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Efficient prediction of anticancer peptides through deep learning
Get PDFBackground
Cancer remains one of the leading causes of mortality globally, with conventional chemotherapy often resulting in severe side effects and limited effectiveness. Recent advancements in bioinformatics and machine learning, particularly deep learning, offer promising new avenues for cancer treatment through the prediction and identification of anticancer peptides.
Objective
This study aimed to develop and evaluate a deep learning model utilizing a two-dimensional convolutional neural network (2D CNN) to enhance the prediction accuracy of anticancer peptides, addressing the complexities and limitations of current prediction methods.
Methods
A diverse dataset of peptide sequences with annotated anticancer activity labels was compiled from various public databases and experimental studies. The sequences were preprocessed and encoded using one-hot encoding and additional physicochemical properties. The 2D CNN model was trained and optimized using this dataset, with performance evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC).
Results
The proposed 2D CNN model achieved superior performance compared to existing methods, with an accuracy of 0.87, precision of 0.85, recall of 0.89, F1-score of 0.87, and an AUC-ROC value of 0.91. These results indicate the model’s effectiveness in accurately predicting anticancer peptides and capturing intricate spatial patterns within peptide sequences.
Conclusion
The findings demonstrate the potential of deep learning, specifically 2D CNNs, in advancing the prediction of anticancer peptides. The proposed model significantly improves prediction accuracy, offering a valuable tool for identifying effective peptide candidates for cancer treatment
Metodología robusta y general para la creación de prototipos virtuales utilizando tecnología de gemelos digitales y optimización de diseño automatizada basada en IA (FUTCAN)
No full textSe llevarán a cabo las siguientes acciones:
1. Revisión de la literatura de algoritmos de aprendizaje automático y aprendizaje por refuerzo empleados en el prototipado virtual de componentes y/o procesos.
2. Selección de los algoritmos más prometedores para ser empleados en el proyecto, considerando una máquina de inyección de termoplásticos como marco de referencia para esa selección de algoritmos.
3. Redacción de informe con los resultados, conclusiones y recomendaciones
Implicações da "tematização da prática", enquanto estratégia formativa, na práxis pedagógica de professoras da Educação Infantil em uma unidade escolar da Rede Municipal de Ensino de Três Lagoas - MS
No full textEsta pesquisa aborda a temática da formação continuada de
professores da Educação Infantil sob o viés metodológico da
pesquisa qualitativa. Os dados coletados foram organizados
em três categorias: dificuldades práticas do início da
docência; formação continuada para o aprimoramento da
prática docente e metodologias formativas e emancipação
crítico-reflexiva docente
Youth Healthy Eating Index (YHEI) and Diet Adequacy in Relation to Country-Specific National Dietary Recommendations in Children and Adolescents in Five Mediterranean Countries from the DELICIOUS Project
No full textBackground/Objectives: The diet quality of younger individuals is decreasing globally, with alarming trends also in the Mediterranean region. The aim of this study was to assess diet quality and adequacy in relation to country-specific dietary recommendations for children and adolescents living in the Mediterranean area. Methods: A cross-sectional survey was conducted of 2011 parents of the target population participating in the DELICIOUS EU-PRIMA project. Dietary data and cross-references with food-based recommendations and the application of the youth healthy eating index (YHEI) was assessed through 24 h recalls and food frequency questionnaires. Results: Adherence to recommendations on plant-based foods was low (less than ∼20%), including fruit and vegetables adequacy in all countries, legume adequacy in all countries except for Italy, and cereal adequacy in all countries except for Portugal. For animal products and dietary fats, the adequacy in relation to the national food-based dietary recommendations was slightly better (∼40% on average) in most countries, although the Eastern countries reported worse rates. Higher scores on the YHEI predicted adequacy in relation to vegetables (except Egypt), fruit (except Lebanon), cereals (except Spain), and legumes (except Spain) in most countries. Younger children (p < 0.005) reporting having 8–10 h adequate sleep duration (p < 0.001), <2 h/day screen time (p < 0.001), and a medium/high physical activity level (p < 0.001) displayed a better diet quality. Moreover, older respondents (p < 0.001) with a medium/high educational level (p = 0.001) and living with a partner (p = 0.003) reported that their children had a better diet quality. Conclusions: Plant-based food groups, including fruit, vegetables, legumes, and even (whole-grain) cereals are underrepresented in the diets of Mediterranean children and adolescents. Moreover, the adequate consumption of other important dietary components, such as milk and dairy products, is rather disregarded, leading to substantially suboptimal diets and poor adequacy in relation to dietary guidelines
StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides
No full textInterleukin-10, a highly effective cytokine recognized for its anti-inflammatory properties, plays a critical role in the immune system. In addition to its well-documented capacity to mitigate inflammation, IL-10 can unexpectedly demonstrate pro-inflammatory characteristics under specific circumstances. The presence of both aspects emphasizes the vital need to identify the IL-10-induced peptide. To mitigate the drawbacks of manual identification, which include its high cost, this study introduces StackIL10, an ensemble learning model based on stacking, to identify IL-10-inducing peptides in a precise and efficient manner. Ten Amino-acid-composition-based Feature Extraction approaches are considered. The StackIL10, stacking ensemble, the model with five optimized Machine Learning Algorithm (specifically LGBM, RF, SVM, Decision Tree, KNN) as the base learners and a Logistic Regression as the meta learner was constructed, and the identification rate reached 91.7%, MCC of 0.833 with 0.9078 Specificity. Experiments were conducted to examine the impact of various enhancement techniques on the correctness of IL-10 Prediction. These experiments included comparisons between single models and various combinations of stacking-based ensemble models. It was demonstrated that the model proposed in this study was more effective than singular models and produced satisfactory results, thereby improving the identification of peptides that induce IL-10
Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data
No full textObjective Epileptic seizures are neurological events that pose significant risks of physical injuries characterized by sudden, abnormal bursts of electrical activity in the brain, often leading to loss of consciousness and uncontrolled movements. Early seizure detection is essential for timely treatments and better patient outcomes. To address this critical issue, there is a need for an advanced artificial intelligence approach for the early detection of epileptic seizure disorder. Methods This study primarily focuses on designing a novel ensemble approach to perform early detection of epileptic seizure disease with high performance. A novel ensemble approach consisting of a fast, independent component analysis random forest (FIR) and prediction probability is proposed, which uses electroencephalography (EEG) data to investigate the efficacy of the proposed approach for early detection of epileptic seizures. The FIR model extracts independent components and class prediction probability features, creating a new feature set. The proposed model combined integrated component analysis (ICA) with predicting probability to enhance seizure recognition accuracy scores. Extensive experimental evaluations demonstrate that FIR assists machine learning models to obtain superior results compared to original features. Results The research gap is addressed using combined features to improve the performance of epileptic seizure detection compared to a single feature set. In particular, the ensemble model FIR with support vector machine (FIR + SVM) outperforms other methods, achieving an accuracy of 98.4% for epileptic seizure detection. Conclusions The proposed FIR has the potential for early diagnosis of epileptic seizures and can significantly help the medical industry with enhanced detection and timely interventions
Federated Learning on Internet of Things: Extensive and Systematic Review
No full textThe proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation. However, FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios. The paper systematically reviewed the available literature using the PRISMA guiding principle. The study aims to provide a detailed overview of the increasing use of FL in IoT networks, including the architecture and challenges. A systematic review approach is used to collect, categorize and analyze FL-IoT-based articles. A search was performed in the IEEE, Elsevier, Arxiv, ACM, and WOS databases and 92 articles were finally examined. Inclusion measures were published in English and with the keywords “FL” and “IoT”. The methodology begins with an overview of recent advances in FL and the IoT, followed by a discussion of how these two technologies can be integrated. To be more specific, we examine and evaluate the capabilities of FL by talking about communication protocols, frameworks and architecture. We then present a comprehensive analysis of the use of FL in a number of key IoT applications, including smart healthcare, smart transportation, smart cities, smart industry, smart finance, and smart agriculture. The key findings from this analysis of FL IoT services and applications are also presented. Finally, we performed a comparative analysis with FL IID (independent and identical data) and non-ID, traditional centralized deep learning (DL) approaches. We concluded that FL has better performance, especially in terms of privacy protection and resource utilization. FL is excellent for preserving privacy because model training takes place on individual devices or edge nodes, eliminating the need for centralized data aggregation, which poses significant privacy risks. To facilitate development in this rapidly evolving field, the insights presented are intended to help practitioners and researchers navigate the complex terrain of FL and IoT
Positive mental health of Latin American university professors: A scientific framework for intervention and improvement
No full textThe post-pandemic stage covid-19 has revealed overloads, ambiguities, and conflicts of teachers in the performance of new roles in hybrid classrooms that demanded an urgent adaptation, this highlighted the need for priority attention to the mental health of teachers, however, there are still insufficient studies that transcend the diagnosis and are committed to establish proposals for improvement. OBJECTIVE: This study aims to establish a proposal for the promotion of positive mental health (PMH). METHODS: The study was deployed from a qualitative approach; using an ethnomethodological design that allowed studying how teachers create meanings and sense in their work context, an appreciative interview was conducted with an affirmative theme that allowed teachers to expose their experiences that were systematized and processed with ATLAS. ti software. The application of the interview was conducted online through a Google form, during the months of February and March 2023. Three hundred university professors who experienced the pandemic in universities in Brazil, Chile, Colombia, Ecuador, Mexico, and Peru participated, based on a convenience sampling. RESULTS: The results of the deductive phase confirmed Lluch's PMH theoretical framework; however, new nuances or variations have been identified, which must be considered in the complex and dynamic nature of each PMH factor. From there, the results of the inductive phase allowed revealing emerging concepts, that is, new categories that would have the function of improving the PMH factors, which is why they have been denominated: dynamizing nuclei. PMH dynamizing nuclei are adjustment to work environment, soft skills, work-family balance, self-motivation, self-efficacy, subjective well-being, proactive strategies, engagement, resilience. CONCLUSIONS: Finally, with the results of both phases, the creation of an integrated model was generated, which was evaluated by six experts in a round of feedback, who highlighted the relevance of the findings and offered recommendations that were considered in the study. The new integrated model has revealed an interesting association, since it not only legitimizes the PMH's dynamizing cores, but also informs on which specific factor of the PMH these cores have the greatest impact, which has a high guiding value for intervention and improvement based on focused strategies
Effect of olive leaf phytochemicals on the anti-acetylcholinesterase, anti-cyclooxygenase-2 and ferric reducing antioxidant capacity
No full textIn this study, the phytochemical profile of fifty olive leaves (OL) extracts from Spain, Italy, Greece, Portugal, and Morocco was characterized and their anti-cholinergic, anti-inflammatory, and antioxidant activities were evaluated. Luteolin-7-O-glucoside, isoharmnentin, and apigenin were involved in the acetylcholinesterase (AChE) inhibitory activity, while oleuropein and hydroxytyrosol showed noteworthy potential. Secoiridoids contributed to the cyclooxygenase-2 inhibitory activity and antioxidant capacity. Compounds such as oleuropein, ligstroside and luteolin-7-O-glucoside, may exert an important role in the ferric reducing antioxidant capacity. It should be also highlighted the role of hydroxytyrosol, hydroxycoumarins, and verbascoside concerning the antioxidant activity. This research provides valuable insights and confirms that specific compounds within OL extracts contribute to distinct anti-cholinergic, anti-inflammatory, and anti-oxidative effects
Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential
No full textGenerative intelligence relies heavily on the integration of vision and language. Much of the research has focused on image captioning, which involves describing images with meaningful sentences. Typically, when generating sentences that describe the visual content, a language model and a vision encoder are commonly employed. Because of the incorporation of object areas, properties, multi-modal connections, attentive techniques, and early fusion approaches like bidirectional encoder representations from transformers (BERT), these components have experienced substantial advancements over the years. This research offers a reference to the body of literature, identifies emerging trends in an area that blends computer vision as well as natural language processing in order to maximize their complementary effects, and identifies the most significant technological improvements in architectures employed for image captioning. It also discusses various problem variants and open challenges. This comparison allows for an objective assessment of different techniques, architectures, and training strategies by identifying the most significant technical innovations, and offers valuable insights into the current landscape of image captioning research