From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care

Biomedicines, Год журнала: 2025, Номер 13(1), С. 167 - 167

Опубликована: Янв. 12, 2025

Background/Objectives: The dual forces of structured inquiry and serendipitous discovery have long shaped neuropsychiatric research, with groundbreaking treatments such as lithium ketamine resulting from unexpected discoveries. However, relying on chance is becoming increasingly insufficient to address the rising prevalence mental health disorders like depression schizophrenia, which necessitate precise, innovative approaches. Emerging technologies artificial intelligence, induced pluripotent stem cells, multi-omics potential transform this field by allowing for predictive, patient-specific interventions. Despite these advancements, traditional methodologies animal models single-variable analyses continue be used, frequently failing capture complexities human conditions. Summary: This review critically evaluates transition serendipity precision-based in research. It focuses key innovations dynamic systems modeling network-based approaches that use genetic, molecular, environmental data identify new therapeutic targets. Furthermore, it emphasizes importance interdisciplinary collaboration human-specific overcoming limitations Conclusions: We highlight precision psychiatry’s transformative revolutionizing care. paradigm shift, combines cutting-edge systematic frameworks, promises increased diagnostic accuracy, reproducibility, efficiency, paving way tailored better patient outcomes

Язык: Английский

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Mapping molecular landscapes in triple-negative breast cancer: insights from spatial transcriptomics

Naunyn-Schmiedeberg s Archives of Pharmacology, Год журнала: 2025, Номер unknown

Опубликована: Март 22, 2025

Язык: Английский

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Machine learning predictions of tumor progression: How reliable are we?

Computers in Biology and Medicine, Год журнала: 2025, Номер 191, С. 110156 - 110156

Опубликована: Апрель 16, 2025

Язык: Английский

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OCR: OmniNet-Fusion: A Hybrid Attention-Based CNN-RNN Model for Multi-Omics Integration in Precision Cancer Drug Response Prediction

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 16, 2025

The increasing complexity of cancer treatment necessitates advanced computational models for accurate drug response prediction. OmniNet-Fusion (OCR) is a hybrid deep learning model designed to integrate multi-omics data—genomics, transcriptomics, proteomics, and metabolomics—enhancing precision medicine. leverages Convolutional Neural Network (CNN) analyze spatial omics data Recurrent (RNN) process sequential data, with an attention mechanism highlighting crucial features across layers. To optimize predictive accuracy, feature selection techniques such as Lasso regression mutual information filtering are utilized, while Principal Component Analysis (PCA) reduces dimensionality, ensuring efficiency. undergoes evaluation using key performance metrics, including precision, recall, F1-score, AUC-ROC, demonstrating superior over existing methods. By integrating fusion learning, OCR enhances biological interpretability facilitates personalized treatment. This approach not only improves prediction but also provides deeper insights into mechanisms, supporting oncology advancing AI-driven therapy.

Язык: Английский

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Advancing chronic myeloid leukemia research with next-generation sequencing: potential benefits, limitations, and future clinical integration

Human Genetics, Год журнала: 2025, Номер unknown

Опубликована: Апрель 21, 2025

Язык: Английский

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