[HTML][HTML] Interpretable and explainable predictive machine learning models for data-driven protein engineering

D Medina-Ortiz, A Khalifeh, H Anvari-Kazemabad… - Biotechnology …, 2024 - Elsevier
Protein engineering through directed evolution and (semi) rational design has become a
powerful approach for optimizing and enhancing proteins with desired properties. The …

[HTML][HTML] Protein language models and machine learning facilitate the identification of antimicrobial peptides

D Medina-Ortiz, S Contreras, D Fernández… - International Journal of …, 2024 - mdpi.com
Peptides are bioactive molecules whose functional versatility in living organisms has led to
successful applications in diverse fields. In recent years, the amount of data describing …

Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides

M Goles, A Daza, G Cabas-Mora… - Briefings in …, 2024 - academic.oup.com
With their diverse biological activities, peptides are promising candidates for therapeutic
applications, showing antimicrobial, antitumour and hormonal signalling capabilities …

Optical fingerprint classification of single upconversion nanoparticles by deep learning

J Liao, J Zhou, Y Song, B Liu, J Lu… - The Journal of Physical …, 2021 - ACS Publications
Highly controlled synthesis of upconversion nanoparticles (UCNPs) can be achieved in the
heterogeneous design, so that a library of optical properties can be arbitrarily produced by …

Peptipedia: a user-friendly web application and a comprehensive database for peptide research supported by machine learning approach

C Quiroz, YB Saavedra, B Armijo-Galdames… - Database, 2021 - academic.oup.com
Peptides have attracted attention during the last decades due to their extraordinary
therapeutic properties. Different computational tools have been developed to take …

Generalized property-based encoders and digital signal processing facilitate predictive tasks in protein engineering

D Medina-Ortiz, S Contreras… - Frontiers in Molecular …, 2022 - frontiersin.org
Computational methods in protein engineering often require encoding amino acid
sequences, ie, converting them into numeric arrays. Physicochemical properties are a …

A novel deep learning prognostic system improves survival predictions for stage III non‐small cell lung cancer

L Yang, X Fan, W Qin, Y Xu, B Zou, B Fan… - Cancer …, 2022 - Wiley Online Library
Background Accurate prognostic prediction plays a crucial role in the clinical setting.
However, the TNM staging system fails to provide satisfactory individual survival prediction …

[HTML][HTML] Development of data-driven machine learning models and their potential role in predicting dengue outbreak

B Mazhar, NM Ali, F Manzoor, MK Khan… - Journal of Vector …, 2024 - journals.lww.com
Dengue fever is one of the most widespread vector-borne viral infections in the world,
resulting in increased socio-economic burden. WHO has reported that 2.5 billion people are …

Exploring machine learning algorithms and numerical representations strategies to develop sequence-based predictive models for protein networks

D Medina-Ortiz, P Salinas, G Cabas-Moras… - … Science and Its …, 2023 - Springer
Predicting the affinity between two proteins is one of the most relevant challenges in
bioinformatics and one of the most useful for biotechnological and pharmaceutical …

Developing and comparing deep learning and machine learning algorithms for osteoporosis risk prediction

C Qiu, K Su, Z Luo, Q Tian, L Zhao, L Wu… - Frontiers in Artificial …, 2024 - frontiersin.org
Introduction Osteoporosis, characterized by low bone mineral density (BMD), is an
increasingly serious public health issue. So far, several traditional regression models and …