Search Results - "Barla, Annalisa"

A telescope GWAS analysis strategy, based on SNPs-genes-pathways ensamble and on multivariate algorithms, to characterize late onset Alzheimer's disease.

Authors: Squillario, Margherita Abate, Giulia Tomasi, Federico et al.

Source: Scientific Reports. 7/21/2020, Vol. 10 Issue 1, p1-12. 12p.

Artificial intelligence and network science as tools to illustrate academic research evolution in interdisciplinary fields: The case of Italian design.

Authors: Pretolesi, Daniele Stanzani, Ilaria Ravera, Stefano et al.

Source: PLoS ONE. 1/14/2025, Vol. 20 Issue 1, p1-17. 17p.

Author Index ISCO 2010.

Source: Cellular Oncology. 2010, Vol. 32 Issue 3, p239-244. 6p.

Compounding Approaches for Wind Prediction From Underwater Noise by Supervised Learning.

Authors: Trucco, Andrea Barla, Annalisa Bozzano, Roberto et al.

Source: IEEE Journal of Oceanic Engineering; Oct2022, Vol. 47 Issue 4, p1172-1187, 16p

A Supervised Learning Approach for Rainfall Detection From Underwater Noise Analysis.

Authors: Trucco, Andrea Bozzano, Roberto Fava, Emanuele et al.

Source: IEEE Journal of Oceanic Engineering; Jan2022, Vol. 47 Issue 1, p213-225, 13p

The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach.

Authors: Brichetto, Giampaolo Monti Bragadin, Margherita Fiorini, Samuele et al.

Source: Neurological Sciences. Feb2020, Vol. 41 Issue 2, p459-462. 4p. 1 Chart.

Subjects: Concept learning, Multiple sclerosis, Machine learning, Proof of concept

Building Kernels From Binary Strings for Image Matching.

Authors: Odone, Francesca Barla, Annalisa Verri, Alessandro

Source: IEEE Transactions on Image Processing; February 2005, Vol. 14 Issue 2, p169-180, 12p

A fully automated deep learning pipeline for micro-CT-imaging-based densitometry of lung fibrosis murine models.

Authors: Vincenzi, Elena Fantazzini, Alice Basso, Curzio et al.

Source: Respiratory Research. 11/11/2022, Vol. 23 Issue 1, p1-14. 14p.

Molecular fingerprinting reflects different histotypes and brain region in low grade gliomas.

Authors: Mascelli, Samantha Barla, Annalisa Raso, Alessandro et al.

Source: BMC Cancer. 2013, Vol. 13 Issue 1, p1-15. 15p. 2 Color Photographs, 1 Diagram, 5 Charts, 1 Graph.

Knowledge Driven Variable Selection (KDVS) - a new approach to enrichment analysis of gene signatures obtained from high-throughput data.

Authors: Zycinski, Grzegorz Barla, Annalisa Squillario, Margherita et al.

Source: Source Code for Biology & Medicine. 2013, Vol. 8 Issue 1, p1-14. 14p. 5 Diagrams, 2 Charts.

PARAMETER SPACE EXPLORATION WITHIN DYNAMIC SIMULATIONS OF SIGNALING NETWORKS.

Authors: DE AMBROSI, CRISTINA BARLA, ANNALISA TORTOLINA, LORENZO et al.

Source: Mathematical Biosciences & Engineering. Feb2013, Vol. 10 Issue 1, p103-120. 18p.

Multi-output learning via spectral filtering.

Authors: Baldassarre, Luca Rosasco, Lorenzo Barla, Annalisa et al.

Source: Machine Learning; Jun2012, Vol. 87 Issue 3, p259-301, 43p

A computational procedure for functional characterization of potential marker genes from molecular data: Alzheimer's as a case study.

Authors: Squillario, Margherita Barla, Annalisa

Source: BMC Medical Genomics. 2011, Vol. 4 Issue 1, p55-70. 16p.

A biology-driven approach identifies the hypoxiagene signature as a predictor of the outcome ofneuroblastoma patients.

Authors: Fardin, Paolo Barla, Annalisa Mosci, Sofia et al.

Source: Molecular Cancer. 2010, Vol. 9, p185-199. 15p.

The l1-l2 regularization framework unmasks the hypoxia signature hidden in the transcriptome of a set of heterogeneous neuroblastoma cell lines.

Authors: Fardin, Paolo Barla, Annalisa Mosci, Sofia et al.

Source: BMC Genomics. 2009, Vol. 10, p474-489. 16p. 1 Black and White Photograph, 1 Diagram, 4 Charts, 6 Graphs.

Machine learning methods for predictive proteomics.

Authors: Barla, Annalisa Jurman, Giuseppe Riccadonna, Samantha et al.

Source: Briefings in Bioinformatics. Mar2008, Vol. 9 Issue 2, p119-128. 10p. 7 Diagrams.

Predicting diabetes second-line therapy initiation in the Australian population via time span-guided neural attention network.

Authors: Fiorini, Samuele Hajati, Farshid Barla, Annalisa et al.

Source: PLoS ONE. 10/18/2019, Vol. 14 Issue 10, p1-17. 17p.

Enhancing Interpretability of Gene Signatures with Prior Biological Knowledge.

Authors: Squillario, Margherita Barbieri, Matteo Verri, Alessandro et al.

Source: Microarrays (2076-3905). Jun2016, Vol. 5 Issue 2, p15. 11p.

Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm.

Authors: Masecchia, Salvatore Coco, Simona Barla, Annalisa et al.

Source: BMC Medical Genomics. 9/11/2015, Vol. 8 Issue 1, p1-11. 11p.

Transplantation Induces Profound Changes in the Transcriptional Asset of Hematopoietic Stem Cells: Identification of Specific Signatures Using Machine Learning Techniques.

Authors: Cilloni, Daniela Petiti, Jessica Campia, Valentina et al.

Source: Journal of Clinical Medicine. Jun2020, Vol. 9 Issue 6, p1670. 1p.