【不提供】
[Not Available].
发表日期：2023 Sep
作者：
Marco Filetti, Manuela Petti, Lorenzo Farina
来源：
MOLECULAR & CELLULAR PROTEOMICS
摘要：
推进对复杂疾病的认识需要在医学领域的人工智能（AI）之外进行跨学科对话。在人类基因组计划完成20年之后，基因测序已经为基因突变相关疾病的靶向治疗提供了便利。然而，这一成就揭示了我们对生命和疾病机制的巨大认知缺口。由于复杂疾病，包括癌症、糖尿病和自身免疫性疾病的多因素性质，仍然难以捉摸。因此，更加整体性的方法将AI与多样化的科学学科结合起来变得迫切。本文强调了遗传学、分子生物学、计算生物学和临床研究之间促进合作的紧迫性，以揭示这些疾病底层复杂性。通过将各领域的专业知识和数据进行协同，我们可以在揭开复杂疾病错综复杂的网络方面取得重大进展，从而实现改善诊断、治疗和最终患者预后的目标。
Advancing our understanding of complex diseases necessitates an interdisciplinary dialogue beyond artificial intelligence (AI) in the field of medicine. Two decades after the completion of the Human Genome Project, genetic sequencing has facilitated targeted therapies for gene mutation-related ailments. However, this achievement has unveiled the immense gaps in our comprehension of life and disease mechanisms. Complex diseases, including cancer, diabetes, and autoimmune disorders, remain elusive due to their multifactorial nature. Consequently, a more holistic approach integrating AI with diverse scientific disciplines becomes imperative. This paper emphasizes the urgency of fostering collaboration among genetics, molecular biology, computational biology, and clinical research to unravel the intricate complexities underlying these diseases. By synergizing expertise and data from various domains, we can make significant strides towards unraveling the intricate web of complex diseases, leading to improved diagnosis, treatment, and ultimately, patient outcomes.