Luo Qibin: The Catalyst Master Transforming Big Data into Products Submits IPO Prospectus

The big data concept, which has been highly favored by the market over the past two years, remains extremely popular today. It is frequently mentioned across various industries, and even the ultimate focus of entrepreneurs’ business plans is often closely tied to big data, albeit typically positioned as part of long-term future planning or as an ultimate development goal.

From the initial euphoria over the value of big data, akin to striking gold, to the current state where the excitement has subsided, people have begun to calmly reflect on how to transform vast amounts of data into useful information and realize its value. Perhaps, leveraging external devices to collect accurate and reliable data is no longer a difficult task; however, analyzing this data to generate standardized information that truly serves various industries remains an arduous and challenging developmental path.

In the healthcare industry, the most notable sectors are genomics and health big data. Prior to 2000, the rise of high-end gene sequencing and analysis equipment manufacturers, represented by Illumina, Thermo Fisher Scientific, and Roche, paved the way for the genomics industry, thereby opening the door to biological genome sequencing.

The field of scientific research has witnessed a significant surge: sequencing the whole genome of a species, integrating bioinformatics, comparing genomic sequences across different species, interpreting genomic information, identifying variant genes and mining functional genes, constructing genetic maps, studying population evolution, and conducting genome-wide association studies, thereby providing a solid theoretical foundation for clinical applications or basic research. The most renowned example is the Human Genome Project, officially launched in 1990, which involved scientists from six countries—the United States, the United Kingdom, France, Germany, Japan, and China—and cost $3.8 billion over more than a decade to complete the draft sequence of the human genome.

In 2003, after the completion of the first human genome sequence, scientists realized that this alone was insufficient to unravel the mysteries of human biology and disease mechanisms. This led to the emergence of a multi-sample sequencing strategy that used the human genome as a reference to compare genetic variations across other organisms and disease types. Subsequently, the U.S. National Human Genome Research Institute proposed reducing the cost of whole-genome sequencing to $1,000, sparking intense competition among various sequencing technologies within the genomics industry. As sequencing costs continued to decline, gene sequencing technology ultimately experienced rapid and significant advancements.

Examining the entire upstream and downstream landscape of the genetic industry, the value chain extends from equipment and consumable suppliers that enable data collection—such as Illumina, Roche, and Life Technologies—to midstream domestic sequencing service providers like BGI Genomics, WuXi AppTec, and Novogene, and further to downstream bioinformatics analytics firms that primarily offer cloud-based big data storage, interpretation, and sharing services, ultimately reaching the end-point suppliers of genetic testing products. In short, a complete industrial chain has been formed, spanning from equipment to big data to applications.

Note: This figure is excerpted from the presentation slides of Luo Qibin’s speech at the NetEase Future Technology Summit.

While the upstream sector of sequencing instruments and consumables has been monopolized by international oligopolies, the downstream and end-user markets for genetic analysis, interpretation, and product offerings remain a vast blue ocean.

Positioning downstream, connecting midstream and end-users

Beijing QiYunNode, which specializes in gene data analysis, is one of the few such companies. At the 2015 NetEase Future Technology Summit, Luo Qibin, CEO of QiYunNode, summarized that there were over 100 third-party sequencing service providers in the midstream of China’s industry chain, and more than 100 end-user genetic testing product providers. However, downstream enterprises bridging the midstream and end-user segments are few in number. In addition to QiYunNode, innovative companies in this space include JiYunHuiKang, JuDao Technology, and L3 in China, as well as Seven Bridges Genomics, Foundation Medicine, GeneDock, and DNA Nexus abroad.

Why has this distribution pattern emerged? Luo Qibin told VCBeat that, first and foremost, the unique nature of the genomics industry dictates that corporate survival and growth depend not on user acquisition or sales volume, but on technological innovation. The depth to which a company can penetrate from red oceans into blue oceans, as well as the breadth of its vertical integration across the upstream and downstream value chain, are both determined by its technological capabilities. It is evident that most leaders in the genomics sector hold doctoral degrees, indicating that the key metric for corporate development in this high-education-intensive industry has shifted toward technological innovation capability. Consequently, such high barriers to entry make it unlikely for a large number of downstream companies specializing in genomic data analysis to emerge on a significant scale.

As for the relative proliferation of companies offering end-user genetic testing products, Luo Qibin believes that most of these firms are concentrated in the field of preventive genetic testing. This is because health prevention projects guided by the interpretation of genetic test reports are easier to launch, compared to diagnostic enterprises, which must undergo dual approval from the National Health and Family Planning Commission (NHFPC) and the China Food and Drug Administration (CFDA). Furthermore, given the early stage of industry development, chaos and irregularities are inevitable.

It is evident that the relatively high technical barriers will limit the participation of more enterprises and constrain industry development. To break this deadlock, Luo Qibin established Beijing QiYunNuoDe Information Technology Co., Ltd. (hereinafter referred to as “QiYunNuoDe”) in October 2014. The company aims to provide end-user enterprises with comprehensive bioinformatics solutions, including solution design, workflow construction, and cloud computing services. This integrated approach is designed to significantly enhance enterprises’ technical depth and capabilities, facilitate connections between end-user enterprises and midstream companies, and streamline the industrial chain. Consequently, traditional enterprises seeking to cross into new sectors or companies intending to expand their gene product businesses can achieve rapid and agile transformation.

On the one-stop bioinformatics big data platform built by Qiyun Nuode, a series of big data products are provided for users in the scientific research and medical fields, including a gene data engine, big data mining software, data visualization tools, and customized workflows. Regardless of whether customers focus on human disease research, animal and plant genomics, or microbial genomics, Qiyun Nuode provides comprehensive coverage for genetic research.



By simply providing project design requirements and experimental samples, clients can leverage QiYunNuoDe’s cloud-based automated computing to generate automated workflows and project management systems, thereby selecting the most optimized, integrated bioinformatics solution. Current institutional partners collaborating with QiYunNuoDe on research include the Beijing Institute of Genomics, Chinese Academy of Sciences; Tsinghua University Hospital; and Germany’s Biomax Bioinformatics Company. Comprehensive bioinformatics analysis applications, combined with flexible and customizable data analysis solutions, are ultimately delivered to clients as interactive, visualized product prototypes through an intuitive user interface.

In addition to independently developing bioinformatics applications and solutions, QiYunNuoDe has built a bioinformatics e-commerce platform that enables all bioinformatics experts in the industry to deploy their self-developed applications and workflows onto the bioinformatics big data platform. Luo Qibin told VCBeat that, with QiYunNuoDe’s support, there will be an increasing number of genetic testing companies and more high-quality genetic products in the future.

“Qiyunuode’s mission is to reduce product manufacturing costs. By leveraging a bioinformatics big data platform and its data analytics search engine, traditional biotechnology companies can rapidly identify partners and establish corporate connections. What once required a team of five researchers a year to develop can now be accomplished by a single researcher in just one week,” said Luo Qibin.

Further Refine Quantified Life

Wang Jun, formerly of BGI Genomics, once proposed that life is quantifiable, and QiYunNuo further refines this quantified data. At the conference, Luo Qibin presented his classification of big health data into four major categories: genetic data (20%), medical data (10%), environmental data (20%), and behavioral data (50%). Behavioral data accounts for the largest proportion at 50%. For instance, choosing to spend five minutes climbing stairs instead of taking the elevator constitutes behavioral data. Currently, many wearable devices are designed to record such human behavioral data. In the future, the Internet of Things (IoT) will primarily focus on data related to health and disease prevention, rather than clinical medical data.

Note: This figure is excerpted from the presentation slides delivered by Luo Qibin at the NetEase Future Technology Summit.

Qiyunuode also operates the “Gene Space” business, which shares the exact same name as Luo Qibin’s science blog. He told VCBeat that Gene Space is currently focused on one primary initiative: ranking. It ranks enterprises, products, key figures, and news within the gene industry. Given the current overload and fragmentation of information, he aims to consolidate all data and filter out the most valuable insights. For instance, by collecting metrics such as readership and engagement levels for each news item across major online platforms, rankings are calculated using mathematical models. Thus, users need only follow the top-ranked curated content on Gene Space, as it represents the most representative, meaningful, and up-to-date information. Following the same logic, artificial intelligence or machine learning methods are employed to analyze corporate business data and generate industry rankings. By integrating fragmented resources into its platform, Gene Space facilitates seamless alignment between technologies and products for enterprises.



How can genetic data be transformed into actionable information? Luo Qibin cited the case of using genetic data to guide drug development. In 2015, U.S. President Barack Obama prioritized the development of precision medicine, and China also announced the launch of its Precision Medicine Initiative this March, with an estimated investment of RMB 60 billion by 2030. This represents a significant policy boon for traditional pharmaceutical companies, driving increased R&D investment in personalized medicines and accelerating the industry’s transition from chemical drugs to targeted therapies.

However, the drug development industry is generally characterized by high investment, high risk, and high returns. To address this, QiYunNuoDe has established a specialized database that spares pharmaceutical companies from starting from scratch, thereby not only saving R&D time and reducing R&D expenditures but also lowering risks while achieving a higher return on investment. Currently, QiYunNuoDe’s database covers medical and genetic data related to more than 100 monogenic diseases, over 30 major cancers, significant cardiovascular diseases, chronic diseases, diagnostic biomarker genes, prognostic biomarker genes, and pharmacogenomic markers. The database features standardized data formats, data encryption, off-site backups, multi-dimensional data collection, real-time updates, and comprehensive manual review of all data.

Anticipate Trends, Strategize Early

Luo Qibin believes that databases should be transformed into information repositories, which in turn should be converted into knowledge bases, from which valuable content for enterprises can be extracted to achieve the transformation of knowledge into products. In reality, however, the slow pace and low efficiency of this product transformation have become a key bottleneck constraining industry development.

“China’s industrial and technological commercialization rate stands at 10%, compared with 40% in the United States and 30%–40% in developed European countries, indicating substantial room for growth in China,” said Luo Qibin. Qi Yunnuo aims to address the productization of technical services and accelerate the pace of product commercialization.

To cultivate more qualified product managers for the genomics industry, Luo Qibin launched the Gene Hackers Bootcamp. He has stated that the genomics sector lacks formal training institutions for product managers, and QiYunNuoD aims to establish a training system on a pro bono basis to fill this industry gap.


China, despite being rife with copycat products, is not lacking in technology; rather, it lacks well-packaged products. Luo Qibin stated that there is a significant difference between a product manager and a technical engineer. While most people in China focus on technology, there are few internationally competitive products. Therefore, product managers will become the scarcest talent in China’s next stage of development. Qi Yunnuode aims to cultivate such talent in advance. He mentioned that the “Gene Hacker Bootcamp” is currently in the Minimum Viable Product (MVP) stage, with the goal of establishing a standardized training system. This would enable the monthly output of hundreds of product managers in the future. It is evident that Qi Yunnuode leverages technological tools to transform big data into small data, small data into valuable information, and extracts this information into knowledge, allowing knowledge to flow and generate value. Luo Qibin believes that achieving this is not enough; it is also necessary to personally train talents for enterprises who can convert knowledge into final products, thereby closing the last link in the value chain.

In fact, Luo Qibin’s foresight is evident not only in talent reserves; it can be said that Qi Yunnuode has been playing a deeply strategic game from the very beginning.

Although personalized gene sequencing has not yet gained widespread traction, the continuous decline in sequencing costs will lead to a steady accumulation of genetic data. A large-scale explosion in the generation and output of such data is inevitable. Once personalized gene sequencing begins to take off, it will spread rapidly like a prairie fire, triggering an exponential surge in data volume within just a few months. If efforts to organize this data and synthesize information are delayed until that point, it will be too late. China, lacking adequate technological reserves, could quickly descend into an era of informational chaos.

“We anticipate that the era of informatization for genetic data will arrive within three years, so we must begin laying the groundwork five years in advance,” said Luo Qibin.

Introduction to Luo Qibin

Co-author of the “Internet Plus” book series and Editor-in-Chief of Internet Plus: Gene Space. Co-author of Internet Plus Healthcare, published by CITIC Press. Member of BioMan at Beike Society. Content Editor-in-Chief of the WeChat official account “Gene Space” and the “Gene World” APP, while also serving as a columnist for several well-known domestic biotechnology websites. Previously engaged in project R&D at the Beijing Institute of Genomics, Chinese Academy of Sciences; served as a Bioinformatics Consultant for Ecoscience Biology and as a Committee Expert for the “Hejun Pharmaceutical Healthcare Salon.” Member of the China Association for Promotion of Rehabilitation Technology Translation and Development.

Graduated from Shantou University in 2004 and entered the genomics industry. Obtained a Master’s degree in Bioinformatics from the Watson Institute of Genetics, Zhejiang University, in 2007, and joined the Beijing Genomics Institute (BGI), the predecessor of BGI Group, at the Chinese Academy of Sciences in the same year to conduct research on the application of bioinformatics algorithms. In 2008, pursued a Ph.D. in the Department of Bioinformatics at the Technical University of Munich, focusing on next-generation sequencing technologies and the application of interaction networks in genomic data. To date, has co-authored seven papers published in international journals in the field of genomics. Left the Chinese Academy of Sciences in 2014 to found QY Genomics and QY NODE.