The role of deep learning in advancing breast cancer detection using different imaging modalities: a systematic review
Simple Summary Breast cancer is the most common cancer, which resulted in the death of
700,000 people around the world in 2020. Various imaging modalities have been utilized to …
700,000 people around the world in 2020. Various imaging modalities have been utilized to …
Classification of skin cancer stages using a AHP fuzzy technique within the context of big data healthcare
Background and objectives Skin conditions in humans can be challenging to diagnose. Skin
cancer manifests itself without warning. In the future, these illnesses, which have been an …
cancer manifests itself without warning. In the future, these illnesses, which have been an …
Application of Fe3O4/SiO2@ ZnO magnetic composites as a recyclable heterogeneous nanocatalyst for biodiesel production from waste cooking oil: Response …
B Maleki, H Esmaeili - Ceramics International, 2023 - Elsevier
Abstract Fe 3 O 4/SiO 2@ ZnO nanocomposite was synthesized using the co-precipitation
method and then used as an efficient and reclaimable heterogeneous catalyst for biodiesel …
method and then used as an efficient and reclaimable heterogeneous catalyst for biodiesel …
Protein function analysis through machine learning
Machine learning (ML) has been an important arsenal in computational biology used to
elucidate protein function for decades. With the recent burgeoning of novel ML methods and …
elucidate protein function for decades. With the recent burgeoning of novel ML methods and …
Development of an optimally designed real-time automatic citrus fruit grading–sorting machine leveraging computer vision-based adaptive deep learning model
Conventional automation approaches for postharvest operations are plagued by time and
data inefficiency seldom leading to suboptimal solutions. Automatic machines often require …
data inefficiency seldom leading to suboptimal solutions. Automatic machines often require …
Peptidebert: A language model based on transformers for peptide property prediction
Recent advances in language models have enabled the protein modeling community with a
powerful tool that uses transformers to represent protein sequences as text. This …
powerful tool that uses transformers to represent protein sequences as text. This …
[HTML][HTML] Generative pretrained autoregressive transformer graph neural network applied to the analysis and discovery of novel proteins
MJ Buehler - Journal of Applied Physics, 2023 - pubs.aip.org
We report a flexible language-model-based deep learning strategy, applied here to solve
complex forward and inverse problems in protein modeling, based on an attention neural …
complex forward and inverse problems in protein modeling, based on an attention neural …
Experimental evaluation, modeling and sensitivity analysis of temperature and cutting force in bone micro-milling using support vector regression and EFAST methods
In orthopedic surgeries, examining the effect of machining conditions are very crucial due to
the possibility of damage to the bone tissue, and the occurrence of thermal necrosis …
the possibility of damage to the bone tissue, and the occurrence of thermal necrosis …
Serverless prediction of peptide properties with recurrent neural networks
We present three deep learning sequence-based prediction models for peptide properties
including hemolysis, solubility, and resistance to nonspecific interactions that achieve …
including hemolysis, solubility, and resistance to nonspecific interactions that achieve …
Protsolm: Protein solubility prediction with multi-modal features
Understanding protein solubility is essential for their functional applications. Computational
methods for predicting protein solubility are crucial for reducing experimental costs and …
methods for predicting protein solubility are crucial for reducing experimental costs and …