人工智能+蛋白质组学:药物研发的生物学底层变革
整体看来,药物研发的体系,是构建于生物学原理与规律的基础之上。生物学的进步,可以在深度与广度上全面影响药物研发的所有领域。
那么人工智能带来的对蛋白质组学认知的进步,会从哪些方面影响和改变药物研发?
为什么药物会脱靶?为什么药物会有重定向?药物临床试验的结果可以预测吗?药物临床试验为什么会失败?
郭天南教授将从蛋白质组大数据与人工智能的角度,对于以上问题给出分析和解读。
整体看来,药物研发的体系,是构建于生物学原理与规律的基础之上。生物学的进步,可以在深度与广度上全面影响药物研发的所有领域。
那么人工智能带来的对蛋白质组学认知的进步,会从哪些方面影响和改变药物研发?
为什么药物会脱靶?为什么药物会有重定向?药物临床试验的结果可以预测吗?药物临床试验为什么会失败?
郭天南教授将从蛋白质组大数据与人工智能的角度,对于以上问题给出分析和解读。
Tiannan talks about the Guomics COVID-19 research progresses in HUPO connect 2020
In search of clues, Guo and colleagues analyzed hundreds of molecular changes in blood samples collected from 53 healthy people and 46 people with COVID-19, including 21 with severe disease involving respiratory distress and decreased blood-oxygen levels. Their studies turned up more than 470 proteins and metabolites that differed in people with COVID-19 compared to healthy people. Of those, levels of about 300 were associated with disease severity. Further analysis revealed that the majority of proteins and metabolites on the list are associated with the suppression or dysregulation of one of three biological processes. Two processes are related to the immune system, including early immune responses and the function of particular scavenging immune cells called macrophages. The third relates to the function of platelets, which are sticky, disc-shaped cell fragments that play an essential role in blood clotting. Such biological insights might help pave the way for potentially effective new ways to treat COVID-19 down the road. Next, the researchers turned to “machine learning” to explore the possibility that such molecular changes also might be used to predict mild versus severe COVID-19.
NIH Director's Blog:
To ensure that people with coronavirus disease 2019 (COVID-19) get the care they need, it would help if a simple blood test could predict early on which patients are most likely to progress to severe and life-threatening illness—and which are more likely to recover without much need for medical intervention. Now, researchers have provided some of the first evidence that such a test might be possible.
Using proteomics in big data-driven precision medicine for the fight against prostate cancer.
“I got diagnosed with prostate cancer on Friday 13th. As my doctor spoke about Gleason scores, probabilities of survival, incontinence and impotence, why surgery would be good and what kind would make the most sense, his voice literally faded out like every movie or TV show about a guy being told he had cancer…,” said Mike.* “Right after I got the news, I promptly got on my computer and Googled ‘men who had prostate cancer.’ As I learned more about my disease (one of the key learnings is not to Google ‘people who died of prostate cancer’ immediately after being diagnosed with prostate cancer), I was able to wrap my head around the fact that I was incredibly fortunate. Fortunate because my cancer was detected early enough to treat and also because my internist gave me a test he didn’t have to. That test saved my life.” Three months later, after successful treatment, Mike was cancer-free.
Shortly after our last release of coronavirus research findings , Westlake University released another breakthrough in COVID-19 research. Tiannan Guo and co-workers identified characteristic molecular changes in the sera from severe #COVID-19 cases, allowing prediction of severe cases using a machine learning model based on serum protein and metabolite biomarkers.
The Guomics Laboratory of Big Proteomic Data, led by Assistant Professor Tiannan Guo, performed the first proteomic and metabolomic characterization of #COVID-19 sera, and managed to identified a series of characteristic biomarkers indicating the severity of COVID-19 patients.
继周强实验室之后,西湖大学生命科学学院PI(研究员)郭天南带领的蛋白质组大数据实验室,近日在新冠病毒研究方面又有重要发现。
Unique insights from thought leaders and researchers in the field of precision medicine, posing and discussing important questions about current challenges and future direction.
High-throughput proteomics of FFPE tissue samples
在生命体内,各个蛋白质就像机械钟表的齿轮般协同合作、互相调控,从而实现一系列精密且复杂的生命活动。因此,检测生物体中蛋白质的种类和含量,对探究生命活动的奥秘有着不可替代的重要意义。
在科研人员的不断探索下,一个新兴的学科——蛋白质组学(proteomics)在1997年诞生了。
蛋白质组学的诞生和发展,离不开多学科和技术的逐渐交叉融合。这些学科技术包括(但不限于)基因组学、生物化学、分析化学、自动化、基于电磁场的精密质谱仪、信号处理、数理统计和计算机科学。近年来,分子医学、大数据技术和人工智能的发展,进一步加速推动了蛋白质组学的成长,使之在精准医疗领域展示出越来越大的应用潜力。