國立虎尾科技大學 |

Statistical Methods for Effective Protein Biomarker Discovery.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Statistical Methods for Effective Protein Biomarker Discovery./
作者:	Cheng, Lin-Yang.
面頁冊數:	1 online resource (123 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
Contained By:	Dissertation Abstracts International79-03B(E).
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9780355253559

Statistical Methods for Effective Protein Biomarker Discovery.
Cheng, Lin-Yang.

Statistical Methods for Effective Protein Biomarker Discovery. - 1 online resource (123 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Effective discovery of protein biomarkers whose expression profiles correlate with (disease) conditions has been of great interest for numerous scientific purposes, such as drug discovery, diagnosis, prognosis, etc. Data independent acquisition (DIA) is the most advanced experimental approach to date for mass spectrometry-based proteomics, as it can quantify protein markers from much wider range of mass, providing opportunities to discover important markers by exploring thousands of unknown ones.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355253559Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Statistical Methods for Effective Protein Biomarker Discovery.
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100 1 $a Cheng, Lin-Yang. $3 1182921
245 1 0 $a Statistical Methods for Effective Protein Biomarker Discovery.
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300 $a 1 online resource (123 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
500 $a Adviser: Bowei Xi.
502 $a Thesis (Ph.D.) $c Purdue University $d 2017.
504 $a Includes bibliographical references
520 $a Effective discovery of protein biomarkers whose expression profiles correlate with (disease) conditions has been of great interest for numerous scientific purposes, such as drug discovery, diagnosis, prognosis, etc. Data independent acquisition (DIA) is the most advanced experimental approach to date for mass spectrometry-based proteomics, as it can quantify protein markers from much wider range of mass, providing opportunities to discover important markers by exploring thousands of unknown ones.
520 $a However, the process of biomarker discovery is complex and costly. Bottom-up experiments with high throughput regularly cause noisy or incorrect signals from DIA data, which then lead to problematic results from subsequent statistical analysis. Meanwhile, data configuration is so complicated that the most effective statistical method for analyzing the data at hand must be determined on a case-by-case basis. Finally, biomarker discovery using DIA data faces sparsity and high dimensionality problems, often causing suboptimal results. Most importantly, we notice that even the best of currently popular statistical methods either mistakenly discover too many irrelevant markers or fail to identify all the relevant markers.
520 $a In this dissertation, our work aims for simplifying the process of biomarker discovery. First, we propose a regularization framework for removing the noisy and deleterious signals incurred during the DIA experiment. Next, we propose a permutationbased method for determining the most effective statistical method in discovering informative markers. Lastly, we propose an effective likelihood ratio-based discriminant statistic, which also has desirable asymptotic properties, for directly finding all relevant markers with much lower false discovery rate than all the existing methods.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
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655 7 $a Electronic books. $2 local $3 554714
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690 $a 0308
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Purdue University. $b Statistics. $3 1181876
773 0 $t Dissertation Abstracts International $g 79-03B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10278050 $z click for full text (PQDT)