利用OMICS組學資料&AI進行卵巢老化藥物研發
利用OMICS組學資料&AI進行卵巢老化藥物研發
主旨
OMICS組學資料資源最新進展包括結合創新的計算工具，為卵巢老化的分子複雜性提供了更深入的見解&加速藥物發現和開發。
從組織層級和單細胞角度擴展與卵巢老化相關的多組學數據，涵蓋基因組、轉錄組、蛋白質組、代謝組和微生物組。
利用這些新興組學資料集的分析可識別新的藥物標靶並指導減緩和逆轉卵巢老化的治療策略。
OMICS組學方法
結合基因組學、轉錄組學、表觀基因組學、DNA甲基化、RNA修飾、組蛋白修飾、蛋白質體學、代謝組學、脂質組學、微生物組、單細胞、全基因組關聯研究（GWAS）、全外顯子組定序、表觀組關聯研究（PheWAS）、孟德爾隨機化（MR）、表觀遺傳組定序、表觀組關聯研究（PheWAS）、孟德爾隨機化（MR）、表觀遺傳組定序、表觀遺傳組關聯研究（PheWAS）、孟德爾隨機化（MR）、表觀遺傳組定序、表觀遺傳學研究、研究卵巢老化的關鍵機制
OMICS多組學研究揭示了導致卵巢老化的關鍵機制，包括 DNA 損傷和修復缺陷、發炎和免疫反應、粒線體功能障礙和細胞死亡。
結果
透過OMICS多組學數據&AI深度學習研判，研究人員可以識別各個生物學層面的關鍵調控因素和機制，從而發現潛在的藥物標靶。
值得注意的例子包括 BRCA2 和 TERT 等遺傳標靶、Tet 和 FTO 等表觀遺傳標靶、sirtuins 和 CD38+ 等代謝標靶、BIN2 和 PDGF-BB 等蛋白質標靶以及 FOXP1 等轉錄因子。
結論
整合尖端OMICS組學技術，尤其是單細胞技術和空間轉錄組學的出現，為指導治療決策提供了可能機轉見解，並對減輕或逆轉卵巢老化的藥物研發提供更有效率研發進程。單細胞多組學數據與人工智慧模型的結合有可能更準確地預測候選藥物標靶。
融合OMICS組學技術為個人化醫療和精準治療提供了一個有希望的新途徑，為卵巢老化的客製化介入新的方向。
Harnessing omics data for drug discovery and development in ovarian aging
Recent advances in omics data resources, combined with innovative computational tools, are offering deeper insight into the molecular complexities of ovarian aging, paving the way for new opportunities in drug discovery and development.
expanding multi-omics data, spanning genome, transcriptome, proteome, metabolome, and microbiome, related to ovarian aging, from both tissue-level and single-cell perspectives. We will specially explore how the analysis of these emerging omics datasets can be leveraged to identify novel drug targets and guide therapeutic strategies for slowing and reversing ovarian aging.
SEARCH METHODS
genomics, transcriptomics, epigenomics, DNA methylation, RNA modification, histone modification, proteomics, metabolomics, lipidomics, microbiome, single-cell, genome-wide association studies (GWAS), whole-exome sequencing, phenome-wide association studies (PheWAS), Mendelian randomization (MR), epigenetic target, drug target, machine learning, artificial intelligence (AI), deep learning, and multi-omics.
OUTCOMES
Multi-omics studies have uncovered key mechanisms driving ovarian aging, including DNA damage and repair deficiencies, inflammatory and immune responses, mitochondrial dysfunction, and cell death.
By integrating multi-omics data, researchers can identify critical regulatory factors and mechanisms across various biological levels, leading to the discovery of potential drug targets.
Notable examples include genetic targets such as BRCA2 and TERT, epigenetic targets like Tet and FTO, metabolic targets such as sirtuins and CD38+, protein targets like BIN2 and PDGF-BB, and transcription factors such as FOXP1.
The advent of cutting-edge omics technologies, especially single-cell technologies and spatial transcriptomics, has provided valuable insights for guiding treatment decisions and has become a powerful tool in drug discovery aimed at mitigating or reversing ovarian aging.
As technology advances, the integration of single-cell multi-omics data with AI models holds the potential to more accurately predict candidate drug targets.
This convergence offers promising new avenues for personalized medicine and precision therapies, paving the way for tailored interventions in ovarian aging.