Bioinformatics and Computational Medicine
Bioinformatics and Computational Medicine (BCM) is an international peer-reviewed, open access journal.
CHINESE POETIC REALM EMPOWERING ASD PRECURSOR DEFECT INTERVENTION: WEARABLE-FREE MULTIMODAL IDENTIFICATION AND PERSONALIZED ADAPTATION IN DIGITAL TWIN METAVERSE
Traditional identification of ASD precursor defects is accompanied by a sense of oppression due to the participation of medical staff, which easily induces fear in children, leads to distorted behavioral expression, and impairs the accuracy of identification. Adhering to the core logic of "no medical staff involvement + digital twin metaverse immersion + oppression-free experience + multimodal dynamic identification + personalized adaptation for each individual", this study takes the Chinese poetic realm as the spiritual core and constructs a metaverse role-playing scenario based on wearable-free somatosensory carriers and digital twin technology, abandoning medical staff participation throughout the process to eliminate children's fear and sense of oppression. Children engage in immersive role-playing in the metaverse space to achieve the dual goals of "intervening while learning and educating through entertainment". Through multi-dimensional data collection, four-dimensional information including motor behavior, voice, emotional response and knowledge acquisition of children during their experience in the poetic realm and role interaction is captured in real time, so as to accurately identify ASD precursor defects, real emotional states (fear, tension, relaxation, etc.) and learning effects. Based on the identification results, a "personalized adaptation for each individual" scheme is generated to dynamically adjust the artistic conception of scenarios, the difficulty of roles, interaction forms and feedback mechanisms. This study constructs an integrated system of "metaverse role-playing as identification, emotion as adaptation basis, poetic realm as intervention carrier, and experience as learning process", which solves the problems of distorted expression caused by oppression and lack of interest in traditional interventions, and provides a new intervention path with humanistic care, identification accuracy, learning value and personalized adaptation for ASD precursor defects.
Personalized adaptation for each individual
Copyright (c) 2026 XiaoQing Wang https://creativecommons.org/licenses/by/4.0
COMPUTATIONAL MEDICINE - COPING WITH THE CHALLENGES OF BIG DATA AND CLINICAL TRANSFORMATION
As an emerging interdisciplinary discipline, computational medicine aims to use computer science and information technology to cope with the challenges of medical big data and promote its clinical transformation. Computational medicine combines knowledge from multiple fields such as medicine, biology, computer science and data science to analyze and interpret large-scale medical data. Through advanced data analysis and machine learning techniques, computational medicine aims to discover biomarkers of diseases, predict disease risks, optimize treatment plans, etc. The amount of medical data is huge, including electronic health records, medical images, genomic data, etc. Computational medicine processes this data through efficient algorithms. Issues such as data heterogeneity, data quality, data security and privacy are key challenges that computational medicine needs to solve. Computational medicine supports clinical decision-making by mining big data, including personalized treatment, early diagnosis of diseases and risk prediction. Algorithm models based on big data can help doctors better understand disease mechanisms and develop precise treatment plans. Computational medicine tools such as medical image analysis and genomic data analysis can be directly applied to clinical practice to improve the quality and efficiency of medical services. Feedback from clinical trials and practical applications can further optimize computational medicine models and promote their clinical transformation. While responding to the challenges of medical big data, computational medicine is gradually transforming from research results to clinical applications, and is expected to provide more accurate and efficient support for medical services.
Copyright (c) 2024 YunRu Lin https://creativecommons.org/licenses/by/4.0
A NOVEL DEEP LEARNING APPROACH FOR BRAIN TUMOR SEGMENTATION AND CLASSIFICATION USING MRI DATA
This study introduces a multi-path Convolutional Neural Network (CNN) for MRI-based brain tumor segmentation and classification, marking a significant stride in medical imaging. The model adeptly segments meningioma and pituitary tumors, as evidenced by robust performance metrics. Challenges persist in glioma segmentation, with a need for enhanced precision. The model's ability to discern glioma regions, despite these obstacles, is promising. The research underscores the necessity for meticulous dataset curation and anatomical knowledge integration to refine specificity and minimize false positives. The findings suggest potential clinical applications in aiding preliminary diagnoses and call for further model refinement. Advanced techniques like 3D convolutional networks and positional encoding are discussed as future enhancements. Overall, the paper contributes to medical imaging advancements, emphasizing the role of innovative deep learning approaches in improving clinical decision-making.
Copyright (c) 2025 Zhao Lu https://creativecommons.org/licenses/by/4.0
SGDSYNERGY: LEVERAGING MULTIMODAL DATA FOR ACCURATE DRUG COMBINATION PREDICTION
The treatment of cancer and other complex diseases require sophisticated therapeutic strategies due to their intricate pathophysiological mechanisms and resistance profiles. While combination therapy has emerged as a cornerstone of precision medicine, many existing computational approaches primarily focus on integrating only simple modalities such as molecular graphs and SMILES sequences, which limits their ability to model the interaction dynamics in an interpretable and biologically meaningful manner. To address these challenges, we propose SGDSynergy that introduces several key innovations in multi-modal fusion. Specifically, in the drug feature extraction stage, we adopt a CLIP-style contrastive learning mechanism to align molecular representations derived from SMILES sequences and molecular graphs, enabling the generation of semantically enriched and modality-aware drug embeddings. For cell line representation, SGDSynergy incorporates external features derived from DDIs data and heterogeneous graph structures, which significantly enhance the biological context of cancer cell line embeddings. Moreover, in the synergy evaluation module, we introduce a Bayesian attention mechanism integrated with MC-dropout to probabilistically weigh synergistic features, thereby reducing overfitting and explicitly quantifying prediction uncertainty. These integrated innovations allow SGDSynergy to achieve superior accuracy in predicting drug combination effects, especially in capturing non-linear dynamics and rare synergy patterns, offering a biologically informed and clinically actionable framework for designing personalized combination therapies and overcoming treatment resistance.
Copyright (c) 2025 ZiWei Ning https://creativecommons.org/licenses/by/4.0