By using deep learning and predictive analytics, researchers have determined who could develop age-related chronic disease based on immune system health.

Researchers from the Buck Institute and Stanford University have created an inflammatory clock for aging (iAge) that uses deep learning and predictive analytics to determine immunological health and chronic diseases associated with aging. By utilizing artificial intelligence technology, researchers studied the blood immunome of 1001 people.

The team of researchers also discovered a modifiable chemokine associated with cardiac aging. This chemokine can be used for early detection of age-related pathology and can help provide targets for interventions.

“Standard immune metrics which can be used to identify individuals most at risk for developing single or even multiple chronic diseases of aging have been sorely lacking,” David Furman, PhD, Buck Institute Associate Professor, Director of the 1001 Immunomes Project at Stanford University School of Medicine and senior author of the study said in a press release.

Dig Deeper

• Deep Learning Aids Prediction of Lung Cancer Immunotherapy Response
• Deep Learning, Genomic Data May Help Predict Alzheimer’s Disease
• Deep Learning Approach May Reduce Knee Pain Disparities

“Bringing biology to our completely unbiased approach allowed us to identify a number of metrics, including a small immune protein which is involved in age-related systemic chronic inflammation and cardiac aging. We now have means of detecting dysfunction and a pathway to intervention before full-blown pathology occurs,” Furman continued.

According to first author Nazish Sayed, MD, PhD, Assistant Professor of Vascular Surgery at Stanford Medicine, the study highlights the soluble chemokine CXCL9 as the major contributor to iAge. Furman describes CXCL0 as a small immune protein typically called to action to attract lymphocytes to infection sites.

“But in this case we showed that CXCL9 upregulates multiple genes implicated in inflammation and is involved in cellular senescence, vascular aging and adverse cardiac remodeling,” Furman stated then added that silencing CXCL9 reversed the loss of function in aging endothelial cells in humans and mice.

According to Furman, the age of one’s immune system provides important information regarding health and longevity.

“On average, centenarians have an immune age that is 40 years younger than what is considered ‘normal’ and we have one outlier, a super-healthy 105 year-old man (who lives in Italy) who has the immune system of a 25 year old,” he said.

 Results for the initial analysis and the cardiac health study were able to be validated. Additionally, Furman said that the researchers found a correlation between CXCL9 and the results from the pulse wave velocity testing.

“These people are all healthy according to all available lab tests and clinical assessments, but by using iAge we were able to predict who is likely to suffer from left ventricular hypertrophy (an enlargement and thickening of the walls of the heart’s main pumping chamber) and vascular dysfunction,” Furman said.

These artificial intelligence tools can be used to track a patient’s risk of developing multiple chronic diseases by assessing the total physiological damage done to their immune system.

Predictive analytics of age-related frailty can be determined by comparing biological immune metrics to information about how long it takes an individual to perform a task, such as standing up from a chair or walking a certain distance.

“Using iAge it’s possible to predict seven years in advance who is going to become frail,” Furman said. “That leaves us lots of room for interventions.”

In 2013, a group of researchers conducted a study on aging and identified nine “hallmarks” in the process. Age-related immune system dysfunction was not one of them.

“It’s becoming clear that we have to pay more attention to the immune system with age, given that almost every age-related malady has inflammation as part of its etiology,” said Furman.

“If you’re chronically inflamed, you will have genomic instability as well as mitochondrial dysfunction and issues with protein stability. Systemic chronic inflammation triggers telomere attrition, as well as epigenetic alterations. It’s clear that all of these nine hallmarks are, by and large, triggered by having systemic chronic inflammation in your body. I think of inflammation as the 10th hallmark,” Furman concluded.

The post Deep Learning, Predictive Analytics Helps Identify Chronic Diseases appeared first on Artificial Intelligence.

Automated predictive analysis is making way for the greatest transformations in the industries!

Automated predictive analysis, or predictive analytics, uses historical data to predict future events. Throughout history, humans are obsessed with predicting the future. The fear of the unknown has led several scientific researchers and professors to develop technologies that can determine the future so that necessary steps can be taken to avoid drastic losses.

Predictive analytics has received a lot of attention in recent years due to its advances in supporting various technologies, particularly in the area of big data and artificial intelligence.

With the increased competition, businesses seek power over their competitors in bringing products and services to crowded markets. Data-driven predictive models can bring companies solutions to long-standing problems in terms of business operations. This technology provides a trove of information from which analytics tools and applications draw insights and predict the upcoming opportunities, suitable investments, and dangers in the market.

Businesses use tools like Hadoop and Spark to extract information from big data. These data sources might consist of transactional databases, equipment log files, images, videos, audios, sensors, and other types of data.

With all this data, tools are necessary to extract insights and trends. Predictive analytics finds patterns in data to build models that predict future outcomes. Other varieties of machine learning techniques are also available, including linear and nonlinear regression, neural networks, support vector machines, decision trees, and other algorithms.

For decades, automated predictive analysis has been used by meteorologists to predict weather and climate forecasts. With time, this concept has been used to study consumer behavior, forecast supply and demand in economic statistics, and related purposes.

• How Can Artificial Intelligence Drive Predictive Analytics To New Heights
• How Predictive Analytics Will Impact Human Resources
• What Is Predictive Analytics And Can It Help You Achieve Business Objectives

Automated predictive technology: Today and beyond 

Data is the core of predictive analytics. Earlier, when there were no computers, businesses used other creative ways to understand what the customers want and predict market conditions. These ways did not involve technological tools or applications.

Currently, one of the most vital industrial applications of predictive models includes energy load forecasting to predict energy demand in the future. Energy producers, grid operators, and traders need accurate predictions of energy load to make decisions for managing tasks in electric grids. Grid operators use data to draw actionable insights.

Artificial intelligence and predictive technology, have revolutionized the way advertisers and marketers work. Targeted advertising uses data like previously purchased products, location, and age to serve the target audience. Today, consumer profiles are much more advanced, and enterprises can gather information from various sources.

Predictive analytics is also used to measure vehicle and pedestrian traffic to coordinate traffic lights, public transportation, and even pedestrian crosswalks to facilitate convenience and efficiency in community design. This also boosts the safety of the public and allocates emergency services more efficiently by predicting the number of officers needed on a task and reassigns posts accordingly.

Automated predictive technology, has played a crucial role in facilitating better medical resources. This technology helps improve the patients’ health outcomes. Rather than completely relying on the patient’s medical history, predictive systems can generate data from a broad spectrum of symptoms, data of other patients, and the treatments used to cure the disease.

AI and machine learning have provided us with various ways through which we can predict the future. With the growing technological evolution in automation and data analysis, our lives will be changed forever and for the better.

The post HOW HAS AUTOMATED PREDICTIVE ANALYSIS DEVELOPED OVER THE YEARS appeared first on Artificial Intelligence.

A new machine learning algorithm system uses predictive analytics to determine which transcription factors are active in individual cells.

Scientists at the University of Illinois Chicago have introduced a new system that uses a machine learning algorithm and predictive analytics to find what transcription factors are most likely to be active in individual cells. The system was created to provide researchers with a more efficient method of identifying the regulators of genes.

Transcription factors are proteins that bind to DNA and have control around what genes are active inside a cell. Understanding and manipulating these signals in a cell is crucial to the biomedical field. Additionally, using this method of manipulating signals within a cell has proven to be an effective way to discover new treatments and illnesses.

However, there are hundreds of transcription factors inside a human cell. It could take years of research, and lots of trial and error, to determine the most active factor.

Dig Deeper

• Machine Learning Predicts Dialysis, Death in COVID-19 Patients
• Machine Learning Gauges Unconsciousness Under Anesthesia
• AI, Predictive Analytics Pave Way for Premature Baby Care

“One of the challenges in the field is that the same genes may be turned ‘on’ in one group of cells but turned ‘off’ in a different group of cells within the same organ,” Jalees Rehman, UIC professor in the department of medicine and the department of pharmacology and regenerative medicine at the College of Medicine, said in a press release.

“Being able to understand the activity of transcription factors in individual cells would allow researchers to study activity profiles in all the major cell types of major organs such as the heart, brain or lungs,” Rehman continued.

The system developed by the University of Illinois Chicago is named BITFAM, standing for Bayesian Inference Transcription Factor Activity Model. The machine learning algorithm system operates by “combining new gene expression profile data gathered from single cell RNA sequencing with existing biological data on transcription factor target genes,” UIC stated in a press release.

With all the information, the system will run multiple computer-based simulations to find the best fit and predict the activity for every transcription factor in the cell.

The system was tested on cells from tissue in the lung, heart, and brain by Rehman and fellow UIC researcher Yang Dai, UIC associate professor in the department of bioengineering at the College of Medicine and the College of Engineering.

“Our approach not only identifies meaningful transcription factor activities but also provides valuable insights into underlying transcription factor regulatory mechanisms,” Shang Gao, first author of the study and a doctoral student in the department of bioengineering said in a press release.

“For example, if 80% of a specific transcription factor’s targets are turned on inside the cell, that tells us that its activity is high. By providing data like this for every transcription factor in the cell, the model can give researchers a good idea of which ones to look at first when exploring new drug targets to work on that type of cell,” Gao continued.

According to the researchers, the machine learning algorithm system is available to the public and could be applied widely. Users can combine the system with additional analysis methods that may be better suited for their own studies. This could include finding new drug targets.

“This new approach could be used to develop key biological hypotheses regarding the regulatory transcription factors in cells related to a broad range of scientific hypotheses and topics. It will allow us to derive insights into the biological functions of cells from many tissues,” Dai said.

Rehman explained the application relevant to his lab is to use the new machine learning algorithm system to focus on factors that increase disease in certain cells.

“For example, we would like to understand if there is transcription factor activity that distinguished a healthy immune cell response from an unhealthy one, as in the case of conditions such as COVID-19, heart disease or Alzheimer’s disease where there is often an imbalance between healthy and unhealthy immune responses,” Rehman said.

The post Machine Learning Algorithm Brings Predictive Analytics to Cell Study appeared first on Artificial Intelligence.

In a digital age, it only takes a few years for research into cutting-edge areas like predictive analytics modelling and artificial intelligence to find practical uses in everyday business contexts. Areas like general usability, user interface, and semantics that are changed to empower a broader cross-section of potential users.

Business-focused users keen to leverage statistical methods may not be capable of or comfortable with interacting in a pure text terminal in Python or R, for example. After all, open-source code is exactly that: can you blame someone for not wanting to make an investment decision based on code found freely on the internet? Business users need an easy way to interface with powerful analytics and need a trusted brand that stands behind them. That’s exactly why Minitab has changed the game, making machine learning easy for everyone.

Today, business users do not have to compromise to do advanced analytics. For statistical analysis, predictive analytics, and machine learning, there is a 50-year-old powerhouse called Minitab.

Over the past couple of years, Minitab has revolutionized the market by bringing the world’s most advanced data gathering, processing, visualizations, and analysis to the masses. Most recently, Minitab broke the barrier to putting “Data Science” into the hands of business professionals. And unlike others who promised this in the past, Minitab delivers.

That’s because, across the company’s product portfolio, there is a strong emphasis on usability and business outcomes – Minitab is deployed by a broad cross-section of the business community.

For decision-makers tasked with a business process or operational improvements, access to data and the ability to use it to achieve clear goals is critical. The lifeblood of today’s organizations is information, so using it to examine what was, what is, and what might happen can result in in lowered costs, higher revenues, and more efficient, timely actions for long-term strategy.

The talk may be of variables and predictors in mathematics and statistics — for the business’s Change Manager, it’s data sources, outcomes, and results. The phraseology might be different, but the required data processing and analysis remain the same. Don’t be intimidated by the term “Machine Learning.” All it refers to is learning from your data, which is effectively what data analysis is. Don’t believe us? Try it yourself.

With Minitab at the core of your data operations, there is immediate “plug and play” access to hundreds of data sources via included connectors that allow companies to access the data silos, repositories and applications across the network and in the cloud. It’s not surprising that Minitab was the highest-rated data integration tool according to Gartner Peer Insights.

Coupled with the statistical core of the Minitab data analysis platform, professionals can get a full picture of their data by leveraging archived information and real-time data streams as they happen in any organization.

Data is cleaned, transformed, and presented, providing the basis for predictive analytics modelling and insights into existing work processes.

As companies begin to scratch the surface of the data resources they have, hidden relationships between events and variables are uncovered. Factors that were never apparent can surface, even to the objective observer, and visual relationships and correlations emerge. Insights gained help both data specialists and line-of-business experts to determine how best to achieve the company’s objectives.

For line-of-business managers and non-data scientists, the visual language of Minitab helps show and correlate the various factors at play. It allows them to create predicted outcomes to proposed changes to operations in safe modelling environments.

The beauty of the Minitab portfolio is its design for use in practical settings. The platform’s openness and user interface mean it can be used in multiple verticals and unexpected use-cases: manufacturer Tate & Lyle used AI techniques and plotted thousands of variables to refine its sweetener consistency for better customer experience, for example. Where one might least expect it, Minitab’s statistical power is creating change.

In a vast range of industries, advanced analytical modelling, analysis, and machine learning algorithms are being deployed by organizations to improve outcomes in thousands of scenarios. At one time, this type of statistical analysis was only seen in finance and high-end medical research and pharma, but not today. Minitab is making real, meaningful differences in thousands of settings.

It integrates with both cloud and on-premises applications and services, from marcomms to stream processors. Minitab installs locally or is now available as a SaaS, ready to be accessed from anywhere with an internet connection.

To learn more about the Minitab suite of offerings and begin leveraging its accessible power to affect change, start your journey here.

The post Improve Your Business’s Processes with Predictive Analytics and Machine Learning appeared first on Artificial Intelligence.

AutoML can solve many of the problems businesses face, but not all. Democratization of machine learning can make a difference. Adam Carrigan, co-founder and COO of MindsDB, explains.

Machine learning and predictive analytics present a massive opportunity for businesses looking to improve existing processes, become more data-driven, and enhance the customer experience. Many companies have instituted some ML projects and are ready to take the next step with more widespread adoption.

Unfortunately, this is one area where companies run into challenges. The expertise and investment required to make ML and predictive analytics successful aren’t readily available. The demand for data scientists continues to snowball. According to LinkedIn, the data science field saw 37 percent job growth last year.

How can companies deal with this shortage of data science resources to take full advantage of AI and predictive analytics? We talk with Adam Carrigan, co-founder and COO of MindsDB, to learn about adding automation to machine learning.

Upside: What are the current challenges with adopting machine learning?

Adam Carrigan: Now that the use of machine learning and predictive analytics is more widespread and investment in this technology is increasing, companies are finding it difficult to scale the benefits to the entire organization. The department that oversees machine learning (often the data science team) has, in many companies, now become a bottleneck within the process to implement these solutions.

Data science teams are usually small, and the process of researching, testing, and deploying machine learning can take anywhere from days to months depending on the complexity of the data. This results in a long list of backlogged projects, where specific projects take priority and others are pushed back or never implemented. This challenge is even more significant for SMBs, which typically don’t have the resources to invest in highly in-demand data scientists.

AutoML now attempts to automate specific tasks in the ML workflow to reduce the strain on data scientists, but this comes with limitations. Many of the solutions available today still require a data scientist to play a significant role in the process. Although many aim to automate the training and testing, this leaves much of the process untouched, including the crucial deployment stage.

How should companies approach automating machine learning?

There are two approaches to automating machine learning inside an organization. The first is automating more of the process for the data scientist, freeing up capacity for them to solve more problems faster. This increases capacity and at least temporarily solves the bottlenecks I talked about earlier. This approach doesn’t help those who don’t yet have a data science team and is primarily a Band-Aid solution.

The second approach, and the one I believe will have a more meaningful impact, is to equip existing employees with the capabilities to solve most machine-learning problems. This eliminates the need to go back and forth between teams because ultimately the team trying to solve the problem is the one that knows it best. This is great for companies without a data science team to begin with. This approach also has an added benefit for those companies already with a data science team. It frees up their time and resources for the more complex and challenging problems. Ultimately, AutoML can solve many of the problems businesses face, but not all. In many instances, data scientists are still essential.

This approach requires a fundamental shift in the way we approach machine learning. Instead of thinking about it as an element within the application layer, it becomes useful to think about it as a data-layer problem. This opens up some exciting opportunities.

When you consider ML as simply other representations of your data, it makes it easier to run models and predictions at the data layer. AI tables — automated ML models as native data tables inside databases — let users execute models by merely running a data query. Instead of using AutoML to streamline the data scientist’s tasks, this approach puts ML into the hands of the end-user of the data.

How can companies educate the end users of predictive analytics?

This is a crucial component to the democratization of machine learning but shouldn’t necessarily be treated any differently than any other method of improving the skills of the enterprise’s employees. These users still need basic education to use those tools. Essential skills include running queries, analyzing data, and understanding how to use ML data to support the human decision-making process.

Now that machine learning is simple enough for the average database user to leverage, these users must now understand the potential strengths and weaknesses in the models they produce. Teaching end users how to determine a level of trust and confidence in the models using explainable AI (XAI) is even more important than the operational education. Thankfully, more AutoML solutions are including explainability as a standard feature. Armed with this information and the generated predictions, users become even more powerful in making impactful changes within the organization.

The post Advice for Automating Machine Learning and Predictive Analytics appeared first on Artificial Intelligence.

Today’s customers overwhelmingly favor the simplicity of subscription models, in which they pay a flat monthly fee for access to a product in the form of a service. For example, consumers no longer buy or rent DVDs; they subscribe to Netflix. And more prefer to use the services of Uber or Lyft over traditional car ownership. As these digital-age consumers enter the industrial workforce in large numbers, they are causing business models to be redefined, leading to an increasing trend toward subscription-based services in manufacturing industries.

This shift toward a subscription-based business model in the manufacturing industry is being referred to as servitization, wherein manufacturers no longer strictly sell new products but instead sell access to and the outcome those products deliver. This new way of delivering services is forcing manufacturers to shift to subscription-based pricing models, where product-as-a-service is the norm. Manufacturers of complex industrial equipment, because they operate in a B2B environment, can attribute this shift to another major cause: Their customers are also dealing with complex operating paradigms, forcing them to improve productivity and capital utilization. In these scenarios, customers are looking to simplify their businesses and increasingly favor suppliers that can help minimize their risks in operating a profitable business.

The full realization of servitization, which could take up to 15 years, especially impacts manufacturers’ after-sales service organizations (the service delivered after the initial sale of a product). In today’s current reactive, break-fix service model, the responsibility to maintain and ensure equipment availability falls on the customer. In a servitization model, however, manufacturers own the responsibility of maintenance and repairs and need to focus on maximizing product uptime, since revenue can only be earned when their products are available to generate output in the field.

Machine Learning and Predictive Analytics in Manufacturing

The use cases of machine learning and predictive analytics are as varied as the industries within manufacturing. However, there are a few common use cases that apply to most manufacturing verticals, typically grouped under terms such as smart manufacturing, industry 4.0 or industrial internet of things (IIoT).

1. Predictive Maintenance: Predictive maintenance is the most well-understood and varied use case in most manufacturing industries. Here, data from process monitoring sensors such as temperatures, pressures, flows, vibrations and more are captured in real-time and used in pattern recognition software to detect the earliest symptoms of wear and tear predictive of eventual functional failures. Early detection and prediction can help prevent failures or at least plan for eventual corrective actions leading to minimized downtime. Downtime—especially unplanned downtime—can be a very expensive event, possibly leading to millions of dollars in losses. Some analysts estimate unplanned downtime in certain industries is worth $20 billion.
2. Process Optimization: Process optimization, in which existing processes are updated and optimized based on historical data, is a critical use case, especially in industries such as power generation, oil and gas refining, petrochemicals and chemicals. In this instance, sensor data feed machine learning algorithms for yield and quality optimization of output components for different combinations and quality of input raw material feedstocks. This also helps with energy efficiency, thus improving sustainability and profitability for these process manufacturers. In the global airline fleet, for example, a 1% fuel savings would save $30 billion over the next 15 years.
3. Supply Chain and Inventory Management: High levels of raw material, work-in-process and finished goods (e.g., replacement service parts) inventory are one of the highest contributors to inefficient capital utilization for discrete manufacturing industries. Using machine learning to improve raw material and demand forecasts while meeting dynamically changing production goals helps improve capital utilization and supports lean and just-in-time manufacturing production goals.

In today’s customer-driven world, manufacturers no longer can rely on selling expensive spare parts and services, since these become costs of supporting a single-price subscription. This shift will require manufacturers to completely re-think how they operate—new organization structures and skilled resources, new incentive models, new KPIs to measure success and new processes replacing ones developed over decades and centuries. They will have to become data-driven organizations, investing in technologies to connect and track products, collect data and efficiently analyze these massive amounts of operational and service data, using technologies such as IoT, machine learning and predictive analytics. This will strain manufacturers’ existing organizations and IT infrastructures, necessitating investment in highly scalable cloud-based solutions to lay the foundation for a successful future.

Manufacturers that embrace these changes will be the winners, while others will struggle to stay relevant. In fact, the ones who can successfully adapt to these paradigm shifts will be able to gain a significant competitive advantage over their competition.

The post Machine Learning and Predictive Analytics Are Reshaping Manufacturing appeared first on Artificial Intelligence.