal ambition but also of navigating a rigidly stratified society. This study addresses two primary issues: the exploitation Balram endures due to his socioeconomic status; and his strategic actions are to attain freedom from the oppressive forces in his life. Using a library research method, the study integrates secondary sources, including books and articles, to deepen the analysis of the novel. Employing a psychological approach, this study examines the psychological complexities of Balram’s character, supported by theories on character development, data credibility, theoretical triangulation, and reliability. Balram is depicted as an astute yet morally ambiguous character, exhibiting selfishness, cunning, irresponsibility, dishonesty, and a lack of remorse. The analysis reveals how his behaviors and attitudes—such as detachment from others and deviant tendencies—are shaped by his environment. India’s entrenched caste system underpins various social issues in the novel, including poverty, unemployment, corruption in education, inadequate healthcare, exploitation, discrimination, and the persistence of hierarchical master-servant relationships. These factors collectively influence Balram’s psychological development, and shape him into a figure both reflective and critical of the society he navigates.

Enhancing Emergency Cognitive Ability in College Students Under Emergencies: A Study of Influencing Factors and Hierarchical Relationships

This study addresses the need to enhance college students’ emergency cognitive ability by identifying key influencing factors and analyzing their hierarchical structure. To fill the gap in understanding these relationships, we used grounded theory to identify 12 influencing factors through a literature review, case analysis, and interviews. The interpretive structural modeling (ISM) method categorized these factors into three levels: direct, key, and root factors. Root factors such as risk awareness, crisis perception, and responsibility are core to the cognitive ability framework and have a profound impact on students’ responses to emergencies. Further, the matrix of cross-impact multiplications applied to classification (MICMAC) analysis categorized the factors based on driving force and dependency, showing strong interrelationships. The integration of ISM-MICMAC methods offers a novel approach to understanding the hierarchical influence among factors, enabling educational institutions and policymakers to design targeted emergency training programs. By incorporating information technology into the educational process, this research provides practical guidance for enhancing students’ preparedness and resilience in emergencies. The findings support policy development and the design of effective educational interventions, offering valuable insights for administrators, policymakers, and emergency management professionals in creating safer, more resilient educational environments.

QLTI-07. COLLABORATIVE RESEARCH SUITE FOR GENOMIC ANALYSIS, DATA REPORTING AND WORKFLOW CUSTOMISATION

Abstract The genomic analysis pipeline involves multiple stages, including data extraction, transformation, and loading (ETL), exploratory data analysis (EDA), machine learning (ML) modeling, and reporting. The ETL stage involves data profiling and quality control measures to ensure data integrity and consistency. The EDA stage employs statistical and visualization methods to understand data distribution, identify patterns, and detect anomalies. Machine learning modelling involves supervised classification techniques, such as logistic regression and support vector machines (SVMs), to predict tumour classification and develop gene signatures. Unsupervised classification techniques, such as K-means clustering and Hierarchical clustering, identify patterns and relationships in genomic data. Quality metrics evaluate data completeness, accuracy, and consistency, ensuring data reliability and validity. The integration of data science techniques into a comprehensive framework can be achieved with the development of Application Dashboards that provide real-time insights into genomic data across every stage of the analysis pipeline. This enables clinical researchers and bioinformaticians to make informed decisions quickly, improving turn-around time and accelerating the discovery of new insights. In contrast to conventional methods of using stand-alone scripts, these dashboards offer a reusable and scalable solution, allowing for easy modification and adaptation to new datasets and analysis pipelines, thereby reducing development effort and increasing productivity. The dashboards constitute a centralized platform for data visualization, exploration, and analysis, facilitating collaboration and communication among research team members. With customizable and interactive visualizations, users can explore genomic data from multiple dimensions and the integration of multiple sources, including genomic and transcriptomic data. The technologies used to power this Research suite include Streamlit for Web App development, Python for data analysis, Opensearch for data storage and other utility libraries for reporting.