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Journal articles on the topic 'Profiling'

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Author: Grafiati
Published: 4 June 2021
Last updated: 6 July 2024

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1

McCarthy, Nicola. "Profiling the profilins." Nature Reviews Cancer 13, no. 1 (December 21, 2012): 8. http://dx.doi.org/10.1038/nrc3435.

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2

NDW. "Profiling the profiling." Journal of the Forensic Science Society 32, no. 4 (October 1992): 365–66. http://dx.doi.org/10.1016/s0015-7368(92)73094-1.

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3

Muschinski, Andreas, Volker Lehmann, Lutz Justen, and Gerd Teschke. "Advanced radar wind profiling." Meteorologische Zeitschrift 14, no. 5 (October 10, 2005): 609–25. http://dx.doi.org/10.1127/0941-2948/2005/0067.

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4

Georgiev, Venelin. "Profiling Human Roles in Cybercrime." Information & Security: An International Journal 43, no. 2 (2019): 145–60. http://dx.doi.org/10.11610/isij.4313.

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5

Goldwyn, Robert M. "Profiling." Plastic and Reconstructive Surgery 111, no. 5 (April 2003): 1756. http://dx.doi.org/10.1097/01.prs.0000053551.45063.3e.

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6

Fernandez, Katya C., Cheri A. Levinson, and Thomas L. Rodebaugh. "Profiling." Social Psychological and Personality Science 3, no. 6 (January 19, 2012): 706–13. http://dx.doi.org/10.1177/1948550611434967.

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7

Vebi Natasya, Salsabila, and Rolly Maulana Awangga. "PROFILING MAHASISWA DAN ALUMNI MENGGUNAKAN METODE DECISION TREE." JATI (Jurnal Mahasiswa Teknik Informatika) 7, no. 2 (September 8, 2023): 1359–63. http://dx.doi.org/10.36040/jati.v7i2.6824.

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Profilling mahasiswa dan alumni merupakan sebuah studi yang bertujuan untuk mengidentifikasi karakteristik individu yang memasuki dan keluar dari sebuah institusi pendidikan. Melalui analisis data yang dikumpulkan, studi ini dapat memberikan informasi yang berharga bagi institusi untuk meningkatkan program-program yang ditawarkan dan membantu mahasiswa serta alumni untuk merencanakan karir mereka. Literature Riview ini akan membeirkan sebuah tinjauan sistematis mengenai profiling mahasiswa dan alumni. Pada penelitian ini digunakan artikel-artikel yang diterbitkan dari tahun 2016-2023 dalam melakukan literature riview. Yang mana artikel tersebut didapat diberbagai database perpustakaan digital seperti IEE Explore, Science Direct, Google Scholar dan masih banyak lagi. Tujuan utama pada penelitian ini yaitu untuk mengetahui atau mencari tren dalam topik yang nantinya akan dilakukan penelitian lebih dalam. Pada penelitian ini menggunakan metode Systematic Literature Review. Hasil dari penelitian Ssystematic Literature Review ini adalah metode yang paling banyak digunakan untuk profiling adalah K-Means, Decision Tree menjadi metode dengan akurasi yang cukup tinggi dalam melakukan profiling, algoritma Decision tree yang paling banyak digunakan untuk profiling adalah C4.5.
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8

Lobon-Iglesias, María-Jesús, Arnault Tauziede-Espariat, Mamy Andrianteranagna, Zhiyan Han, Julien Masliah-Planchon, and Franck Bourdeaut. "ATRT-27. COST-EFFECTIVE ASSAYS TO SUBGROUP ATRT IN THE DAILY ROUTINE." Neuro-Oncology 22, Supplement_3 (December 1, 2020): iii281. http://dx.doi.org/10.1093/neuonc/noaa222.026.

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Abstract Three atypical teratoid rhabdoid tumors (ATRT) molecular subgroups with different bio-clinical characteristics have been reported (TYR, SHH and MYC). Molecular subgrouping relies on either methylation profiling (reference methods), or expression profiling. However, the cost-effectiveness of such pangenomic screening is questionable. This work aims to study the reliability of alternative techniques for subgroup classification in the daily routine. Illumina EPIC-arrays were performed on 46 samples. Among those cases, expression profiling were analysed by RNAseq (n=30). We designed a 26-gene panel to assess expression profiling using the Nanostring technology; this was applied to 35 tumors. Immunohistochemistry (IHC) was used for 20 samples; it relied on the expression of MITF, TYR, OTX2 and MYC. We first assessed the concordance between DNA methylation and RNAseq based profilings; then, between RNAseq and Nanostring and, finally, between methylation profiling and Nanostring or IHC, the two rapidest and cheapest tools. The concordance between the two expression-based profiling was 19/21. EPIC-arrays and RNAseq or Nanostring were concordant in 26/30 and 30/35 samples, respectively. The concordance was perfect for methylation-defined MYC subtype. Finally, 17/20 tumor samples were classified in the same subgroup by EPIC-arrays and IHC; the 3/20 misclassified tumors were SHH by methylation but consistently MYC by IHC, Nanostring and RNAseq. There was 90–100% of concordance for TYR subgroup (all techniques). We have designed a gene panel-based expression signature that shows promising concordance with RNAseq and methylation profiling. Nanostring assay and IHC well predict ATRT subgroup classification for MYC and TYR subclass, but less so for methylation-defined SHH ones.
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9

Yoon, Ung-Han, Yeon-Ki Kim, Chang-Kug Kim, Jang-Ho Hahn, Dong-Hern Kim, Tae-Ho Lee, Gang-Seob Lee, Soo-Chul Park, and Baek-Hie Nahm. "Current status on expression profiling using rice microarray." Journal of Plant Biotechnology 37, no. 2 (June 30, 2010): 144–52. http://dx.doi.org/10.5010/jpb.2010.37.3.144.

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10

Tsai, Alexander C. "Prescriber Profiling." Annals of Internal Medicine 148, no. 1 (January 1, 2008): 81. http://dx.doi.org/10.7326/0003-4819-148-1-200801010-00012.

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11

Pratt, Stephen E. "Prescriber Profiling." Annals of Internal Medicine 148, no. 1 (January 1, 2008): 81. http://dx.doi.org/10.7326/0003-4819-148-1-200801010-00013.

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12

Kirk, Stephen M. "Prescriber Profiling." Annals of Internal Medicine 148, no. 1 (January 1, 2008): 82. http://dx.doi.org/10.7326/0003-4819-148-1-200801010-00014.

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13

Fugh-Berman, Adriane. "Prescriber Profiling." Annals of Internal Medicine 148, no. 1 (January 1, 2008): 82. http://dx.doi.org/10.7326/0003-4819-148-1-200801010-00015.

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14

Natália Santos, Machado, and Franck Wilson. "Criminal profiling." International Multidisciplary Scientific Database 1, no. 1 (2019): 1–4. http://dx.doi.org/10.31789/imscid-2019-001.

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15

Peppelenbosch, Maikel P. "Kinome Profiling." Scientifica 2012 (2012): 1–13. http://dx.doi.org/10.6064/2012/306798.

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The use of arrays in genomics has led to a fast and reliable way to screen the transcriptome of an organism. It can be automated and analysis tools have become available and hence the technique has become widely used within the past few years. Signal-transduction routes rely mainly on the phosphorylation status of already available proteins; therefore kinases are central players in signal-transduction routes. The array technology can now also be used for the analysis of the kinome. To enable array analysis, consensus peptides for kinases are spot on a solid support. After incubation with cell lysates and in the presence of radioactive ATP, radioactive peptides can be visualized and the kinases that are active in the cells can be determined. The present paper reviews comprehensively the different kinome array platforms available and results obtained hitherto using such platforms. It will appear that this technology does not disappoint its high expectations and is especially powerful because of its species independence. Nevertheless, improvements are still possible and I shall also sketch future possible directions.
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16

Viscido, Toni, Sebastian Mathar, and Matthias Lammermann. "Powernet Profiling." ATZautotechnology 9, no. 3 (May 2009): 38–41. http://dx.doi.org/10.1007/bf03247118.

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17

Fatima, Fatima Fatima. "Forensic Profiling." International Journal for Electronic Crime Investigation 4, no. 2 (March 23, 2020): 12. http://dx.doi.org/10.54692/ijeci.2020.040256.

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This review paper brings out technical and legal association between profiling and forensic science. It emphasizes on trace evidences used as valuable information in perspective of forensic science and criminal justice system. The aim of forensic profiling is to interpret and investigate with reasoning purposes. It explains available forms of data, data processing methods and technologies, and obstacles to data treatment. Proceeding with reconstruction process that formulates a possible hypothesis of profile, different forms of forensic profiling with judicial perspective are discussed. Other factors such as crime prepetition, crime series, and tactical, operational and strategic analysis etc. that can help in developing of profiles are also argued. It distinguishes the profiles significance into new technologies of DNA databases, digital image forensics, 3D and CCTV surveillance cameras, forensic drug profiling and intelligence based expertise centers. Other fields related to security such as detection, risk analysis and surveillance are discussed in profiling perspective
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18

Kigerl, Alex. "Profiling Cybercriminals." Social Science Computer Review 36, no. 5 (September 20, 2017): 591–609. http://dx.doi.org/10.1177/0894439317730296.

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Cybercrime has become a growing business. The marketplaces for such businesses tend to be online forums. Much of the research on carding forums has been qualitative, but there have been quantitative analyses as well. One such type of analysis is topic modeling, a clustering technique that groups forum users according to the textual comments they leave. However, this type of research so far has been exclusively quantitative, without qualitatively examining the topics. The following study attempts to add to this research by analyzing the comment histories from 30,469 users from three carding forums. The results have revealed that users belong to one or more of 21 different topics. The topics are grouped into six broader categories, consisting of a customer base, identity fraud market, crimeware market, free content market, and two others. Descriptives are provided displaying how the topics are distributed across the three websites and directions for future research are discussed.
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19

Mansmann, U. "Genomic Profiling." Methods of Information in Medicine 44, no. 03 (2005): 454–60. http://dx.doi.org/10.1055/s-0038-1633993.

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Summary Objectives: The current literature on the use of micro-arrays to generate prognostic profiles is still a methodological wasteland. Many valid questions, such as how profiling studies should be evaluated and what conclusions can be drawn, remain unanswered. Or how can flaws in the collection, analysis and interpretation of data be detected? Undiscovered imperfections can lead to a waste of valuable resources as well as be a latent source of false conclusions. Methods: Three seminal papers on the prognosis of breast cancer using genomic profiling will be discussed. Six principles of good experimental design will be used for methodological guidance: defining relevant endpoints, avoiding systematic bias, generalizability of results, appropriately sized samples to achieve sufficient power, simple design to improve interpretability, and avoiding artificial assumptions. Results: Severe violations of at least one of the six principles of good experimental design can be found in each of the three papers. A strategy is presented to assess whether a study has achieved a high level of methodological quality. This strategy also helps to establish a suitable protocol for future profiling projects. Conclusions: Determining the design of a study in a protocol is a first step to avoid impending pitfalls. The protocol should deal with the problem of understanding the complex reality behind genomic profiling. There are basic guiding principles which can help handle the complex task of designing prognostic studies to find genomic signatures.
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20

Vaisman-Tzachor, Reuben. "Profiling Terrorists." Journal of Police Crisis Negotiations 7, no. 1 (March 6, 2007): 27–61. http://dx.doi.org/10.1300/j173v07n01_03.

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21

Hill, Alex, and Steve Brown. "Strategic profiling." International Journal of Operations & Production Management 27, no. 12 (November 13, 2007): 1333–61. http://dx.doi.org/10.1108/01443570710835642.

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22

Frohlick, Susan, and Paula Migliardi. "HETEROSEXUAL PROFILING." Australian Feminist Studies 26, no. 67 (March 2011): 73–88. http://dx.doi.org/10.1080/08164649.2010.546329.

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23

Turco, Ronald N. "Psychological Profiling." International Journal of Offender Therapy and Comparative Criminology 34, no. 2 (September 1990): 147–54. http://dx.doi.org/10.1177/0306624x9003400207.

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24

Vinson, Valda. "Profiling coronaviruses." Science 370, no. 6520 (November 26, 2020): 1053.12–1055. http://dx.doi.org/10.1126/science.370.6520.1053-l.

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25

COOK, PATRICK E., and DAYLE L. HINMAN. "Criminal Profiling." Journal of Contemporary Criminal Justice 15, no. 3 (August 1999): 230–41. http://dx.doi.org/10.1177/1043986299015003002.

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26

EGGER, STEVEN A. "Psychological Profiling." Journal of Contemporary Criminal Justice 15, no. 3 (August 1999): 242–61. http://dx.doi.org/10.1177/1043986299015003003.

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27

Risse, Mathias, and Richard Zeckhauser. "Racial Profiling." Philosophy Public Affairs 32, no. 2 (April 2004): 131–70. http://dx.doi.org/10.1111/j.1088-4963.2004.00009.x.

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28

Higgins, George E. "Racial Profiling." Journal of Ethnicity in Criminal Justice 6, no. 1 (March 5, 2008): 1–2. http://dx.doi.org/10.1300/j222v06n01_01.

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29

Zaparanuks, Dmitrijs, and Matthias Hauswirth. "Algorithmic profiling." ACM SIGPLAN Notices 47, no. 6 (August 6, 2012): 67–76. http://dx.doi.org/10.1145/2345156.2254074.

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30

Rotella, Carlo. "Profiling “Money”." Public Culture 27, no. 1 75 (January 2015): 7–19. http://dx.doi.org/10.1215/08992363-2798427.

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31

Spicer, Jeree. "CYBERCRIMINAL PROFILING." EDPACS 60, no. 3 (September 2, 2019): 1–17. http://dx.doi.org/10.1080/07366981.2019.1675965.

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32

Aicher, R. "Physician profiling." Aesthetic Surgery Journal 21, no. 2 (March 2001): 185–86. http://dx.doi.org/10.1067/maj.2001.115055.

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33

Hall, Erika V., Adam D. Galinsky, and Katherine W. Phillips. "Gender Profiling." Personality and Social Psychology Bulletin 41, no. 6 (April 6, 2015): 853–68. http://dx.doi.org/10.1177/0146167215580779.

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34

Delsol, Rebekah. "Racial profiling." Criminal Justice Matters 101, no. 1 (April 3, 2015): 34–35. http://dx.doi.org/10.1080/09627251.2015.1080944.

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35

Mellish, David. "Profiling juveniles." Criminal Justice Matters 28, no. 1 (June 1997): 23. http://dx.doi.org/10.1080/09627259708553128.

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36

Werner, Rachel M., David A. Asch, and Daniel Polsky. "Racial Profiling." Circulation 111, no. 10 (March 15, 2005): 1257–63. http://dx.doi.org/10.1161/01.cir.0000157729.59754.09.

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37

Wells, William. "Metabolite profiling." Genome Biology 1 (2000): spotlight—20001108–03. http://dx.doi.org/10.1186/gb-spotlight-20001108-03.

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38

Weitzman, Jonathan B. "Nephrogenesis profiling." Genome Biology 2 (2001): spotlight—20010504–02. http://dx.doi.org/10.1186/gb-spotlight-20010504-02.

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39

Weitzman, Jonathan B. "PTEN profiling." Genome Biology 2 (2001): spotlight—20011107–01. http://dx.doi.org/10.1186/gb-spotlight-20011107-01.

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40

Weitzman, Jonathan B. "Placental profiling." Genome Biology 2 (2001): spotlight—20011108–01. http://dx.doi.org/10.1186/gb-spotlight-20011108-01.

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41

Merino, J. G. "Profiling patients." Neurology 80, no. 18 (April 17, 2013): 1632–33. http://dx.doi.org/10.1212/wnl.0b013e318293e39c.

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42

Bell, Karen M. "Physician profiling." Journal of Ambulatory Care Management 19, no. 1 (January 1996): 81–85. http://dx.doi.org/10.1097/00004479-199601000-00007.

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43

Newman, Carrie, Suzanne White, and Donna Burman. "Physician Profiling." Journal of Ambulatory Care Management 19, no. 4 (October 1996): 49–57. http://dx.doi.org/10.1097/00004479-199610000-00008.

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44

Weitzman, Jonathan B. "Phospho-profiling." Genome Biology 4 (2003): spotlight—20030109–02. http://dx.doi.org/10.1186/gb-spotlight-20030109-02.

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45

Honour, John W. "Steroid Profiling." Annals of Clinical Biochemistry: International Journal of Laboratory Medicine 34, no. 1 (January 1997): 32–44. http://dx.doi.org/10.1177/000456329703400106.

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46

Weedn, Victor Walter. "DNA Profiling." American Journal of Forensic Medicine and Pathology 11, no. 4 (December 1990): 348. http://dx.doi.org/10.1097/00000433-199012000-00020.

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47

Hudson, John. "Demographic profiling." Ubiquity 2002, December (December 2002): 1. http://dx.doi.org/10.1145/764008.763951.

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48

Ford, Matthew W., and Bertie M. Greer. "Profiling Change." Journal of Applied Behavioral Science 42, no. 4 (December 2006): 420–46. http://dx.doi.org/10.1177/0021886306293437.

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49

MULLER, DAMON A. "Criminal Profiling." Homicide Studies 4, no. 3 (August 2000): 234–64. http://dx.doi.org/10.1177/1088767900004003003.

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50

Bahcall, Orli. "Profiling epistasis." Nature Reviews Genetics 6, no. 5 (May 2005): 349. http://dx.doi.org/10.1038/nrg1626.

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