東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

More is Better than One: The Effect ...

Stern, Jacob A.

Linked to FindBook

Google Book

Amazon

博客來

More is Better than One: The Effect of Ensembling on Deep Learning Performance in Biochemical Prediction Problems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	More is Better than One: The Effect of Ensembling on Deep Learning Performance in Biochemical Prediction Problems./
Author:	Stern, Jacob A.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	44 p.
Notes:	Source: Masters Abstracts International, Volume: 85-06.
Contained By:	Masters Abstracts International85-06.
Subject:	Diabetes. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30725947
ISBN:	9798381018462

More is Better than One: The Effect of Ensembling on Deep Learning Performance in Biochemical Prediction Problems.
Stern, Jacob A.

More is Better than One: The Effect of Ensembling on Deep Learning Performance in Biochemical Prediction Problems. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 44 p.

Source: Masters Abstracts International, Volume: 85-06.

Thesis (M.Sc.)--Brigham Young University, 2023.

This item must not be sold to any third party vendors.

This thesis presents two papers addressing important biochemical prediction challenges. The first paper focuses on accurate protein distance predictions and introduces updates to the ProSPr network. We evaluate its performance in the Critical Assessment of techniques for Protein Structure Prediction (CASP14) competition, investigating its accuracy dependence on sequence length and multiple sequence alignment depth. The ProSPr network, an ensemble of three convolutional neural networks (CNNs), demonstrates superior performance compared to individual networks. The second paper addresses the issue of accurate ligand ranking in virtual screening for drug discovery. We propose MILCDock, a machine learning consensus docking tool that leverages predictions from five traditional molecular docking tools. MILCDock, an ensemble of eight neural networks, outperforms single-network approaches and other consensus docking methods on the DUD-E dataset. However, we find that LIT-PCBA targets remain challenging for all methods tested. Furthermore, we explore the effectiveness of training machine learning tools on the biased DUD-E dataset, emphasizing the importance of mitigating its biases during training. Collectively, this work emphasizes the power of ensembling in deep learning-based biochemical prediction problems, highlighting improved performance through the combination of multiple models. Our findings contribute to the development of robust protein distance prediction tools and more accurate virtual screening methods for drug discovery.

ISBN: 9798381018462Subjects--Topical Terms:

544344
Diabetes.

More is Better than One: The Effect of Ensembling on Deep Learning Performance in Biochemical Prediction Problems.
LDR:02733nmm a2200361 4500 001 2394928
005 20240513061047.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798381018462
035 $a (MiAaPQ)AAI30725947
035 $a (MiAaPQ)BrighamYoung11132
035 $a AAI30725947
040 $a MiAaPQ $c MiAaPQ
100 1 $a Stern, Jacob A. $3 3764425
245 1 0 $a More is Better than One: The Effect of Ensembling on Deep Learning Performance in Biochemical Prediction Problems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 44 p.
500 $a Source: Masters Abstracts International, Volume: 85-06.
500 $a Advisor: Wingate, David;Corte, Dennis Della;Moody, James D.;Fulda, Nancy.
502 $a Thesis (M.Sc.)--Brigham Young University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a This thesis presents two papers addressing important biochemical prediction challenges. The first paper focuses on accurate protein distance predictions and introduces updates to the ProSPr network. We evaluate its performance in the Critical Assessment of techniques for Protein Structure Prediction (CASP14) competition, investigating its accuracy dependence on sequence length and multiple sequence alignment depth. The ProSPr network, an ensemble of three convolutional neural networks (CNNs), demonstrates superior performance compared to individual networks. The second paper addresses the issue of accurate ligand ranking in virtual screening for drug discovery. We propose MILCDock, a machine learning consensus docking tool that leverages predictions from five traditional molecular docking tools. MILCDock, an ensemble of eight neural networks, outperforms single-network approaches and other consensus docking methods on the DUD-E dataset. However, we find that LIT-PCBA targets remain challenging for all methods tested. Furthermore, we explore the effectiveness of training machine learning tools on the biased DUD-E dataset, emphasizing the importance of mitigating its biases during training. Collectively, this work emphasizes the power of ensembling in deep learning-based biochemical prediction problems, highlighting improved performance through the combination of multiple models. Our findings contribute to the development of robust protein distance prediction tools and more accurate virtual screening methods for drug discovery.
590 $a School code: 0022.
650 4 $a Diabetes. $3 544344
650 4 $a Deep learning. $3 3554982
650 4 $a Biochemistry. $3 518028
650 4 $a Neural networks. $3 677449
650 4 $a Amino acids. $3 558768
650 4 $a Correlation analysis. $3 3684579
650 4 $a Annotations. $3 3561780
650 4 $a Enzymes. $3 520899
650 4 $a Medicine. $3 641104
650 4 $a Public health. $3 534748
690 $a 0487
690 $a 0800
690 $a 0564
690 $a 0573
710 2 $a Brigham Young University. $3 1017451
773 0 $t Masters Abstracts International $g 85-06.
790 $a 0022
791 $a M.Sc.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30725947