bioinformatics

MCLab: Model Checking Lab

MCLab: Model Checking Lab

Our primary research activity focuses on AI and Model Checking based algorithms and tools for the automatic design and verification of mission or safety-critical systems with an emphasis on intelligent or autonomous systems.

 

URL: http://mclab.di.uniroma1.it

MCLab: Model Checking Lab

MCLab: Model Checking Lab

Our primary research activity focuses on AI and Model Checking based algorithms and tools for the automatic design and verification of mission or safety-critical systems with an emphasis on intelligent or autonomous systems.

 

URL: http://mclab.di.uniroma1.it

MCLab: Model Checking Lab

MCLab: Model Checking Lab

Our primary research activity focuses on AI and Model Checking based algorithms and tools for the automatic design and verification of mission or safety-critical systems with an emphasis on intelligent or autonomous systems.

 

URL: http://mclab.di.uniroma1.it

MCLab: Model Checking Lab

MCLab: Model Checking Lab

Our primary research activity focuses on AI and Model Checking based algorithms and tools for the automatic design and verification of mission or safety-critical systems with an emphasis on intelligent or autonomous systems.

 

URL: http://mclab.di.uniroma1.it

MCLab: Model Checking Lab

MCLab: Model Checking Lab

Our primary research activity focuses on AI and Model Checking based algorithms and tools for the automatic design and verification of mission or safety-critical systems with an emphasis on intelligent or autonomous systems.

 

URL: http://mclab.di.uniroma1.it

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and

Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery

In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein–protein interaction network (PPI, or interactome) to predict novel disease–disease relationships (i.e., a SWIM-informed diseasome).

Ancient plant DNA in lake sediments

Recent advances in sequencing technologies now permit the analyses of plant DNA from fossil samples (ancient plant DNA, plant aDNA), and thus enable the molecular reconstruction of palaeofloras. Hitherto, ancient frozen soils have proved excellent in preserving DNA molecules, and have thus been the most commonly used source of plant aDNA. However, DNA from soil mainly represents taxa growing a few metres from the sampling point.

BioWebEngine: a generation environment for bioinformatics research

With technologies for massively parallel genome sequencing available, bioinformatics has entered the “big data” era. Developing applications in this field involves collaboration of domain experts with IT specialists to specify programs able to query several sources, obtain data in several formats, search them for significant patterns and present the obtained results according to several types of visualisation.

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