Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Guntuboina, Chakradhar"'
In recent years, natural language processing (NLP) models have demonstrated remarkable capabilities in various domains beyond traditional text generation. In this work, we introduce PeptideGPT, a protein language model tailored to generate protein se
Externí odkaz:
http://arxiv.org/abs/2410.19222
Peptides are essential in biological processes and therapeutics. In this study, we introduce Multi-Peptide, an innovative approach that combines transformer-based language models with Graph Neural Networks (GNNs) to predict peptide properties. We com
Externí odkaz:
http://arxiv.org/abs/2407.03380
The pursuit of novel alloys tailored to specific requirements poses significant challenges for researchers in the field. This underscores the importance of developing predictive techniques for essential physical properties of alloys based on their ch
Externí odkaz:
http://arxiv.org/abs/2403.19783
Intrinsically Disordered Proteins (IDPs) constitute a large and structure-less class of proteins with significant functions. The existence of IDPs challenges the conventional notion that the biological functions of proteins rely on their three-dimens
Externí odkaz:
http://arxiv.org/abs/2403.19762
Efficient catalyst screening necessitates predictive models for adsorption energy, a key property of reactivity. However, prevailing methods, notably graph neural networks (GNNs), demand precise atomic coordinates for constructing graph representatio
Externí odkaz:
http://arxiv.org/abs/2309.00563
Recent advances in Language Models have enabled the protein modeling community with a powerful tool since protein sequences can be represented as text. Specifically, by taking advantage of Transformers, sequence-to-property prediction will be amenabl
Externí odkaz:
http://arxiv.org/abs/2309.03099
Akademický článek
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Akademický článek
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Publikováno v:
Dipòsit Digital de Documents de la UAB
Universitat Autònoma de Barcelona
Universitat Autònoma de Barcelona
This paper proposes a computationally inexpensive method for automatic key-event extraction and sub-sequent summarization of sports videos using scoreboard detection. A database consisting of 1300 imageswas used to train (using transfer learning) a s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::6129893e39b29ce7a37892252eae16ed
https://ddd.uab.cat/record/241089
https://ddd.uab.cat/record/241089
Autor:
Guntuboina C; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States., Das A; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States., Mollaei P; Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States., Kim S; Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States., Barati Farimani A; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.; Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.; Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
Publikováno v:
The journal of physical chemistry letters [J Phys Chem Lett] 2023 Nov 23; Vol. 14 (46), pp. 10427-10434. Date of Electronic Publication: 2023 Nov 13.
