{"id":2984,"date":"2023-10-19T09:40:22","date_gmt":"2023-10-19T08:40:22","guid":{"rendered":"https:\/\/springerhealthcare.nl\/france\/?p=2984"},"modified":"2023-10-20T10:09:57","modified_gmt":"2023-10-20T09:09:57","slug":"how-to-spice-up-your-bioinformatics-skill-set-with-ai","status":"publish","type":"post","link":"https:\/\/springerhealthplus.nl\/shmigrate\/how-to-spice-up-your-bioinformatics-skill-set-with-ai\/","title":{"rendered":"How to spice up your bioinformatics skill set with AI"},"content":{"rendered":"

[vc_row full_width=”stretch_row” content_placement=”middle” css=”.vc_custom_1663187695627{padding-bottom: 37px !important;background-image: url(http:\/\/springerhealthcare.nl\/wp-content\/uploads\/sites\/24\/2022\/09\/science_connect_back.png?id=2486) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}” el_class=”sciencetitle”][vc_column][vc_custom_heading text=”Science Connect” font_container=”tag:h2|text_align:left|color:%23ffffff” use_theme_fonts=”yes” link=”url:https%3A%2F%2Fspringerhealthcare.nl%2Ffrance%2Fscience-connect-october-2023%2F”][vc_column_text css=”.vc_custom_1663186747780{margin-bottom: 40px !important;}”]A Springer Healthcare Initiative For Pharma Professionals<\/span>[\/vc_column_text][vc_column_text]OCTOBER 2023
\n<\/span>[\/vc_column_text][\/vc_column][\/vc_row][vc_row css=”.vc_custom_1538651642953{padding-top: 75px !important;}”][vc_column width=”3\/4″ css=”.vc_custom_1538657061395{border-right-width: 1px !important;padding-top: 0px !important;border-right-color: #8faec1 !important;border-right-style: solid !important;}”][vc_custom_heading source=”post_title” font_container=”tag:h1|font_size:40px|text_align:left|line_height:60px” google_fonts=”font_family:Merriweather%3Aregular%2Citalic|font_style:700%20regular%3A700%3Anormal” el_class=”title” css=”.vc_custom_1664358136969{margin-bottom: 30px !important;}”][vc_column_text css=”.vc_custom_1686833173052{padding-bottom: 0px !important;}”]Incorporating machine-learning tools into data analysis can accelerate discovery and free up valuable time.<\/strong><\/span>[\/vc_column_text][vc_column_text el_class=”intro-box”]By Rachael Pells<\/span>[\/vc_column_text][vc_single_image image=”2991″ img_size=”full” el_class=”hero-img”][vc_column_text css=”.vc_custom_1695902816200{margin-bottom: 30px !important;}” el_class=”sc-img-caption”]Artificial-intelligence programs can speed up monotonous tasks in research \u2014 and the learning curve is not too steep. Credit: Luis Alvarez\/Getty[\/vc_column_text][vc_column_text]

Image-analysis tools can do amazing things. Yet despite their power, Fernanda Garcia Fossa was frustrated. A biology PhD student at the State University of Campinas, Brazil, Garcia Fossa specializes in nanotoxicology. Image-based profiling of human cells is a core part of her research. But when she started out, the process was slow and error-prone.<\/p>\n

\u201cI spent a lot of my time analysing my images individually by hand, looking for differences and patterns,\u201d Garcia Fossa explains. She was looking for evidence of the subtle effects of silver nanoparticles on liver cells. But the number of hours it took to compare scanned images of each cell one by one was overwhelming, she says. \u201cI thought, there has to be a faster way to do this.\u201d<\/p>\n

Trawling online biology forums, she stumbled on CellProfiler, an image-analysis tool based on artificial intelligence (AI) developed at the Broad Institute of MIT and Harvard in Cambridge, Massachusetts. Within hours, she had identified an algorithm tailored to her needs, which she used to analyse her images automatically. \u201cIt was exciting,\u201d she says. \u201cSuddenly, I found I had more time to do other tasks related to my research, because the program was analysing all my images for me.\u201d<\/p>\n

She\u2019s not alone; bioinformatics skills have become essential in the life sciences. Scientists are typically trained on the algorithms that drive that research \u2014 how they work and how to use them efficiently. But informaticians are increasingly using machine learning or AI \u2014 including large language models, such as the ChatGPT chatbot \u2014 rather than algorithms to find patterns or features in sequences and images.<\/p>\n

Uptake is growing fast, but it could be faster, says Shantanu Singh, a data scientist and senior group leader at the Broad Institute\u2019s Imaging Platform. Although a large number of researchers are now working with these platforms, many lack data-management skills \u2014 which, coupled with a shortage of resources, is holding the field back. \u201cSome things, like data-storage solutions, are getting simpler \u2014 but it\u2019s still not enough,\u201d he says.<\/p>\n

Those who have already made the transition to using AI are reaping the benefits of vastly accelerated workflows and targeted decision-making in data analysis. But for bioinformaticians who remain on the fence, there are challenges to consider when taking the leap.<\/p>\n

Get familiar with AI tools<\/strong><\/h2>\n

Image-analysis algorithms help researchers to compare cell characteristics faster and more quantitatively than when they do the work manually; AI further accelerates the process through adaptive learning that is specific to the researcher\u2019s needs. AI can often detect differences or modes of comparison that the user had never considered. \u201cThe benefit of bringing AI into imaging is that it allows researchers to reason with biological images in high dimensions, not just focus on one or two predefined measurements,\u201d explains Singh. By converting what it \u2018sees\u2019 into numerical data, AI effectively transforms a biologically complicated image into a relatively straightforward mathematics problem. \u201cOnce you have those numbers, the rest of it is all data science.\u201d<\/p>\n

CellProfiler, for example, is an online open-source tool that allows users to set up their own workflows \u2014 often called pipelines \u2014 to automate their analyses (for example, quantifying shapes, characteristics or patterns). It can run machine-learning algorithms from companion tools such as CellProfiler Analyst, and is evolving to also use deep learning \u2014 a richer, more complex approach to recognizing intricate patterns in data.<\/p>\n

\"Portrait
Fernanda Garcia Fossa uses CellProfiler, an image-analysis tool, in her PhD research. Credit: Marcelo Bispo de Jesus\/NanoCell Interactions Lab<\/span><\/figcaption><\/figure>\n

 <\/p>\n

According to Beth Cimini, CellProfiler\u2019s project lead, integrating deep learning into tools such as CellProfiler is the natural next step for image-based research. Deep learning and image analysis have been used together \u201cfor as long as we\u2019ve had the computational abilities to do so\u201d, she says \u2014 whether that\u2019s tagging friends on Facebook and Instagram, or cleaning up photomicrographs and finding and counting objects in them.<\/p>\n

Garcia Fossa liked CellProfiler because of its \u201ceasy interface, and the fact I didn\u2019t need to know how to code; it was just a matter of practising to get the hang of it\u201d. But several other open-source, AI-based tools have emerged for cell and image analysis in the past few years, which also require little to no coding expertise. These include ilastik, made by the Swiss Federal Institute of Technology in Zurich; QuPath, an open-source digital pathology platform developed at the University of Edinburgh, UK; and CDeep3M, from the National Center for Microscopy and Imaging Research at the University of California, San Diego.<\/p>\n

Bridge your skills gaps<\/strong><\/h2>\n

Bioinformaticians who wish to build their own AI tools need to be good coders, says Ga\u00ebl Varoquaux, \u201cand by this, I mean a good software engineer \u2014 being very specific about how you track the modifications, how to do quality assurance on the code\u201d.<\/p>\n

Varoquaux is a research director at the French National Institute for Research in Digital Science and Technology (Inria) in Paris, and co-founder of scikit-learn, a popular library of free machine-learning algorithms for the Python programming language. \u201cPython is a generalist language,\u201d Varoquaux says: \u201cYou can do many things with it \u2014 text processing, scientific computing, web servers. It\u2019s useful for science because more often than we think we end up having to do auxiliary tasks, but also, it\u2019s good to have if ever you\u2019re looking for a job outside of academia,\u201d he notes.<\/p>\n

\"Portrait
\u201cFoundations are important,\u201d says Gael Varoquaux, co-developer of scikit-learn. Credit: Inria\/Photo B. Fourrier<\/span><\/figcaption><\/figure>\n

 <\/p>\n

To this end, he advises that knowing some software engineering and investing in those skills, as well as in your mathematics and statistics abilities, can further your career. \u201cThe foundations are important,\u201d he says. \u201cPeople avoid it, but it bites them back.\u201d<\/p>\n

That said, interactive tools, such as ChatGPT, can ease the transition, says Kyogo Kawaguchi, a research scientist at the Riken Center for Biosystems Dynamics Research in Kobe, Japan. That\u2019s because programming is challenging, both on its own and because of the skills involved, \u201clike setting up your environment, debugging and being able to ask the questions with the correct words\u201d, he says. Chatbots lower the bar by allowing users to find solutions through experimentation and by asking candid questions.<\/p>\n

Whatever the AI, scientists can become good at using it through a combination of formal education, self-study and practical experience. Start by exploring online tutorials and courses offered by universities and on platforms such as Coursera, edX and Udacity. Many of these are available at no cost, include step-by-step videos and can be taken in the learner\u2019s own timeframe. Andrew Ng, a computer scientist at Stanford University in California and founder of DeepLearning.AI, for example, has a popular\u00a0collection of tutorials on machine- and deep-learning programming on Coursera<\/a>\u00a0(which he co-founded).<\/p>\n

Live and in-person learning opportunities are also available. The European Molecular Biology Laboratory\u2019s European Bioinformatics Institute (EMBL-EBI) in Hinxton, UK, for example, hosts live training sessions, both in-person and online, for individuals and groups around the world. This year\u2019s five-day on-site courses will cost each attendee \u00a3825 (US$1,014), which includes four nights\u2019 accommodation and catering; five-day virtual courses usually cost \u00a3200. Course materials,\u00a0on-demand training<\/a>\u00a0and online webinars are free and open to everyone.<\/p>\n

The French government\u00a0backs a free online course<\/a>, maintained by scikit-learn, that typically takes around 35 hours to complete, says Varoquaux. \u201cThere is a lot of coding, but that\u2019s by design; we think this is useful.\u201d<\/p>\n

Dayane Rodrigues Ara\u00fajo, a scientific training officer at the EMBL-EBI, says that newcomers are often surprised by how easy it is to get started. A significant part of her work, she explains, \u201cis getting the message out that they may not need to start from scratch with writing an algorithm; the materials to start are already available\u201d. As a publicly funded, intergovernmental organization, the EMBL-EBI offers a\u00a0bank of free resources<\/a>\u00a0as well as on-demand online courses that anyone can use, without restriction.<\/p>\n

Don\u2019t panic<\/strong><\/h2>\n

As with many new technologies, it might seem impossible to keep pace with AI\u2019s rapid evolution. But often, you don\u2019t have to.<\/p>\n

Varoquaux explains that scitkit-learn uses \u201cconventional\u201d machine learning over deep learning because the goal of the platform is to \u201cdemocratize and simplify\u201d AI, not to compete with bigger Internet players such as Google.<\/p>\n

But beyond this, chasing the latest technology isn\u2019t always necessary, he says. \u201cSure, AI evolves extremely fast. But I don\u2019t think science at large changes on a weekly basis.\u201d<\/p>\n

\u201cIf we\u2019re trying to integrate the latest tools, we\u2019re always going to be running after the literature, and it\u2019s going to be exhausting and we\u2019re going to fail,\u201d he continues. \u201cBetter to take a step back and wait to see what emerges as the most useful.\u201d<\/p>\n

That\u2019s prudent advice. But there are practical challenges to consider when incorporating AI into your analysis \u2014 in particular, uncertainty and natural human bias.<\/p>\n

Virginie Uhlmann leads a bioimage-quantification research group at the EMBL-EBI, where she works on the design of AI programs for image analysis. One advantage of delegating biological-image analysis to a computer, she explains, is that it helps to mitigate our innate human limitations: \u201cOne of the things we are very, very bad at is understanding what brings us a decision; how do we determine that this is \u2018object A\u2019 and this is \u2018object B\u2019 in an image, for example.\u201d<\/p>\n

With machine learning, she continues, \u201cthe real power is, you\u2019re not trying to determine and write the rules yourself; you\u2019re leaving it up to the machine\u201d.<\/p>\n

But relying too heavily on the AI comes with its own risks, she warns.<\/p>\n

\"Portrait
Virginie Uhlmann, who leads a bioimage-quantification research group, suggests that you carefully evaluate what an AI tells you, to understand its decision.Credit: Jeff Dowling\/EMBL-EBI<\/span><\/figcaption><\/figure>\n

 <\/p>\n

Uhlmann\u2019s advice: carefully consider what the AI tells you, to understand how and why it made its decision. \u201cThere are lots of very famous examples of very dumb decision-making that somehow leads to the right conclusion.\u201d<\/p>\n

Uhlmann\u2019s team has a useful test for any AI: giving it a task for which you already know the solution. \u201cThis is a good way to check the algorithm is working as it should be and also maintain confidence in it,\u201d she says.<\/p>\n

Image analysis, for example, can depend heavily on the conditions under which the cells or tissue images were captured \u2014 perhaps the light was better on one day, or a different person was behind the microscope. Machine-learning developers and users can address this challenge by being \u201cmindful about the information they put in\u201d, Uhlmann says: \u201cI have to think, \u2018Was I biased in the way I selected my examples of A and B? Is that really representative of the variation between A and B?\u2019\u201d<\/p>\n

Also challenging is data management. As Singh explains, some projects generate hundreds of terabytes of images and measurement data, but the data-science expertise needed to analyse them isn\u2019t always available. \u201cWe definitely need more people who are able to work with high-dimensional data, who can tease apart the noise,\u201d he says.<\/p>\n

Learn from the community<\/strong><\/h2>\n

Inspired by CellProfiler and its potential, Garcia Fossa e-mailed the Broad Institute\u2019s Imaging Platform to learn more about the tool and its development. To her surprise, lab leader and co-developer Cimini replied almost instantly, inviting her to see the lab\u2019s work at first hand.<\/p>\n

Garcia Fossa spent a year in Massachusetts, where she worked on her doctorate while helping to develop CellProfiler. \u201cDon\u2019t be afraid to contact the developers of AI tools,\u201d she advises. \u201cIn my experience, they want to share their knowledge and get that feedback from the community to make the tools better.\u201d<\/p>\n

And for people who can\u2019t attend training in person, there is a flourishing online community of AI-adopters in bioscience, whose members offer support and share resources on several global and regional forums. Singh recommends websites such as\u00a0forum.image.sc<\/a>, a discussion group for scientific image software, sponsored by the Center for Open Bioimage Analysis, a collaboration between the Broad Institute and the University of Wisconsin\u2013Madison. Other options include\u00a0BioStars.org<\/a>\u00a0and GitHub, which bioinformaticians use for online discussions and to share practical examples and code.<\/p>\n

Ultimately, the best way to hone AI skills is through practice, and the data-science community platform\u00a0Kaggle<\/a>\u00a0can offer some incentives. Informaticians can enter AI-related competitions on the platform and can win monetary prizes. It also offers a space for users to stress-test and compare their designs.<\/p>\n

But win or lose, don\u2019t shy away from mistakes, advises Garcia Fossa \u2014 they\u2019re neither particularly expensive nor difficult to clean up. \u201cIt\u2019s important to play around with the program and learn through doing,\u201d she says. \u201cThat way, it will become second nature before you know it.\u201d<\/p>\n

Nature<\/em>\u00a0622<\/strong>, S1-S3 (2023)<\/p>\n

doi: https:\/\/doi.org\/10.1038\/d41586-023-03067-6<\/em><\/p>\n[\/vc_column_text][vc_column_text]\u00a9 2023 Springer Nature Limited.[\/vc_column_text][\/vc_column][vc_column width=”1\/4″ css=”.vc_custom_1538657003956{padding-top: 0px !important;padding-bottom: 0px !important;}”][vc_custom_heading text=”ISSUES” font_container=”tag:h3|font_size:12px|text_align:left|color:%23333333|line_height:18px” use_theme_fonts=”yes” el_class=”titlewidget”][vc_column_text el_class=”columnflex” css=”.vc_custom_1664404002021{margin-bottom: 40px !important;}”]

October 2023<\/a><\/p>\r\n

September 2023<\/a><\/p>\r\n

August 2023<\/a><\/p>[\/vc_column_text][vc_custom_heading text=”OTHER ARTICLES IN THIS ISSUE” font_container=”tag:h3|font_size:12px|text_align:left|color:%23333333|line_height:18px” use_theme_fonts=”yes” el_class=”titlewidget” css=”.vc_custom_1674576179369{margin-bottom: 15px !important;}”][vc_basic_grid post_type=”post” max_items=”-1″ style=”load-more” items_per_page=”4″ element_width=”12″ orderby=”rand” item=”538″ grid_id=”vc_gid:1697708781777-845f0bce-cf8a-10″ taxonomies=”32″][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"Incorporating machine-learning tools into data analysis can accelerate discovery and free up valuable time. ...more<\/a>","protected":false},"author":29,"featured_media":2978,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[7],"tags":[29,32],"class_list":{"0":"post-2984","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-science-connect","8":"tag-nature-briefings","9":"tag-october-2023"},"acf":[],"yoast_head":"\nHow to spice up your bioinformatics skill set with AI - 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