Author

Ali Mostafa Anwar

Åbo Akademi UniversityORCID 0000-0002-5201-387XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:statistical model· 3 Assay:proteomics· 2 Research Focus:batch correction / integration· 1 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Organism:human· 1 Research Focus:imputation· 1

Frequent Co-Authors

Eleanor T. CoffeyiD· 2 shared Leo LahtiiD· 2 shared Akewak JebaiD· 1 shared Saif M. KhodaryiD· 1 shared Salma BayoumiiD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 5 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Eleanor T. CoffeyORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintMay 22, 2026

A Unified Framework for Batch Correction and Imputation in Modern Mass Spectrometry Proteomics

Ali Mostafa Anwar, Salma Bayoumi, Leo Lahti, Eleanor T. Coffey

NMFBatch reduces technical variation and preserves biological structure in proteomics data through batch correction and imputation.

DOI 10.64898/2026.05.19.726178

proteomicsstatistical model

𝕏 PostBluesky

BioinformaticsBioinformaticsPeer-reviewedOct 10, 2025

LimROTS: A Hybrid Method Integrating Empirical Bayes and Reproducibility-Optimized Statistics for Robust Differential Expression Analysis

Ali Mostafa Anwar, Akewak Jeba, Leo Lahti, Eleanor T. Coffey

LimROTS improves differential expression analysis in proteomics by combining empirical Bayes and reproducibility-optimized statistics for better accuracy and reproducibility.

2 citations

proteomicsstatistical model

𝕏 PostBluesky

GenomicsbioRxivPreprintApr 1, 2020

Insights into The Codon Usage Bias of 13 Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Isolates from Different Geo-locations

Saif M. Khodary, Ali Mostafa Anwar

This study analyzes codon usage bias in 13 SARS-CoV-2 isolates to understand its evolutionary forces and adaptation to humans.

3 citations

humanwhole-genome sequencing

𝕏 PostBluesky