Nome |
# |
Chromatin Velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin, file e2913fdf-3385-f688-e053-3705fe0a67e0
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559
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Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors, file fd93249c-b4f5-45e3-b5c8-1e641f5a0410
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164
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Automatising the analysis of stochastic biochemical time-series, file e2913fdd-7045-f688-e053-3705fe0a67e0
|
102
|
Algorithmic methods to infer the evolutionary trajectories in cancer progression, file e2913fdc-efb4-f688-e053-3705fe0a67e0
|
79
|
Design of the TRONCO BioConductor Package for TRanslational ONCOlogy, file e2913fdd-7646-f688-e053-3705fe0a67e0
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75
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Gene switching rate determines response to extrinsic perturbations in the self-activation transcriptional network motif, file e2913fdd-a25f-f688-e053-3705fe0a67e0
|
68
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Inferring Tree Causal Models of Cancer Progression with Probability Raising, file e2913fdd-0336-f688-e053-3705fe0a67e0
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65
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A Bayesian method to cluster single-cell RNA sequencing data using Copy Number Alterations, file 7aa3e7ac-aa0d-4a93-9f50-da9b5d9397cd
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56
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Measuring evolutionary cancer dynamics from genome sequencing, one patient at a time, file e2913fde-f92b-f688-e053-3705fe0a67e0
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55
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Confirming and investigating the role of breast cancer PIK3CA-ERBB2 genes in Anti-Cancer Drug Resistance with a new
framework for the inference of cancer progression graphs using vector integration sites data, file e2913fdd-8079-f688-e053-3705fe0a67e0
|
46
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Modeling Cumulative Biological Phenomena with Suppes-Bayes Causal Networks, file e2913fdd-ac33-f688-e053-3705fe0a67e0
|
46
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Subclonal reconstruction of tumors by using machine learning and population genetics, file e2913fdf-0a2f-f688-e053-3705fe0a67e0
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45
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Phenotypic plasticity and genetic control in colorectal cancer evolution, file 236cbbba-803d-49ac-88d5-5e1e684e21a2
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42
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The Influence of Nutrients Diffusion on a Metabolism-driven Model of a Multi-cellular System, file e2913fdf-a265-f688-e053-3705fe0a67e0
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37
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Signatures of TOP1 transcription-associated mutagenesis in cancer and germline, file e2913fdf-5b43-f688-e053-3705fe0a67e0
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31
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Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data, file e2913fdd-ac15-f688-e053-3705fe0a67e0
|
28
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The co-evolution of the genome and epigenome in colorectal cancer, file 4c1c69c0-d822-4785-b39a-313e8d0060e2
|
25
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Data symmetries and Learning in fully connected neural networks, file 856df9ab-4e98-4bab-b71e-591336d25db0
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22
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The Preliminary Results from the 100,000 Genomes Project: The Genomic Landscape of Colorectal Cancer, file e2913fdd-52ac-f688-e053-3705fe0a67e0
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22
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Evolutionary dynamics of residual disease in human glioblastoma, file e2913fdd-ac14-f688-e053-3705fe0a67e0
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21
|
Automatising the analysis of stochastic biochemical time-series, file e2913fdd-7046-f688-e053-3705fe0a67e0
|
18
|
Inferring Tree Causal Models of Cancer Progression with Probability Raising, file e2913fdd-7645-f688-e053-3705fe0a67e0
|
18
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Exploiting evolutionary steering to induce collateral drug sensitivity in cancer, file e2913fde-12d5-f688-e053-3705fe0a67e0
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17
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Germline MBD4 deficiency causes a multi-tumor predisposition syndrome, file 578d5d1d-2aba-4e04-8353-0af440481c85
|
15
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Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors, file 707606ca-0b1b-48ba-8ff6-e51f6f6c0f38
|
15
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The MOBSTER R package for tumour subclonal deconvolution from bulk DNA whole-genome sequencing data, file e2913fdd-ccca-f688-e053-3705fe0a67e0
|
14
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Gene switching rate determines response to extrinsic perturbations in the self-activation transcriptional network motif, file e2913fdd-7644-f688-e053-3705fe0a67e0
|
12
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PMCE: efficient inference of expressive models of cancer evolution with high prognostic power, file e2913fdf-0313-f688-e053-3705fe0a67e0
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12
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Pan-cancer landscape of AID-related mutations, composite mutations, and their potential role in the ICI response, file 240e2584-47d5-45e8-b26b-b68f35f728be
|
9
|
cyTRON and cytron/js: Two cytoscape-based applications for the inference of cancer evolution models, file e2913fdf-5ba6-f688-e053-3705fe0a67e0
|
9
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Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors, file 11166ba4-c170-466f-b778-1f40a8fb6ee4
|
7
|
Clonal KEAP1 mutations with loss of heterozygosity share reduced immunotherapy efficacy and low immune cell infiltration in lung adenocarcinoma, file fdfcb630-0e70-4a3f-b532-563511469e9a
|
6
|
Mathematical modeling of neuroblastoma associates evolutionary patterns with outcomes, file 02a0bb57-ffb0-49b0-bd57-27ea59a3344d
|
5
|
Detecting repeated cancer evolution from multiregion tumor sequencing data, file e2913fdd-19da-f688-e053-3705fe0a67e0
|
5
|
Signatures of TOP1 transcription-associated mutagenesis in cancer and germline, file e2913fdf-a8d8-f688-e053-3705fe0a67e0
|
5
|
Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors, file 3c08d67f-468f-4215-bb66-d9a434fe4011
|
4
|
Cancer genomes tolerate deleterious coding mutations through somatic copy number amplifications of wild-type regions, file 89c12dac-f6bf-4812-b015-f78baaa3e6f0
|
4
|
Exploiting evolutionary steering to induce collateral drug sensitivity in cancer, file e2913fde-1279-f688-e053-3705fe0a67e0
|
4
|
Learning the structure of Bayesian Networks via the bootstrap, file e2913fdf-80be-f688-e053-3705fe0a67e0
|
4
|
Clinical application of tumour-in-normal contamination assessment from whole genome sequencing, file e1d87eb4-d19e-455b-ae0d-0f11b270d264
|
3
|
Detecting repeated cancer evolution from multiregion tumor sequencing data, file e2913fdd-0337-f688-e053-3705fe0a67e0
|
3
|
A Bayesian method to cluster single-cell RNA sequencing data using Copy Number Alterations, file e2913fdf-a2d3-f688-e053-3705fe0a67e0
|
3
|
Matching models across abstraction levels with Gaussian processes, file e2913fdb-3ed9-f688-e053-3705fe0a67e0
|
2
|
Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data, file e2913fdc-efb8-f688-e053-3705fe0a67e0
|
2
|
Learning the structure of Bayesian Networks via the bootstrap, file e2913fdf-26c2-f688-e053-3705fe0a67e0
|
2
|
cyTRON and cytron/js: Two cytoscape-based applications for the inference of cancer evolution models, file e2913fdf-2b82-f688-e053-3705fe0a67e0
|
2
|
The Influence of Nutrients Diffusion on a Metabolism-driven Model of a Multi-cellular System, file e2913fdf-4d84-f688-e053-3705fe0a67e0
|
2
|
Bounded noises as a natural tool to model extrinsic fluctuations in biomolecular networks, file 391f8dc6-90e5-46c2-a63b-1df407a15f12
|
1
|
Effects of delayed immune-response in tumor immune-system interplay, file 730c877e-1ce8-4d54-9d3a-4bcdf5608d28
|
1
|
Clinical application of tumour-in-normal contamination assessment from whole genome sequencing, file 779ffdf7-547b-4ef9-af13-13cd0204bb76
|
1
|
CABeRNET: a Cytoscape app for Augmented Boolean models of gene Regulatory NETworks, file e2913fdc-efb5-f688-e053-3705fe0a67e0
|
1
|
Reply to 'Neutral tumor evolution?', file e2913fdc-efb9-f688-e053-3705fe0a67e0
|
1
|
Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data, file e2913fdc-efbc-f688-e053-3705fe0a67e0
|
1
|
CoGNaC: a Chaste plugin for the multiscale simulation of gene regulatory networks driving the spatial dynamics of tissues and cancer, file e2913fdd-04da-f688-e053-3705fe0a67e0
|
1
|
An Intermediate Language for the Stochastic Simulation of Biological Systems, file e2913fdd-4601-f688-e053-3705fe0a67e0
|
1
|
TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data, file e2913fdd-7047-f688-e053-3705fe0a67e0
|
1
|
Subclonal reconstruction of tumors by using machine learning and population genetics, file e2913fde-1359-f688-e053-3705fe0a67e0
|
1
|
Totale |
1.800 |