Filippo Utro

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Research Scientist: Computational Biology
Thomas J. Watson Research Center, Yorktown Heights, NY USA.
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Professional Associations: ACM | BITS | International Society for Computational Biology

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2021

A common methodological phylogenomics framework for intra-patient heteroplasmies to infer SARS-CoV-2 sublineages and tumor clones
F. Utro, C. Levovitz, K. Rhrissorrakrai, L. Parida
BMC Genomics22, 581, 2021

Convergence of resistance patterns in breast cancer after multiple lines of treatment through analysis of rapid autopsy samples
Ignaty Leshchiner, Elizabeth E. Martin, Christopher T. Chen, Elizaveta Leshchiner, Thomas Zhang, Christopher Pinto, Filippo Utro, Kahn Rhrissorrakrai, Chaya Levovitz, Brian P. Danysh, Kara Slowik, Raquel A. Jacobs, Laxmi Parida, Gad Getz, Dejan Juric
Cancer Research 81(13 Supplement), 37--37, American Association for Cancer Research, 2021
Abstract

Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PADetermining patterns and mechanisms of drug resistance is fundamentally required for improving clinical outcome of cancer treatments. The ability to study multiple samples from different metastatic sites of the same patient is a clinically challenging task, which has become possible with the advent of \textquotedblleftrapid\textquotedblright autopsy procedures (\<6 hours from death) conducted on the patients deceased from cancer. We have obtained and analyzed whole-exome, whole genome and transcriptome sequencing data from advanced breast cancer samples derived from multiple cancer lesions of the same patient post-treatment through the Massachusetts General Hospital Rapid Autopsy program. We have integrated genomic and transcriptomic data through advanced clonal reconstruction methods (PhylogicNDT) to investigate how the patients developed resistance to multiple lines of anti-cancer therapy. Additionally, we have used previously collected cell-free DNA samples to establish both a spatial and temporal picture of cancer drug resistance and progression. We have examined clonal heterogeneity and resistance to targeted therapies in 10 metastatic ER+ breast cancer patients over 120 distinct metastatic sites. The majority of patients in this cohort were treated with CDK4/6 cell cycle inhibitors in addition to hormone therapy. We discovered that multiple metastatic sites often share similar clonal structure, but overall progeny of several sibling clones spreads throughout the body, creating \textquotedblleftfamilies\textquotedblright of more closely related lesions. These distinct lesion populations often develop independent resistance mechanisms to the many lines of treatment that the patents have received. At least 8 out of 10 patients showed multiple known and potentially novel resistance mechanisms in separate branches of the phylogenetic tree, often converging on distinct mutations in the same resistance genes. Notably we have identified ESR1, KRAS and chromatin modifier recurrent and convergent mutations across the cohort. Temporal analysis using cfDNA taken over the course of treatment allows us to identify clones that expanded in response to a given treatment. Using RNA expression and pathway analysis, we identify unique transcriptional programs, differentially expressed genes and ER-signaling changes between distinct clones within one patient, as well as compare genetically similar clones across patents.Citation Format: Ignaty Leshchiner, Elizabeth E. Martin, Christopher T. Chen, Elizaveta Leshchiner, Thomas Zhang, Christopher Pinto, Filippo Utro, Kahn Rhrissorrakrai, Chaya Levovitz, Brian P. Danysh, Kara Slowik, Raquel A. Jacobs, Laxmi Parida, Gad Getz, Dejan Juric. Convergence of resistance patterns in breast cancer after multiple lines of treatment through analysis of rapid autopsy samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 37.

Mechanisms of primary and acquired resistance to venetoclax in chronic lymphocytic leukemia (CLL)
I. Murali, J. Cha, I. Leshchiner, Y. Kuang, K. Vasquez, J. Khalsa, S.M. Fernandes, F. Utro, K. Rhrissorrakrai, C. Levovitz, B.P. Danysh, K. Slowik, R.A. Jacobs, C.P. Paweletz, L. Parida, G. Getz, J.R. Brown
Cancer Research 81(13 Supplement), 1097--1097, American Association for Cancer Research, 2021
Abstract

Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PAMechanisms of resistance to venetoclax, a highly selective oral BCL2 inhibitor approved for therapy of CLL, remain poorly understood and we sought to characterize the clonal evolution of resistance in patients developing progressive disease on venetoclax. We performed whole-exome sequencing (WES) on 7 relapsed/refractory CLL patients with disease progressing on venetoclax. We observed del17p in 43\% (3/7), TP53 mutation in 86\% (6/7) and unmutated IGHV in 71\% (5/7) of patients. A median of four longitudinal tumor samples were sequenced per patient (total Nsamples= 25). We performed digital droplet PCR (ddPCR) analysis to look at the emergence of BCL2 G101V mutations that have been previously linked with venetoclax resistance after long times on therapy. G101V mutation was detected by ddPCR in two patients at low variant allelic fractions (VAF) of 0.03\% and 4.68\% respectively, and not detected by WES. We further detected one droplet positive for G101V mutation in two other patients in venetoclax progression bone marrow samples. Due to the very low VAF of the BCL2 G101V mutations, we suspected that other mechanisms of acquired resistance were more significant in this cohort of relatively early relapses. Analysis of WES data showed no somatic single nucleotide variants (sSNVs) selected in more than one patient with resistance. However, copy number analysis revealed acquired del8p in 3 patients, resulting in large subclonal expansions. Furthermore, del 8p co-occurred with amp 1q21.2-21.3 affecting the MCL1 gene in 2 patients, and with amp 8q, del 17p, del 18q22.1-23 and del 9p23-21.2 (containing the MYC, TP53, BCL2 and CDKN2A/B genes respectively) in a third. Two other patients showed expansion of clones harboring del 10q23.31-24.1, that includes PTEN, while a third contains an expanding clone with two IRF8 mutations (S283C and SFF416fs). Resistance in the final patient is likely associated with marked expansion of clones with TP53 and SF3B1 mutations. RNA-seq analysis comparing pre-venetoclax to post-venetoclax resistant samples from 8 CLL patients showed downregulation of the BCR, FCGR and MAPK signaling pathways, with upregulation of mitochondrial translation, oxidative phosphorylation and the TCA/citric acid cycle at the time of progression. RNA-seq analysis focusing on the patients with del 8p shows significant downregulation of the TNFRSF10A/10B genes (TRAIL-Rs), with gene set enrichment analysis (GSEA) showing positive enrichment for WNT5A/FZD4 signaling and CREB signaling via the PKC and MAPK pathways, concomitant with downregulation of the BCR and FCGR pathways at progression. Our data suggest several mechanisms of venetoclax resistance in CLL, including loss of TNFRSF10A/B, sometimes with MCL1 upregulation, as well as WNT pathway upregulation and BCR pathway downregulation, which we are now validating in primary patient PBMCs and relevant cell line models.Citation Format: Ishwarya Murali, Justin Cha, Ignaty Leshchiner, Yanan Kuang, Kevin Vasquez, Jasneet Khalsa, Stacey M. Fernandes, Filippo Utro, Kahn Rhrissorrakrai, Chaya Levovitz, Brian P. Danysh, Kara Slowik, Raquel A. Jacobs, Cloud P. Paweletz, Laxmi Parida, Gad Getz, Jennifer R. Brown. Mechanisms of primary and acquired resistance to venetoclax in chronic lymphocytic leukemia (CLL) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1097.

Parallel genomic alterations of antigen and payload targets mediate polyclonal acquired clinical resistance to sacituzumab govitecan in triple-negative breast cancer
J.T. Coates, S. Sun, I. Leshchiner, N. Thimmiah, E.E. Martin, D. McLoughlin, B.P. Danysh, K. Slowik, R.A. Jacobs, K. Rhrissorrakrai, F. Utro, C. Levovitz, E. Denault, C.S. Walmsley, A. Kambadakone, J.R. Stone, S.J. Isakoff, L. Parida, D. Juric, G. Getz, A. Bardia and L.W. Ellisen
Cancer Discovery, 2021

Multiple Loci Selection with Multi-Way Epistasis in Coalescence with Recombination
A. Bose, F. Utro, D.E. Platt, L. Parida
Algorithms14, 2021

Functional profiling of COVID-19 respiratory tract microbiomes
N. Haiminen, F. Utro, E. Seabolt, L. Parida
Scientific Reports11, 2021

Re-purposing software for functional characterization of the microbiome
L.-J. Gardiner, N. Haiminen, F. Utro, L. Parida, E. Seabolt, R. Krishna and J.H. Kaufman
Microbiome, 2021

K-Mer Analyses Reveal Different Evolutionary Histories of Alpha, Beta, and Gamma Papillomaviruses
Z. Chen, F. Utro, D. Platt, R. DeSalle, L. Parida, P.K.S. Chan, R.D. Burk
International Journal of Molecular Sciences 22(17), 2021
Abstract

2020

Estimation of lesion-specific mutation shedding in cfDNA from rapid autopsy study
F. Utro, K. Rhrissorrakrai, C. Levovitz, I. Leshchiner, L. Martin, C. Chen, C. Pinto, B. Danysh, K. Slowik, L. Goyal, R. Allen, M.W. Broudo, D. Juric, G. Getz, L. Parida
Cancer Research, AACR, 80, 1509-1509, 2020
Abstract

Sampling cfDNA using liquid biopsies offers clinically important benefits for monitoring of cancer progression. A single cfDNA sample represents a mixture of shed tumor DNA from all known and unknown lesions within the patient. However, the amount of DNA shed by any one specific lesion is not well characterized. We design an LSM (Lesion Shedding Model) that is able to order the lesions from the strongest to the poorest shedding for a given patient. Our framework intrinsically models for missing/hidden lesions and operates on blood and lesion cfDNA assays to estimate the potential relative shedding levels of lesions into the blood. We verify the accuracy of the model under controlled conditions using a simulation approach to create a synthetic blood cfDNA sample per patient, where specific lesions are assigned predefined shedding levels. The simulated data mirrors real data lesion genomic similarities. The LSM correctly obtains a partial order of the lesions, i.e. accurately stratifies the lesions by their assigned shedding levels, in 83% (10/12) simulated data sets. We then applied LSM to a cohort of 12 cancer patients with available cfDNA blood samples and multiple biopsied lesions and found that indeed there were lesions that were consistently shedding more than other lesions into the patients9 blood. From this analysis we are then able to probe whether there are detectable tissue-specific differences in the shedding behavior of lesions. In most patients a top shedding lesion(s) was identified. Trends emerged across lesions whereby pancreatic and gallbladder lesions tended to be higher shedders than other lesions and abdominal cavity lesions tended to be low shedding lesions. The LSM represents one of the first attempts to characterize the contributions of distinct lesions in cfDNA. With an accurate understanding of lesion cfDNA contribution, we can begin to look for patterns in shedding behavior that associate with clinical features of the lesions, such as response to treatment, tissue of origin, age, growth, etc. As sample size increases, the LSM will also be able to address the question of whether a cfDNA biopsy would provide sufficient coverage of the entire space of lesions present in the patient, or, if biopsies of individual lesions would be necessary. Such insights would further increase the impact and applicability of liquid biopsies in the clinic. 374 Citation Format: Filippo Utro, Kahn Rhrissorrakrai, Chaya Levovitz, Ignaty Leshchiner, Liz Martin, Christopher Chen, Christopher Pinto, Brian Danysh, Kara Slowik, Lipika Goyal, Read Allen, Maida Williams Broudo, Dejan Juric, Gad Getz, Laxmi Parida. Estimation of lesion-specific mutation shedding in cfDNA from rapid autopsy study [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1509.

Genetic Determinants and Evolutionary History of Richters Syndrome
E.M. Parry, R. Guieze, I. Leshchiner, D. Rosebrock, B. Persaud, C.K. Lemvigh, E. Tausch, M.S. Davids, J. Bloehdorn, C. Schneider, L. Elagina, A.Z. Martinez, A. Taylor-Weiner, N. Jain, W.G. Wierda, L.Z. Rassenti, T.J. Kipps, J. Broseus, F. Cymbalista, N.E. Kay, K.J. Livak, S. Li, T. Huang, N. Purroy, A.J. Anandappa, S.M. Fernandes, F. Utro, K. Rhrissorrakrai, C. Levovitz, B Danish, K. Slowik, S.A. Parikh, J.R. Brown, L. Parida, D.S. Neuberg, S. Stilgenbauer, G. Getz, C.J. Wu
Blood, ASH, 136, 47-48, 2020

Hierarchically labeled database indexing allows scalable characterization of microbiomes
F. Utro, N. Haiminen, E. Siragusa, L.-J. Gardiner, E. Seabolt, R. Krishna, J.H. Kaufman, L. Parida
iScience, 2020

Simultaneous Phasing of Multiple Polyploids
L. Parida and F. Utro
CIBB 2018: Computational Intelligence Methods for Bioinformatics and Biostatistics, pp. 50-68, 2020

2019

Dark-matter matters: Discriminating subtle blood cancers using the darkest DNA
L. Parida, C. Haferlach, K. Rhrissorrakrai, F. Utro, C. Levovitz, W. Kern, N. Nadarajah, S. Twardziok, S. Hutter, M. Meggendorfer, W. Walter, C. Baer, T. Haferlach
PLoS Comput Biology 15(8), 2019
Abstract

Transcriptome characterization and differentially expressed genes under flooding and drought stress in the biomass grasses Phalaris arundinacea and Dactylis glomerata
M. Klaas, N. Haiminen, J. Grant, P. Cormican, J. Finnan, S.K. Arojju, F. Utro, T. Vellani, L. Parida, S. Barth
Annals of botany, 2019

Sequencing and curation strategies for identifying candidate glioblastoma treatments
M.O. Frank, T. Koyama, K. Rhrissorrakrai, N. Robine, F. Utro, and others
BMC Medical Genomics 12(56), 2019

A Quantitative and Qualitative Characterization of k-mer Based Alignment-Free Phylogeny Construction
F. Utro, D.E. Platt, L. Parida
Computational Intelligence Methods for Bioinformatics and Biostatistics , pp. 19-31, Springer, 2019
Abstract

Liquid versus tissue biopsy for detecting acquired resistance and tumor heterogeneity in gastrointestinal cancers
A.R. Parikh, I. Leshchiner, L. Elagina, L. Goyal, C. Levovitz, G. Siravegna, D. Livitz, K. Rhrissorrakrai, E.E. Martin, E.E. Van Seventer, M. Hanna, K. Slowik, F. Utro, et al
Nature Medicine25, 1415-1421, 2019
Abstract

2018

Essential Simplices in Persistent Homology and Subtle Admixture Detection
S. Basu, F. Utro, L. Parida
18th International Workshop on Algorithms in Bioinformatics (WABI 2018), pp. 14:1-14:10
Abstract

DNA combinatorial messages and Epigenomics: The case of chromatin organization and nucleosome occupancy in eukaryotic genomes
R. Giancarlo, S.E. Rombo, F. Utro
Theoretical Computer Science, 2018
Abstract

Broad/IBM Project: Discovery of treatment resistance mechanisms through use of liquid biopsy genomics services
G. Getz, C. Cibulskis, I. Leshchiner, M. Hanna, D. Livitz, K. Slowik, C. Levovitz, F. Utro, K. Rhrissorrakrai, D. Rotem, and others
Cancer Research, AACR, 2018
Abstract

The Broad/IBM Cancer Resistance Project has partnered with Broad Genomics to pilot the use of cutting edge sequencing technology for the analysis of cell free DNA in blood biopsies. Working closely with the Broad9s Cancer Program, Broad Genomics has developed a suite of liquid biopsy sequencing products designed to provide optimal flexibility in conducting research studies with a broad range of applications including; biomarker discovery, treatment resistance monitoring, and detection of minimal residual disease (MRD) post-surgery. Cell-free DNA is extracted from the blood, and a dual unique-molecular-indexed library is created. From this library, low coverage whole genome (ultra-low-pass 0.1x coverage) data is generated to survey sample quality and evaluate the tumor fraction in the liquid specimen. Utilizing the same library, additional assays can be selected for processing based on the research aim (Targeted Panel Assays, MRD Detection or Whole Exomes). Since our approach utilizes the same genomic material for whole genome and targeted sequencing assays, it is possible to maximize the information learned from each valuable and limited liquid biopsy specimen. Our study design takes advantage of the discovery potential of combined tissue-based sequencing and serial liquid biopsy analysis to elucidate mechanisms of cancer resistance by tracking the evolution of clonal and subclonal populations in patients samples over time. This collaboration will utilize the ultra-low-pass sequencing and whole exome sequencing together with custom analysis pipelines to correlate the genomic events with patient clinical data. We aim to process 3,000 samples from 1,000 patients over the next 3 years. To date we have processed close to 500 samples through the ultra-low-pass pipeline and 100 samples through the whole exome sequencing pipeline (results to be provided).The ability to successfully investigate treatment resistant cancers from non-invasive liquid biopsies presents new opportunities for identifying markers, understanding dynamics and monitoring tumor dissemination and clonal evolution.

In vitro vs in vivo compositional landscapes of histone sequence preferences in eucaryotic genomes
R. Giancarlo, S.E. Rombo, F. Utro
Bioinformatics, 2018
Abstract

2017

Computation Cluster Validation in the Big Data Era
R. Giancarlo, F. Utro
Reference Module in Life Sciences, 2017

Enhancing Next-Generation Sequencing-Guided Cancer Care Through Cognitive Computing
M.M. Patel, V.V. Michelini, J.M. Snell, S. Balu, A.P. Hoyle, J.S. Parker, M.C. Hayward, D.A. Eberhard, A.H. Salazar, P. McNeillie, and others
The Oncologist, AlphaMed Press, 2017
Abstract

Linear time algorithms to construct populations fitting multiple constraint distributions at genomic scales
E. Siragusa, N. Haiminen, F. Utro, L. Parida
IEEE/ACM Transactions on Computational Biology and Bioinformatics PP(99), 2017
Abstract

Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma
K.O. Wrzeszczynski, M.O. Frank, T. Koyama, K. Rhrissorrakrai, N. Robine, F. Utro, A.-K. Emde, B.-J. Chen, K. Arora, M. Shah, and others
Neurology Genetics 3(4), e164, AAN Enterprises, 2017
Abstract

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

The intrinsic combinatorial organization and information theoretic content of a sequence are correlated to the DNA encoded nucleosome organization of eukaryotic genomes
F. Utro, V. Di Benedetto and F.F.V. Corona, R. Giancarlo
Bioinformatics 32(6), 835-842, Oxford Univ Press, 2016

Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins
Z. Waks, O. Weissbrod, B. Carmeli, R. Norel, F. Utro, Y. Goldschmidt
Scientific Reports 6, 38988, Nature Publishing Group, 2016
Abstract

2015

Implementation of Watson Genomic Analytics processing to improve the efficiency of interpreting whole genome sequencing data on patients with advanced cancers
T. Koyama, S. Jones, F. Utro, Y. Ma, K. Rhrissorrakrai, Y. Shen, B. Carmeli, M.Jones, Z. Waks, E. Pleasance, R. Norel, R. Moore, E. Bilal, A.J. Mungall, K. Beaty, J. Schein, V.V. Michelini, M. Marra, A. Royyuru, J.J. Laskin
Journal of Clinical Oncology33, 2015
Abstract whole genome sequencing, transcriptome sequencing, transcriptome, rationalization, pathology, genome, data integrity, computational biology, cancer, biology, bioinformatics, analytics

Epigenomic k-mer dictionaries: shedding light on how sequence composition influences in vivo nucleosome positioning
R. Giancarlo, S.E. Rombo, F. Utro
Bioinformatics 31(18), 2939-2946, Oxford Univ Press, 2015

Topological Signatures for Population Admixture
L. Parida, F. Utro, D. Yorukoglu, A.P. Carrieri, D. Kuhn, S. Basu
RECOMB 2015: Research in Computational Molecular Biology, pp. 261-275, Springer
Abstract

ValWorkBench: an open source Java library for cluster validation, with applications to microarray data analysis
R. Giancarlo, D. Scaturro, F. Utro
Computer Methods and Programs in Biomedicine, Elsevier, 2015

2014

Comparative exomics of Phalaris cultivars under salt stress
N. Haiminen, M. Klaas, Z. Zhou, F. Utro, P. Cormican, T. Didion, C. Sig Jensen, C.E. Mason, S. Barth, L. Parida
BMC Genomics 15(Suppl 6), S18, BioMed Central Ltd, 2014
Abstract

Bayesian versus data driven model selection for microarray data
R. Giancarlo, G. Lo Bosco, F. Utro
Natural Computing, 1-10, Springer Netherlands, 2014
Abstract

Effect of sampling on the extent and accuracy of the inferred genetic history of recombining genome
D.E Platt, F. Utro, L. Parida
Computational Biology and Chemistry, Elsevier, 2014

2013

The Three Steps of Clustering In The Post-Genomic Era
R. Giancarlo, G. Lo Bosco, L. Pinello, F. Utro
Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data, pp. 519--556, Wiley Online Library, 2013
Abstract

Compressive biological sequence analysis and archival in the era of high-throughput sequencing technologies
R. Giancarlo, S.E. Rombo, F. Utro
Briefings in bioinformatics, bbt088, Oxford Univ Press, 2013
Abstract

Genetic History of Populations: Limits to Inference
D.E Platt, F. Utro, M. Pybus, L. Parida
Models and Algorithms for Genome Evolution, pp. 309--323, Springer, 2013

Efficient in silico Chromosomal Representation of Populations via Indexing Ancestral Genomes
N. Haiminen, F. Utro, C. Lebreton, P. Flament, Z. Karaman, L. Parida
Algorithms 6(3), 430--441, 2013
Abstract

iXora: Exact haplotype inferencing and trait association
F. Utro, N. Haiminen, D. Livingstone, O.E. Cornejo, S. Royaert, R. Schnell, J.C. Motamayor, D.N. Kuhn, L. Parida
BMC Genetics 14(1), 48, BioMed Central Ltd, 2013
Abstract

The genome sequence of the most widely cultivated cacao type and its use to identify candidate genes regulating pod color
J.C. Motamayor*, K. Mockaitis*, J. Schmutz*, N. Haiminen*, D. Livingstone, O.E. Cornejo, S. Findley, P. Zheng, F. Utro, S. Royaert, C. Saski, J. Jenkins, R. Podicheti, M. Zhao, B. Scheffler, others
Genome Biology 14(6), R53, BioMed Central Ltd, 2013
Abstract IBM 100 Icons of Progress: https://www.ibm.com/ibm/history/ibm100/us/en/icons/cocoagene/

A methodology to assess the intrinsic discriminative ability of a distance function and its interplay with clustering algorithms for microarray data analysis
R. Giancarlo, G. Lo Bosco, L. Pinello, F. Utro
BMC Bioinformatics 14(Suppl 1), S6, BioMed Central Ltd, 2013

Sum of parts is greater than the whole: inference of common genetic history of populations
F. Utro, M. Pybus, L. Parida
BMC Genomics 14(Suppl 1), S10, BioMed Central Ltd, 2013

2012

ARG-based genome-wide analysis of cacao cultivars
F. Utro, O.E. Cornejo, D. Livingstone, J.C. Motamayor, L. Parida
BMC Bioinformatics 13(Suppl 19), S17, BioMed Central Ltd, 2012

GenomicTools: an open source platform for developing high-throughput analytics in genomics
A. Tsirigos, N. Haiminen, E. Bilal, F. Utro
Open source software in life science research: Practical solutions to common challenges in the pharmaceutical industry and beyond, Woodhead Publishing Series in Biomedicine No. 16, 2012

Stability-Based Model Selection for High Throughput Genomic Data: An Algorithmic Paradigm
R. Giancarlo, F. Utro
Artificial Immune Systems, pp. 260--270, Springer, 2012

Algorithmic paradigms for stability-based cluster validity and model selection statistical methods, with applications to microarray data analysis
R. Giancarlo, F. Utro
Theoretical Computer Science, Elsevier, 2012

GenomicTools: a computational platform for developing high-throughput analytics in genomics
A. Tsirigos, N. Haiminen, E. Bilal, F. Utro
Bioinformatics 28(2), 282--283, Oxford University Press, 2012
Abstract

Textual data compression in computational biology: Algorithmic techniques
R Giancarlo, D Scaturro, F Utro
Computer Science Review, Elsevier, 2012

Genetic inactivation of the polycomb repressive complex 2 in T cell acute lymphoblastic leukemia
P. Ntziachristos, A. Tsirigos, P. Van Vlierberghe, J. Nedjic, T. Trimarchi, M. Sol Flaherty, D. Ferres-Marco, V. da Ros, Z. Tang, J. Siegle, others
Nature medicine 18(2), 298--303, Nature Publishing Group, 2012

2011

The Three Steps of Clustering in the Post-Genomic Era: A Synopsis
R Giancarlo, G Lo Bosco, L Pinello, F Utro
Computational Intelligence Methods for Bioinformatics and Biostatistics, pp. 13-30, Springer Berlin / Heidelberg, 2011
10.1007/978-3-642-21946-7_2

IRiS: Construction of ARG networks at genomic scales
A Javed, M Pybus, M Melè, F Utro, J Bertranpetit, F Calafell, L Parida
Bioinformatics, 2011
Abstract

Speeding up the Consensus Clustering methodology for microarray data analysis
R Giancarlo, F Utro
Algorithms for Molecular Biology 6(1), 1, BioMed Central Ltd, 2011

2010

Functional Information, Biomolecular Messages and Complexity of BioSequences and Structures (Extended Abstract)
D Corona, V Di Benedetto, A Gabriele, R Giancarlo, F Utro
Structure Discovery in Biology: Motifs, Networks & Phylogenies, 2010

2009

Basic Statistical Indices for SeqAn
SE Rombo, F Utro, R Giancarlo
Biological Sequence Analysis Using the SeqAn C++ Library, Taylor and Francis-CRC, 2009

Statistical Indices for Computational and Data Driven Class Discovery in Microarray Data
R Giancarlo, D Scaturro, F Utro
Biological Data Mining, pp. 295, Chapman & Hall, 2009

Textual data compression in computational biology: a synopsis
R Giancarlo, D Scaturro, F Utro
Bioinformatics 25(13), 1575, Oxford Univ Press, 2009

2008

A Tutorial on Computational Cluster Analysis with Applications to Pattern Discovery in Microarray Data
R Giancarlo, D Scaturro, F Utro
Mathematics in Computer Science 1(4), 655--672, Springer, 2008

Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of Merit, Gap Statistics and Model Explorer
R Giancarlo, D Scaturro, F Utro
BMC bioinformatics 9(1), 462, BioMed Central Ltd, 2008

2007

A basic analysis toolkit for biological sequences
R Giancarlo, A Siragusa, E Siragusa, F Utro
Algorithms for Molecular Biology 2(1), 10, 2007

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Filippo Utro