Why breast cancer signatures are no better than random signatures explained

Why breast cancer signatures are no better than random signatures explained

Random signature superiority (RSS) occurs when random gene signatures outperform published and/or known signatures. Unlike reproducibility and generalizability issues, RSS is relatively underexplored. Yet, understanding it is imperative for better analytical outcome. In breast cancer, RSS correlates...

Bibliografiset tiedot
Päätekijät:	Goh, Wilson Wen Bin, Wong, Limsoon
Muut tekijät:	School of Biological Sciences
Aineistotyyppi:	Journal Article
Kieli:	English
Julkaistu:	2020
Aiheet:	Science::Biological sciences Bioinformatics Statistics
Linkit:	https://hdl.handle.net/10356/137544

Samankaltaisia teoksia

Turning straw into gold : building robustness into gene signature inference
Tekijä: Goh, Wilson Wen Bin, et al.
Julkaistu: (2020)

How to do quantile normalization correctly for gene expression data analyses
Tekijä: Zhao, Yaxing, et al.
Julkaistu: (2021)

Dealing with confounders in omics analysis
Tekijä: Goh, Wilson Wen Bin, et al.
Julkaistu: (2020)

Structural analysis of genomic and proteomic signatures reveal dynamic expression of intrinsically disordered regions in breast cancer
Tekijä: Nicole Zatorski, et al.
Julkaistu: (2024-09-01)

Pan-cancer analysis of the interplay between mutational signatures and cellular signaling
Tekijä: Anna Hakobyan, et al.
Julkaistu: (2024-06-01)

PROTREC: a probability-based approach for recovering missing proteins based on biological networks
Tekijä: Kong, Weijia, et al.
Julkaistu: (2022)

ProJect: a powerful mixed-model missing value imputation method
Tekijä: Kong, Weijia, et al.
Julkaistu: (2023)

Human alpha cell transcriptomic signatures of types 1 and 2 diabetes highlight disease-specific dysfunction pathways
Tekijä: Bosi, Emanuele, et al.
Julkaistu: (2023)

PROSE: phenotype-specific network signatures from individual proteomic samples
Tekijä: Wong, Bertrand Jern Han, et al.
Julkaistu: (2023)

Dealing with missing values in proteomics data
Tekijä: Kong, Weijia, et al.
Julkaistu: (2023)

Advanced bioinformatics methods for practical applications in proteomics
Tekijä: Goh, Wilson Wen Bin, et al.
Julkaistu: (2020)

Utilization of interpro domain signatures for broadening pathways knowledge /
Tekijä: Mardhiah Alias, 1988-, author, et al.
Julkaistu: (2011)

Utilization of interpro domain signatures for broadening pathways knowledge /
Tekijä: Mardhiah Alias, 1988-, author, et al.
Julkaistu: (2011)

Examining the practical limits of batch effect-correction algorithms : when should you care about batch effects?
Tekijä: Zhou, Longjian, et al.
Julkaistu: (2021)

ProInfer: an interpretable protein inference tool leveraging on biological networks
Tekijä: Peng, Hui, et al.
Julkaistu: (2023)

Evaluating network-based missing protein prediction using p-values, Bayes Factors, and probabilities
Tekijä: Goh, Wilson Wen Bin, et al.
Julkaistu: (2023)

A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods
Tekijä: Foo, Reuben Jyong Kiat, et al.
Julkaistu: (2023)

Understanding missing proteins : a functional perspective
Tekijä: Zhou, Longjian, et al.
Julkaistu: (2020)

Doppelgänger spotting in biomedical gene expression data
Tekijä: Wang, Li Rong, et al.
Julkaistu: (2023)

Thermodynamic analysis of DNA hybridization signatures near mitochondrial DNA deletion breakpoints
Tekijä: Lakshmanan, Lakshmi Narayanan, et al.
Julkaistu: (2022)

Resolving missing protein problems using functional class scoring
Tekijä: Wong, Bertrand Jern Han, et al.
Julkaistu: (2023)

How doppelgänger effects in biomedical data confound machine learning
Tekijä: Wang, Li Rong, et al.
Julkaistu: (2022)

What can scatterplots teach us about doing data science better?
Tekijä: Goh, Wilson Wen Bin, et al.
Julkaistu: (2022)

Gene x gene interactions in genome wide association studies
Tekijä: Bhattacharya, K
Julkaistu: (2014)

Proteomic investigation of intra-tumor heterogeneity using network-based contextualization - a case study on prostate cancer
Tekijä: Goh, Wilson Wen Bin, et al.
Julkaistu: (2021)

Cohort-specific boolean models highlight different regulatory modules during Parkinson’s disease progression
Tekijä: Ahmed Abdelmonem Hemedan, et al.
Julkaistu: (2024-10-01)

Identifying gene signatures contributing to progression after chemotherapy in DLBCL patients
Tekijä: Vijay, Varsheni
Julkaistu: (2023)

Exploring the fold space preferences of ancient and newborn protein superfamilies
Tekijä: Edwards, H, et al.
Julkaistu: (2014)

Perspectives for better batch effect correction in mass-spectrometry-based proteomics
Tekijä: Phua, Ser-Xian, et al.
Julkaistu: (2023)

Modelling and comparing protein interaction networks using subgraph counts
Tekijä: Chegancas Rito, TM
Julkaistu: (2012)

Mechanical signatures of microbial biofilms in micropillar-embedded growth chambers
Tekijä: Chew, S. C., et al.
Julkaistu: (2021)

Statistical models for single-cell data
Tekijä: Pierson, E
Julkaistu: (2015)

Data integration in the life sciences : second international workshop, DILS 2005, San Diego, CA, USA, July 20-22, 2005 : proceedings /
Tekijä: DILS 2005 (2005 : San Diego, Calif.), et al.
Julkaistu: (2005)

Defining gene signature underlying impaired hippocampal neurogenesis in triple transgenic Alzheimer’s disease mice
Tekijä: Ng, Yi Lin
Julkaistu: (2016)

Generalised networks for protein interaction analysis
Tekijä: Klimm, F
Julkaistu: (2018)

Genetic signatures of HIV-1 envelope-mediated bystander apoptosis
Tekijä: Joshi, Anjali, et al.
Julkaistu: (2018)

Networks in nature: dynamics, evolution, and modularity
Tekijä: Agarwal, S
Julkaistu: (2012)

Ten quick tips for ensuring machine learning model validity
Tekijä: Goh, Wilson Wen Bin, et al.
Julkaistu: (2024)

Bioinformatic, phylogenetic and chemical analysis of the UV-absorbing compounds scytonemin and mycosporine-like amino acids from the microbial mat communities of Shark Bay, Australia
Tekijä: D'Agostino, Paul M., et al.
Julkaistu: (2019)

An integrated genomic approach for the identification and analysis of single nucleotide polymorphisms that affect cancer in humans
Tekijä: Repapi, E
Julkaistu: (2013)