Anna Paola Carrieri

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Manager & Senior Research Scientist
IBM Research Laboratory, UK

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2021

Efficient computation of Faiths phylogenetic diversity with applications in characterizing microbiomes
George W Armstrong, Kalen Cantrell, Shi Huang, Daniel McDonald, Niina Haiminen, Anna Paola Carrieri, Qiyun Zhu, Antonio Gonzalez, Imran McGrath, Kristen Beck, Daniel Hakim, Aki S Havulinna, Guillaume Meric, Teemu Niiranen, Leo Lahti, Veikko Salomaa, Mohit Jain, Michael Inouye, Austin D Swafford, Ho-Cheol Kim, Laxmi Parida, Yoshiki Vazquez-Baeza, Rob Knight
Genome Research, Cold Spring Harbor Laboratory, 2021
Abstract phylogenetic diversity, alpha diversity, metric, phylogenetic tree, metagenomics, diversity, scale, data mining, computation, biology

Combining explainable machine learning, demographic and multi-omic data to identify precision medicine strategies for inflammatory bowel disease
Gardiner L, Carrieri Ap, Bingham K, Macluskie G, Bunton D, McNeil M, Pyzer-Knapp Eo
medRxiv, Cold Spring Harbor Laboratory Press, 2021
Abstract precision medicine, disease, inflammatory bowel disease, efficacy, drug, ulcerative colitis, omics, transcriptome, medicine, machine learning, artificial intelligence

Abstract 1 Inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohns disease, affect several million individuals worldwide. These diseases are heterogeneous at the clinical, immunological and genetic levels and result from a complex interaction between the host and environmental factors. Investigating drug efficacy in cultured human fresh IBD tissues can improve our understanding of the reasons why certain medications are more or less effective for different patients. 67 We propose an explainable machine learning (ML) approach that combines bioinformatics and domain insight, to informatively integrate multi-modal data to predict inter-patient specific variation in drug response. Using explanation of our models, we interpret the models predictions inferring unique combinations of important features associated with human tissue pharmacological responses. The inferred multi-modal features originate from multi-omic data (genomic and transcriptomic), demographic, medicinal and pharmacological data and all are associated with drug efficacy generated by a preclinical human fresh IBD tissue assay. 149 To pharmacologically assess patient variation in response to IBD treatment, we used the reduction in the release of the inflammatory cytokine TNF from the fresh IBD tissues in the presence or absence of test drugs, as a measure of drug efficacy. The TNF pathway is a common target in current therapies for IBD; we initially explored the effects of a mitogen-activated protein kinase (MAPK) inhibitor on the production of TNF; however, we later show the approach can be applied to other targets, test drugs or mechanisms of interest. Our best model was able to predict TNF levels from a combination of integrated demographic, medicinal and genomic features with an error as low as 4.98% on unseen patients. We incorporated transcriptomic data to validate and expand insights from genomic features. Our results showed variations in drug effectiveness between patients that differed in gender, age or condition and linked new genetic polymorphisms in our cohort of IBD patients to variation in response to the anti-inflammatory treatment BIRB796 (Doramapimod). 316 Our approach models drug response in a relevant human tissue model of IBD while also identifying its most predictive features as part of a transparent ML-based precision medicine strategy.

Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function
Gardiner L, Rusholme-Pilcher R, Colmer J, Rees H, Crescente Jm, Carrieri Ap, Duncan S, Pyzer-Knapp Eo, Krishna R, Halll A
bioRxiv, Cold Spring Harbor Laboratory, 2021
Abstract circadian clock, circadian rhythm, expression, function, identification, machine learning, computer science, artificial intelligence, circadian regulation, experimental work, model interpretation

Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences
Anna Paola Carrieri, Niina Haiminen, Sean Maudsley-Barton, Laura-Jayne Gardiner, Barry Murphy, Andrew Mayes, Sarah Paterson, Sally Grimshaw, Martyn Winn, Cameron Shand, Panagiotis Hadjidoukas, Will Rowe, Stacy Hawkins, Ashley MacGuire-Flanagan, Jane Tazzioli, John Kenny, Laxmi Parida, Michael Hoptroff, Edward O Pyzer-Knapp
Scientific Reports 11(4565), 2021
Abstract Blog: https://www.ibm.com/blogs/research/2021/02/ai-explains-microbiome

SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment
C. Marotz, P. Belda-Ferre, F. Ali, P. Das, K. Cantrell, L. Jiang, C. Martino, R. E. Diner, G. Rahman, D. McDonald, G. Armstrong, S. Kodera, S. Donato, G. Ecklu-Mensah, N. Gottel M. C. Salas Garcia, L. Y. Chiang, R. A. Salido, J. P. Shaffer, M. K. Bryant, K. Sanders, G. Humphrey, G. Ackerman, N. Haiminen, K. L. Beck, H-C. Kim, A. P. Carrieri, L. Parida, Y. Vazquez-Baeza, F. J. Torriani, R. Knight, J. Gilbert, D. A. Sweeney, S. M. Allard
Microbiome 9(132), 2021
Abstract Learn more: https://health.ucsd.edu/news/releases/Pages/2021-06-09-sars-cov-2-detectable-though-likely-not-transmissible-on-hospital-surfaces.aspx

Multi-omics profiling of Earths biomes reveals that microbial and metabolite composition are shaped by the environment
Shaffer Jp, Nothias L, Nothias L, Thompson Lr, Thompson Lr, Sanders Jg, Salido Ra, Couvillion Sp, Brejnrod Ad, Huang S, Lejzerowicz F, Lutz Hl, Zhu Q, Martino C, Morton Jt, Karthikeyan S, Nothias-Esposito M, Nothias-Esposito M, Duhrkop K, Bocker S, Kim H, Aksenov Aa, Aksenov Aa, Bittremieux W, Bittremieux W, Bittremieux W, Minich Jj, Marotz C, Bryant Mm, Sanders K, Schwartz T, Humphrey G, Vasquez-Baeza Y, Tripathi A, Tripathi A, Parida L, Carrieri Ap, Haiminen N, Beck Kl, Das P, Gonzalez A, McDonald D, Kars
bioRxiv, Cold Spring Harbor Laboratory, 2021
Abstract metabolome, metagenomics, metabolite, microbiome, genome, computational biology, biome, biology, profiling, multi omics

EMPress Enables Tree-Guided, Interactive, and Exploratory Analyses of Multi-omic Data Sets
Kalen Cantrell, Marcus W. Fedarko, Gibraan Rahman, Daniel McDonald, Yimeng Yang, Thant Zaw, Antonio Gonzalez, Stefan Janssen, Mehrbod Estaki, Niina Haiminen, Kristen L. Beck, Qiyun Zhu, Erfan Sayyari, James T. Morton, George Armstrong, Anupriya Tripathi, Julia M. Gauglitz, Clarisse Marotz, Nathaniel L. Matteson, Cameron Martino, Jon G. Sanders, Anna Paola Carrieri, Se Jin Song, Austin D. Swafford, Pieter C. Dorrestein, Pieter C. Dorrestein, Kristian G. Andersen, Laxmi Parida, Ho Cheol Kim, Yoshiki Vazquez-B
mSystems 6(2), American Society for Microbiology, 2021
Abstract context, tree, data structure, workflow, data science, scalability, ordination, computer science, microbiome, scale

Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data
Lingjing Jiang, Niina Haiminen, Anna-Paola Carrieri, Shi Huang, Yoshiki Vazquez-Baeza, Laxmi Parida, Ho-Cheol Kim, Austin D. Swafford, Rob Knight, Loki Natarajan
Biometrics, 2021
Abstract CMI blog: https://cmi.ucsd.edu/2021/06/21/refining-research-reproducibility-understanding-the-utility-of-stability-criteria-in-choosing-feature-selection-methods/

Challenges in benchmarking metagenomic profilers
Zheng Sun, Shi Huang, Meng Zhang, Qiyun Zhu, Niina Haiminen, Anna-Paola Carrieri, Yoshiki Vazquez-Baeza, Laxmi Parida, Ho-Cheol Kim, Rob Knight, Yang-Yu Liu
Nature Methods, 2021
Abstract CMI blog: https://cmi.ucsd.edu/2021/09/09/pondering-problems-in-probing-profilers-exploring-the-challenges-in-comparing-metagenomic-profilers/

2020

Human Skin, Oral, and Gut Microbiomes Predict Chronological Age
Shi Huang, Niina Haiminen, Anna-Paola Carrieri, Rebecca Hu, Lingjing Jiang, Laxmi Parida, Baylee Russell, Celeste Allaband, Amir Zarrinpar, Yoshiki V\'azquez-Baeza, Pedro Belda-Ferre, Hongwei Zhou, Ho-Cheol Kim, Austin D. Swafford, Rob Knight, Zhenjiang Zech Xu
mSystems 5(1), American Society for Microbiology Journals, 2020
Abstract

Human gut microbiomes are known to change with age, yet the relative value of human microbiomes across the body as predictors of age, and prediction robustness across populations is unknown. In this study, we tested the ability of the oral, gut, and skin (hand and forehead) microbiomes to predict age in adults using random forest regression on data combined from multiple publicly available studies, evaluating the models in each cohort individually. Intriguingly, the skin microbiome provides the best prediction of age (mean \textpm standard deviation, 3.8 \textpm 0.45 years, versus 4.5 \textpm 0.14 years for the oral microbiome and 11.5 \textpm 0.12 years for the gut microbiome). This also agrees with forensic studies showing that the skin microbiome predicts postmortem interval better than microbiomes from other body sites. Age prediction models constructed from the hand microbiome generalized to the forehead and vice versa, across cohorts, and results from the gut microbiome generalized across multiple cohorts (United States, United Kingdom, and China). Interestingly, taxa enriched in young individuals (18 to 30 years) tend to be more abundant and more prevalent than taxa enriched in elderly individuals (\>60 yrs), suggesting a model in which physiological aging occurs concomitantly with the loss of key taxa over a lifetime, enabling potential microbiome-targeted therapeutic strategies to prevent aging.IMPORTANCE Considerable evidence suggests that the gut microbiome changes with age or even accelerates aging in adults. Whether the age-related changes in the gut microbiome are more or less prominent than those for other body sites and whether predictions can be made about a person\textquoterights age from a microbiome sample remain unknown. We therefore combined several large studies from different countries to determine which body site\textquoterights microbiome could most accurately predict age. We found that the skin was the best, on average yielding predictions within 4 years of chronological age. This study sets the stage for future research on the role of the microbiome in accelerating or decelerating the aging process and in the susceptibility for age-related diseases.

Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity
Laura-Jayne Gardiner, Anna Paola Carrieri, Jenny Wilshaw, Stephen Checkley, Edward O. Pyzer-Knapp, Ritesh Krishna
Scientific Reports 10(1), 2020

2019

Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development
Laura-Jayne Gardiner, Anna Paola Carrieri, Jenny Wilshaw, Stephen Checkley, Edward O Pyzer-Knapp, Ritesh Krishna
arXiv preprint arXiv:1911.04374, 2019
Abstract trustworthiness, model selection, machine learning, genomics, gaussian process, drug discovery, drug development, biology, bayesian probability, bayesian inference, artificial intelligence

A Fast Machine Learning Workflow for Rapid Phenotype Prediction from Whole Shotgun Metagenomes
Anna Paola Carrieri, Will P. M. Rowe, Martyn D. Winn, Edward O. Pyzer-Knapp
Proceedings of the AAAI Conference on Artificial Intelligence, pp. 9434-9439, 2019
Abstract workflow, terabyte, shotgun, precision agriculture, microbiome, metagenomics, machine learning, computer science, cloud computing, big data, artificial intelligence

Streaming histogram sketching for rapid microbiome analytics
WP Rowe, Anna Paola Carrieri, Cristina Alcon-Giner, Shabhonam Caim, Alex Shaw, Kathleen Sim, J Simon Kroll, Lindsay Hall, Edward O Pyzer-Knapp, Martyn D Winn
Microbiome, 40, BioMed Central, 2019

2018

Accelerating molecular discovery through data and physical sciences: Applications to peptide-membrane interactions
Flaviu Cipcigan, Anna Paola Carrieri, Edward O Pyzer-Knapp, Ritesh Krishna, Ya-Wen Hsiao, Martyn Winn, Maxim G Ryadnov, Colin Edge, Glenn Martyna, Jason Crain
The Journal of chemical physics, 2018

2017

A colored graph approach to the perfect phylogeny with persistent characters
Bonizzoni P., Carrieri A.P., Della Vedova G., Trucco, G.
Theoretical Computer Science658, 60-73, 2017

Host phenotype prediction from differentially abundant microbes using RoDEO
A.P. Carrieri*, N. Haiminen*, L. Parida
In: Bracciali A., Caravagna G., Gilbert D., Tagliaferri R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics, Lecture Notes in Computer Science, pp. 27-41, Springer, 2017
Abstract

Species-Driven Persistent Phylogeny
Bonizzoni, P., Carrieri, A. P., Della Vedova, G., Rizzi, R., Trucco, G.
Fundamenta Informaticae 154(1-4), 47-63, IOS Press, 2017

2016

Dimension reduction of metagenome data using RoDEO improves phenotype prediction
Carrieri A.P., Haiminen N., Parida L.
In: Computational Intelligence Methods for Bioinformatics and Biostatistics, Proc. 13th International Meeting CIBB, Bracciali A., Gilbert D., MacKenzie G. (Eds.), pp. 51-56, 2016

Sampling ARG of multiple populations under complex configurations of subdivision and admixture
A.P. Carrieri, F. Utro, L. Parida
Bioinformatics 32(7), 1048-1056, Oxford Univ Press, 2016

2015

Fixed-parameter algorithms for scaffold filling
Bulteau, L., Carrieri, A. P., Dondi, R.
Theoretical Computer Science, pp. 72-83, Elsevier, 2015

Topological Signatures for Population Admixture
Parida L., Utro F., Yorukoglu D., Carrieri A.P., David Kuhn D., Basu S.
In: Research in Computational Molecular Biology, RECOMB 2015, Lecture Notes in Computer Science, Teresa M. Przytycka (Ed.), pp. 261-275, Springer

2014

Algorithms for the Constrained Perfect Phylogeny with Persistent Characters
Bonizzoni, P., Carrieri, A. P., Della Vedova, G., Trucco, G.
arXiv preprint arXiv:1405.7497, 2014

SimRA: Rapid & Accurate Simulation of Populations based on Random-Graph Models of ARG
Carrieri A.P., Parida L.
Poster: RECOMB-CG, Cold Spring Harbor (NY) , 2014

Fixed-Parameter Algorithms for Scaffold Filling
Bulteau, L., Carrieri, A.P., Dondi, R.
In: Proc. Third International Symposium on Combinatorial Optimization, ISCO 2014, Lisbon, Portugal, Lecture Notes in Computer Science, P. Fouilhoux, L. E. Neves Gouveia, A. Ridha Mahjoub, V. T. Paschos (Eds), pp. 137-148, Springer

Explaining Evolution via Constrained Persistent Perfect Phylogeny
Bonizzoni P., Carrieri A.P., Della Vedova G., Trucco G.
BMC Genomics 15(6), 2014

2013

When and how the perfect phylogeny model explains evolution
Bonizzoni, P., Carrieri, A. P., Della Vedova, G., Dondi, R., Przytycka, T. M.
Discrete and Topological Models in Molecular Biology, Jonoska N., Saito M. (Eds), pp. 67-83, Springer, 2013

2012

An in-silico framework for comparing and validating transcripts predicted from single and paired-end reads
Beretta S., Bonizzoni P., Carrieri A.P., Della Vedova G., Pirola Y., Pesole G., Picardi E., Rizzi R.
Poster: Next Generation Sequencing Workshop, Bari (Italy) , 2012

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