Cell therapies are powerful technologies in which human cells are reprogrammed for therapeutic applications such as killing cancer cells or replacing defective cells. The technologies underlying cell therapies are increasing in effectiveness and complexity, making rational engineering of cell therapies more difficult. Creating the next generation of cell therapies will require improved experimental approaches and predictive models. Artificial intelligence (AI) and machine learning (ML) methods have revolutionized several fields in biology including genome annotation, protein structure prediction, and enzyme design. In this review, we discuss the potential of combining experimental library screens and AI to build predictive models for the development of modular cell therapy technologies. Advances in DNA synthesis and high-throughput screening techniques enable the construction and screening of libraries of modular cell therapy constructs. AI and ML models trained on this screening data can accelerate the development of cell therapies by generating predictive models, design rules, and improved designs.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/imr.13236

Harnessing the power of artificial intelligence to advance cell therapy.

(1) Background: Artificial intelligence (AI) has existed in some form for decades, but recent rapid advances in a subset called machine learning (ML) — and more specifically deep learning (DL), a neural network-based approach — have made headlines for the potential to revolutionize and automate multiple large sectors of society, including scientific research and the healthcare field. Furthermore, large language models (LLMs) — which are built on DL — could lead to a more seamless, natural interaction between humans and computers. (2) Methods: We reviewed numerous publications on this subject from recent years. (3) Results: We found these studies collectively show that AI is positively disrupting both biomedical research and medical practice, such as optical imaging in surgery guidance. (4) Conclusions: However, we recommend caution in over-reliance on AI in the laboratory or the clinic due to anticipated risks and current limitations. 

New Advances in Artificial Intelligence for Biomedical Research and Clinical Decision-Making

In recent years, there has been an exponential increase in the generation and accessibility of electronic healthcare data, often referred to as “real-world data”. The landscape of data sources has significantly expanded to encompass traditional databases and newer sources such as the social media, wearables, and mobile devices. Advances in information technology, along with the growth in computational power and the evolution of analytical methods relying on bioinformatic tools and/or artificial intelligence techniques, have enhanced the potential for utilizing this data to generate real-world evidence and improve clinical practice. Indeed, these innovative analytical approaches enable the screening and analysis of large amounts of data to rapidly generate evidence. As such numerous practical uses of artificial intelligence in medicine have been successfully investigated for image processing, disease diagnosis and prediction, as well as the management of pharmacological treatments, thus highlighting the need to educate health professionals on these emerging approaches. This narrative review provides an overview of the foremost opportunities and challenges presented by artificial intelligence in pharmacology, and specifically concerning the drug post-marketing safety evaluation.

Artificial intelligence for optimizing benefits and minimizing risks of pharmacological therapies: challenges and opportunities

The low motivation of students/santri in learning the Koran is still one of the causes of the low quality of education, especially in the ability to read the Koran. One of the efforts to increase the motivation of students/students in learning to read the Qur'an is by using the appropriate method. Along with the development of information technology, especially in the advancement of multimedia-based teaching media, the teaching of the Qur'an can use media more effectively. The design and development of this media uses a software development model with research stages including: data collection, analysis, design, implementation and report writing. The stages of data collection were carried out using the method of literature study and interviews, while in the analysis stage, the MDLC method was used. At the design stage a structured approach is used. Process design is carried out using flow chart tools. The results of the process design are used as a basis for developing user interface designs. The design of the resulting learning media was developed using Adobe Flash CS 4 and Adobe Flash CS 6 software. With this design, it is hoped that TPA Miftakhul Jannah Mosque can increase students' learning motivation in reading the Qur'an 

Design and Build Learning Media to Read and Write Al-Qur'an Flash-Based

Abstract Artificial intelligence (AI) is revolutionizing the current approach to medicine. AI uses machine learning algorithms to predict the success of therapeutic procedures or assist the clinician in the decision-making process. To date, machine learning studies in the andrological field have mainly focused on prostate cancer imaging and management. However, an increasing number of studies are documenting the use of AI to assist clinicians in decision-making and patient management in andrological diseases such as varicocele or sexual dysfunction. Additionally, machine learning applications are being employed to enhance success rates in assisted reproductive techniques (ARTs). This article offers the clinicians as well as the researchers with a brief overview of the current use of AI in andrology, highlighting the current state-of-the-art scientific evidence, the direction in which the research is going, and the strengths and limitations of this approach. 

Artificial intelligence in andrology – fact or fiction: essential takeaway for busy clinicians

This editorial article aims to highlight advances in artificial intelligence (AI) technologies in five areas: Collaborative AI, Multimodal AI, Human-Centered AI, Equitable AI, and Ethical and Value-based AI in order to cope with future complex socioeconomic and public health issues.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9703021

Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world

GEN BiotechnologyVol. 1, No. 2 Views & NewsFree AccessArtificial Intelligence: Bioengineers' Ultimate Best FriendSimone BiancoSimone Bianco*Address correspondence to: Simone Bianco, Bay Area Institute, Altos Labs, Redwood City, California, USA, E-mail Address: sbianco@altolabs.comBay Area Institute, Altos Labs, Redwood City, California, USA.Search for more papers by this authorPublished Online:20 Apr 2022https://doi.org/10.1089/genbio.2022.29027.sbiAboutSectionsPDF/EPUB ToolsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail An exciting new study in Nature from Aviv Regev's group at Genentech brings us closer to the rational engineering of biological systems through the use of advanced computational methods.Artificial intelligence (AI) has come a long way in biology. What was once a rather exotic, although obviously useful, tool used by computer wizards to magically generate accurate segmentation maps of microscopic images has matured recently to powering Alphafold1—a discovery so important that it has been heralded as one of the most important achievements in AI.2It is not an understatement that AI has taken biology by storm. Even hard-core biologists are now caving in! Yes, AI is useful, important, and even essential. Neural networks, the prototypical and most widely used examples of the larger class of algorithms that is referred to as AI3 and developed by the Turing Award winners, Bengio, Hinton and LeCun, are now part of the essential curriculum of a biologist. And something needs to be said of the development of frameworks such as TensorFlow, Keras, and Pytorch, among many others, that have democratized the adoption of very advanced and mostly obscure tools by making their use as part of simple codes almost trivial.I like to tell my students that one can write a neural network with two lines of code. Whereas in jest, there is some truth to this statement. Without recapping the history or even the major milestones of AI in biology, suffice to say that it has become so common to be now routinely omitted from the titles of the articles.And yet, once again AI surprises by being instrumental for yet another milestone in biology, as beautifully presented in a recent article in Nature by Aviv Regev and colleagues (Vaishnav et al.).4 Let me attempt at describing, in a nutshell, the article.The ultimate frontier of AI is—most certainly and, I would argue, rather controversially—the unbiased de novo engineering of biological systems: Can an algorithm be so powerful and so “smart” as to precisely design a complex biological system? Can an algorithm really understand the rules of evolution and encode the inner workings of life to design a new never before observed living system? A new take on the Promethean myth that now sees a machine as the creator of complex biological entities.To be fair, synthetic biology has been rather successfully aided for decades now by computational methods, but these tools have been, for the most part, focused on the mathematical and computational description of genetic networks (both natural and synthetic). Thus, at least a partial understanding of the dynamics of the system has been necessary to be able to suggest potential actionable modifications. Using the methods described in Vaishnav et al., one would instead be able to produce predictions driven only by the data, unbiased and unencumbered by the “eye of the investigator.”Optimal SolutionsUsing a gross oversimplification, one can understand evolution by considering that every living organism is trying to find an optimal solution to a given set of complex dynamical problems, at multiple scales. Evolution is, in a nutshell, the collection of all the solutions of those problems. The characterization of this space of solutions is by itself an exciting research problem.The first notable result of Vaishnav et al.4 is to present a computational model capable of predicting expression for an unknown sequence when trained on a comprehensive data set of yeast expression. Basically, the model learns the space of possible solutions and it is capable of generalizing to a new set of solutions. This is of paramount importance because expression engineering is a fundamental part of industrial bioengineering, especially metabolic engineering. But it is even more important because it allows for uncovering the whole complexity of the organism fitness landscape, which can now be computationally revealed on call.And that is exactly what the authors, brilliantly, do. They introduce a new indicator—the expression conservation coefficient—that shows the difference between the in silico prediction of the expression of a given promoter and the expression of the same promoter if random variation occurred. In this way, one is able to recapitulate the potential for a gene—any gene—to evolve to a specific environment (as low variance on native sequence indicates stabilizing selection).Besides the examples reported in the article, which are naturally confirmatory of existing biological functions and show that the methods are capable of predicting expression conservation, fitness, mutational robustness, etc., the potential for bioengineering and cellular engineering is enormous. In fact, testing for evolvability can open the door to more stable industrial bioengineering pipelines, as just one example. Mutational robustness shapes the fitness landscape so that fitness peaks are tall but wide instead of narrow, indicating that a broader set of mutations is now buffered and can be tolerated without significant loss of fitness. Selecting for a robust system opens the door, in my opinion, to more efficient production pipelines.In addition to these important results, the authors also show (again using their AI framework) that evolution to complex solutions of biological problems is not favored, as de novo generated regulatory sequences evolve to existing or random ones in a reasonable time frame. This suggests that existing sequences have been optimized by evolution to minimize regulatory complexity, here defined intuitively as the resistance of a regulatory sequence to change expression levels in time.There is much more to be learnt from this article, which is but a starting point. More and more interesting work is appearing that directly challenges the current dogma of bioengineering, synthetic biology, and cellular engineering as driven by prior knowledge and painstaking trial and error. A bit like a pathologist, the bioengineer nowadays turns to sometimes decades of (collective) expertise to get a sensible answer to a biological question. And just as in pathology, machine learning and AI can aid the work of the practitioner. But unlike pathology, where the flow of information is reactive to the acquisition of a sample, in synthetic biology machines can take a central role as designers. They can be the driver of innovation and discovery, by providing actionable answers to very precise questions.And there is more: New algorithms still need to enter the field. Generative models, that is, algorithms that are capable of answers by generating data not contained in the existing databases are only now starting to appear as exceptional arrows to the bow of synthetic biologists.5 And I would argue that reinforcement learning models can also play an important part to sample more intelligently the space of solutions and give us strategies for combining several biological building blocks.Finally, hybrid models, where the combination of interpretable and noninterpretable parts can provide a solid ground to the predictions, can be paramount to this new field of AI-driven synthetic biology. So, yes, there is much more to biology than evolution has shown us. And there is much more to synthetic biology than random chance. Computers can help. Let us allow them!References1. Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583–589. DOI: 10.1038/s41586-021-03819-2. Crossref, Medline, Google Scholar2. Callaway E. ‘It will change everything’: DeepMind's AI makes gigantic leap in solving protein structures. Nature. 2020;588:203–204. DOI: https://doi.org/10.1038/d41586-020-03348-4. Google Scholar3. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–444. DOI: 10.1038/nature14539. Crossref, Medline, Google Scholar4. Vaishnav ED, de Boer CG, Molinet J, et al. The evolution, evolvability and engineering of gene regulatory DNA. Nature. 2022;603:1–9. DOI: 10.1038/s41586-022-04506-6. Crossref, Google Scholar5. Das P, Sercu T, Wadhawan K, et al. Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations. Nat Biomed Eng. 2021;5:613–623. DOI: 10.1038/s41551-021-00689-x. Crossref, Medline, Google ScholarFiguresReferencesRelatedDetails Volume 1Issue 2Apr 2022 InformationCopyright 2022, Mary Ann Liebert, Inc., publishersTo cite this article:Simone Bianco.Artificial Intelligence: Bioengineers' Ultimate Best Friend.GEN Biotechnology.Apr 2022.140-141.http://doi.org/10.1089/genbio.2022.29027.sbiPublished in Volume: 1 Issue 2: April 20, 2022PDF download 

Artificial Intelligence: Bioengineers' Ultimate Best Friend

Creative and generative artificial intelligence for personalized medicine and healthcare: Hype, reality, or hyperreality?

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Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world

Artificial Intelligence: Bioengineers' Ultimate Best Friend

Creative and generative artificial intelligence for personalized medicine and healthcare: Hype, reality, or hyperreality?