In recent years, research has been drastically altered by the advancement and integration of technologies such as artificial intelligence (AI). While AI is a computer science tool, it has been increasingly utilized for various applications across many scientific disciplines, ranging from diagnostics to genomics. This list will give you a glimpse into some of these applications.
Artificial Intelligence Detects New Family of Genes in Gut Bacteria
In a study published inPNAS，，，，researchers from UT Southwestern reported on the use of an AI program called AlphaFold to assist in the discovery of a new family of sensing genes in enteric bacteria.
In previous research co-led by金·奥特（Kim Orth），博士，，，，professor of Molecular Biology and Biochemistry and Lisa Kinch, PhD, a bioinformatics specialist in the Department of Molecular Biology, the structures of two proteins, VtrA and VtrC complex, were characterized. These proteins work together in a bacterial species often the cause of food poisoning from contaminated shellfish,Vibrio parahaemolyticus。In order to find out whether a homolog for VtrC existed, Orth and team used AlphaFold, an AI program that an accurately predict the structure of some proteins based on the genetic sequence that codes for them, and found homologs for VtrC in several other enteric bacteria species responsible for human disease.
“我们确定了这些蛋白质中的相似之处，与通常的方式相反。丽莎没有使用序列，而是寻找结构中的匹配项，” - Kim Orth，PhD。
野火烟雾正在逆转空气质量的增长并创造极端的污染水平
Researchers from Stanford University used statistical modeling and AI techniques to determine exposure levels to fine particulate matter from wildfire smoke.
该研究发表在环境科学技术，，，，focused on a type of particle pollution known as PM2.5 and utilized a trained machine learning model to accurately predict PM2.5 concentrations from wildfire smoke in areas that don’t have monitors. The researchers, led by玛丽莎·柴尔德斯（Marissa Childs），博士，现在是哈佛大学环境中心的博士后学者发现，每年至少每年至少一份200微克米的人数增加了11,000倍。
“Smoke pollution is particularly challenging to measure, both because it’s difficult to know which portion of particulate matter is from smoke and because we only have pollution monitors at a limited number of locations in the U.S.,” – Marissa Childs, PhD.
创建了第一个基于AI的基于AI的方法
根据发表在Cell Reports Methods，，，，AI can support DNA analysis of ancient human remains, in which the DNA is significantly degraded.
Radiocarbon dating “revolutionized” the field of archaeological science and is the traditional “gold standard” method for dating in archelogy，，，，but can be unstable and is affected by the quality of the material being examined. Researchers from Lund University have developed a novel dating method for ancient genome data called Temporal Population Structure (TPS) that uses supervised machine learning. TPS is trained on the temporal components (unique allelic combinations that characterize the historical period when individuals lived) of thousands of ancient – and modern – genomes and “learns” how to predict their age.
“由于它们与时间的关联，可以利用时间成分将基因组数据转换为时间，并仅从基因型数据中预测样本的年龄”， - -Eran Elhaik博士，隆德大学等人分子细胞生物学副教授。
AI Traffic Light System Aims To End Congestion
在呈现的论文中国际自主代理和多基因系统联合会议论文集，阿斯顿大学的研究人员描述了一个AI系统，该系统读取现场摄影镜头并更改交通信号灯以补偿，减少拥塞。
该系统使用深度强化学习，其中一个程序何时表现不佳，尝试不同的事情，或者在做得好时，它会继续改善。可以设置该程序以查看任何交通交界处（真实或模拟），并将自动学习。
“我们之所以基于学习行为的原因是，它可以理解以前从未明确体验过的情况。我们已经用物理障碍物对此进行了测试，该障碍会导致交通拥堵，而不是交通量阶段，并且该系统仍然做得很好。只要有因果关系链接，计算机最终就会弄清楚该链接是什么。这是一个强大的系统。”-乔治·沃吉亚斯（George Vogiatzis）博士，，，，senior lecturer in Computer Science at Aston University.
基于AI的筛查方法可以改善新药的发现
根据发表在生物信息学的简报，，，，
the process of drug development could be sped up using an AI system called AttentionSiteDTI.
Researchers from University of Central Florida, led by Mehdi
Yazdani-Jahromi，UCF工程与计算机学院的博士生
科学使用了一种使用药物和靶向蛋白质相互作用的方法
自然语言处理技术，在识别有希望的候选药物方面达到了97％的准确性。这项研究可以帮助药物设计师确定
critical protein binding sites along with their functional properties, which is
确定药物是否有效的关键。Actentionsitedti是第一个
使用蛋白质结合位点的语言可以解释的模型。
“This method also allows the researchers to identify the best binding site of a virus’s protein to focus on in drug design.” – Mehdi Yazdani-Jahromi.
