A machine-learning-based prediction method for hypertension outcomes based on medical data
W Chang, Y Liu, Y Xiao, X Yuan, X Xu, S Zhang… - Diagnostics, 2019 - mdpi.com
The outcomes of hypertension refer to the death or serious complications (such as
myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes …
myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes …
An Efficient hybrid filter-wrapper metaheuristic-based gene selection method for high dimensional datasets
J Pirgazi, M Alimoradi, T Esmaeili Abharian… - Scientific reports, 2019 - nature.com
Feature selection problem is one of the most significant issues in data classification. The
purpose of feature selection is selection of the least number of features in order to increase …
purpose of feature selection is selection of the least number of features in order to increase …
Combining Machine Learning and Edge Computing: Opportunities, Challenges, Platforms, Frameworks, and Use Cases
In recent years, we have been observing the rapid growth and adoption of IoT-based
systems, enhancing multiple areas of our lives. Concurrently, the utilization of machine …
systems, enhancing multiple areas of our lives. Concurrently, the utilization of machine …
Incorporating distance-based top-n-gram and random forest to identify electron transport proteins
X Ru, L Li, Q Zou - Journal of Proteome Research, 2019 - ACS Publications
Cellular respiration provides direct energy substances for living organisms. Electron storage
and transportation should be completed through electron transport chains during the cellular …
and transportation should be completed through electron transport chains during the cellular …
ML-based clinical decision support models based on metabolomics data
M Burdukiewicz, J Chilimoniuk, K Grzesiak… - TrAC Trends in …, 2024 - Elsevier
Abstract Machine learning-based clinical decision support models in healthcare emulate
clinicians' cognitive processes, leveraging artificial intelligence to analyze intricate medical …
clinicians' cognitive processes, leveraging artificial intelligence to analyze intricate medical …
FAD-BERT: improved prediction of FAD binding sites using pre-training of deep bidirectional transformers
The electron transport chain is a series of protein complexes embedded in the process of
cellular respiration, which is an important process to transfer electrons and other …
cellular respiration, which is an important process to transfer electrons and other …
DeepETC: A deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes
An electron transport chain is a series of protein complexes embedded in the transport
protein, which is an important process to transfer electrons and other macromolecules …
protein, which is an important process to transfer electrons and other macromolecules …
Prediction of FMN binding sites in electron transport chains based on 2-D CNN and PSSM profiles
Flavin mono-nucleotides (FMNs) are cofactors that hold responsibility for carrying and
transferring electrons in the electron transport chain stage of cellular respiration. Without …
transferring electrons in the electron transport chain stage of cellular respiration. Without …
ET-GRU: using multi-layer gated recurrent units to identify electron transport proteins
Background Electron transport chain is a series of protein complexes embedded in the
process of cellular respiration, which is an important process to transfer electrons and other …
process of cellular respiration, which is an important process to transfer electrons and other …
iMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou's 5-step rule
Motor proteins are the driving force behind muscle contraction and are responsible for the
active transportation of most proteins and vesicles in the cytoplasm. There are three …
active transportation of most proteins and vesicles in the cytoplasm. There are three …