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Relevant bibliographies by topics / LINCS L1000

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Academic literature on the topic 'LINCS L1000'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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Journal articles on the topic "LINCS L1000"

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Liu, Chenglin, Jing Su, Fei Yang, Kun Wei, Jinwen Ma, and Xiaobo Zhou. "Compound signature detection on LINCS L1000 big data." Molecular BioSystems 11, no. 3 (2015): 714–22. http://dx.doi.org/10.1039/c4mb00677a.

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The Library of Integrated Network-based Cellular Signatures (LINCS) L1000 big data provide gene expression profiles induced by over 10 000 compounds, shRNAs, and kinase inhibitors using the L1000 platform.

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Qiu, Yue, Tianhuan Lu, Hansaim Lim, and Lei Xie. "A Bayesian approach to accurate and robust signature detection on LINCS L1000 data." Bioinformatics 36, no. 9 (2020): 2787–95. http://dx.doi.org/10.1093/bioinformatics/btaa064.

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Abstract Motivation LINCS L1000 dataset contains numerous cellular expression data induced by large sets of perturbagens. Although it provides invaluable resources for drug discovery as well as understanding of disease mechanisms, the existing peak deconvolution algorithms cannot recover the accurate expression level of genes in many cases, inducing severe noise in the dataset and limiting its applications in biomedical studies. Results Here, we present a novel Bayesian-based peak deconvolution algorithm that gives unbiased likelihood estimations for peak locations and characterize the peaks w

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Kort, Eric J., and Stefan Jovinge. "Streamlined analysis of LINCS L1000 data with the slinky package for R." Bioinformatics 35, no. 17 (2019): 3176–77. http://dx.doi.org/10.1093/bioinformatics/btz002.

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Abstract Summary The L1000 dataset from the NIH LINCS program holds the promise to deconvolute a wide range of biological questions in transcriptional space. However, using this large and decentralized dataset presents its own challenges. The slinky package was created to streamline the process of identifying samples of interest and their corresponding control samples, and loading their associated expression data and metadata. The package can integrate with workflows leveraging the BioConductor collection of tools by encapsulating the L1000 data as a SummarizedExperiment object. Availability a

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Wen, Huaming, Ryan A. Gallo, Xiaosheng Huang, et al. "Incorporating Differential Gene Expression Analysis with Predictive Biomarkers to Identify Novel Therapeutic Drugs for Fuchs Endothelial Corneal Dystrophy." Journal of Ophthalmology 2021 (June 28, 2021): 1–8. http://dx.doi.org/10.1155/2021/5580595.

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Purpose. Based on the differential gene expression analysis for predictive biomarkers with RNA-Sequencing data from Fuchs endothelial corneal dystrophy (FECD) patients, we are aiming to evaluate the efficacy of Library of Integrated Network-based Cellular Signatures (LINCS) perturbagen prediction software to identify novel pharmacotherapeutic targets that can revert the pathogenic gene expression signatures and reverse disease phenotype in FECD. Methods. A publicly available RNA-seq dataset was used to compare corneal endothelial specimens from controls and patients with FECD. Based on the dif

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Wang, Zichen, Neil R. Clark, and Avi Ma’ayan. "Drug-induced adverse events prediction with the LINCS L1000 data." Bioinformatics 32, no. 15 (2016): 2338–45. http://dx.doi.org/10.1093/bioinformatics/btw168.

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Duan, Qiaonan, Corey Flynn, Mario Niepel, et al. "LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures." Nucleic Acids Research 42, W1 (2014): W449—W460. http://dx.doi.org/10.1093/nar/gku476.

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Szalai, Bence, Vigneshwari Subramanian, Christian H. Holland, Róbert Alföldi, László G. Puskás, and Julio Saez-Rodriguez. "Signatures of cell death and proliferation in perturbation transcriptomics data—from confounding factor to effective prediction." Nucleic Acids Research 47, no. 19 (2019): 10010–26. http://dx.doi.org/10.1093/nar/gkz805.

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Abstract Transcriptional perturbation signatures are valuable data sources for functional genomics. Linking perturbation signatures to screenings opens the possibility to model cellular phenotypes from expression data and to identify efficacious drugs. We linked perturbation transcriptomics data from the LINCS-L1000 project with cell viability information upon genetic (Achilles project) and chemical (CTRP screen) perturbations yielding more than 90 000 signature–viability pairs. An integrated analysis showed that the cell viability signature is a major factor underlying perturbation signatures

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Suter, Robert, Vasileios Stathias, Anna Jermakowicz, et al. "COMP-16. COMPREHENSIVE TRANSCRIPTOMIC ANALYSIS OF SINGLE CELLS FROM RECURRENT AND PRIMARY GLIOBLASTOMA TO PREDICT CELL-TYPE SPECIFIC THERAPEUTICS." Neuro-Oncology 21, Supplement_6 (2019): vi64. http://dx.doi.org/10.1093/neuonc/noz175.259.

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Abstract Glioblastoma (GBM) remains the most common adult brain tumor, with poor survival expectations, and no new therapeutic modalities approved in the last decade. Our laboratories have recently demonstrated that the integration of a transcriptional disease signature obtained from The Cancer Genome Atlas’ GBM dataset with transcriptional cell drug-response signatures in the LINCS L1000 dataset yields possible combinatorial therapeutics. Considering the extreme intra-tumor heterogeneity associated with the disease, we hypothesize that the utilization of single-cell RNA-sequencing (scRNA-seq)

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Lee, Hanbi, and Wankyu Kim. "Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data." Pharmaceutics 11, no. 8 (2019): 377. http://dx.doi.org/10.3390/pharmaceutics11080377.

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Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important question is which features for targets are most relevant to DTI prediction. As an early attempt to answer t

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Ferguson, Laura B., Shruti Patil, Bailey A. Moskowitz, et al. "A Pathway-Based Genomic Approach to Identify Medications: Application to Alcohol Use Disorder." Brain Sciences 9, no. 12 (2019): 381. http://dx.doi.org/10.3390/brainsci9120381.

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Chronic, excessive alcohol use alters brain gene expression patterns, which could be important for initiating, maintaining, or progressing the addicted state. It has been proposed that pharmaceuticals with opposing effects on gene expression could treat alcohol use disorder (AUD). Computational strategies comparing gene expression signatures of disease to those of pharmaceuticals show promise for nominating novel treatments. We reasoned that it may be sufficient for a treatment to target the biological pathway rather than lists of individual genes perturbed by AUD. We analyzed published and un

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Dissertations / Theses on the topic "LINCS L1000"

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White, Shana. "Application and Development of Novel Methods for Pathway Analysis and Visualization of the LINCS L1000 Dataset." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623241379918016.

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Mahi, Naim. "Connectivity Analysis of Single-cell RNA-seq Derived Transcriptional Signatures." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613748441148963.

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Chen, Tzu-Yao, and 陳子堯. "Investigating the functions of unannotated genes using LINCS L1000 big data." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/05564794452357217247.

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碩士<br>國立陽明大學<br>生物醫學資訊研究所<br>105<br>Abstract Library of Integrated Network-based Cellular Signatures (LINCS) is an NIH program which aims to understand biology by cataloging changes in gene expression and other cellular processes that occur when cells are exposed to a variety of perturbing agents. L1000 gene expression data, which include about 1.3 million samples, are the most comprehensive data in LINCS. Since each sample indicates the gene expression status under the treatment of different perturbagens, such as chemical compounds, shRNAs, to normal or cancer cells, the aim of this study is

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Liao, Pei-Han, and 廖珮函. "Inferring Drug-Target Interactions Based on Perturbational Profiles in LINCS L1000 Data." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/dw9egx.

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碩士<br>國立臺灣大學<br>生醫電子與資訊學研究所<br>106<br>The journey of a drug, from being selected in the laboratory to finally be sold on the market, is tedious, money-consuming and full of risks. It is an urgent need to shorten the process of drug discovery and development. Either accelerating the initial phase – drug discovery or repurposing existing drugs for new indications could be beneficial to achieve the goal. In this study, we have developed an analysis pipeline for predicting potential targets of drugs based on only perturbational profiles in L1000 data. Through analyzing the associations between com

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Conference papers on the topic "LINCS L1000"

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Huang, Chia-Ling, Andrew Yang, Ted Natoli, et al. "Abstract 2467: Heme-CMap: Generation and characterization of ~20K L1000 profiles across 11 hematologic malignant lines." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.am2019-2467.

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Huang, Chia-Ling, Andrew Yang, Ted Natoli, et al. "Abstract 2467: Heme-CMap: Generation and characterization of ~20K L1000 profiles across 11 hematologic malignant lines." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.sabcs18-2467.

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