The rapid development of high-throughput sequencing technologies has generated massive valuable human brain atlases, providing great opportunities for systematically investigating molecular characteristics across various brain regions throughout a series of developmental stages. Analyzing the spatiotemporal characteristics of normal brain development and function is of vital importance to determine the causes of a variety of complicated neurological disorders. Particularly, single-cell sequencing provides new opportunities to parse the complex cellular composition of the human brain. However, analysis of such high-dimensional multi-omics data remains substantially complex and requires more effective and sophisticated computational methods and models. Recent progress in computational biology fields has facilitated integrative analyses with high precision to obtain new insights into the molecular characteristics of many human diseases (e.g., cancers). However, with the emergence of new Neuro-omics data, developing novel systematic approaches to identify new molecular underpinnings of the brain is still a big challenge.
This Research Topic aims to provide an international forum for:
- bringing together the greatest research efforts in normal/diseased brain-specific molecular/network signature identification by integrating multi-omics/multi-level data;
- exploring future-generation interesting and practical biomedical applications in AI, machine learning, big data sciences, knowledge-based system, etc., to provide novel ideas and solutions in mathematical modeling for neurodegeneration, drug resistance, and targeting effect prediction;
- addressing the real-world challenges in the fields of AI-based patient diagnosis or disease progression prediction by utilizing modern machine learning or statistical strategies, and produce a more reliable and promising application environment to develop those technologies.
1) Integration of multi-omics data with deep learning to identify new genes/pathways and their associated molecular mechanisms in human brain in normal and disease states;
2) Developing novel computational biology approaches to reveal the heterogeneous molecular regulatory mechanisms in different brain regions;
3) Developing translational bioinformatics approaches to bridging the gap between circadian alteration and neurodegeneration;
4) Developing 3D systems biology approaches by modeling the dynamics of neurodegeneration to identify new targets, such as AD, PD, HD, etc.
5) Developing novel computational strategies to analyze the spatial transcriptome data for getting a closer look at the laminar topography of the brain region;
6) Combing single-cell transcriptomics with clinical data to understand the immune response of brain cell types to in disease states, e.g., virus infection, neurodegeneration, etc.
7) Integrating molecular data with brain imaging data to infer the evolution trajectory of brain diseases.
8) AI-oriented computational approaches analyze EEG signals for deeply understanding brain activities.
9) Establishing on-line knowledge platforms or databases of molecular and network signatures of human brain in normal or disease states.
The rapid development of high-throughput sequencing technologies has generated massive valuable human brain atlases, providing great opportunities for systematically investigating molecular characteristics across various brain regions throughout a series of developmental stages. Analyzing the spatiotemporal characteristics of normal brain development and function is of vital importance to determine the causes of a variety of complicated neurological disorders. Particularly, single-cell sequencing provides new opportunities to parse the complex cellular composition of the human brain. However, analysis of such high-dimensional multi-omics data remains substantially complex and requires more effective and sophisticated computational methods and models. Recent progress in computational biology fields has facilitated integrative analyses with high precision to obtain new insights into the molecular characteristics of many human diseases (e.g., cancers). However, with the emergence of new Neuro-omics data, developing novel systematic approaches to identify new molecular underpinnings of the brain is still a big challenge.
This Research Topic aims to provide an international forum for:
- bringing together the greatest research efforts in normal/diseased brain-specific molecular/network signature identification by integrating multi-omics/multi-level data;
- exploring future-generation interesting and practical biomedical applications in AI, machine learning, big data sciences, knowledge-based system, etc., to provide novel ideas and solutions in mathematical modeling for neurodegeneration, drug resistance, and targeting effect prediction;
- addressing the real-world challenges in the fields of AI-based patient diagnosis or disease progression prediction by utilizing modern machine learning or statistical strategies, and produce a more reliable and promising application environment to develop those technologies.
1) Integration of multi-omics data with deep learning to identify new genes/pathways and their associated molecular mechanisms in human brain in normal and disease states;
2) Developing novel computational biology approaches to reveal the heterogeneous molecular regulatory mechanisms in different brain regions;
3) Developing translational bioinformatics approaches to bridging the gap between circadian alteration and neurodegeneration;
4) Developing 3D systems biology approaches by modeling the dynamics of neurodegeneration to identify new targets, such as AD, PD, HD, etc.
5) Developing novel computational strategies to analyze the spatial transcriptome data for getting a closer look at the laminar topography of the brain region;
6) Combing single-cell transcriptomics with clinical data to understand the immune response of brain cell types to in disease states, e.g., virus infection, neurodegeneration, etc.
7) Integrating molecular data with brain imaging data to infer the evolution trajectory of brain diseases.
8) AI-oriented computational approaches analyze EEG signals for deeply understanding brain activities.
9) Establishing on-line knowledge platforms or databases of molecular and network signatures of human brain in normal or disease states.
