M. Chierici

发表

A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control consortium

David P. Kreil, Todd M. Smith, Thomas M. Blomquist, 2014, Nature Biotechnology.

Predictability of drug-induced liver injury by machine learning

C. Furlanello, Giuseppe Jurman, M. Chierici, 2020, Biology Direct.

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

May D. Wang, Wenwei Zhang, Simon M Lin, 2015, Genome Biology.

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

David P. Kreil, Paweł P. Łabaj, C. Furlanello, 2014, Nature Biotechnology.

Variability in GWAS analysis: the impact of genotype calling algorithm inconsistencies

C. Furlanello, F. Goodsaid, R. Wolfinger, 2010, The Pharmacogenomics Journal.

Batch effects in the BRLMM genotype calling algorithm influence GWAS results for the Affymetrix 500K array

Li Zhang, C. Furlanello, F. Goodsaid, 2010, The Pharmacogenomics Journal.

An interactive effect of batch size and composition contributes to discordant results in GWAS with the CHIAMO genotyping algorithm

C. Furlanello, M. Chierici, K. Miclaus, 2010, The Pharmacogenomics Journal.

Assessment of variability in GWAS with CRLMM genotyping algorithm on WTCCC coronary artery disease

C. Furlanello, F. Goodsaid, R. Wolfinger, 2010, The Pharmacogenomics Journal.

A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning

David L. Gibbs, C. Furlanello, M. Chierici, 2019, Cancers.

Multi-omics integration for neuroblastoma clinical endpoint prediction

Cesare Furlanello, Giuseppe Jurman, Marco Chierici, 2018, Biology Direct.

Cellular and gene signatures of tumor-infiltrating dendritic cells and natural-killer cells predict prognosis of neuroblastoma

R. Boldrini, C. Furlanello, Giuseppe Jurman, 2020, Nature Communications.

Surfactant disaturated-phosphatidylcholine kinetics in acute respiratory distress syndrome by stable isotopes and a two compartment model

C. Cobelli, M. Chierici, G. Toffolo, 2007, Respiratory research.

TOFwave: reproducibility in biomarker discovery from time-of-flight mass spectrometry data.

C. Furlanello, M. Chierici, D. Albanese, 2012, Molecular bioSystems.

Tumor-infiltrating T cells and PD-L1 expression in childhood malignant extracranial germ-cell tumors

R. Boldrini, C. Furlanello, Giuseppe Jurman, 2018, Oncoimmunology.

Focal adhesion kinase depletion reduces human hepatocellular carcinoma growth by repressing enhancer of zeste homolog 2

C. Furlanello, M. Chierici, C. Leonetti, 2017, Cell Death and Differentiation.

Automatically detecting Crohn’s disease and Ulcerative Colitis from endoscopic imaging

V. Osmani, Giuseppe Jurman, M. Chierici, 2022, BMC Medical Informatics and Decision Making.

Dexamethasone Therapy in Preterm Infants Developing Bronchopulmonary Dysplasia: Effect on Pulmonary Surfactant Disaturated-Phosphatidylcholine Kinetics

C. Cobelli, M. Chierici, P. Cogo, 2008, Pediatric Research.

A promoter-level mammalian expression atlas

Cesare Furlanello, Finn Drabløs, Carsten O. Daub, 2015 .

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Martin S. Taylor, Jay W. Shin, Thomas J. Ha, 2021, Nature Communications.

The Constrained Maximal Expression Level Owing to Haploidy Shapes Gene Content on the Mammalian X Chromosome

Robert S. Young, J. Blake, T. Meehan, 2015, PLoS biology.

Differential diagnosis of systemic lupus erythematosus and Sjögren's syndrome using machine learning and multi-omics data

Giuseppe Jurman, M. Chierici, P. Carmona-Sáez, 2022, Comput. Biol. Medicine.

Identification of GALNT14 as a novel neuroblastoma predisposition gene

M. Chierici, G. Tonini, S. Ferrini, 2015, Oncotarget.

PD-L1 Is a Therapeutic Target of the Bromodomain Inhibitor JQ1 and, Combined with HLA Class I, a Promising Prognostic Biomarker in Neuroblastoma

R. Boldrini, Giuseppe Jurman, M. Chierici, 2017, Clinical Cancer Research.

Prioritization of putatively detrimental variants in euploid miscarriages

V. Colonna, Q. Ayub, M. Chierici, 2021, Scientific Reports.

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling

Cesare Furlanello, Giuseppe Jurman, Marco Chierici, 2020, bioRxiv.

Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples

F. Goodsaid, H. Fang, W. Ge, 2010, The Pharmacogenomics Journal.

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency

David P. Kreil, Paweł P. Łabaj, Jeff S. Jasper, 2021, Genome biology.

Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions

Paul M. Rindler, David P. Kreil, Paweł P. Łabaj, 2021, Genome biology.

Phylogenetic convolutional neural networks in metagenomics

Cesare Furlanello, Giuseppe Jurman, Claudio Agostinelli, 2017, BMC Bioinformatics.

Monitoring Perinatal Gut Microbiota in Mouse Models by Mass Spectrometry Approaches: Parental Genetic Background and Breastfeeding Effects

M. Chierici, B. Dallapiccola, L. Pieroni, 2016, Front. Microbiol..

Transcriptomics data availability and reusability in the transition from microarray to next-generation sequencing

Roger E Bumgarner, John Quackenbush, A. Brazma, 2021, bioRxiv.

Integrating deep and radiomics features in cancer bioimaging

C. Furlanello, V. Maggio, D. Salvalai, 2019, bioRxiv.

A metaproteomic-based gut microbiota profiling in children affected by autism spectrum disorders.

A. Gasbarrini, M. Chierici, P. Venuti, 2021, Journal of proteomics.

Microbial community structure in vineyard soils across altitudinal gradients and in different seasons.

M. Chierici, L. Cappellin, I. Pertot, 2013, FEMS microbiology ecology.

Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma

Cesare Furlanello, Giuseppe Jurman, Valerio Maggio, 2018, PloS one.

Evaluating reproducibility of AI algorithms in digital pathology with DAPPER

Cesare Furlanello, Giuseppe Jurman, Andrea Bizzego, 2018, bioRxiv.

Genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients' noninvolved lung tissue

Giuseppe Jurman, M. Chierici, T. Dragani, 2022, Cancer science.

Machine learning models for predicting endocrine disruption potential of environmental chemicals

Cesare Furlanello, Giuseppe Jurman, Nicole Bussola, 2018, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.

Predictability of drug-induced liver injury by machine learning

Giuseppe Jurman, M. Chierici, Margherita Francescatto, 2020, Biology Direct.

Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer

Cesare Furlanello, Manlio De Domenico, Giuseppe Jurman, 2017, 1710.05918.

Machine learning methods for predictive proteomics

Cesare Furlanello, Annalisa Barla, Giuseppe Jurman, 2007, Briefings Bioinform..

Fast algorithms for nonparametric population modeling of large data sets

Giuseppe De Nicolao, Claudio Cobelli, Gianluigi Pillonetto, 2009, Autom..

Single-base mismatch profiles for NGS samples

Cesare Furlanello, Giuseppe Jurman, Marco Chierici, 2011, 1109.1108.

Efficient Nonparametric Population Modeling for Large Data Sets

Giuseppe De Nicolao, Claudio Cobelli, Gianluigi Pillonetto, 2007, 2007 American Control Conference.

Machine learning methods for predictive proteomics

Giuseppe Jurman, S. Merler, A. Barla, 2007, Briefings Bioinform..

A promoter-level mammalian expression atlas

Martin S. Taylor, Jay W. Shin, Thomas J. Ha, 2014, Nature.

Endoscopy-based IBD identification by a quantized deep learning pipeline

M. Chierici, Matteo Pozzi, Marcello Dorian Donzella, 2023, BioData Mining.