Demo Data from SIGIR 2026 / reviewers in the wild / expert
Mario Miglietta
dblp:146/9814
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › drug discovery
drug mode of action analysis |
0.2 | 1 | 2014 | Mantra 2.0: an online collaborative resource for drug mode of action and repurposing by network analysis · Bioinform. 2014 |
Bioinformatics and computational biology › drug discovery
drug repurposing |
0.1 | 1 | 2014 | Mantra 2.0: an online collaborative resource for drug mode of action and repurposing by network analysis · Bioinform. 2014 |
Methods — techniques the papers use, named apart from their topics
transcriptional profile similarity · 0.2network analysis · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Mantra 2.0: an online collaborative resource for drug mode of action and repurposing by network analysisabstractSUMMARY: Elucidation of molecular targets of a compound [mode of action (MoA)] and its off-targets is a crucial step in drug development. We developed an online collaborative resource (MANTRA 2.0) that supports this process by exploiting similarities between drug-induced transcriptional profiles. Drugs are organized in a network of nodes (drugs) and edges (similarities) highlighting 'communities' of drugs sharing a similar MoA. A user can upload gene expression profiles before and after drug treatment in one or multiple cell types. An automated processing pipeline transforms the gene expression profiles into a unique drug 'node' embedded in the drug-network. Visual inspection of the neighbouring drugs and communities helps in revealing its MoA and to suggest new applications of known drugs (drug repurposing). MANTRA 2.0 allows storing and sharing user-generated network nodes, thus making MANTRA 2.0 a collaborative ever-growing resource. AVAILABILITY AND IMPLEMENTATION: The web tool is freely available for academic use at http://mantra.tigem.it. Diego Carrella, Francesco Napolitano, Rossella Rispoli, Mario Miglietta, Annamaria Carissimo, Luisa Cutillo, Francesco Sirci, Francesco Gregoretti, Diego di Bernardo |
Bioinform. | 4 |