Artificial Intelligence Applications in Biomedicine

Vlamos, Panayiotis; Lefkimmiatis, Konstantinos; Cocianu, Cătălina; State, Luminiţa; Luo, Zhiyuan

doi:10.1155/2013/219137

Cited by 3 publications

(1 citation statement)

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“…Vlachakis et al proposed computational methods to simulate catalytic mechanisms, complete drug design, and model protein three-dimensional structures [13][14][15][16][17]. Vlamos et al developed several intelligent disease diagnosis applications and hybrid models for vulnerability detection [18][19][20][21][22][23][24][25]. Some researchers also introduced computational methods into the medical field and developed several automated diagnostic models [26,27].…”

Section: Introductionmentioning

confidence: 99%

Predicting Protein Interactions Using a Deep Learning Method‐Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine

Wang

You

et al. 2018

Complexity

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Protein-protein interactions (PPIs), as an important molecular process within cells, are of pivotal importance in the biochemical function of cells. Although high-throughput experimental techniques have matured, enabling researchers to detect large amounts of PPIs, it has unavoidable disadvantages, such as having a high cost and being time consuming. Recent studies have demonstrated that PPIs can be efficiently detected by computational methods. Therefore, in this study, we propose a novel computational method to predict PPIs using only protein sequence information. This method was developed based on a deep learning algorithm-stacked sparse autoencoder (SSAE) combined with a Legendre moment (LM) feature extraction technique. Finally, a probabilistic classification vector machine (PCVM) classifier is used to implement PPI prediction. The proposed method was performed on human, unbalanced-human, H. pylori, and S. cerevisiae datasets with 5-fold cross-validation and yielded very high predictive accuracies of 98.58%, 97.71%, 93.76%, and 96.55%, respectively. To further evaluate the performance of our method, we compare it with the support vector machine- (SVM-) based method. The experimental results indicate that the PCVM-based method is obviously preferable to the SVM-based method. Our results have proven that the proposed method is practical, effective, and robust.

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Section: Introductionmentioning

confidence: 99%

Predicting Protein Interactions Using a Deep Learning Method‐Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine

Wang

You

et al. 2018

Complexity

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AI Gender Biases in Women’s Healthcare: Perspectives from the United Kingdom and the European Legal Space

Lau

2023

YSEC Yearbook of Socio-Economic Constitutions

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Accelerating UN Sustainable Development Goals with AI-Driven Technologies: A Systematic Literature Review of Women’s Healthcare

2023

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In this paper, we critically examine if the contributions of artificial intelligence (AI) in healthcare adequately represent the realm of women’s healthcare. This would be relevant for achieving and accelerating the gender equality and health sustainability goals (SDGs) defined by the United Nations. Following a systematic literature review (SLR), we examine if AI applications in health and biomedicine adequately represent women’s health in the larger scheme of healthcare provision. Our findings are divided into clusters based on thematic markers for women’s health that are commensurate with the hypotheses that AI-driven technologies in women’s health still remain underrepresented, but that emphasis on its future deployment can increase efficiency in informed health choices and be particularly accessible to women in small or underrepresented communities. Contemporaneously, these findings can assist and influence the shape of governmental policies, accessibility, and the regulatory environment in achieving the SDGs. On a larger scale, in the near future, we will extend the extant literature on applications of AI-driven technologies in health SDGs and set the agenda for future research.

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Artificial Intelligence Applications in Biomedicine

Cited by 3 publications

References 0 publications

Predicting Protein Interactions Using a Deep Learning Method‐Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine

Predicting Protein Interactions Using a Deep Learning Method‐Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine

AI Gender Biases in Women’s Healthcare: Perspectives from the United Kingdom and the European Legal Space

Accelerating UN Sustainable Development Goals with AI-Driven Technologies: A Systematic Literature Review of Women’s Healthcare

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