Alzheimer's diseases detection by using deep learning algorithms: a mini-review

S Al-Shoukry, TH Rassem, NM Makbol - IEEE Access, 2020 - ieeexplore.ieee.org
The accurate diagnosis of Alzheimer's disease (AD) plays an important role in patient
treatment, especially at the disease's early stages, because risk awareness allows the …

[HTML][HTML] Deep learning techniques for the effective prediction of Alzheimer's disease: a comprehensive review

KA Shastry, V Vijayakumar, MKM V, M BA, C BN - Healthcare, 2022 - mdpi.com
“Alzheimer's disease”(AD) is a neurodegenerative disorder in which the memory shrinks and
neurons die.“Dementia” is described as a gradual decline in mental, psychological, and …

Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳ s general PseAAC

A Dehzangi, R Heffernan, A Sharma, J Lyons… - Journal of theoretical …, 2015 - Elsevier
Protein subcellular localization is defined as predicting the functioning location of a given
protein in the cell. It is considered an important step towards protein function prediction and …

Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain …

H Zhou, Y Yang, HB Shen - Bioinformatics, 2017 - academic.oup.com
Motivation Protein subcellular localization prediction has been an important research topic
in computational biology over the last decade. Various automatic methods have been …

SIFLoc: a self-supervised pre-training method for enhancing the recognition of protein subcellular localization in immunofluorescence microscopic images

Y Tu, H Lei, HB Shen, Y Yang - Briefings in Bioinformatics, 2022 - academic.oup.com
With the rapid growth of high-resolution microscopy imaging data, revealing the subcellular
map of human proteins has become a central task in the spatial proteome. The cell atlas of …

Label-free assessment of the drug resistance of epithelial ovarian cancer cells in a microfluidic holographic flow cytometer boosted through machine learning

L Xin, W Xiao, L Che, JJ Liu, L Miccio, V Bianco… - ACS …, 2021 - ACS Publications
About 75% of epithelial ovarian cancer (EOC) patients suffer from relapsing and develop
drug resistance after primary chemotherapy. The commonly used clinical examinations and …

Low dimensional representation of fisher vectors for microscopy image classification

Y Song, Q Li, H Huang, D Feng… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
Microscopy image classification is important in various biomedical applications, such as
cancer subtype identification, and protein localization for high content screening. To achieve …

Bioimage classification with handcrafted and learned features

L Nanni, S Brahnam, S Ghidoni… - IEEE/ACM transactions …, 2018 - ieeexplore.ieee.org
Bioimage classification is increasingly becoming more important in many biological studies
including those that require accurate cell phenotype recognition, subcellular localization …

Multi-modal classifier fusion with feature cooperation for glaucoma diagnosis

NE Benzebouchi, N Azizi, AS Ashour… - … of Experimental & …, 2019 - Taylor & Francis
Glaucoma is a major public health problem that can lead to an optic nerve lesion, requiring
systematic screening in the population over 45 years of age. The diagnosis and …

General purpose (GenP) bioimage ensemble of handcrafted and learned features with data augmentation

L Nanni, S Brahnam, S Ghidoni, G Maguolo - arXiv preprint arXiv …, 2019 - arxiv.org
Bioimage classification plays a crucial role in many biological problems. In this work, we
present a new General Purpose (GenP) ensemble that boosts performance by combining …