A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.
TL;DR: The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.
About: This article is published in Cell.The article was published on 2017-11-30 and is currently open access. It has received 1943 citations till now.
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TL;DR: In this article , the authors used a weighted gene co-expression network analysis (WGCNA) to create a coexpression network based on the top 25% genes in the GSE24124, GSE33926, and GSE86166 datasets obtained from the Gene Expression Omnibus.
Abstract: We used bioinformatics analysis to identify potential biomarker genes and their relationship with breast cancer (BC).We used a weighted gene co-expression network analysis (WGCNA) to create a co-expression network based on the top 25% genes in the GSE24124, GSE33926, and GSE86166 datasets obtained from the Gene Expression Omnibus. We used the DAVID online platform to perform GO and KEGG pathway enrichment analyses and the Cytoscape CytoHubba plug-in to screen the potential genes. Then, we related the genes to prognostic values in BC using the Oncomine, GEPIA, and Kaplan-Meier Plotter databases. Findings were validated by immunohistochemical (IHC) staining in the Human Protein Atlas and the TCGA-BRCA cohort. LinkedOmics identified the interactive expressions of hub genes. We used UALCAN to evaluate the methylation levels of these hub genes. MethSurv and SurvivalMeth were used to assess the multilevel prognostic value. Finally, we assessed hub gene association with immune cell infiltration using TIMER.The mRNA levels of MKI67, UBE2C, GTSE1, CCNA2, and MND1 were significantly upregulated in BC, whereas ESR1, THSD4, TFF1, AGR2, and FOXA1 were significantly downregulated. The DNA methylation signature analysis showed a better prognosis in the low-risk group. Further subgroup analyses revealed that MND1 might serve as an independent risk factor for unfavorable BC prognosis. Additionally, MND1 expression levels positively correlate with the immune infiltration statuses of CD4+ T cells, CD8+ T cells, B cells, neutrophils, dendritic cells, and macrophages.Our results indicate that the ten hub genes may be involved in BC's carcinogenesis, development, or metastasis, and MND1 may be a potential biomarker and therapeutic target for BC.
5 citations
TL;DR: The biomedical relevance of GESgnExt is demonstrated further in multiple case studies, providing mechanistic insights into its knowledge discovery process.
Abstract: The gene expression omnibus (GEO) repository harbours an exponentially increasing number of gene expression studies. The expression data, as well as the related metadata, provides an abundant resource for knowledge discovery. Each study in GEO focuses on the gene expression perturbation of a specific subject (e.g., gene, drug, and disease). The identification of those subjects and the associations among them are beneficial for further in-depth studies. However, they cannot be directly inferred from the studies. A unified representation of those subjects (i.e., gene expression signatures) is desired. We developed GESgnExt for the automatic construction of gene expression signatures. The resultant 6542 signatures are built on 1934 series and 35 919 samples from GEO. To evaluate its significance, we calculated the similarities among those signatures and compared the discovered associations against the existing interaction databases. The signatures connect the genes, drugs, and diseases, covering most of the experimentally validated interactions. Besides, we have discovered 3307 novel signatures and their related associations, complementing the existing signature knowledge. The biomedical relevance of GESgnExt is demonstrated further in multiple case studies, providing mechanistic insights into its knowledge discovery process.
5 citations
TL;DR: In this article, the authors used gene expression profiles from 13 human cell lines, as well as molecular properties of drugs to build machine learning models of drug-induced liver injury (DILI).
Abstract: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI can bring a significant reduction in the cost of clinical trials. In this work we examined whether occurrence of DILI can be predicted using gene expression profile in cancer cell lines and chemical properties of drugs. We used gene expression profiles from 13 human cell lines, as well as molecular properties of drugs to build Machine Learning models of DILI. To this end, we have used a robust cross-validated protocol based on feature selection and Random Forest algorithm. In this protocol we first identify the most informative variables and then use them to build predictive models. The models are first built using data from single cell lines, and chemical properties. Then they are integrated using Super Learner method with several underlying methods for integration. The entire modelling process is performed using nested cross-validation. We have obtained weakly predictive ML models when using either molecular descriptors, or some individual cell lines (AUC ∈(0.55−0.61)). Models obtained with the Super Learner approach have a significantly improved accuracy (AUC=0.73), which allows to divide substances in two categories: low-risk and high-risk.
5 citations
TL;DR: In this paper, the authors employed an integrated in silico and in vivo approach to identify potential treatments for cisplatin-induced nephrotoxicity (CIN) using publicly available mouse kidney and human kidney organoid transcriptome datasets, and then used the bioinformatics database Cmap and Lincs Unified Environment (CLUE) to identify drugs expected to counter the expression signature for CIN.
Abstract: Cisplatin is widely used to treat various types of cancers, but it is often limited by nephrotoxicity. Here, we employed an integrated in silico and in vivo approach to identify potential treatments for cisplatin-induced nephrotoxicity (CIN). Using publicly available mouse kidney and human kidney organoid transcriptome datasets, we first identified a 208-gene expression signature for CIN and then used the bioinformatics database Cmap and Lincs Unified Environment (CLUE) to identify drugs expected to counter the expression signature for CIN. We also searched the adverse event database, Food and Drug Administration. Adverse Event Reporting System (FAERS), to identify drugs that reduce the reporting odds ratio of developing cisplatin-induced acute kidney injury. Palonosetron, a serotonin type 3 receptor (5-hydroxytryptamine receptor 3 (5-HT3R)) antagonist, was identified by both CLUE and FAERS analyses. Notably, clinical data from 103 patients treated with cisplatin for head and neck cancer revealed that palonosetron was superior to ramosetron in suppressing cisplatin-induced increases in serum creatinine and blood urea nitrogen levels. Moreover, palonosetron significantly increased the survival rate of zebrafish exposed to cisplatin but not to other 5-HT3R antagonists. These results not only suggest that palonosetron can suppress CIN but also support the use of in silico and in vivo approaches in drug repositioning studies.
5 citations
01 Jan 2021
TL;DR: This chapter describes how reinforcement learning (RL) algorithms can be applied to generative AI for better real-world effectiveness while better utilizing modern distributed hardware assets.
Abstract: A drug-like-molecule library can contain 1023–1060 molecules, among which only approximately 1012 molecules may be synthesized in labs. However, it is still challenging for researchers to find the most promising candidates among the vast number of synthesizable compounds in a reasonable time. Moreover, although molecules are picked for their predicted bioactivities, their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties are often difficult to predict and modify. This is often a bottleneck for downstream studies and applications. It would be more productive if candidate molecules are generated, rather than screened from libraries, with suitable ADMET properties as prerequisites at the beginning of the molecule design process. Recently, artificial intelligence (AI)-based generative models have been described for designing drug candidates using prior biological and chemical knowledge. A spectacular example was the use of a combination of AI generative techniques and reinforcement learning by the biotechnology company, Insilico Medicine, to successfully create new DDR1 kinase inhibitors to treat fibrosis in only 21 days. We will describe how reinforcement learning (RL) algorithms can be applied to generative AI for better real-world effectiveness while better utilizing modern distributed hardware assets. In this chapter, we will review simple and advanced AI generative models and discuss the advantages and disadvantages of each model.
5 citations
References
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TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
34,830 citations
Journal Article•
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Abstract: We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets.
30,124 citations
TL;DR: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data and provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-power gene expression and genomic hybridization experiments.
Abstract: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
10,968 citations
TL;DR: How BLAT was optimized is described, which is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences.
Abstract: Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments A new tool, BLAT, is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences BLAT's speed stems from an index of all nonoverlapping K-mers in the genome This index fits inside the RAM of inexpensive computers, and need only be computed once for each genome assembly BLAT has several major stages It uses the index to find regions in the genome likely to be homologous to the query sequence It performs an alignment between homologous regions It stitches together these aligned regions (often exons) into larger alignments (typically genes) Finally, BLAT revisits small internal exons possibly missed at the first stage and adjusts large gap boundaries that have canonical splice sites where feasible This paper describes how BLAT was optimized Effects on speed and sensitivity are explored for various K-mer sizes, mismatch schemes, and number of required index matches BLAT is compared with other alignment programs on various test sets and then used in several genome-wide applications http://genomeucscedu hosts a web-based BLAT server for the human genome
8,326 citations
TL;DR: This paper proposed parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples.
Abstract: SUMMARY Non-biological experimental variation or “batch effects” are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes (>25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
6,319 citations