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GEN April 2022 Page 36

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"With MatchMaker at hand, we were able to replace our reliance on conventional molecular docking in our fl agship proteome screening platform Ligand Express," the blog post detailed. "MatchMaker also plays a critical role in our newly launched Ligand Design technology for multi-objective drug design. Taken together, Ligand Design and Ligand Express, our fi rst-generation off-target profi ling platform, offer a unique end-to-end AI-augmented drug discovery platform to design ad-vanced lead-like molecules while minimizing off-target effects." Turning speci c details into generalizable rules Molly Gibson, PhD, is the co-founder of Generate Bio-medicines, a biotech company that uses a machine learning platform called Generative Biology to expedite the discovery of protein-based drugs. The platform, which leverages statistics to uncover patterns linking amino acid sequence, structure, and function, is designed to expand the available search space for novel biomedicines.


Team develops a universal AI algorithm for in-depth cleaning of single cell genomic data

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Just as asking a single person about their health will provide tailored, personalized information impossible to glean from a large poll, an individual cell's genome or transcriptome can provide much more information about their place in living systems than sequencing a whole batch of cells. But until recent years, the technology didn't exist to get that high resolution genomic data--and until today, there wasn't a reliable way to ensure the high quality and usefulness of that data. Researchers from the University of North Carolina at Charlotte, led by Dr. Weijun Luo and Dr. Cory Brouwer, have developed an artificial intelligence algorithm to "clean" noisy single-cell RNA sequencing (scRNA-Seq) data. The study, "A Universal Deep Neural Network for In-Depth Cleaning of Single-Cell RNA-Seq Data," was published in Nature Communications on April 7, 2022. From identifying the specific genes associated with sickle cell anemia and breast cancer to creating the mRNA vaccines in the ongoing COVID-19 pandemic, scientists have been searching genomes to unlock the secrets of life since the Human Genome Project of the 1990s.


AI Widens Search Spaces and Promises More Hits in Drug Discovery

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Traditional drug discovery techniques are all about brute force--and a little bit of luck. Basically, large-scale, high-throughput screening is used to cover a search space. The process is a little like conducting antisubmarine warfare without the benefit of sonar. Unsurprisingly, very few of the depth charges (drug candidates) hit their targets and achieve the desired results (successful clinical trials). The seas are simply too vast.


The Basic Essentials: Statistics For Machine Learning

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Knowledge of statistics is important if you work with data. Having a firm grasp of some fundamental concepts goes a long way in your ability to effectively communicate. You'll also understand the proper methods to collect, analyze, make decisions, and effectively present results that have been discovered from data. In this article, we are going to be using the Breast Cancer Wisconsin dataset from sklearn to cover some fundamental statistics concepts. Below we've imported the necessary frameworks and loaded our data into memory.


Artificial Intelligence's Promise and Peril

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John Quackenbush was frustrated with Google. It was January 2020, and a team led by researchers from Google Health had just published a study in Nature about an artificial intelligence (AI) system they had developed to analyze mammograms for signs of breast cancer. The system didn't just work, according to the study, it worked exceptionally well. When the team fed it two large sets of images to analyze--one from the UK and one from the U.S.--it reduced false positives by 1.2 and 5.7 percent and false negatives by 2.7 and 9.4 percent compared with the original determinations made by medical professionals. In a separate test that pitted the AI system against six board-certified radiologists in analyzing nearly 500 mammograms, the algorithm outperformed each of the specialists. The authors concluded that the system was "capable of surpassing human experts in breast cancer prediction" and ready for clinical trials. An avalanche of buzzy headlines soon followed. "Google AI system can beat doctors at detecting breast cancer," a CNN story declared.


Artificial Intelligence Can See Breast Cancer Before It Happens

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The use of artificial intelligence (AI) and deep learning (DL) in the medical and healthcare field has been increasing at an astonishing rate. While the Health Insurance Portability and Accountability Act (HIPAA) is important for the protection of personal health information, it presented as the biggest barrier for gathering large data sets required for deep learning. Several strategies have been successfully implemented to gather lots of data for training medical AI systems without risking patient privacy. AI continues to have a significant impact on medical imaging and deep learning models are constantly being developed to look for anomalies such as bone fractures or possible cancer. The introduction of breast cancer screening has helped to reduce cancer mortality rates in women as well as provide a consistent source of image data.


The Scope Of Computer Vision In Nuclear Medicine

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The inclusion of technologies such as AI and computer vision in healthcare can greatly enhance high-precision applications like nuclear medicine. Nuclear medicine is a subfield of radiology that involves the use of minute amounts of radiation and radiation-based medicines, known as radiopharmaceuticals, to evaluate the composition and functioning of bones and tissue in patients. Today, nuclear medicine and radiology are prominent components of modern healthcare, especially for cancer diagnosis and treatment. X-rays and CT scans are some of the methods that involve radiation usage in healthcare. The use of powerful radiation beams to inhibit the growth of tumors in cancer patients is also a common healthcare application.


Modality specific U-Net variants for biomedical image segmentation: a survey - Artificial Intelligence Review

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With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net architectures are most widely utilized in biomedical image segmentation to address the automation in identification and detection of the target regions or sub-regions. In recent studies, U-Net based approaches have illustrated state-of-the-art performance in different applications for the development of computer-aided diagnosis systems for early diagnosis and treatment of diseases such as brain tumor, lung cancer, alzheimer, breast cancer, etc., using various modalities. This article contributes in presenting the success of these approaches by describing the U-Net framework, followed by the comprehensive analysis of the U-Net variants by performing (1) inter-modality, and (2) intra-modality categorization to establish better insights into the associated challenges and solutions. Besides, this article also highlights the contribution of U-Net based frameworks in the ongoing pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19. Finally, the strengths and similarities of these U-Net variants are analysed along with the challenges involved in biomedical image segmentation to uncover promising future research directions in this area.


Serve your first model with Scikit-Learn + Flask + Docker

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One of the first steps in achieving this is to create a process to serve machine learning models to the organization. This is usually done by creating an application to run the prediction model and return the prediction, in the example in this post we are going to use a handy stack to create and serve models. We will be using Python as the base programming language, the Scikit-Learn package for building the model pipeline: preprocessing the data, training the model and saving the model into a file, the Flask package to develop a web application for the interaction between the client and the prediction model and finally Docker for containerizing the application to prepare it for deployment. In this example we are going to work with the dataset: Breast Cancer Wisconsin (Diagnostic) [1], a widely used dataset for testing machine learning models. In this dataset features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass and it was first introduced in K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23–34].


Emerging Applications of Artificial Intelligence in Cancer Care - American Association for Cancer Research (AACR)

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Now, we trust the complex processes underlying artificial intelligence (AI) with everything from navigation to movie recommendations to targeted advertising. Can we also trust machine learning with our health care? The integration of AI and cancer care was a popular topic in 2021, as evidenced by prominent sessions at two of last year's AACR conferences: the 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved, held virtually October 6-8, 2021, and the San Antonio Breast Cancer Symposium (SABCS), held in a hybrid format December 7-10, 2021. During these sessions, experts gave an overview of how machine learning works, shared data on new applications of AI technologies, and emphasized important considerations for making algorithms equitable. Recognizing that a diverse audience of breast cancer clinicians and researchers may have questions about the fundamentals of AI, the SABCS session "Artificial Intelligence: Beyond the Soundbites" opened with a talk titled, "Everything You Always Wanted to Know About AI But Were Afraid to Ask," presented by Regina Barzilay, PhD, the AI faculty lead at the Jameel Clinic of the Massachusetts Institute of Technology.