Combination of machine learning-based bulk and single-cell genomics reveals necroptosis-related molecular subtypes and immunological features in autism spectrum disorder

A bibliometric analysis of autism spectrum disorder signaling pathways research in the past decade

Background

This study employs bibliometric methods to comprehensively understand the fundamental structure of research about Autism Spectrum Disorder (ASD) Signaling Pathways by examining key indicators such as nations, institutions, journals, authors, and keywords.

Methodology

We utilized the WoScc database to retrieve literature relevant to ASD Signaling Pathways published between 2013 and 2023. Through visual analysis and tools like CiteSpace and VosViewer, we explored nations, institutions, journals, authors, and keywords, thereby constructing relevant networks.

Results

26 The study encompasses 1,396 articles, revealing a consistent increase in publications. The United States, China, and Germany are leading nations in this literature. Regarding research institutions, the University of California system and Eric Klann have garnered significant attention due to their substantial contributions to the field of ASD Signaling Pathways. Most relevant research is published in the journal "Molecular Autism." Research interests are concentrated across various themes, including "elevating neuronal β-catenin levels," "Tunisian children," "Fmr1 knockout (KO) mice," "de novo mutations," "autistic children," "local translation," "propionic acid-induced mouse models," "neurosystems," "glucose metabolism," and "neuronal migration." Future research may emphasize exploring aspects such as gut microbiota, genes, stress, maternal immune activation, memory, and neurodevelopmental disorders of ASD.

Conclusion

This study, through bibliometric analysis of key indicators such as nations, institutions, journals, authors, and keywords, provides a comprehensive overview of the current state of research on ASD Signaling Pathways. These investigations predominantly focus on molecular mechanisms, animal model studies, population-based research, and the structure and function of neurosystems. Future research directions are also clearly proposed. First, in-depth research on the genes and neurodevelopmental disorders associated with ASD will continue to reveal the genetic basis and provide support for precise treatments. At the same time, attention to the gut microbiota will help explore its association with ASD, which may provide clues for new treatments. In addition, the relationship between stress and ASD will become the focus of research to understand better the emotional and behavioral characteristics of ASD patients in stressful situations. Maternal immune activation will also be further studied to explore how environmental factors influence the risk and development of ASD. Finally, a deeper understanding of the cognitive functions of patients with ASD, especially memory and learning, will help develop individualized treatment strategies to improve patients' quality of life. These directions will work together and are expected to provide a more comprehensive understanding of Signaling Pathways research in ASD and provide new ideas and opportunities for future intervention and treatment.

Optimised stacked machine learning algorithms for genomics and genetics disorder detection in the healthcare industry

With recent advances in precision medicine and healthcare computing, there is an enormous demand for developing machine learning algorithms in genomics to enhance the rapid analysis of disease disorders. Technological advancement in genomics and imaging provides clinicians with enormous amounts of data, but prediction is still mostly subjective, resulting in problematic medical treatment. Machine learning is being employed in several domains of the healthcare sector, encompassing clinical research, early disease identification, and medicinal innovation with a historical perspective. The main objective of this study is to detect patients who, based on several medical standards, are more susceptible to having a genetic disorder. A genetic disease prediction algorithm was employed, leveraging the patient's health history to evaluate the probability of diagnosing a genetic disorder. We developed a computationally efficient machine learning approach to predict the overall lifespan of patients with a genomics disorder and to classify and predict patients with a genetic disease. The SVM, RF, and ETC are stacked using two-layer meta-estimators to develop the proposed model. The first layer comprises all the baseline models employed to predict the outcomes based on the dataset. The second layer comprises a component known as a meta-classifier. Results from the experiment indicate that the model achieved an accuracy of 90.45% and a recall score of 90.19%. The area under the curve (AUC) for mitochondrial diseases is 98.1%; for multifactorial diseases, it is 97.5%; and for single-gene inheritance, it is 98.8%. The proposed approach presents a novel method for predicting patient prognosis in a manner that is unbiased, accurate, and comprehensive. The proposed approach outperforms human professionals using the current clinical standard for genetic disease classification in terms of identification accuracy. The implementation of stacked will significantly improve the field of biomedical research by improving the anticipation of genetic diseases.