Application of learning techniques to splicing site recognition

Most genes of eukaryotic genomes are disrupted by introns. The application of a learning technique which uses both statistic and syntactic analysis lead to the establishment of logical rules enabling the recognition of intron/exon junctions between uncoding and coding sequences. The rules were tested on rat actin gene sequences containing some or all of the introns and 50 exon nucleotides on either side of the intron. The results show good recognition of the excision site. This recognition is more ambiguous when the sequence is short; for the acceptor sequence it presents a good selection. The learning achieved with both the donor and acceptor sequence does not lead to recognition. This result indicates that it is not the relationship between donor and acceptor sites in the same intron which determines sequence selection or the splicing mechanism.     

Search for promoter sites of prokaryotic DNA using learning techniques

Using learning techniques previously described in this journal, we have built an expert system able to point to the start DNA point of a sequence and therefore to recognize a promoter. However, to build this system, we have focused on the TATA box and its environment. We have used this expert system to look for new promoters and also to construct new promoters. The results obtained are discussed.     

Computer search of calcium binding sites in a gene data bank: use of learning techniques to build an expert system

Using a learning set of 28 sequences able to bind calcium (each sequence is 12 residues long), we have built two filters by learning on this set. The first filter uses a pattern-matching technique and the second one takes into account the environment of amino-acids. These two filters have been used to find new calcium-binding proteins in a data bank. The results are discussed.     

基于深度学习的作物基因组学和遗传改良

随着世界人口的不断增长、食物需求量的不断增加,以及气候的不断变化,如何提高农作物产量已成为人类面临的一个巨大挑战。传统设计育种耗时长、效率低,已经不能满足新时代的育种需求。随着基因型和表型数据成本的不断降低,以及各种组学数据的爆炸式增长,人工智能技术作为能够在大数据中高效率挖掘信息的工具,在生物学领域受到了广泛关注。人工智能指导的设计育种将大大加快育种的效率,给育种带来革命性的变化。介绍了人工智能特别是深度学习在作物基因组学和遗传改良中的应用,并进行了总结与展望,以期为智能设计育种提供新的思路。     

Localization of the initiation of translation in messenger RNAs of prokaryotes by learning techniques

Learning processes are applied to the recognition of protein coding regions in prokaryotes. Non-contradictory, statistical and logical rules are deduced from a set of known examples of coding sequences. These rules enable to build characteristic patterns on the m-RNA upstream of the initiating codon. These rules are applied with success to recognize more than 180 coding sequences and to detect and/or eliminate hypothetical reading frames or unknown genes.     

机器学习在蛋白质功能预测领域的研究进展

蛋白质是有机生命体内不可或缺的化合物,在生命活动中发挥着多种重要作用,了解蛋白质的功能有助于医学和药物研发等领域的研究。此外,酶在绿色合成中的应用一直备受人们关注,但是由于酶的种类和功能多种多样,获取特定功能酶的成本高昂,限制了其进一步的应用。目前,蛋白质的具体功能主要通过实验表征确定,该方法实验工作繁琐且耗时耗力,同时,随着生物信息学和测序技术的高速发展,已测序得到的蛋白质序列数量远大于功能获得注释的序列数量,高效预测蛋白质功能变得至关重要。随着计算机技术的蓬勃发展,由数据驱动的机器学习方法已成为应对这些挑战的有效解决方案。本文对蛋白质功能及其注释方法以及机器学习的发展历程和操作流程进行了概述,聚焦于机器学习在酶功能预测领域的应用,对未来人工智能辅助蛋白质功能高效研究的发展方向提出了展望。     

组学时代下机器学习方法在临床决策支持中的应用

随着组学技术的不断发展,对于不同层次和类型的生物数据的获取方法日益成熟。在疾病诊治过程中会产生大量数据,通过机器学习等人工智能方法解析复杂、多维、多尺度的疾病大数据,构建临床决策支持工具,辅助医生寻找快速且有效的疾病诊疗方案是非常必要的。在此过程中,机器学习等人工智能方法的选择显得尤为重要。基于此,本文首先从类型和算法角度对临床决策支持领域中常用的机器学习等方法进行简要综述,分别介绍了支持向量机、逻辑回归、聚类算法、Bagging、随机森林和深度学习,对机器学习等方法在临床决策支持中的应用做了相应总结和分类,并对它们的优势和不足分别进行讨论和阐述,为临床决策支持中机器学习等人工智能方法的选择提供有效参考。     

A user-friendly biological workstation

Learning methods developed by artificial intelligence research teams are very efficient for biological sequences analysis but they need running on large computers accessed by terminals. These computers are interfaced with standard displays involving long and unpleasant alphanumerical data handling. The "biological work station" is a personal computer with a color graphic screen providing a user-friendly interface for the artificial intelligence learning programs running on large computers. It provides to biologist a graphical convenient tool for sequence analysis built with efficient man-machine communication methods such as multiwindows, icons and mouse selection. It allows the biologist to edit and display sequences in an efficient and natural way, showing off directly on color pictures the data and the results of learning programs.     

Maternal Vaccination to Prevent Adverse Pregnancy Outcomes: An Underutilized Molecular Immunological Intervention?

Adverse pregnancy outcomes including maternal mortality, stillbirth, preterm birth, intrauterine growth restriction cause millions of deaths each year. More effective interventions are urgently needed. Maternal immunization could be one such intervention protecting the mother and newborn from infection through its pathogen-specific effects. However, many adverse pregnancy outcomes are not directly linked to the infectious pathogens targeted by existing maternal vaccines but rather are linked to pathological inflammation unfolding during pregnancy. The underlying pathogenesis driving such unfavourable outcomes have only partially been elucidated but appear to relate to altered immune regulation by innate as well as adaptive immune responses, ultimately leading to aberrant maternal immune activation. Maternal immunization, like all immunization, impacts the immune system beyond pathogen-specific immunity. This raises the possibility that maternal vaccination could potentially be utilised as a pathogen-agnostic immune modulatory intervention to redirect abnormal immune trajectories towards a more favourable phenotype providing pregnancy protection. In this review we describe the epidemiological evidence surrounding this hypothesis, along with the mechanistic plausibility and present a possible path forward to accelerate addressing the urgent need of adverse pregnancy outcomes.     

Animal learning and intelligence

The ability to learn is common to most animal species: the need to exploit past experience being obviously extremely important for survival, many animals have evolved ways of coping with it. Although the complexity of learning needed for optimal survival may be different in different species, the basic mechanisms appear to be fairly constant even in phylogenetically distant ones. This homogeneity across species in learning mechanisms is in some ways surprising in view of the large phylogenetic differences and of the considerable variability not only in the general plan of their bodily structures, but also, more specifically, in their neural organization and in their behavioral adaptations. One possible explanation is that animals have acquired learning very precociously, and that the original and basic mechanisms have proved so efficient and faultproof as to be preserved from then on without any significant modification. Most researchers of the subject seem to accept the equation «intelligence=learning capability», operationally very useful because it leads to a variety of formal tests. Some researchers, stressing that behavior is subject to the same evolutionary principles as any other character of the organism and acknowledging some problems in the accepted laws of learning, have tried to find a satisfactory answer to the question of animal intelligence by attempting a synthesis between the concepts of animal learning psychology and those of ethology. To some extent, dissatisfaction with established learning theories originated within the theories themselves: the study of phenomena such as autoshaping, selective attention, preferential learning of some responses amongst the many possible, conditioned learning of taste aversions, etc. Further difficulties for conditioning theories arose from the discovery of ethological phenomena. Other researchers have attempted to check the hypothesis that animals possess cognition. A number of complex experimental situtations have been devised to this purpose, but the results still are far from conclusive.     

基于精神影像和人工智能的抑郁症客观生物学标志物研究进展

抑郁症是当今社会上造成首要危害且病因和病理机制最为复杂的精神疾病之一,寻找抑郁症的客观生物学标志物一直是精神医学研究和临床实践的重点和难点,而结合人工智能技术的磁共振影像(magnetic resonance imaging,MRI)技术被认为是目前抑郁症等精神疾病中最有可能率先取得突破进展的客观生物学标志物.然而,当前基于精神影像学的潜在抑郁症客观生物学标志物还未得到一致结论 .本文从精神影像学和以机器学习(machine learning,ML)与深度学习(deep learning, DL)等为代表的人工智能技术相结合的角度,首次从疾病诊断、预防和治疗等三大临床实践环节对抑郁症辅助诊疗的相关研究进行归纳分析,我们发现:a.具有诊断价值的脑区主要集中在楔前叶、扣带回、顶下缘角回、脑岛、丘脑以及海马等;b.具有预防价值的脑区主要集中在楔前叶、中央后回、背外侧前额叶、眶额叶、颞中回等;c.具有预测治疗反应价值的脑区主要集中在楔前叶、扣带回、顶下缘角回、额中回、枕中回、枕下回、舌回等.未来的研究可以通过多中心协作和数据变换提高样本量,同时将多元化的非影像学数据应用于数据挖掘,这将有利于提高人工智能模型的辅助分类能力,为探寻抑郁症的精神影像学客观生物学标志物及其临床应用提供科学证据和参考依据.