Neven Sumonja
University of Belgrade
10 Papers
3 Citations
Neven Sumonja is an academic researcher from University of Belgrade. The author has contributed to research in topics: Computer science & Feature selection. The author has an hindex of 7, co-authored 10 publications.
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Papers
The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
Naihui Zhou,Yuxiang Jiang,Timothy Bergquist,Alexandra J. Lee,Balint Z. Kacsoh,Alex W. Crocker,Kimberley A. Lewis,George Georghiou,Huy N Nguyen,Nafiz Hamid,Larry Davis,Tunca Doğan,Tunca Doğan,Volkan Atalay,Ahmet Sureyya Rifaioglu,Alperen Dalkiran,Rengul Cetin Atalay,Chengxin Zhang,Rebecca L. Hurto,Peter L. Freddolino,Yang Zhang,Prajwal Bhat,Fran Supek,José M. Fernández,Branislava Gemovic,Vladimir Perovic,Radoslav Davidovic,Neven Sumonja,Nevena Veljkovic,Ehsaneddin Asgari,Mohammad R. K. Mofrad,Giuseppe Profiti,Giuseppe Profiti,Castrense Savojardo,Pier Luigi Martelli,Rita Casadio,Florian Boecker,Heiko Schoof,Indika Kahanda,Natalie Thurlby,Alice C. McHardy,Alexandre Renaux,Alexandre Renaux,Rabie Saidi,Julian Gough,Alex A. Freitas,Magdalena Antczak,Fabio Fabris,Mark N. Wass,Jie Hou,Jianlin Cheng,Zheng Wang,Alfonso E. Romero,Alberto Paccanaro,Haixuan Yang,Haixuan Yang,Tatyana Goldberg,Chenguang Zhao,Liisa Holm,Petri Törönen,Alan Medlar,Elaine Zosa,Itamar Borukhov,Ilya Novikov,Angela D. Wilkins,Olivier Lichtarge,Po-Han Chi,Wei-Cheng Tseng,Michal Linial,Peter W. Rose,Christophe Dessimoz,Christophe Dessimoz,Christophe Dessimoz,Vedrana Vidulin,Saso Dzeroski,Ian Sillitoe,Sayoni Das,Jonathan G. Lees,Jonathan G. Lees,David T. Jones,David T. Jones,Cen Wan,Cen Wan,Domenico Cozzetto,Domenico Cozzetto,Rui Fa,Rui Fa,Mateo Torres,Alex Warwick Vesztrocy,Alex Warwick Vesztrocy,Jose Manuel Rodriguez,Michael L. Tress,Marco Frasca,Marco Notaro,Giuliano Grossi,Alessandro Petrini,Matteo Re,Giorgio Valentini,Marco Mesiti,Marco Mesiti,Daniel B. Roche,Jonas Reeb,David W. Ritchie,Sabeur Aridhi,Seyed Ziaeddin Alborzi,Seyed Ziaeddin Alborzi,Marie-Dominique Devignes,Marie-Dominique Devignes,Da Chen Emily Koo,Richard Bonneau,Vladimir Gligorijević,Meet Barot,Hai Fang,Stefano Toppo,Enrico Lavezzo,Marco Falda,Michele Berselli,Silvio C. E. Tosatto,Marco Carraro,Damiano Piovesan,Hafeez Ur Rehman,Qizhong Mao,Qizhong Mao,Shanshan Zhang,Slobodan Vucetic,Gage S. Black,Dane Jo,Erica Suh,Jonathan B. Dayton,Dallas J. Larsen,Ashton Omdahl,Liam J. McGuffin,Danielle A Brackenridge,Patricia C. Babbitt,Jeffrey M. Yunes,Paolo Fontana,Feng Zhang,Shanfeng Zhu,Ronghui You,Zihan Zhang,Suyang Dai,Shuwei Yao,Weidong Tian,Weidong Tian,Renzhi Cao,Caleb Chandler,Miguel Amezola,Devon Johnson,Jia-Ming Chang,Wen-Hung Liao,Yi-Wei Liu,Stefano Pascarelli,Yotam Frank,Robert Hoehndorf,Maxat Kulmanov,Imane Boudellioua,Gianfranco Politano,Stefano Di Carlo,Alfredo Benso,Kai Hakala,Filip Ginter,Farrokh Mehryary,Suwisa Kaewphan,Suwisa Kaewphan,Jari Björne,Jari Björne,Hans Moen,Martti Tolvanen,Tapio Salakoski,Tapio Salakoski,Daisuke Kihara,Daisuke Kihara,Aashish Jain,Tomislav Šmuc,Adrian M. Altenhoff,Adrian M. Altenhoff,Asa Ben-Hur,Burkhard Rost,Steven E. Brenner,Christine A. Orengo,Constance J. Jeffery,Giovanni Bosco,Deborah A. Hogan,Maria Jesus Martin,Claire O'Donovan,Sean D. Mooney,Casey S. Greene,Predrag Radivojac,Iddo Friedberg +188 more
TL;DR: The third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed, concluded that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not.
The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
Naihui Zhou,Yuxiang Jiang,Timothy Bergquist,Alexandra J. Lee,Balint Z. Kacsoh,Alex W. Crocker,Kimberley A. Lewis,George Georghiou,Huy N Nguyen,Nafiz Hamid,Larry Davis,Tunca Doğan,Tunca Doğan,Volkan Atalay,Ahmet Sureyya Rifaioglu,Alperen Dalkiran,Rengul Cetin-Atalay,Chengxin Zhang,Rebecca L. Hurto,Peter L. Freddolino,Yang Zhang,Prajwal Bhat,Fran Supek,José M. Fernández,Branislava Gemovic,Vladimir Perovic,Radoslav Davidovic,Neven Sumonja,Nevena Veljkovic,Ehsaneddin Asgari,Mohammad R. K. Mofrad,Giuseppe Profiti,Giuseppe Profiti,Castrense Savojardo,Pier Luigi Martelli,Rita Casadio,Florian Boecker,Indika Kahanda,Natalie Thurlby,Alice C. McHardy,Alexandre Renaux,Alexandre Renaux,Rabie Saidi,Julian Gough,Alex A. Freitas,Magdalena Antczak,Fabio Fabris,Mark N. Wass,Jie Hou,Jianlin Cheng,Zheng Wang,Alfonso E. Romero,Alberto Paccanaro,Haixuan Yang,Tatyana Goldberg,Chenguang Zhao,Liisa Holm,Petri Törönen,Alan Medlar,Elaine Zosa,Itamar Borukhov,Ilya Novikov,Angela D. Wilkins,Olivier Lichtarge,Po-Han Chi,Wei-Cheng Tseng,Michal Linial,Peter W. Rose,Christophe Dessimoz,Christophe Dessimoz,Vedrana Vidulin,Saso Dzeroski,Ian Sillitoe,Sayoni Das,Jonathan G. Lees,Jonathan G. Lees,David T. Jones,David T. Jones,Cen Wan,Cen Wan,Domenico Cozzetto,Domenico Cozzetto,Rui Fa,Rui Fa,Mateo Torres,Alex Warwick Vesztrocy,Alex Warwick Vesztrocy,Jose Manuel Rodriguez,Michael L. Tress,Marco Frasca,Marco Notaro,Giuliano Grossi,Alessandro Petrini,Matteo Re,Giorgio Valentini,Marco Mesiti,Daniel B. Roche,Jonas Reeb,David W. Ritchie,Sabeur Aridhi,Seyed Ziaeddin Alborzi,Marie-Dominique Devignes,Da Chen Emily Koo,Richard Bonneau,Vladimir Gligorijević,Meet Barot,Hai Fang,Stefano Toppo,Enrico Lavezzo,Marco Falda,Michele Berselli,Silvio C. E. Tosatto,Marco Carraro,Damiano Piovesan,Hafeez Ur Rehman,Qizhong Mao,Qizhong Mao,Shanshan Zhang,Slobodan Vucetic,Gage S. Black,Dane Jo,Dallas J. Larsen,Ashton Omdahl,Luke W Sagers,Erica Suh,Jonathan B. Dayton,Liam J. McGuffin,Danielle A Brackenridge,Patricia C. Babbitt,Jeffrey M. Yunes,Paolo Fontana,Feng Zhang,Shanfeng Zhu,Ronghui You,Zihan Zhang,Suyang Dai,Shuwei Yao,Weidong Tian,Renzhi Cao,Caleb Chandler,Miguel Amezola,Devon Johnson,Jia-Ming Chang,Wen-Hung Liao,Yi-Wei Liu,Stefano Pascarelli,Yotam Frank,Robert Hoehndorf,Maxat Kulmanov,Imane Boudellioua,Gianfranco Politano,Stefano Di Carlo,Alfredo Benso,Kai Hakala,Filip Ginter,Farrokh Mehryary,Suwisa Kaewphan,Suwisa Kaewphan,Jari Björne,Jari Björne,Hans Moen,Martti Tolvanen,Tapio Salakoski,Tapio Salakoski,Daisuke Kihara,Daisuke Kihara,Aashish Jain,Tomislav Šmuc,Adrian M. Altenhoff,Asa Ben-Hur,Burkhard Rost,Steven E. Brenner,Christine A. Orengo,Constance J. Jeffery,Giovanni Bosco,Deborah A. Hogan,Maria Jesus Martin,Claire O'Donovan,Sean D. Mooney,Casey S. Greene,Predrag Radivojac,Iddo Friedberg +181 more
TL;DR: It is reported that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bioontologies, working together to improve functional annotation, computational function prediction, and the ability to manage big data in the era of large experimental screens.
Biofilm-forming ability and infection potential of Pseudomonas aeruginosa strains isolated from animals and humans.
Dušan Milivojević,Neven Sumonja,Strahinja Medic,Aleksandar Pavic,Ivana Moric,Branka Vasiljevic,Lidija Senerovic,Jasmina Nikodinovic-Runic +7 more
TL;DR: For the first time an ensemble machine learning approach used on the in vitro virulence data determined the highest relative predictive importance of the submerged biofilm formation for the cytotoxicity, as an indicator of the infection ability.
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IDPpi: Protein-Protein Interaction Analyses of Human Intrinsically Disordered Proteins.
Vladimir Perovic,Neven Sumonja,Lindsey A. Marsh,Sandro Radovanovic,Milan Vukicevic,Stefan G. E. Roberts,Nevena Veljkovic +6 more
TL;DR: This work developed a method that relies on features of the interacting and non-interacting protein pairs and utilizes machine learning to classify and predict IDP PPIs and confirms that this method predicts interactions of the IDP of interest even on the proteome-scale.
Automated feature engineering improves prediction of protein–protein interactions
TL;DR: A unified method, HP-GAS, is introduced for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features, and represents currently the most efficient method for proteome-wide forecasting of protein interactions.
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