about
CAPRI: efficient inference of cancer progression models from cross-sectional data.TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data.Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning.Inferring tree causal models of cancer progression with probability raising.Algorithmic methods to infer the evolutionary trajectories in cancer progression.OncoScore: a novel, Internet-based tool to assess the oncogenic potential of genes.SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning.Modeling Cumulative Biological Phenomena with Suppes-Bayes Causal Networks.Improved Survival of Cancer Patients Admitted to the Intensive Care Unit between 2002 and 2011 at a U.S. Teaching HospitalMulti-omic tumor data reveal diversity of molecular mechanisms that correlate with survivalLearning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic SchemesWithholding or withdrawing invasive interventions may not accelerate time to death among dying ICU patientsLearning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing dataDetecting repeated cancer evolution from multi-region tumor sequencing dataMachine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patientsComprehensive genomic characterization of breast tumors with BRCA1 and BRCA2 mutationsCharacterization of intra-host SARS-CoV-2 variants improves phylogenomic reconstruction and may reveal functionally convergent mutations
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description
researcher
@en
wetenschapper
@nl
հետազոտող
@hy
name
Daniele Ramazzotti
@ast
Daniele Ramazzotti
@en
Daniele Ramazzotti
@es
Daniele Ramazzotti
@nl
type
label
Daniele Ramazzotti
@ast
Daniele Ramazzotti
@en
Daniele Ramazzotti
@es
Daniele Ramazzotti
@nl
prefLabel
Daniele Ramazzotti
@ast
Daniele Ramazzotti
@en
Daniele Ramazzotti
@es
Daniele Ramazzotti
@nl
P106
P31
P496
0000-0002-6087-2666