Evidence map›Paper›PMID 34012763›Full record

ArticleNetwork modeling and analysis in health informatics and bioinformatics2021

Benchmarking mass spectrometry based proteomics algorithms using a simulated database.

Muaaz Gul Awan, Abdullah Gul Awan, Fahad Saeed

Abstract read
In one paragraph

Article in Network modeling and analysis in health informatics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Muaaz Gul AwanLawrence Berkeley National Laboratory, Berkeley, CA, USA.
Abdullah Gul AwanAl-Khwarizmi Institute of Computer Science (KICS), University of Engineering & Technology (UET), Lahore, Pakistan.
Fahad SaeedSchool of Computing and Information Sciences, Florida International University, Miami, FL, USA.ORCID 0000-0002-3410-9552

Funding

Multimodal Machine-Learning and High Performance Computing Strategies for Big MS Proteomics DataR01GM134384 · NIGMS · FLORIDA INTERNATIONAL UNIVERSITY · PI SAEED, FAHAD · 2020 to 2022
$1.3M
NIGMS NIH HHS R01 GM134384
6 · The paper itself

Abstract

Protein sequencing algorithms process data from a variety of instruments that has been generated under diverse experimental conditions. Currently there is no way to predict the accuracy of an algorithm for a given data set. Most of the published algorithms and associated software has been evaluated on limited number of experimental data sets. However, these performance evaluations do not cover the complete search space the algorithmand the software might encounter in real-world. To this end, we present a database of simulated spectra that can be used to benchmark any spectra to peptide search engine. We demonstrate the usability of this database by bench marking two popular peptide sequencing engines. We show wide variation in the accuracy of peptide deductions and a complete quality profile of a given algorithm can be useful for practitioners and algorithm developers. All benchmarking data is available at https://users.cs.fiu.edu/~fsaeed/Benchmark.html.

Indexed as

BenchmarkingMass-spectrometryPeptide search algorithmsProteomics

Identifiers

PMID34012763
PMCPMC8130870

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.