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Relevant bibliographies by topics / Omic network inference

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Journal articles

Academic literature on the topic 'Omic network inference'

Author: Grafiati

Published: 20 April 2024

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Journal articles on the topic "Omic network inference"

1

Nagpal, Sunil, Rashmi Singh, Deepak Yadav, and Sharmila S. Mande. "MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks." Nucleic Acids Research 48, W1 (2020): W572—W579. http://dx.doi.org/10.1093/nar/gkaa254.

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Abstract Microbial association networks are frequently used for understanding and comparing community dynamics from microbiome datasets. Inferring microbial correlations for such networks and obtaining meaningful biological insights, however, requires a lengthy data management workflow, choice of appropriate methods, statistical computations, followed by a different pipeline for suitably visualizing, reporting and comparing the associations. The complexity is further increased with the added dimension of multi-group ‘meta-data’ and ‘inter-omic’ functional profiles that are often associated wit

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2

Dohlman, Anders B., and Xiling Shen. "Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference." Experimental Biology and Medicine 244, no. 6 (2019): 445–58. http://dx.doi.org/10.1177/1535370219836771.

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Advances in high-throughput sequencing have ushered in a new era of research into the gut microbiome and its role in human health and disease. However, due to the unique characteristics of microbiome survey data, their use for the detection of ecological interaction networks remains a considerable challenge, and a field of active methodological development. In this review, we discuss the landscape of existing statistical and experimental methods for detecting and characterizing microbial interactions, as well as the role that host and environmental metabolic signals play in mediating the behav

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Ramos, Susana Isabel, Zarmeen Mussa, Bruno Giotti, Alexander Tsankov, and Nadejda Tsankova. "EPCO-25. MULTI-OMIC ANALYSIS OF THE GLIOBLASTOMA EPIGENOME AND TRANSCRIPTOME INFORMS OF MIGRATORY INTERNEURON-LIKE DEVELOPMENTAL REGULATORS." Neuro-Oncology 24, Supplement_7 (2022): vii121. http://dx.doi.org/10.1093/neuonc/noac209.460.

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Abstract Recent studies have demonstrated that, despite their nomenclature, gliomas recapitulate an interneuron progenitor-like state that drives tumor progression. During human neurodevelopment, interneurons arise from the subcortical ganglionic eminences and migrate tangentially into the neocortex, settling in the cortical plate where they integrate local neurocircuitry. Analogously, malignant glioblastoma (GBM) cells migrate from the tumor core into the surrounding healthy tissue. This innate infiltrative property renders these malignant cells elusive to surgical resection, leading to tumor

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4

Grund, Eric M., A. James Moser, Corinne L. DeCicco, et al. "Abstract 5145: Project Survival®: Discovery of a molecular-clinical phenome biomarker panel to detect pancreatic ductal adenocarcinoma among at risk populations using high-fidelity longitudinal phenotypic and multi-omic analysis." Cancer Research 82, no. 12_Supplement (2022): 5145. http://dx.doi.org/10.1158/1538-7445.am2022-5145.

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Abstract Delayed diagnosis and rapid progression are major drivers of poor survival outcomes for pancreatic ductal adenocarcinoma (PDAC). PDAC is expected to be the second leading cause of cancer death and has a dismal 5 year survival rate of 10%. There is an urgent unmet need to detect the disease at an early stage and stratify patients into more effective treatment regimens within clinically meaningful timeframes. To accomplish this, robust quality controlled OMIC molecular profiling platforms and analytic solutions need to be deployed into precision medicine protocols to discover actionable

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5

Nathasingh, Brandon, Derek Walkama, Laurel Mayhew, et al. "Abstract LB181: Infer cancer cell gene dependency in multiple myeloma using causal AI in-silico patient model." Cancer Research 83, no. 8_Supplement (2023): LB181. http://dx.doi.org/10.1158/1538-7445.am2023-lb181.

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Abstract Recent advances in artificial intelligence (AI) and availability of multimodal patient datasets have enabled the construction of complex network models to derive disease molecular mechanisms and predict the impact of therapeutic intervention. However, observational datasets are commonly affected by confounding factors making causal interpretation challenging. Causal inference network methods are particularly suited to facilitate therapeutic intervention studies by inferring the causal structure from sufficiently detailed multi-omic molecular data. The learned models enable in-silico l

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6

Ye, Qing, and Nancy Lan Guo. "Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets." Cells 12, no. 1 (2022): 101. http://dx.doi.org/10.3390/cells12010101.

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There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages o

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7

Alanis-Lobato, Gregorio, Thomas E. Bartlett, Qiulin Huang, et al. "MICA: a multi-omics method to predict gene regulatory networks in early human embryos." Life Science Alliance 7, no. 1 (2023): e202302415. http://dx.doi.org/10.26508/lsa.202302415.

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Recent advances in single-cell omics have transformed characterisation of cell types in challenging-to-study biological contexts. In contexts with limited single-cell samples, such as the early human embryo inference of transcription factor-gene regulatory network (GRN) interactions is especially difficult. Here, we assessed application of different linear or non-linear GRN predictions to single-cell simulated and human embryo transcriptome datasets. We also compared how expression normalisation impacts on GRN predictions, finding that transcripts per million reads outperformed alternative met

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8

Wang, Pei. "Network biology: Recent advances and challenges." Gene & Protein in Disease 1, no. 2 (2022): 101. http://dx.doi.org/10.36922/gpd.v1i2.101.

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Biological networks have garnered widespread attention. The development of biological networks has spawned the birth of a new interdisciplinary field – network biology. Network biology involves the exploration of complex biological systems through biological networks for better understanding of biological functions. This paper reviews some of the recent development of network biology. On the one hand, various approaches to constructing different types of biological networks are reviewed, and the pros and cons of each approach are discussed; on the other hand, the recent advances of information

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9

Yan, Yan, Feng Jiang, Xinan Zhang, and Tianhai Tian. "Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm." Entropy 24, no. 5 (2022): 693. http://dx.doi.org/10.3390/e24050693.

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One of the key challenges in systems biology and molecular sciences is how to infer regulatory relationships between genes and proteins using high-throughout omics datasets. Although a wide range of methods have been designed to reverse engineer the regulatory networks, recent studies show that the inferred network may depend on the variable order in the dataset. In this work, we develop a new algorithm, called the statistical path-consistency algorithm (SPCA), to solve the problem of the dependence of variable order. This method generates a number of different variable orders using random sam

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10

Bonnet, Eric, Laurence Calzone, and Tom Michoel. "Integrative Multi-omics Module Network Inference with Lemon-Tree." PLOS Computational Biology 11, no. 2 (2015): e1003983. http://dx.doi.org/10.1371/journal.pcbi.1003983.

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