ai training for biology experts

ai training for biology experts

AI Training for Biology Experts: Leveraging Artificial Intelligence to Enhance Research and Discovery

1. Introduction to Artificial Intelligence in Biology

We are poised for a revolution in life sciences. Advances in multiple scientific and engineering disciplines are defining a new era in which we will have the ability to define, model, analyze, and construct complex biological systems with purposeful control of their organization, function, and behavior. Researchers are increasingly able to draw on data from a wide range of sources – genomics, proteomics, single cell measurements, organoids, service models, CRISPR screens – as well as state-of-the-art experimental technologies. Machine learning methods hold promise for helping us make new discoveries from the rich data resources flying from this rapidly evolving data infrastructure. Advanced computational techniques can help biologists tilt the balance toward discoveries over hypothesis testing, and enhance model systems to provide uses to place these discoveries into a larger context.

This paper provides a guide on how to most effectively combine experiments with advanced computational-driven discovery to make biological inroads leading to improving human health outcomes. Our focus will be on leveraging artificial intelligence (AI) to enhance the capabilities of life science researchers. In this unfolding research era, advances in machine learning will provide opportunities for existing and novel researchers across scales, across life science subfields, across data modalities, and from public/private sector activities to best contribute to health improvement and individual wellness. Executing an AI strategy effectively will require new tools, existing tools, and many tech-savvy researchers eager to enhance their capacity for querying the deep structures of our molecular lives.

2. Key Applications of AI in Biology Research

As the amount of data keeps increasing, artificial intelligence (AI) has in recent years become an indispensable tool in many areas of biology research. In our daily research activities, we frequently send out emails to the biology research community and update our lab group’s web pages with new developments. However, due to widely recognized information retrieval overload, information exchange via email and literature has important limitations, possibly not efficiently reaching all interested researchers. AI offers unprecedented opportunities to help solve time-consuming constraints and identify new or unknown potential applications hidden within voluminous literature reports or in large databases. The daily number of new reports in text, audio, video, or data format requires AI solutions to filter, synthesize, link, analyze, evaluate, and store new findings discoverable in all kinds of biological databases, popular open access, and institutional researchers’ literatures. Squaring the circle of biological data is a genome information trend that is challenging computational tasks and is frequently diminished due to sheer data size, redundant computational efforts, and the treat as-you-analyze capacity information that grows faster and does not fit in standard laboratory wall cabinets. So far, cost-effective state-of-the-art solutions for all relevant life science tasks are exactly what biologists researching, teaching, and interacting with society need. The most widespread types of AI systems used in biology are knowledge-based systems, machine learning classifiers, natural language processing and understanding, deep learning, neural networks, deep belief networks, and reinforcement learning, that were already employed countless times. Products of AI systems are approaching human expert performance, both on complex large-scale biomedical data sets and in relevant clinical benchmark tasks. uibKBCubi combines three technologies to make a unique knowledge-based chatbot: knowledge graphs, symbolic rules, and reinforcement learning, being capable of filtering parts of the web pages that are commonly asked in real-time by chatbot users. These technologies have been proposed to turn classic databases into friendlier, more accessible, and convincing databases, as they keep updated reasoning patterns that imitate a human expert who can answer natural language questions and facilitate chatbot interface and interaction with the final user. More questions about AI capabilities in biomedical knowledge accessible by chatbots would possibly raise subsequent amendments in related databases to provide better question-answering about global as well as user-customized personalized biomedical data properties conveyed by investigated biomolecules, cells, tissues, and organs involved in human disease and health visitors.

3. Challenges and Opportunities in AI Training for Biology Experts

It is a common perception that AI is a standalone, insular field. Indeed, AI has relatively mature pedagogic and training theory, transcending narrow disciplinary confines. However, AI is also a mechanical set of rules and strict requirements, often requiring singular focus during training, with seminal achievements predominantly by those with sustained engagement. Under which specific conditions are specialists essential? Who are truly congruent AI specialists, essential to all applied fields, and what does an optimally designed primer in AI, keenly focused on high-impact domain problems to resolve, look like? Are there constructs inherent to specific domains that could distinctively enhance AI training for specific audiences, making the imbued knowledge and skills not just generally useful but also supremely applicable within specific fields?

Which new problem domains would so enriched domain AI specialists most uniquely attack with startling success? This chapter in the context of aspiring biology specialists provides an in-depth rationale, backdrop, parameters of specificity, representative research domain examples, realization considerations (including structural foibles and constraints to overcome), supporting infrastructure, and recommended practical constructs underpinning rigorous day-to-day meanwhile learning. These intricately intertwine classical machine learning, deep learning, and databases indices with the consensus domain knowledge articulated as formal BIOLL principles, for biology-focused analyses. Untangled real-world biology research problems, including developed statistical and AI models are presented investigating lipid particles, preventing untimely overtraining, and imputing burgeoning data from cutting light sheet microscopy. Contemplation of broader applications leveraging subsets or components of the proposed introduction to deploy specialists in other fields is addressed.

4. Best Practices and Strategies for Integrating AI into Biological Research

If all goes well, scientists will have many opportunities to use these artificial intelligence features and rapidly answer new and burning questions. In this chapter, we present some guidelines and strategies for bringing these artificial intelligence and machine learning methods into your lab. Although these methods have previously been widely available, newer machine learning models and systems capable of high quality predictions for large-scale biological tasks, such as classification of resulting images, prediction of the effect of a mutation on protein function, and prediction of chemical activity, etc., have only been available for the past few years. Additionally, many of the methods, and their available implementations, return higher quality results when identifying available and large-scale benchmarking datasets for training the underlying deep learning models. This has made the process of integrating these tools, which previously required extensive or careful feature engineering and other pre-processing steps, more accessible to the average scientific researcher.

Best practices can help you avoid many of the pitfalls associated with deploying and integrating with this technique, as well as allow you to make more accurate predictions and draw more meaningful conclusions. Moreover, using these techniques as the basis for additional studies can help you find follow-up insights or leverage your current biological research in numerous ways. Lastly, by openly and transparently adopting these methods, you can help combat self-reinforcing biases and misunderstandings about the nature of the training, the implications of the action, and the interpretation of the results that may be present in the use of models whose sole purpose is to solve these tasks.

5. Future Trends and Implications of AI in the Field of Biology

Ray Kurzweil’s and others’ prediction that AI will be developed to the point of human-like capabilities is indirect validation of the current trend toward AI being employed in “sideways” applications that involve the practice of specialized professions. The AI and society-like changes of the resulting revolution may make these applications secondary. Here we describe the primary revolution that AI will bring to the practice of the field of biology, and to turn biology into a quantitative “hard” science to a hitherto unappreciated extent.

AI is increasingly being applied, as in the Go application, not as a pure game but to areas of interest of professional practitioners requiring the expertise built up during learning, including economics, law, journalism, etc. Requiring less time and expense during the learning phase for a human to achieve a similar level of expertise.

While the direct effect of these AI application advances on society is presumed to suggest dramatic alterations to human society, there are economical and other concerns related to algorithmic practices, the readiness of the target industry sector for this application, etc., that planets of extramarital origin cannot address. The solutions for the application areas are part of the first revolution that AI will bring to biology.

AI is already facilitating the work of the specialized professional practitioner. As algorithms become more sophisticated, these practitioners will rely more heavily on them and the associated skills will lose the importance they now enjoy. Significant numbers of the professional commercial artist have already disappeared – the first wave. With time it becomes conceivable that the observable appearance and behavior of a work of art will no longer even possess an intrinsic value, only its provenance. This is already true for certain other closely related pursuits.