China Net/China Development Portal News In 2007, Turing Award winner Newzealand Sugar proposed by Jim Gray Four categories of paradigms for scientific research have been identified, which are basically widely recognized by the scientific community. The first paradigm is experimental (empirical) science, which mainly describes natural phenomena and summarizes laws through experiments or experiences; the second paradigm is theoretical science, where scientists summarize and form scientific theories through mathematical models; the third paradigm is computational science, which uses computers to Simulate scientific experiments; the fourth paradigm is data science, which uses large amounts of data collected by instruments or generated by simulation calculations for analysis and knowledge extraction. The paradigm change in scientific research reflects the evolution of the depth, breadth, method and efficiency of human exploration of the universe.
The development of life sciences has gone through multiple stages, and the evolution of its research paradigms also has its own unique disciplinary attributes. In the early stages of the development of life sciences, biologists mainly explored the general forms of biological existence and the common laws of evolution by observing the morphology and behavioral patterns of different organisms. The representative of this stage was Darwin, who accumulated a large number of species knowledge through global surveys. The appearance describes the data and puts forward the theory of evolution. Starting from the middle of the 20th century, marked by the revelation of the double helix structure of DNA, anyone with an affectionate Zelanian Escort will not marry you. “A monarch is all made up. It’s nonsense. Do you understand? Zelanian sugar?” Life science research has entered the era of molecular biology. Scientists began to study the basic composition and operating laws of life at a deeper level. At this stage, biologists still mainly summarize rules and knowledge through observation and experiments of biological phenomena. With the further development of life sciences and the rapid emergence of new biotechnologies, scientists can conduct more extensive explorations of life sciences at different levels and at different resolutions, which has also led to explosive growth in data in the field of life sciences. Combining high-throughput, multi-dimensional omics data analysis with experimental science to more precisely describe and analyze biological processes has become the norm in modern life science research.
However, living systems have multi-level complexity, covering different levels from molecules, cells to individuals, as well as the population relationship between individuals and the interaction between the organism and the environment, showing multi-level, high-level Dimensional, highly interconnected, and dynamically regulated. When faced with such complex living systems, the existing experimental scientific research paradigm can often only observe, describe and study a limited number of samples at a specific scale, making it difficult to fully understand the operating mechanism of biological networks; and is highly dependent on human experience and a prioriKnowledge explores specific biological relationships, Zelanian EscortIt is difficult to efficiently extract hidden associations and mechanisms from large-scale, diverse, and high-dimensional data . In the face of complex non-linear relationships and unpredictable characteristics in life phenomena, artificial intelligence (AI) technology has demonstrated powerfulSugar Daddy capabilities , and has shown disruptive application potential in protein structure prediction and gene regulatory network simulation analysis, pushing the first paradigm of life science research dominated by experimental science to a new paradigm of life science research driven by artificial intelligence – Chapter 1 Five paradigms (Figure 1).
This article will focus on typical examples of AI-driven life science research, life science NZ Escorts Systematically discusses three aspects: the connotation and key elements of the new paradigm, the frontiers of life science research empowered by the new paradigm, and the challenges faced by our country.
Typical examples of life science research driven by artificial intelligence
Life is a complex system with multiple levels, multi-scales, dynamic interconnection and mutual influence. When faced with the extreme complexity of life phenomena, multi-scale spans, and dynamic changes in space and time, traditional life science research paradigms can often only start from a local perspective and establish limited biological systems through experimental verification or limited-level omics data analysisZelanian EscortThe relationship between molecules and phenotypes. However, even if a huge cost is spent, it is usually only possible to discover a single linear correlation mechanism in a specific situation, which is significantly different in complexity from the nonlinear properties of life activities, making it difficult to fully understand the operating mechanism of the entire network.
AI technology, especially technologies such as deep learning and pre-trained large models, with its superior pattern recognition and feature extraction capabilities, can surpass human rational reasoning ability in the case of huge parameter stacking, and extract data from data. Better understand patterns in complex biological systems. The continuous development of modern biotechnology has led to a leapfrog growth in data in the field of life sciences. In the past, global life science researchNewzealand SugarIn research, humans have accumulated a large amount of data based on experimental description and verification, Sugar Daddy created the foundation for AI to crack the underlying laws of life sciences]. When there are sufficient and high-quality data and algorithms adapted to life sciences, AI models can be used in “low-dimensional” multi-level massive data Newzealand Sugar data predicts “high-dimensional” information and patterns, achieving a leap from low-dimensional data such as gene sequences and expressions to revealing the patterns of high-dimensional complex biological processes such as cells and organisms, and analyzing complex non-linear relationships, such as The generation rules of biological macromolecular structures, gene expression regulation mechanisms, and even the underlying rules in complex biological systems where multiple factors such as ontogeny and aging intersect. Under this development trend, in recent years, a number of typical examples of AI-driven development of life science research have emerged in the field of life sciences, such as protein structure analysis and gene regulation analysis.
Examples of protein structure analysis
As the executors of key functions in organisms, proteins directly affect important functions such as transport, catalysis, binding and immunity. biological processes. Although sequencing technology can reveal the amino acid sequence contained in a protein, any protein chain with a known amino acid sequence may fold into any of the astronomical number of possible conformations, which makes accurate analysis of Cai Xiu helpless, and she has to catch up quickly. He called the lady honestly, “Miss, my lady asked you to stay in the yard all day and don’t leave the yard.” Protein structure has become a long-term challenge. Using traditional techniques such as nuclear magnetic resonance, X-ray crystallography, cryo-electron microscopy and other methods to analyze protein structures of known sequences, it takes several years to delineate the shape of a single protein, which is expensive, time-consuming and cannot guarantee the successful analysis of its structure. Therefore, capturing the underlying laws of protein folding to achieve accurate prediction of protein structure has always been one of the most important challenges in the field of structural biology.
AlphaFold 2 uses a deep learning algorithm based on the attention mechanism Zelanian sugar to conduct analysis on a large amount of protein sequence and structure data. training, and combined with prior knowledge of physics, chemistry, and biology, a protein structure analysis model including feature extraction, encoding, and decoding modules was constructed. In the 2020 International Protein Structure Prediction Competition (CASP14), AlphaFold NZ Escorts2 has achieved remarkable results, and its protein three-dimensional structure prediction accuracy is even comparable to the results of experimental analysis. This breakthrough has brought a new breakthrough to the field of life sciences. Perspectives and unprecedented opportunities are mainly reflected in three points.
It has had a direct impact on the field of drug discovery. Most drugs trigger changes in protein function by binding to special structural domains of proteins in the body. AlphaFold 2 can Quickly calculate the structures of massive target proteins to design drugs to effectively bind to these proteins.
The rational design of proteins provides new possibilities. Once AI understands the underlying layers of protein folding With a deep understanding of the laws, we can use this knowledge to designZelanian Escort protein sequences that fold into the desired structure. This makes biology Scientists can freely design and modify the structure of proteins or enzymes according to their needs, such as designing higher activity gene editing enzymes, or even protein structures that do not exist in nature. At the same time, it also promotes people’s understanding of the structural projection rules of genetically encoded information at the protein level. To be sure, she asked her mother and Cai Xiu again, and the answers she got were similar to what she thought. Cai Yi had no scheming, so the maid who was dowry decided to choose Cai Xiu and Cai Yi. It happened that Cai Yi’s Transformation capabilities.
AlphaFold 2 completely changes the research paradigm in the field of protein structure analysis. It has transformed from the ability to analyze protein structures only through time-consuming and laborious traditional experimental techniques to low-threshold, high-precision, and high-throughput prediction of protein three-dimensional A new paradigm of structure, proving that by combining protein knowledge Sugar Daddy with AI technology, high-dimensional and complex knowledge can be extracted and learned , to promote a deeper understanding of the physical structure and function of proteins.
Analysis of gene Zelanian sugar regulation rules Example
The Human Genome Project is hailed as one of the three major scientific projects of mankind in the 20th century, unveiling the mystery of life. Although the genetic information encoding living individuals is stored in DNA sequences, The fate and phenotype of each cell vary greatly due to its unique spatiotemporal background. This complex life process is controlled by a sophisticated gene expression regulatory system, and exploring the ubiquitous gene regulatory mechanisms of life is the most important step after the human genome project. One of the important life science issues. Gene expression profiles of different cells are an ideal window for understanding gene regulatory activities within biological systems. However, only through biological experimentsTo fully understand the gene regulation mechanism experimentally, it is necessary to capture different cell types of different biological individuals in different environmental backgrounds for observation. Traditional biological information analysis methods can only process a small amount of data, and it is difficult to capture the complex nonlinear relationships in the large-scale, high-dimensional biological big data that lacks accurate annotation.
In recent years, continuous breakthroughs in natural language processing technology, especially the rapid development of large language models, can make the model have the ability to understand human language description knowledge through training corpus data, which has brought great success to solving problems in this field. Here comes a new idea. Multiple international research teams have learned from the training ideas of large language models and based on tens of millions of human single-cell transcriptome profiles. Data and huge computing resources, using advanced algorithms such as Transformer and a variety of biological knowledge, have built multiple basic life models with the ability to understand the dynamic relationships of genes, such as GeneCompass, scGPT, Geneformer and scFoundation. These large life basic models are trained based on underlying life activity information such as gene expression, and use machines to learn and understand these “low-dimensional” life science data and complex “high-dimensional” gene expression regulatory networks, cell fate transitions and other underlying life mechanisms. The correlation and corresponding rules between them enable effective simulation and prediction of high-dimensional information with low-dimensional data. This kind of simulation of gene expression regulatory networks can show excellent performance in a wide range of downstream tasks, providing a new way to deeply understand the laws of gene regulation.
Existing successful cases of AI-driven life science research prove to us that in the face of deeper and more systematic life science problems, AI is expected to break through the dilemmas that are difficult to solve with traditional research methods and build a system from the basic biological level. Projection theoretical system to the entire life system, and further promote the development of life science to a higher stage, opening a new paradigm of life science research.
The connotation and key elements of the new paradigm of life science research
With the continuous advancement of biotechnology, life science dataZelanian sugar‘s rapid growth, AINewzealand Sugar‘s rapid development and its relationship with With deep cross-integration in the field of life, AI has demonstrated an in-depth understanding and generalization ability of life science knowledge, which not only improves the research height and breadth of life sciences, but also promotes the first paradigm of life science research to be dominated by experimental science, leapfrogging Entering a new paradigm of AI-driven life science research (the fifth paradigm, hereinafter referred to as the “new paradigm”).
ThroughThrough an in-depth analysis of typical examples of AI-driven life science research, the author believes that the new paradigm of life science research is like an intelligent new energy vehicle, benchmarking the battery system, electronic control system, motor system, and assisted driving system of the new energy vehicle. , chassis systems and other core technologies, the new paradigm should have five key elements: life science big data, intelligent algorithm models, computing power platforms, expert prior knowledge and cross-research teams (Figure 2). Just like a battery system provides energy for a vehicle, life science big data provides basic resources for scientific research; algorithm models are like intelligent electronic control systems, enabling in-depth understanding of the operation of biological systemsNewzealand Sugar‘s operating mechanism; the computing power platform can be compared to a motor system, responsible for processing massive scientific data and complex computing tasks; expert prior knowledge is like an assisted driving system, providing direction guidance and implementation for scientists Experience; Cross Newzealand Sugar The cross research team is similar to a chassis system, responsible for integrating knowledge and skills in different fields to improve research efficiency through interdisciplinary collaboration , to promote the development of life sciences.
Key element one: life science big data
Life science big data is the “battery” system of the new paradigm “car”. With the development of new biotechnology, life science big data with the characteristics of multi-modal, multi-dimensional, dispersed distribution, hidden association, and multi-level intersection has gradually formed; only by effectively integrating life science big data and fully utilizing innovative AI technology Only by mining data can we break the cognitive limitations of human scientists, promote the generation of new discoveries, and expand the scope of life science exploration. For example, the large medical vision model realizes a variety of applications under few-sample and zero-sample conditions by integrating multi-source, multi-modal, and multi-task medical image data; the large cross-species life-based model GeneCompass effectively integrates global open source Based on the single cell data of more than 120 million single cells, it has realized the analysis of multiple life science issues such as panoramic learning and understanding of gene expression regulation rules.
Key element two: intelligent algorithm model
The intelligent algorithm model is the “electronic control” system of the new paradigm “car”. New laws and new knowledge of life emerge from the vast sea of life science big data, which requires innovative AI algorithms and models; how to develop and utilize lifeScientifically adapted AI algorithms, extracting effective biological characteristics, and constructing large-scale biological process dynamic models are the basis of the current new paradigm. central question. For example, the results of the Gerstein team using the Bayesian network algorithm to predict protein interactions were published in Science, laying the foundation for the development of classic machine learning in the field of biological information; the graph convolutional neural network algorithm was used to analyze protein-protein interaction networks and Biomolecular networks such as gene regulatory networks have expanded research directions in the field of life sciences; AlphaFolZelanian sugard 2 uses the Transformer model to be able to The ability to quickly calculate the structures of a large number of proteins based on accuracy demonstrates the importance of AI algorithm models in the new paradigm of life science research.
Key element three: computing power platform
The computing power platform is the “motor” system of the new paradigm “car”. Computing power is the basis for AI operation. The continuous development of AI algorithm models suitable for new paradigms in life science research, such as deep learning and large model technology, requires the support of more powerful and efficient computing power platforms for AI model training. Facing the new paradigm, in the future we should build a hardware capability platform that can support AI-enabled life science research, including building high-speed and large-capacity storage systems, building high-performance and high-throughput supercomputers, and developing research and development specifically for processing lifeZelanian sugarScientific data chips, special processors designed to accelerate biological model reasoning and training, etc., provide efficient and reliable computing and processing capabilities for life science research, In order to cope with the massive data generated in the field of life sciences, meet the computing needs of complex model construction in the field of life sciences, and ensure the application and innovation of AI in the field of life sciences.
Key element four: Expert prior knowledge
Expert prior knowledge is the “assisted driving” system of the new paradigm “car”. Under the new paradigm, existing life science knowledge will provide valuable training constraints, important background and feature relationships for AI algorithm models, help explain and understand the complexity of life science data, and verify and optimize the application of AI in the field of life sciences. ; It can play an important guiding role in AI algorithm design and model construction, promote more accurate and efficient solutions to life science problems, and promote the development of life science research in a more in-depth and comprehensive direction. For example, by embedding the prior knowledge of life science experts and encoding human annotation information, the new gene expression pre-trained large model improves the understanding of complex feature relationships between biological data.The explanation of the relationship shows better model performance.
Key element five: Cross-research team
The cross-research team is the “chassis” system of the new paradigm “car”. Under the new paradigm, a multidisciplinary research team composed of AI experts, data scientists, biologists, and medical scientists is crucial to achieving leap-forward life science discoveries. A cross-research team with diverse backgrounds that collaborates closely can integrate Zelanian sugar professional knowledge in AI, biology, medicine and other fields to provide diversified Perspectives and methods provide a solid foundation for comprehensively understanding and solving complex mechanism problems in life sciences, providing more possibilities for innovative solutions, thereby promoting breakthrough discoveries and progress in the field of life sciences.
The frontiers of life science research empowered by the new paradigm and the challenges faced by our country
The traditional research paradigm’s exploration of life is like peeking through a tube. Different subdivisions of life sciences are struggling on their own. With the continuous development of new paradigms, life science research will usher in new research modalities characterized by AI prediction, guidance, hypothesis proposing, and verification of hypotheses, bursting out a number of rapidly developing cutting-edge research directions in the new paradigm of life sciences, and demonstrating and the development gains brought about by new paradigm changes. However, accelerating the establishment and promotion of a new paradigm for life science research in my country under current conditions still faces a series of huge challenges.
The frontier of life science research empowered by new paradigms
Structural biology. Currently, in the field of structural biology, AI application technology represented by AlphaFold is still stuck in the “from sequence to structure” protein structure prediction and design stage, and cannot yet achieve the simulation and prediction of protein structure and function under complex physiological conditions. The emergence of higher-quality, larger-scale protein data and new algorithms is expected to systematically analyze the structure and function of biological macromolecules under different physiological states and spatio-temporal conditions, and realize protein “from sequence to function” or even “from sequence”. Intelligent structural analysis and refined design to multi-scale interactions.
Systems biology. Current omics data analysis is still limited to lower-dimensional biological omics observation levels, and has not yet formed full-dimensional observations from the gene level to the cell level or even to the individual or even group omics level. The new paradigm will integrate multi-dimensional and multi-modal biological big data and expert prior knowledge, extract key features of biological phenotypes, build multi-scale biological process analytical models, restore the underlying laws of the operation of complex biological systems, and form a foundation that is widely applicable A new system of systems biology research.
Genetics. With the accumulation of multi-omics data and the emergence of new large genetic models, genetics research has entered a stage of rapid development driven by new paradigms.The self-supervised pre-trained large model of expression profile data is expected to become a powerful tool for analyzing gene regulation rules, predicting disease targets, and expanding the exploration boundaries of genetic research.
Drug design and development. With the emergence of AlphaFold and the development of a number of molecular dynamics models, AI models have been used to predict and screen drug candidate molecules. In the future, the new paradigm will further promote the development of this field. It is expected that an AI-assisted full-process drug design and development system will emerge, which can independently complete the optimized design of drug structure and properties, realize the simulation prediction of the effectiveness and safety of candidate drugs, and efficiently generate drugs. Synthesis and production process solutions greatly accelerate the development and production process of drugs.
Precision medicine. AI technologies such as computer vision, natural language processing and machine learning have widely penetrated into precision medicine subfields such as biological imaging, medical imaging, intelligent disease analysis and target predictionZelanian sugar. For example, AI-based diagnostic systems are already comparable to or even surpassing experienced clinicians in accuracy in some aspects. However, most of the existing models are subject to data preferences and have problems such as poor robustness and low versatility. With the emergence of universal precision medicine models driven by new paradigms, they will help diagnose diseases and analyze diseases more quickly and accurately. Molecular mechanisms of diseases, discovery of new therapeutic targets, and improvement of human health.
Challenges facing the new paradigm of life science research in my country
Faced with the new situation and new requirements of the development of the new paradigm of life science research, our country still faces high-quality There are huge challenges such as the lack of life science data resource systems, the lack of key AI technologies and infrastructure, and the lack of new ecosystems for cross-innovation scientific research under the new paradigm.
Lack of high-quality life science data resource system
Although my country’s investment in scientific research in the field of life continues to increase, in some frontier fields, Chinese scientists still rely on Foreign high-quality data, while the construction and use of domestic data are relatively lagging behind. my country’s life science data resources still have uneven distribution problems. Better coordination and resource integration are needed to achieve efficient aggregation and systematization of high-quality life science data resources. promote. In addition, during the collection, transmission and storage of life science data, data security issues need to be strengthened urgently. In particular, the privacy and security issues of biological data still need to be paid attention to.
Facing these challenges, our country needs to strengthen the integration and sharing of scientific data resources and promote life science dataZelanian Escort Sustainable development of data resources, improving data quality and security, strengthening the transformation of data management and supply models, and promoting cross-domain and multi-modal scientific and technological resource integration service capabilitiesImprovement to meet the development of scientific research needs under the new paradigm.
Insufficient AI key technologies and infrastructure
my country’s core technologies for AI-driven new scientific research paradigms are relatively scarce, and independent and original algorithms, models, and tools are still needed. Develop. In view of the massive, high-dimensional, sparse distribution and other characteristics of life science big data, there is an urgent need to develop advanced computing and analysis methods for complex data. In the future, hardware, software and new computing media that are more suitable for life science applications should be developed, and new computing-biology interaction models should be explored during the integration of life sciences and computing sciences. In short, new paradigm research has put forward new requirements for the comprehensive capabilities of data, networks, computing power and other resources. It is necessary to accelerate the construction of a new generation of information infrastructure and solve the problem of “stuck neck” in computing power.
The lack of new ecology for cross-innovation scientific research under the new paradigm
Most of the existing AI-driven life science research methods are “small workshops” spontaneously assembled by research groups ” model and lacks the cross-innovation environment required for the development of new paradigms. The United States also emphasizes the interdisciplinary nature of artificial intelligence research in the updated version of the “National Artificial Intelligence Research and Development Strategic Plan” released in 2023 NZ Escorts The importance of cross-development. Therefore, the scientific research ecology under the new paradigm should encourage more extensive multidisciplinary “big crossover” and “big integration”, establish a new research model that combines dry and wet methods, and integrate theory and practice, and continue to cultivate high-level compound cross-research talents.
Under the new situation, our country has also begun to extensively deploy and promote the development of Newzealand Sugar interdisciplinary subjects. The “Fourteenth Five-Year Plan for National Economic and Social Development of the People’s Republic of China and the Outline of Long-term Goals for 2035” points out the need to promote the depth of various industries such as the Internet, big data, and artificial intelligenceNZ EscortsFusion. Combined with the actual development of my country’s life sciences field, the development of my country’s life sciences field should focus on integrating the paradigm change of AI-enabled life science research into my country’s national development vision layout in the new era, so as to achieve an overall effect of point-to-point and area-wide effects and establish a more open new model. Scientific research ecology and development environment.
In recent years, the field of life sciences has been undergoing unprecedented changes. The development of this field is not only driven by biotechnology and information technology, but also by AI. The huge impact of technological progress. The core of this change lies in the evolution from the traditional scientific research paradigm driven by hypotheses and experiments that mainly rely on human experience to a new research paradigm driven by big data and AI. this meansWe no longer rely solely on experiments and hypotheses, but proactively reveal the mysteries of life through big data analysis and AI technology. More broadly, this evolution will widely change or promote changes in scientific research activities at different levels, covering epistemology, methodology, research organization forms, economic society, ethics and laws, and many other levels.
To sum up, we are living in an era full of change and hope. The innovation of life sciences and the advancement of science and technology jointly draw a future blueprint for mankind’s deeper exploration of the mysteries of life. It is foreseeable that with the further development of general AI, life science research will realize a new model of dry and wet integration and human-machine collaboration in the near future, ushering in the “unprecedented” AI self-driven abstraction of new knowledge and new laws. , a new era of science that thinks about things no one has ever thought about.
(Author: Li Xin, Institute of Zoology, Chinese Academy of Sciences, Beijing Institute of Stem Cell and Regenerative Medicine; Yu Hanchao, Bureau of Frontier Science and Education, Chinese Academy of Sciences. Contributor to “Proceedings of the Chinese Academy of Sciences”)