Výsledky vyhledávání - "Ebenezer Nanor"

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Semi-supervised Federated Learning on Evolving Data Streams

Autor: Cobbinah B. Mawuli, Jay Kumar, Ebenezer Nanor, Shangxuan Fu, Liangxu Pan, Qinli Yang, Wei Zhang, Junming Shao

Publikováno v: Information Sciences. :119235

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::83e6d36ef773181c52272e655a6fcd42
https://doi.org/10.1016/j.ins.2023.119235

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Prediction of Drug Permeability to the Blood-Brain Barrier using Deep Learning

Autor: Ebenezer Nanor, Wei-Ping Wu, Abena Achiaa Atwereboannah

Publikováno v: 4th International Conference on Biometric Engineering and Applications.

In our quest to design new drugs, conventional means of designing drugs characterized by experiments is a time and resource demanding process. It involves exploring many molecular combinations, to find potential drugs to target indications. Machine L

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a5fec09529f8bc1326608c7d5621fddc
https://doi.org/10.1145/3476779.3476797

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Algorithmic Generation of Positive Samples for Compound-Target Interaction Prediction

Autor: Victor K. Agbesi, Wei-Ping Wu, Strato Angsoteng Bayitaa, Ebenezer Nanor, Brighter Agyemang

Publikováno v: ICMLC

Machine Learning (ML) methods have become the preferred computational methods for Compound-Target Interaction (CTI) prediction in small drug development in Bioinformatics, because they have been proven to be very efficient. However, the extremely imb

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::dc432a7f97b6c1c2279a069665d5736a
https://doi.org/10.1145/3457682.3457689

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Deep inverse reinforcement learning for structural evolution of small molecules

Autor: Wei-Ping Wu, Brighter Agyemang, Michael Y. Kpiebaareh, Daniel Addo, Charles Roland Haruna, Ebenezer Nanor

Publikováno v: Briefings in Bioinformatics.

The size and quality of chemical libraries to the drug discovery pipeline are crucial for developing new drugs or repurposing existing drugs. Existing techniques such as combinatorial organic synthesis and high-throughput screening usually make the p

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e4f351a73976d0ee9945e9a52368acbe
https://doi.org/10.1093/bib/bbaa364

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Graph Based Analytics Enhanced by Deep Learning

Autor: Brighter Agyemang, Ebenezer Nanor, Strato Angsoteng Bayitaa, Michael Y. Kpiebaareh, Wei-Ping Wu

Publikováno v: ICCDE

We explore the use of graph analytics and deep learning to generate insight for decision support systems. The solution consists of two phases: graph analytics, and deep learning. In the first phase, we modelled data into graph and execute graph analy

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d0c67d6370dc72ab78db7bf5077cc96c
https://doi.org/10.1145/3379247.3379286

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Drug-Target Indication Prediction by Integrating End-to-End Learning and Fingerprints

Autor: Wei-Ping Wu, Brighter Agyemang, Michael Y. Kpiebaareh, Ebenezer Nanor

Computer-Aided Drug Discovery research has proven to be a promising direction in drug discovery. In recent years, Deep Learning approaches have been applied to problems in the domain such as Drug-Target Interaction Prediction and have shown improveme

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6ee4162b2381f8957c275637f2c10cc1
http://arxiv.org/abs/1912.01163

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Multi-view self-attention for interpretable drug–target interaction prediction

Autor: Ebenezer Nanor, Brighter Agyemang, Lei Chen, Michael Y. Kpiebaareh, Zhihua Lei, Wei-Ping Wu

Publikováno v: Journal of Biomedical Informatics. 110:103547

The drug discovery stage is a vital aspect of the drug development process and forms part of the initial stages of the development pipeline. In recent times, machine learning-based methods are actively being used to model drug-target interactions for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb3a0d7bb8b73fad2a992d21abefc52f
https://doi.org/10.1016/j.jbi.2020.103547

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