Cesare Furlanello

Prioritization of putatively detrimental variants in euploid miscarriages

The paper identifies potentially harmful genetic variants in euploid miscarriage embryos and highlights specific genes linked to miscarriage risk.

Orchestrating and sharing large multimodal data for transparent and reproducible research

The paper introduces a cloud-based platform called ORCESTRA for reproducible processing and sharing of multimodal biomedical data in a FAIR manner.

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

The paper analyzes transcriptomics data availability and reusability between microarray and RNA-seq experiments published between 2009 and 2013, and provides recommendations to improve RNA-seq data sharing.

Integrative Network Fusion: a multi-omics approach in molecular profiling

The paper introduces INF, a multi-omics framework that improves cancer classification by integrating gene expression, protein, and copy number data, achieving better accuracy and smaller feature sets than single-omics models.

Integrating deep and radiomics features in cancer bioimaging

The paper introduces a framework combining deep learning and radiomics for cancer prognosis using CT and PET imaging, showing improved accuracy over single-method approaches.

Evaluating reproducibility of AI algorithms in digital pathology with DAPPER

This study evaluates the reproducibility of AI algorithms in digital pathology using the DAPPER framework, analyzing accuracy and feature stability across multiple deep learning models and datasets.

DBD-RCO: Derivative Based Detection And Reverse Combinatorial Optimization To Improve Heart Beat Detection For Wearable Devices

This paper introduces and validates a pipeline for heartbeat detection and HRV analysis in wearable devices, showing improved accuracy over existing methods.

CIDER: a pipeline for detecting waves of coordinated transcriptional regulation in gene expression time-course data

CIDER identifies waves of coordinated gene transcription in time-course data by combining clustering and motif enrichment to detect regulatory interactions.