Publications

Anderson P. Avila Santos, Breno L. S. de Almeida, Robson P. Bonidia, Peter F. Stadler, Polonca Stefanic, Ines Mandic-Mulec, Ulisses Rocha, Danilo S. Sanches, and André C.P.L.F. de Carvalho. BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification. RNA Biology, (21)1:410–421, Informa UK Limited, March 2024. [PUMA: BioDeepfuse RNA classification deep extraction feature learning non-coding] URL

Lena Jurkschat, Gregor Wiedemann, Maximilian Heinrich, Mattes Ruckdeschel, and Sunna Torge. Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate. In Nicoletta Calzolari, Frederic Bechet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Helene Mazo, Jan Odijk, and Stelios Piperidis (Eds.), 2022 Language Resources and Evaluation Conference, LREC 2022, 663--672, European Language Resources Association (ELRA), 2022. [PUMA: FIS_scads argument aspect-based aspects, classification discourse, energy few-shot frames, learning, mining, nuclear text]

Christopher Klapproth, Rituparno Sen, Peter F Stadler, Sven Findeiß, and Jörg Fallmann. Common features in lncRNA annotation and classification: A survey. Noncoding RNA, (7)4:77, MDPI AG, December 2021. [PUMA: classification coding extraction; feature learning lncRNA; machine problems; sequence;]

Akshay Akshay, Masoud Abedi, Navid Shekarchizadeh, Fiona C Burkhard, Mitali Katoch, Alex Bigger-Allen, Rosalyn M Adam, Katia Monastyrskaya, and Ali Hashemi Gheinani. MLcps: machine learning cumulative performance score for classification problems. Gigascience, (12)December 2022. [PUMA: topic_federatedlearn Python classification evaluation evaluation; learning; machine model package; problems; score unified]

Akshay Akshay, Mitali Katoch, Navid Shekarchizadeh, Masoud Abedi, Ankush Sharma, Fiona C Burkhard, Rosalyn M Adam, Katia Monastyrskaya, and Ali Hashemi Gheinani. Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis. Gigascience, (13)January 2024. [PUMA: topic_federatedlearn AutoML; analysis; classification data learning; machine problems; visualization]