Interesting Properties of Profile Data Analysis in the Understanding and Utilization of the Effects of Drugs.

Profile data is defined as data which describes the properties of an object. Omics data of a specimen is profile data because its comprehensiveness supports the idea that omics data is numeric information which reflects biological information of the specimen. In general, omics data analysis utilizes an existing body of biological knowledge, while some profile data analysis methods are independent of existing knowledge, which is suitable for uncovering unidentified aspects of a specimen of interest. The effects of a small compound, such as drugs, are multiple, and include unrecognized effects, even by the developers. To uncover such unrecognized effects, it is useful to employ profile data analysis independent of existing knowledge. In this review, we summarize what profile data is, properties of profile data analysis, and current applications of profile data in order to understand and utilize the effects of small compounds, in particular, in a recently developed method to decompose multiple effects of a drug.

[1]  M. Petzold,et al.  Economic Impact of Adverse Drug Events – A Retrospective Population-Based Cohort Study of 4970 Adults , 2014, PloS one.

[2]  Patrick Meyer,et al.  On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid , 2008, Eur. J. Oper. Res..

[3]  Erin L. Schenk,et al.  Clinical outcomes of patients on check point inhibitor therapy who receive routine vaccinations. , 2017 .

[4]  S. Lipton.  Failures and successes of NMDA receptor antagonists: Molecular basis for the use of open-channel blockers like memantine in the treatment of acute and chronic neurologic insults , 2011, NeuroRX.

[5]  Jaideep Srivastava,et al.  Selecting the right objective measure for association analysis , 2004, Inf. Syst..

[6]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Zaynab Mousavian,et al.  Identification of Key Components in Colon Adenocarcinoma Using Transcriptome to Interactome Multilayer Framework , 2020, Scientific Reports.

[8]  Tomaso Aste,et al.  Correction: Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods , 2014, PloS one.

[9]  Brendan MacLean,et al.  Building high-quality assay libraries for targeted analysis of SWATH MS data , 2015, Nature Protocols.

[10]  Adam Ertel,et al.  Monocyte-macrophage differentiation of acute myeloid leukemia cell lines by small molecules identified through interrogation of the Connectivity Map database , 2015, Cell cycle.

[11]  H. Kusuhara,et al.  Successful treatment with 4‐phenylbutyrate in a patient with benign recurrent intrahepatic cholestasis type 2 refractory to biliary drainage and bilirubin absorption , 2016, Hepatology Research.

[12]  T. Sharpe,et al.  Syrosingopine sensitizes cancer cells to killing by metformin , 2016, Science Advances.

[13]  Paul A Clemons,et al.  The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease , 2006, Science.

[14]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[15]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[16]  John A. Tallarico,et al.  Integrating high-content screening and ligand-target prediction to identify mechanism of action. , 2008, Nature chemical biology.

[17]  Michiel Schinkel,et al.  Short Keynote Paper: Mainstreaming Personalized Healthcare–Transforming Healthcare Through New Era of Artificial Intelligence , 2020, IEEE Journal of Biomedical and Health Informatics.

[18]  F. Albericio,et al.  The Pharmaceutical Industry in 2016. An Analysis of FDA Drug Approvals from a Perspective of the Molecule Type , 2017, Molecules.

[19]  John D. Storey,et al.  Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis , 2007, PLoS genetics.

[20]  E. Hovig,et al.  Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses , 2015, Biostatistics.

[21]  Laleh Haghverdi,et al.  Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors , 2018, Nature Biotechnology.

[22]  R. W. Hansen,et al.  Journal of Health Economics , 2016 .

[23]  Tadahaya Mizuno,et al.  Development of a Novel Platform of Proteome Profiling Based on an Easy-to-Handle and Informative 2D-DIGE System. , 2019, Biological & pharmaceutical bulletin.

[24]  Wei Keat Lim,et al.  The transcriptional network for mesenchymal transformation of brain tumors , 2009, Nature.

[25]  Anne E Carpenter,et al.  Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes , 2016, Nature Protocols.

[26]  S. Horvath,et al.  A General Framework for Weighted Gene Co-Expression Network Analysis , 2005, Statistical applications in genetics and molecular biology.

[27]  Anushya Muruganujan,et al.  Large-scale gene function analysis with the PANTHER classification system , 2013, Nature Protocols.

[28]  Makoto Muroi,et al.  Application of proteomic profiling based on 2D-DIGE for classification of compounds according to the mechanism of action. , 2010, Chemistry & biology.

[29]  Ludovic C. Gillet,et al.  Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps , 2015, Nature Medicine.

[30]  Anushya Muruganujan,et al.  PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools , 2018, Nucleic Acids Res..

[31]  R. Shields,et al.  mRNA Expression Signatures of Human Skeletal Muscle Atrophy Identify a Natural Compound that Increases Muscle Mass , 2011, Cell metabolism.

[32]  E. Kenny,et al.  Personalized Medicine and the Power of Electronic Health Records , 2019, Cell.

[33]  Jinwen Ma,et al.  Compound signature detection on LINCS L1000 big data. , 2015, Molecular bioSystems.

[34]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[35]  Angela N. Brooks,et al.  A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles , 2017, Cell.

[36]  Katsuhisa Horimoto,et al.  Identification of drug candidate against prostate cancer from the aspect of somatic cell reprogramming , 2013, Cancer science.

[37]  H. Kusuhara,et al.  Development of Orthogonal Linear Separation Analysis (OLSA) to Decompose Drug Effects into Basic Components , 2019, Scientific Reports.