Andrea Tangherloni

Andrea Tangherloni

Assistant Professor in Computer Science

Journal Articles

You can also find my articles on my Google Scholar profile.

MAGNETO: Marker pAnels GeNEraTor with multi-Objective optimization

Published in Journal of Biomedical Informatics, 2023

MAGNETO is a fully-automatic framework that builds compact, optimal marker panels from single-cell gene expression data by solving a tailored bi-objective optimization problem.

Recommended citation: Tangherloni A., Riva S.G., Myers B., Buffa F.M., Cazzaniga P. (2023). MAGNETO: Marker pAnels GeNEraTor with multi-Objective optimization. Journal of Biomedical Informatics, 147: 104510.
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Simplifying Fitness Landscapes using Dilation Functions evolved with Genetic Programming

Published in IEEE Computational Intelligence Magazine, 2023

We propose GP4DFs, a Genetic Programming method that automatically evolves effective Dilation Functions to manipulate the fitness landscape and improve the optimization process.

Recommended citation: Papetti D.M., Tangherloni A., Farinati D., Cazzaniga P., Vanneschi L. (2023). Simplifying Fitness Landscapes using Dilation Functions evolved with Genetic Programming. IEEE Computational Intelligence Magazine, 18(1): 22-31.
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SMGen: A generator of synthetic models of biochemical reaction networks

Published in Symmetry, 2022

SMGen automatically generates synthetic yet realistic models of biochemical reaction networks to benchmark simulation and analysis tools in computational systems biology.

Recommended citation: Riva S.G., Cazzaniga P., Nobile M.S., Spolaor S., Rundo L., Besozzi D., Tangherloni A. (2022). SMGen: A generator of synthetic models of biochemical reaction networks. Symmetry, 14(1): 119.
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Salp Swarm Optimization: a Critical Review

Published in Expert Systems with Applications, 2022

A critical review of the Salp Swarm Optimization algorithm that exposes its mathematical flaws, proposes a corrected variant (ASSO), and questions its advantages over classic metaheuristics.

Recommended citation: Castelli M., Manzoni L., Mariot L., Nobile M.S., Tangherloni A. (2022). Salp Swarm Optimization: a Critical Review. Expert Systems with Applications, 189: 116029.
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FiCoS: a fine- and coarse-grained GPU-powered deterministic simulator for biochemical networks

Published in PLoS Computational Biology, 2021

FiCoS is a GPU-powered deterministic simulator combining fine- and coarse-grained parallelization to simulate large-scale biochemical models with dramatic speed-ups.

Recommended citation: Tangherloni A., Nobile M.S., Cazzaniga P., Capitoli G., Spolaor S., Rundo L., Mauri G., Besozzi D. (2021). FiCoS: a fine- and coarse-grained GPU-powered deterministic simulator for biochemical networks. PLoS Computational Biology, 17(9): e1009410.
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Analysis of single-cell RNA sequencing data based on autoencoders

Published in BMC Bioinformatics, 2021

scAEspy is a unifying autoencoder-based tool for the low-dimensional representation and integration of single-cell RNA-Seq datasets.

Recommended citation: Tangherloni A., Ricciuti F., Besozzi D., Liò P., Cvejic A. (2021). Analysis of single-cell RNA sequencing data based on autoencoders. BMC Bioinformatics, 22(1): 309.
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Integrative Single-Cell RNA-Seq and ATAC-Seq Analysis of Human Developmental Hematopoiesis

Published in Cell Stem Cell, 2021

An integrative single-cell RNA-Seq and ATAC-Seq analysis of human developmental hematopoiesis that reveals epigenetic priming of stem cells prior to lineage commitment.

Recommended citation: Ranzoni A.M., Tangherloni A., Berest I., Riva S.G., Myers B., Strzelecka P.M., Xu J., Panada E., Mohorianu I., Zaugg J.B., Cvejic A. (2021). Integrative Single-Cell RNA-Seq and ATAC-Seq Analysis of Human Developmental Hematopoiesis. Cell Stem Cell, 28(3): 472-487.
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A CUDA-powered method for the feature extraction and unsupervised analysis of medical images

Published in The Journal of Supercomputing, 2021

CHASM is a GPU-accelerated method for Haralick feature extraction and self-organizing-map analysis of medical images.

Recommended citation: Rundo L., Tangherloni A., Cazzaniga P., Mistri M., Galimberti S., Woitek R., Sala E., Mauri G., Nobile M.S. (2021). A CUDA-powered method for the feature extraction and unsupervised analysis of medical images. The Journal of Supercomputing, 77(8): 8514-8531.
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ACDC: Automated cell detection and counting for time-lapse fluorescence microscopy

Published in Applied Sciences, 2020

ACDC is an automated method for detecting and counting fluorescently labeled cell nuclei in time-lapse microscopy, without relying on large annotated datasets.

Recommended citation: Rundo L., Tangherloni A., Tyson D.R., Betta R., Militello C., Spolaor S., Nobile M.S., Besozzi D., Lubbock A.L.R., Quaranta V., Mauri G., Lopez C.F., Cazzaniga P. (2020). ACDC: Automated cell detection and counting for time-lapse fluorescence microscopy. Applied Sciences, 10(18): 6187.
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Computational Intelligence for Life Sciences

Published in Fundamenta Informaticae, 2020

A survey showing how Computational Intelligence methods can solve complex optimization problems in life sciences, from protein folding to parameter estimation.

Recommended citation: Besozzi D., Manzoni L., Nobile M.S., Spolaor S., Castelli M., Vanneschi L., Cazzaniga P., Ruberto S., Rundo L., Tangherloni A. (2020). Computational Intelligence for Life Sciences. Fundamenta Informaticae, 171(1-4): 57-80.
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USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

Published in Neurocomputing, 2019

USE-Net incorporates Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation, achieving strong cross-dataset generalization on multi-institutional MRI.

Recommended citation: Rundo L., Han C., Nagano Y., Zhang J., Hataya R., Militello C., Tangherloni A., Nobile M.S., Ferretti C., Besozzi D., Gilardi M.C., Vitabile S., Mauri G., Nakayama H., Cazzaniga P. (2019). USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. Neurocomputing, 365: 31-43.
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Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design

Published in Applied Soft Computing, 2019

We show that benchmark functions do not fully capture real-world optimization difficulty, using biochemical parameter estimation as a case study and highlighting the impact of solution representation.

Recommended citation: Tangherloni A., Spolaor S., Cazzaniga P., Besozzi D., Rundo L., Mauri G., Nobile M.S. (2019). Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design. Applied Soft Computing, 81: 105494.
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A novel framework for MR image segmentation and quantification by using MedGA

Published in Computer Methods and Programs in Biomedicine, 2019

A novel evolutionary framework that uses MedGA as a pre-processing stage to improve the enhancement and segmentation of bimodal MR images.

Recommended citation: Rundo L., Tangherloni A., Cazzaniga P., Nobile M.S., Russo G., Gilardi M.C., Vitabile S., Mauri G., Besozzi D., Militello C. (2019). A novel framework for MR image segmentation and quantification by using MedGA. Computer Methods and Programs in Biomedicine, 176: 159-172.
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MedGA: A Novel Evolutionary Method for Medical Image Enhancement in Medical Imaging Systems

Published in Expert Systems with Applications, 2019

MedGA is a Genetic Algorithm-based image enhancement method for images with a bimodal gray-level histogram, applied to contrast-enhanced MR image analysis.

Recommended citation: Rundo L., Tangherloni A., Nobile M.S., Militello C., Besozzi D., Mauri G., Cazzaniga P. (2019). MedGA: A Novel Evolutionary Method for Medical Image Enhancement in Medical Imaging Systems. Expert Systems with Applications, 119: 387-399.
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GenHap: A Novel Computational Method Based on Genetic Algorithms for Haplotype Assembly

Published in BMC Bioinformatics, 2019

GenHap is a Genetic Algorithm-based method for haplotype assembly that yields accurate solutions and is substantially faster than state-of-the-art phasing tools.

Recommended citation: Tangherloni A., Spolaor S., Rundo L., Nobile M.S., Cazzaniga P., Mauri G., Liò P., Merelli I., Besozzi D. (2019). GenHap: A Novel Computational Method Based on Genetic Algorithms for Haplotype Assembly. BMC Bioinformatics, 20(Suppl 4): 172.
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NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique

Published in International Journal of Imaging Systems and Technology, 2018

NeXt is a fully automatic method based on Fuzzy C-Means for extracting necrotic regions in brain tumor MRI to support neuro-radiosurgery.

Recommended citation: Rundo L., Militello C., Tangherloni A., Russo G., Vitabile S., Gilardi M.C., Mauri G. (2018). NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique. International Journal of Imaging Systems and Technology, 28(1): 21-37.
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LASSIE: simulating large-scale models of biochemical systems on GPUs

Published in BMC Bioinformatics, 2017

LASSIE is a black-box GPU-accelerated deterministic simulator designed for large-scale biochemical models, achieving up to 92× speed-up over CPU integration.

Recommended citation: Tangherloni A., Nobile M.S., Besozzi D., Mauri G., Cazzaniga P. (2017). LASSIE: simulating large-scale models of biochemical systems on GPUs. BMC Bioinformatics, 18(1): 246.
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Gillespie’s Stochastic Simulation Algorithm on MIC Coprocessor

Published in The Journal of Supercomputing, 2017

A parallel implementation of Gillespie’s Stochastic Simulation Algorithm on the Intel Many Integrated Core (Xeon Phi) coprocessor to accelerate stochastic simulations of biochemical networks.

Recommended citation: Tangherloni A., Nobile M.S., Cazzaniga P., Besozzi D., Mauri G. (2017). Gillespie’s Stochastic Simulation Algorithm on MIC Coprocessor. The Journal of Supercomputing, 73(2): 676-686.
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