Browsing by Subject "Omics"

Now showing 1 - 5 of 5
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    Correlation Analysis of Histopathology and Proteogenomics Data for Breast Cancer
    (American Society for Biochemistry and Molecular Biology, 2019-08-09) Zhan, Xiaohui; Cheng, Jun; Huang, Zhi; Han, Zhi; Helm, Bryan; Liu, Xiaowen; Zhang, Jie; Wang, Tian-Fu; Ni, Dong; Huang, Kun; Medicine, School of Medicine
    Tumors are heterogeneous tissues with different types of cells such as cancer cells, fibroblasts, and lymphocytes. Although the morphological features of tumors are critical for cancer diagnosis and prognosis, the underlying molecular events and genes for tumor morphology are far from being clear. With the advancement in computational pathology and accumulation of large amount of cancer samples with matched molecular and histopathology data, researchers can carry out integrative analysis to investigate this issue. In this study, we systematically examine the relationships between morphological features and various molecular data in breast cancers. Specifically, we identified 73 breast cancer patients from the TCGA and CPTAC projects matched whole slide images, RNA-seq, and proteomic data. By calculating 100 different morphological features and correlating them with the transcriptomic and proteomic data, we inferred four major biological processes associated with various interpretable morphological features. These processes include metabolism, cell cycle, immune response, and extracellular matrix development, which are all hallmarks of cancers and the associated morphological features are related to area, density, and shapes of epithelial cells, fibroblasts, and lymphocytes. In addition, protein specific biological processes were inferred solely from proteomic data, suggesting the importance of proteomic data in obtaining a holistic understanding of the molecular basis for tumor tissue morphology. Furthermore, survival analysis yielded specific morphological features related to patient prognosis, which have a strong association with important molecular events based on our analysis. Overall, our study demonstrated the power for integrating multiple types of biological data for cancer samples in generating new hypothesis as well as identifying potential biomarkers predicting patient outcome. Future work includes causal analysis to identify key regulators for cancer tissue development and validating the findings using more independent data sets.
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    Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics
    (Elsevier, 2025-01-10) Budhkar, Aishwarya; Song, Qianqian; Su, Jing; Zhang, Xuhong; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    The widespread adoption of Artificial Intelligence (AI) and machine learning (ML) tools across various domains has showcased their remarkable capabilities and performance. Black-box AI models raise concerns about decision transparency and user confidence. Therefore, explainable AI (XAI) and explainability techniques have rapidly emerged in recent years. This paper aims to review existing works on explainability techniques in bioinformatics, with a particular focus on omics and imaging. We seek to analyze the growing demand for XAI in bioinformatics, identify current XAI approaches, and highlight their limitations. Our survey emphasizes the specific needs of both bioinformatics applications and users when developing XAI methods and we particularly focus on omics and imaging data. Our analysis reveals a significant demand for XAI in bioinformatics, driven by the need for transparency and user confidence in decision-making processes. At the end of the survey, we provided practical guidelines for system developers.
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    Genome-wide circadian rhythm detection methods: systematic evaluations and practical guidelines
    (Oxford University Press, 2021-05-20) Mei, Wenwen; Jiang, Zhiwen; Chen, Yang; Chen, Li; Sancar, Aziz; Jiang, Yuchao; Medicine, School of Medicine
    Circadian rhythms are oscillations of behavior, physiology and metabolism in many organisms. Recent advancements in omics technology make it possible for genome-wide profiling of circadian rhythms. Here, we conducted a comprehensive analysis of seven existing algorithms commonly used for circadian rhythm detection. Using gold-standard circadian and non-circadian genes, we systematically evaluated the accuracy and reproducibility of the algorithms on empirical datasets generated from various omics platforms under different experimental designs. We also carried out extensive simulation studies to test each algorithm’s robustness to key variables, including sampling patterns, replicates, waveforms, signal-to-noise ratios, uneven samplings and missing values. Furthermore, we examined the distributions of the nominal equation M1-values under the null and raised issues with multiple testing corrections using traditional approaches. With our assessment, we provide method selection guidelines for circadian rhythm detection, which are applicable to different types of high-throughput omics data.
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    Multiple Myeloma Insights from Single-Cell Analysis: Clonal Evolution, the Microenvironment, Therapy Evasion, and Clinical Implications
    (MDPI, 2025-02-14) Li, Sihong; Liu, Jiahui; Peyton, Madeline; Lazaro, Olivia; McCabe, Sean D.; Huang, Xiaoqing; Liu, Yunlong; Shi, Zanyu; Zhang, Zhiqi; Walker, Brian A.; Johnson, Travis S.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Multiple myeloma (MM) is a complex and heterogeneous hematologic malignancy characterized by clonal evolution, genetic instability, and interactions with a supportive tumor microenvironment. These factors contribute to treatment resistance, disease progression, and significant variability in clinical outcomes among patients. This review explores the mechanisms underlying MM progression, including the genetic and epigenetic changes that drive clonal evolution, the role of the bone marrow microenvironment in supporting tumor growth and immune evasion, and the impact of genomic instability. We highlight the critical insights gained from single-cell technologies, such as single-cell transcriptomics, genomics, and multiomics, which have enabled a detailed understanding of MM heterogeneity at the cellular level, facilitating the identification of rare cell populations and mechanisms of drug resistance. Despite the promise of advanced technologies, MM remains an incurable disease and challenges remain in their clinical application, including high costs, data complexity, and the need for standardized bioinformatics and ethical considerations. This review emphasizes the importance of continued research and collaboration to address these challenges, ultimately aiming to enhance personalized treatment strategies and improve patient outcomes in MM.
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    Primary and Metastatic Cutaneous Melanomas Discriminately Enrich Several Ligand-Receptor Interactions
    (MDPI, 2023-01-08) Diaz, Michael J.; Fadil, Angela; Tran, Jasmine T.; Batchu, Sai; Root, Kevin T.; Tran, Andrew X.; Lucke-Wold, Brandon; Dermatology, School of Medicine
    Introduction: Cutaneous melanoma remains a leading cancer with sobering post-metastasis mortality rates. To date, the ligand-receptor interactome of melanomas remains weakly studied despite applicability to anti-cancer drug discovery. Here we leverage established crosstalk methodologies to characterize important ligand-receptor pairs in primary and metastatic cutaneous melanoma. Methods: Bulk transcriptomic data, representing 470 cutaneous melanoma samples, was retrieved from the Broad Genome Data Analysis Center Firehose portal. Tumor and stroma compartments were computationally derived as a function of tumor purity estimates. Identification of preferential ligand-receptor interactions was achieved by relative crosstalk scoring of 1380 previously established pairs. Results: Metastatic cutaneous melanoma uniquely enriched PTH2-PTH1R for tumor-to-stroma signaling. The Human R-spondin ligand family was involved in 4 of the 15 top-scoring stroma-to-tumor interactions. Receptor ACVR2B was involved in 3 of the 15 top-scoring tumor-to-tumor interactions. Conclusions: Numerous gene-level differences in ligand-receptor crosstalk between primary and metastatic cutaneous melanomas. Further investigation of notable pairings is warranted.