Generative AI, which refers to artificial intelligence systems that can generate new content based on learned patterns and data, has transformative potential across a wide range of industries. Here’s a deeper look into how this technology can be applied in healthcare, entertainment, and design:

Healthcare

1. Drug Discovery and Development:

Generative AI can accelerate the drug discovery process by predicting molecular behavior and generating new compounds that might be effective against specific diseases. This reduces the time and cost associated with traditional drug discovery methods.

2. Personalized Medicine:

AI models can generate personalized treatment plans by analyzing patient data, including genetic information, lifestyle, and previous health records. This can lead to more effective and tailored treatments for individual patients.

3. Medical Imaging:

AI can enhance image analysis in radiology and pathology. Generative models can improve the clarity of medical images, generate synthetic data for training purposes, and even help in reconstructing missing or corrupted data.

4. Prosthetics and Implants Design:

AI can assist in designing custom prosthetics and implants by generating models that perfectly fit the unique anatomical structure of patients. This can improve comfort and functionality for the user.

Entertainment

1. Content Creation:

In film, music, and gaming, generative AI can create new scripts, compose music, or develop new gaming environments and scenarios. This can lead to more innovative and engaging content.

2. Virtual Reality and Augmented Reality:

AI can generate immersive environments that are indistinguishable from real life, enhancing the user experience in VR and AR applications. This technology can create dynamic scenarios that react to the user’s actions in real-time.

3. Animation and Visual Effects:

Generative AI can automate part of the animation process, creating realistic and complex animations that would be time-consuming and costly to produce manually. It can also be used to enhance visual effects in movies and video games.

Design

1. Architectural and Industrial Design:

AI can help designers by generating multiple design alternatives based on specific criteria like space utilization, energy efficiency, or aesthetic preferences. This allows designers to explore more options and optimize designs more efficiently.

2. Fashion and Textile Design:

In fashion, AI can predict trends and generate new designs based on past styles, current trends, and emerging preferences. It can also help in creating custom clothing by generating patterns and designs that fit individual customers.

3. User Experience (UX) and Interface Design:

AI can generate design elements that are optimized for usability and aesthetic value, helping UX designers create more effective interfaces. It can also simulate user interactions to predict how changes to the design will impact user experience.

Cross-Industry Applications

1. Automation of Creative Processes:

Across industries, generative AI can automate repetitive and time-consuming tasks, allowing humans to focus on more strategic and creative aspects of their work.

2. Enhanced Decision Making:

By generating forecasts, scenarios, and models, AI can aid in complex decision-making processes, providing insights that might not be apparent through traditional methods.

3. Training and Simulation:

Generative AI can create realistic scenarios for training purposes across various fields, from piloting aircraft to medical surgery simulations, enhancing the learning experience without the associated risks of real-world training.

Generative AI’s ability to analyze vast amounts of data and generate insightful outputs makes it a powerful tool in these and many other industries, potentially leading to innovations that can transform the way we live and work.

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May 20, 2020 – A big data analytics tool that uses information from multiple cancer types could help researchers identify potential treatments and accelerate precision medicine, a study published in the Journal of Clinical Oncology Clinical Cancer Informatics revealed.

Developed by researchers at the University of Michigan Rogel Cancer Center, the tool combines multiple datasets to help turn information into meaningful clinical insights. Recent efforts to categorize the molecular data of multiple cancer types has produced an overwhelming amount of data, researchers noted, and this tool could help researchers make sense of it all.

“Our idea was to combine three sources of data sets – molecular data from both cancer cell lines and patients and drug profiling data – to understand proper preclinical models that are most representative of these tumors,” said Veerabhadran Baladandayuthapani, PhD, professor of biostatistics at the University of Michigan School of Public Health.

The tool, called TransPRECISE, uses data from 7,714 patient samples across 31 cancer types, collected as part of the Cancer Proteome Atlas. This information is combined with 640 cancer cell lines from the MD Anderson Cell Lines Project and drug sensitivity data representing 481 drugs from the Genomics of Drug Sensitivity in Cancer model system.

“The good thing is this is a very dynamic process. We can have this whole system set up in a computer. As new patients come in or new data comes in, you can keep adding it,” said Rupam Bhattacharrya, MStat, a doctoral student and first author on the paper. 

The new tool builds on an earlier model from the team, called PRECISE (personalized cancer-specific integrated network estimation model). The PRECISE model aimed to analyze the changes that occur to the molecular structure of individual patients’ individual tumors.

TransPRECISE adds in data from cell lines and drug sensitivity, which will be helpful for researchers translating cancer cell biology into drug discovery.

“Now that we have tens of thousands of tumors on these patients, we can evaluate what might be the potential therapeutic efficiency of these drugs. The key idea was to develop an analytic tool to do that,” said Baladandayuthapani, who is also director of the Rogel Cancer Center’s cancer data science shared resource. 

The research team validated the tool by comparing known drug responses and clinical outcomes in patient data. The model identified the differences in proteins among individual tumors, and accurately tied it back to actual patient outcomes.

Researchers also looked at several pathways to predict potential drug targets, which generated results that reflected current treatment recommendations or targets being tested in clinical trials, such as ibrutinib for BRCA-positive breast cancer.

In addition to the published study, researchers have made a comprehensive database and visualization of their findings publicly available. The team expects the tool to lead to accelerated drug discovery for different types of cancer.

“We have so much data, how do we drill it down to make it more informative so an oncologist can understand? Our work would potentially help oncologists or researchers develop concrete hypotheses based on which mechanism is working, potentially bringing to the top drugs that might warrant more evaluation,” Baladandayuthapani said.

The results demonstrate the potential for analytics tools to advance precision medicine for cancer and other types of complex diseases.

“In summary, TransPRECISE offers the potential to bridge the gap between human and preclinical models to delineate actionable cancer-pathway-drug interactions to assist personalized systems biomedicine approaches in the clinic,” researchers stated.

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February 24, 2020 – With the prevalence of antibiotic resistance rapidly rising, a new study published in Cell is offering a more efficient, cost-efficient strategy for antibiotic discovery using machine learning.

There is an innate need to discover new antibiotics due to the emergence of antibiotic-resistant bacteria. And antibiotics have become the cornerstone of modern medicine since the discovery of penicillin. 

In the past, antibiotics were discovered through screening soil-swelling microbes for secondary metabolites that prevented the growth of pathogenic bacteria, the study stated. But lately, the discovery of antibiotics has proved difficult. 

“Natural product discovery is now plagued by the dereplication problem, wherein the same molecules are being repeatedly discovered,” researchers said in the study. 

Many antibiotic discovery programs have looked to screening large synthetic chemical libraries. But the challenge lies in high cost due to the thousands to millions of molecules it contains. 

Machine learning offers to enhance the antibiotic discovery process, researchers found using its own model to predict antibacterial compounds in silico from a collection of >107 million compounds.. 

“[T]he development of new approaches that can substantially decrease the cost and increase the rate of antibiotic discovery is essential to reinfuse the pipeline with a steady stream of candidates that show promise as next-generation therapeutics,” they wrote in the study. “The adoption of machine learning approaches is ideally suited to address these hurdles.”

Researchers intended to find how the combination of computerized predictions and empirical investigations can further the discovery of new antibiotics. They also looked to obtain a training dataset that was inexpensive and chemically diverse to allow for the development of a robust model for antibiotic discovery. 

First, using a collection of 2,335 molecules, researchers trained a deep neutral network model to predict growth inhibition of Escherichia coli. Then they identified potential lead compounds with activity against E. Coli. 

The resulting model was used to predict antibacterial compounds. It performed well with a 51.5 percent accuracy rate. The model approach was capable of generalization and permitted access to new antibiotic chemistry. 

Using machine learning for antibiotic discovery can help process years of data to make informed decisions about the development of various medications, researchers confirmed with this study.

“Given recent advancements in machine learning, the field is now ripe for the application of algorithmic solutions for molecular property prediction to identify novel structural classes of antibiotics,” researchers explained. 

Previously, machine learning has lacked accuracy and been insufficient to substantially change traditional drug discovery. But with recent algorithmic advances, there is an opening to influence the drug discovery system. 

Traditionally, molecules were represented by their fingertip vectors which showed the presence or absence of functional groups in the molecule, the study highlighted. These neural network vectors had the ability to learn the representation automatically.

While neural network models narrowed the performance gap between analytical and experimental approaches, there is some disparity. 

 “It is important to emphasize that machine learning is imperfect,” researchers highlighted. There are many elements that must be taken into consideration when applying machine learning including the design for training, the composition of the training data itself, and the predication prioritization.  

“Overall, our results suggest that the time is ripe for the application of modern machine learning approaches for antibiotic discovery. Deep learning could therefore enable us to expand our antibiotic arsenal and help outpace the dissemination of resistance,” researchers concluded.

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January 22, 2020 – Machine learning algorithms have the potential to accelerate and refine the drug development process, but the industry should expand data access and create consistent data standards to ensure drug companies can fully leverage these tools, according to a report from the Government Accountability Office (GAO).

Drug companies spend ten to 15 years bringing a drug to market, often at high costs. Only about one in every 10,000 chemical compounds initially tested for drug potential makes it through the research and development pipeline, GAO noted, and is then approved by the FDA for marketing in the US. Machine learning tools could accelerate and improve the drug development process.

“Machine learning can make drug development more efficient and effective, decreasing the time and cost required to bring potentially more effective drugs to market,” GAO said.

“Both of these improvements could save lives and reduce suffering by getting drugs to patients in need more quickly. Lower research and development costs could also allow researchers to invest more resources in disease areas that are currently not considered profitable to pursue, such as rare or orphan diseases.”

Although drug companies already use machine learning throughout the drug development process, there are several challenges that hinder its advancement in this area, including barriers to data access and sharing.

“According to one industry representative, collecting data from the early drug discovery phase can be cost prohibitive. This representative said that certain health-related data may cost tens of thousands of dollars, as compared to just cents for other consumer related data that many technology companies use,” GAO stated.

“Data sharing also presents unique legal issues. According to stakeholders, privacy laws such as HIPAA can make it difficult for drug companies, especially those that are not regulated by HIPAA, to share or access data.”

To increase data sharing and access, GAO recommended that policymakers create mechanisms or incentives to share data held by private or public sectors, while also ensuring patient information is protected.

“To promote greater availability of data, policymakers could consider forming or facilitating research consortia that allow for secure data sharing,” the organization wrote.

“Policymakers could also consider creating a data repository through encouraging an industry-driven solution, establishing a public-private partnership, or creating a repository of all data under their control.”

In creating new ways to share and access data, stakeholders should ensure they adhere to laws around information exchange.

“Improper data sharing or use could have legal consequences. Increased data sharing could therefore require a careful review of the legal ramifications, because data are often gathered through a wide variety of mechanisms and governed by multiple legal frameworks,” GAO advised.

In addition to data sharing and access, policymakers will need to address the lack of quality data in the drug development process.

“Machine learning requires a large amount of accurate and representative data. This poses a unique challenge in drug development, as much of the data were not originally collected with machine learning in mind and may not be machine-readable or model-ready,” GAO wrote.

“Furthermore, according to an industry representative, data collected across different organizations and environments come in different formats, and this lack of standardization in data quality is a barrier.”

READ MORE: Data Standards, Governance Will Address Social Determinants of Health

Overcoming data quality issues will require policymakers to collaborate with appropriate stakeholders to establish data standards, GAO said.

“For example, a standard that defines synthetic data and how they can be used can help reduce bias by allowing researchers to generate data that could be used to better represent currently underrepresented communities,” the agency stated.

“Similarly, a standard data format for uploading and sharing data across platforms could reduce the need for data scientists to spend time converting data sets to machine-readable formats.”

GAO also named drug development research gaps as an obstacle to machine learning use.

“Research gaps present a significant challenge to advancing the use of machine learning in drug development. These gaps fall into two broad categories: gaps in understanding of fundamental biology and chemistry, and gaps in domain-specific machine learning research,” GAO said.

“Experts in the field have noted that addressing these issues may be transformational for future applications of machine learning in drug development.”

GAO suggested that policymakers promote basic research to generate new and better data to improve understanding of machine learning in drug development.

“Policymakers could promote the field in multiple ways, including approaches such as support for intramural research, grants, or other subsidies. Policymakers could choose to use one of these approaches or combine them,” GAO said.

“Policymakers could also support collaboration across sectors. The Machine Learning for Pharmaceutical Discovery and Synthesis Consortium (MLPDS) is a collaboration between large drug companies such as Pfizer, Merck, and Novartis with the Chemical Engineering, Chemistry, and Computer Science departments at the Massachusetts Institute of Technology, and has published a variety of papers at the intersection of machine learning and drug development.”

With these recommendations, policymakers and other stakeholders can advance the use of machine learning in drug development, refining and speeding the process to benefit patients.

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Researchers at the Sanford Burnham Prebys Medical Discovery Institute say that machine learning’s powerful ability to detect patterns in complex data is revolutionizing how scientists diagnose disease and, now, how they discover new drugs.

The Sanford Burnham team has developed a machine-learning algorithm that gleans information from microscope images that allow for high-throughput epigenetic drug screens. They believe that this approach (“Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape”), described in eLife, could unlock new treatments for cancer, heart disease, mental illness, and other diseases.

“High-content phenotypic screening has become the approach of choice for drug discovery due to its ability to extract drug-specific multi-layered data. In the field of epigenetics, such screening methods have suffered from a lack of tools sensitive to selective epigenetic perturbations. Here we describe a novel approach, Microscopic Imaging of Epigenetic Landscapes (MIEL), which captures the nuclear staining patterns of epigenetic marks and employs machine learning to accurately distinguish between such patterns,” the investigators wrote.

“We validated the MIEL platform across multiple cells lines and using dose-response curves, to insure the fidelity and robustness of this approach for high content high-throughput drug discovery. Focusing on noncytotoxic glioblastoma treatments, we demonstrated that MIEL can identify and classify epigenetically active drugs. Furthermore, we show MIEL was able to accurately rank candidate drugs by their ability to produce desired epigenetic alterations consistent with increased sensitivity to chemotherapeutic agents or with induction of glioblastoma differentiation.”

“In order to identify the rare few drug candidates that induce desired epigenetic effects, scientists need methods to screen hundreds of thousands of potential compounds,” said Alexey Terskikh, PhD, associate professor in Sanford Burnham Prebys’ development, aging and regeneration program and senior author of the study. “Our study describes a powerful image-based approach that enables high-throughput epigenetic drug discovery.”

Several medicines that target epigenetic alterations are approved by the FDA for the treatment of cancer, and researchers are working to find additional epigenetic-based therapies. However, drug discovery has been slowed by a lack of a high-throughput screening method, explained Terskikh. Scientists currently visualize epigenetic changes using special dyes and traditional microscopy methods.

In the study, the scientists trained a machine-learning algorithm using a set of more than 220 drugs known to work epigenetically. The resulting method (MIEL) was able to detect active drugs, classify the compounds by their molecular function, spot epigenetic changes across multiple cell lines and drug concentrations, and help identify how unknown compounds work. The scientists used the approach to identify epigenetic compounds that may be able to help treat glioblastoma, a deadly brain cancer.

“Our method is ready for immediate use by pharmaceutical companies looking to develop epigenetic drug screens,” said Chen Farhy, PhD, a postdoctoral researcher in the Terskikh lab and first author of the study. “Industry and academic researchers working on mechanistic studies may also benefit from this method, as the algorithm can detect and categorize epigenetic changes induced by experimental treatments, genetic manipulations, or other approaches.”

Terskikh and his team are already using the algorithm to study epigenetic changes in aging cells, with the aim of developing compounds that promote healthy aging—the single greatest risk factor for disease. This work is conducted in collaboration with Sanford Burnham Prebys professor Peter Adams, PhD. Terskikh said he is eager to broaden the technology from 2D images to 3D videos, which will expand the power of the approach.

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