TY  - JOUR
A1  - Jiang, Yuxiang
A1  - Oron, Tal Ronnen
A1  - Clark, Wyatt T.
A1  - Bankapur, Asma R.
A1  - D'Andrea, Daniel
A1  - Lepore, Rosalba
A1  - Funk, Christopher S.
A1  - Kahanda, Indika
A1  - Verspoor, Karin M.
A1  - Ben-Hur, Asa
A1  - Koo, Da Chen Emily
A1  - Penfold-Brown, Duncan
A1  - Shasha, Dennis
A1  - Youngs, Noah
A1  - Bonneau, Richard
A1  - Lin, Alexandra
A1  - Sahraeian, Sayed M. E.
A1  - Martelli, Pier Luigi
A1  - Profiti, Giuseppe
A1  - Casadio, Rita
A1  - Cao, Renzhi
A1  - Zhong, Zhaolong
A1  - Cheng, Jianlin
A1  - Altenhoff, Adrian
A1  - Skunca, Nives
A1  - Dessimoz, Christophe
A1  - Dogan, Tunca
A1  - Hakala, Kai
A1  - Kaewphan, Suwisa
A1  - Mehryary, Farrokh
A1  - Salakoski, Tapio
A1  - Ginter, Filip
A1  - Fang, Hai
A1  - Smithers, Ben
A1  - Oates, Matt
A1  - Gough, Julian
A1  - Törönen, Petri
A1  - Koskinen, Patrik
A1  - Holm, Liisa
A1  - Chen, Ching-Tai
A1  - Hsu, Wen-Lian
A1  - Bryson, Kevin
A1  - Cozzetto, Domenico
A1  - Minneci, Federico
A1  - Jones, David T.
A1  - Chapman, Samuel
A1  - BKC, Dukka
A1  - Khan, Ishita K.
A1  - Kihara, Daisuke
A1  - Ofer, Dan
A1  - Rappoport, Nadav
A1  - Stern, Amos
A1  - Cibrian-Uhalte, Elena
A1  - Denny, Paul
A1  - Foulger, Rebecca E.
A1  - Hieta, Reija
A1  - Legge, Duncan
A1  - Lovering, Ruth C.
A1  - Magrane, Michele
A1  - Melidoni, Anna N.
A1  - Mutowo-Meullenet, Prudence
A1  - Pichler, Klemens
A1  - Shypitsyna, Aleksandra
A1  - Li, Biao
A1  - Zakeri, Pooya
A1  - ElShal, Sarah
A1  - Tranchevent, Léon-Charles
A1  - Das, Sayoni
A1  - Dawson, Natalie L.
A1  - Lee, David
A1  - Lees, Jonathan G.
A1  - Sillitoe, Ian
A1  - Bhat, Prajwal
A1  - Nepusz, Tamás
A1  - Romero, Alfonso E.
A1  - Sasidharan, Rajkumar
A1  - Yang, Haixuan
A1  - Paccanaro, Alberto
A1  - Gillis, Jesse
A1  - Sedeño-Cortés, Adriana E.
A1  - Pavlidis, Paul
A1  - Feng, Shou
A1  - Cejuela, Juan M.
A1  - Goldberg, Tatyana
A1  - Hamp, Tobias
A1  - Richter, Lothar
A1  - Salamov, Asaf
A1  - Gabaldon, Toni
A1  - Marcet-Houben, Marina
A1  - Supek, Fran
A1  - Gong, Qingtian
A1  - Ning, Wei
A1  - Zhou, Yuanpeng
A1  - Tian, Weidong
A1  - Falda, Marco
A1  - Fontana, Paolo
A1  - Lavezzo, Enrico
A1  - Toppo, Stefano
A1  - Ferrari, Carlo
A1  - Giollo, Manuel
A1  - Piovesan, Damiano
A1  - Tosatto, Silvio C. E.
A1  - del Pozo, Angela
A1  - Fernández, José M.
A1  - Maietta, Paolo
A1  - Valencia, Alfonso
A1  - Tress, Michael L.
A1  - Benso, Alfredo
A1  - Di Carlo, Stefano
A1  - Politano, Gianfranco
A1  - Savino, Alessandro
A1  - Rehman, Hafeez Ur
A1  - Re, Matteo
A1  - Mesiti, Marco
A1  - Valentini, Giorgio
A1  - Bargsten, Joachim W.
A1  - van Dijk, Aalt D. J.
A1  - Gemovic, Branislava
A1  - Glisic, Sanja
A1  - Perovic, Vladmir
A1  - Veljkovic, Veljko
A1  - Almeida-e-Silva, Danillo C.
A1  - Vencio, Ricardo Z. N.
A1  - Sharan, Malvika
A1  - Vogel, Jörg
A1  - Kansakar, Lakesh
A1  - Zhang, Shanshan
A1  - Vucetic, Slobodan
A1  - Wang, Zheng
A1  - Sternberg, Michael J. E.
A1  - Wass, Mark N.
A1  - Huntley, Rachael P.
A1  - Martin, Maria J.
A1  - O'Donovan, Claire
A1  - Robinson, Peter N.
A1  - Moreau, Yves
A1  - Tramontano, Anna
A1  - Babbitt, Patricia C.
A1  - Brenner, Steven E.
A1  - Linial, Michal
A1  - Orengo, Christine A.
A1  - Rost, Burkhard
A1  - Greene, Casey S.
A1  - Mooney, Sean D.
A1  - Friedberg, Iddo
A1  - Radivojac, Predrag
A1  - Veljkovic, Nevena
T1  - An expanded evaluation of protein function prediction methods shows an improvement in accuracy
JF  - Genome Biology
N2  - Background
A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging.

Results
We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2.

Conclusions
The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.
KW  - Protein function prediction
KW  - Disease gene prioritization
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-166293
VL  - 17
IS  - 184
ER  - 
TY  - JOUR
A1  - Rao, Luigia
A1  - Giannico, Donato
A1  - Leone, Patrizia
A1  - Solimando, Antonio Giovanni
A1  - Maiorano, Eugenio
A1  - Caporusso, Concetta
A1  - Duda, Loren
A1  - Tamma, Roberto
A1  - Mallamaci, Rosanna
A1  - Susca, Nicola
A1  - Buonavoglia, Alessio
A1  - Da Vià, Matteo Claudio
A1  - Ribatti, Domenico
A1  - De Re, Vallì
A1  - Vacca, Angelo
A1  - Racanelli, Vito
T1  - HB-EGF−EGFR signaling in bone marrow endothelial cells mediates angiogenesis associated with multiple myeloma
JF  - Cancers
N2  - Epidermal growth factor receptor (EGFR) and its ligand heparin-binding EGF-like growth factor (HB-EGF) sustain endothelial cell proliferation and angiogenesis in solid tumors, but little is known about the role of HB-EGF–EGFR signaling in bone marrow angiogenesis and multiple myeloma (MM) progression. We found that bone marrow endothelial cells from patients with MM express high levels of EGFR and HB-EGF, compared with cells from patients with monoclonal gammopathy of undetermined significance, and that overexpressed HB-EGF stimulates EGFR expression in an autocrine loop. We also found that levels of EGFR and HB-EGF parallel MM plasma cell number, and that HB-EGF is a potent inducer of angiogenesis in vitro and in vivo. Moreover, blockade of HB-EGF–EGFR signaling, by an anti-HB-EGF neutralizing antibody or the EGFR inhibitor erlotinib, limited the angiogenic potential of bone marrow endothelial cells and hampered tumor growth in an MM xenograft mouse model. These results identify HB-EGF–EGFR signaling as a potential target of anti-angiogenic therapy, and encourage the clinical investigation of EGFR inhibitors in combination with conventional cytotoxic drugs as a new therapeutic strategy for MM.
KW  - multiple myeloma
KW  - HB-EGF
KW  - EGFR
KW  - bone marrow angiogenesis
KW  - endothelial cells
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-200786
SN  - 2072-6694
VL  - 12
IS  - 1
ER  -