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James Zou, PhD, assistant professor of biomedical data science at Stanford University, discusses machine learning and the different ways oncologists are utilizing it for the management, treatment, and diagnosis of cancer.

Machine learning is being applied in both early- and late-stage disease, and aids clinicians in providing the best treatment plans and options for their patients with cancer. In this video, Zou further discusses some of the specific methods the algorithm is trained to look at.

Transcription:

0:09 | Machine learning and artificial intelligence are seeing a lot of applications in oncology. For example, in diagnosis, often the clinicians are working with different kinds of imaging data could be mammography images or CT scans. Machine learning AI algorithms can be very helpful in helping clinicians to analyze those kinds of images for them to identify or to segment relevant regions.

0:39 | There are different stages where machine learning is being applied. They will go all the way from early stages in diagnosis to later stages in terms of treatment planning and treatment recommendations. [On the] diagnosis side, we are seeing a lot of these computer vision algorithms, which is a type of AI or machine learning models that are trained to really understand and analyze different images. For example, now there are algorithms that are looking at histopathology images and slides, and then try to diagnose and predict patient outcomes based on those histology images.

1:18 | There are also algorithms that are trained to look at mammography images and try to detect tumors, legions from these mammography images as other diagnosis sites and other treatment planning sites. People also develop machine learning models that look at, for example, mutation profiles of patients, right from their somatic mutations, and then try to predict based on these mutation profiles if immunotherapy or some other treatments are likely to be a good treatment for this particular patient.

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Using Machine Learning and AI in Oncology - Targeted Oncology

Machine learning (ML) has become a game-changer in many areas of life, and its potential in scientific research is remarkable. In the world of plant biology, ML algorithms are proving instrumental in detecting and measuring stomata the microscopic pores on the surface of leaves that allow for gas exchange. However, the application of these algorithms has been limited by the availability and quality of stomatal images.

To address this limitation, a vast collection of around 11,000 unique images of temperate broadleaf angiosperm tree leaf stomata has been compiled. This dataset includes over 7,000 images of 17 commonly encountered hardwood species, and over 3,000 images of 55 genotypes from seven Populus taxa. The inner guard cell walls and the whole stomata were labeled meticulously, and a corresponding YOLO label file was created for each image.

This dataset has been designed to enable the use of cutting-edge machine learning models to identify, count, and quantify leaf stomata. By leveraging the power of machine learning, scientists can explore the diverse range of stomatal characteristics and develop new indices for measuring stomata. This approach could revolutionize our understanding of stomatal response to environmental factors, as well as enhance our ability to predict and manage ecosystem changes.

The use of machine learning algorithms, such as deep learning and convolutional neural networks, offers the exciting possibility of automated stomatal detection and measurement. The application of AI in stomatal studies could lead to high-throughput methods that drastically reduce the time, cost, and labor involved in manual stomatal counting.

Despite the promise of AI, the full potential of machine learning in stomatal studies remains untapped due to the small dataset sizes and laborious manual processes involved in current research approaches. There is a pressing need for large stomatal image datasets to improve the accuracy and reliability of machine learning algorithms in stomatal detection and measurement.

The creation of a publicly accessible leaf stomatal image database presents an exciting opportunity to overcome the limitations of current approaches. Such a database would provide a rich source of data for developing machine learning-based stomatal measuring methods. This would be a valuable resource for ecologists, plant biologists, and ecophysiologists, facilitating more extensive and detailed research into stomatal function and its role in plant health and ecosystem sustainability.

The compilation of a comprehensive stomatal image dataset and the use of machine learning algorithms for stomatal detection and measurement represent significant advancements in plant biology research. By harnessing the power of AI, scientists can gain new insights into stomatal function, improve our understanding of the plant response to environmental changes, and contribute to the development of effective strategies for ecosystem management.

Link:
Machine Learning in Stomatal Detection and Measurement: A Game-Changer in Plant Biology Research - Medriva

Written by Aruna Pattam, Head Generative AI Analytics & Data Science, Insights & Data, Asia Pacific region, Capgemini.

Welcome to the brave new world of data, a world that is not just evolving but also actively being reshaped by remarkable technologies.

It is a realm where our traditional understanding of data is continuously being challenged and transformed, paving the way for revolutionary methodologies and innovative tools.

Among these cutting-edge technologies, two stand out for their potential to dramatically redefine our data-driven future: Generative AI and Synthetic Data.

In this blog post, we will delve deeper into these fascinating concepts.

We will explore what Generative AI and Synthetic Data are, how they interact, and most importantly, how they are changing the data landscape.

So, strap in and get ready for a tour into the future of data.

Generative AI refers to a subset of artificial intelligence, particularly machine learning, that uses algorithms like Generative Adversarial Networks (GANs) to create new content. Its generative because it can generate something new and unique from random noise or existing data inputs, whether that be an image, a piece of text, data, or even music.

GANs are powerful algorithms comprise two neural networks the generator, which produces new data instances, and the discriminator, which evaluates them for authenticity. Over time, the generator learns to create more realistic outputs.

Today, the capabilities of Generative AI have evolved significantly, with models like OpenAIs GPT-4 showcasing a staggering potential to create human-like text. The technology is being refined and optimized continuously, making the outputs increasingly indistinguishable from real-world data.

Synthetic data refers to artificially created information that mimics the characteristics of real-world data but does not directly correspond to real-world events. It is generated via algorithms or simulations, effectively bypassing the need for traditional data collection methods.

In our increasingly data-driven world, the demand for high-quality, diverse, and privacy-compliant data is soaring.

Across industries, companies are grappling with data-related challenges that prevent them from unlocking the full potential of artificial intelligence (AI) solutions.

These hurdles can be traced to various factors, including regulatory constraints, sensitivity of data, financial implications, and data scarcity.

Data regulations have placed strict rules on data usage, demanding transparency in data processing. These regulations are in place to protect the privacy of individuals, but they can significantly limit the types and quantities of data available for developing AI systems.

Moreover, many AI applications involve customer data, which is inherently sensitive. The use of production data poses significant privacy risks and requires careful anonymization, which can be a complex and costly process.

Financial implications add another layer of complexity. Non-compliance with regulations can lead to severe penalties.

Furthermore, AI models typically require vast amounts of high-quality, historical data for training. However, such data is often hard to come by, posing a challenge in developing robust AI models.

This is where synthetic data comes in.

Synthetic data can be used to generate rich, diverse datasets that resemble real-world data but do not contain any personal information, thus mitigating any compliance risks. Additionally, synthetic data can be created on-demand, solving the problem of data scarcity and allowing for more robust AI model training.

By leveraging synthetic data, companies can navigate the data-related challenges and unlock the full potential of AI.

Synthetic data refers to data thats artificially generated rather than collected from real-world events. Its a product of advanced deep learning models, which can create a wide range of data types, from images and text to complex tabular data.

Synthetic data aims to mimic the characteristics and relationships inherent in real data, but without any direct linkage to actual events or individuals.

A synthetic data generating solution can be a game-changer for complex AI models, which typically require massive volumes of data for training. These models can be fed with synthetically generated data, thereby accelerating their development process and enhancing their performance.

One of the key features of synthetic data is its inherent anonymization.

Because its not derived from real individuals or events, it doesnt contain any personally identifiable information (PII). This makes it a powerful tool for data-related tasks where privacy and confidentiality are paramount.

As such, it can help companies navigate stringent data protection regulations, such as GDPR, by providing a rich, diverse, and compliant data source for various purposes.

In essence, synthetic data can be seen as a powerful catalyst for advanced AI model development, offering a privacy-friendly, versatile, and abundant alternative to traditional data.

Its generation and use have the potential to redefine the data landscape across industries.

Synthetic data finds significant utility across various industries due to its ability to replicate real-world data characteristics while maintaining privacy.

Here are a few key use cases:

In Testing and Development, synthetic data can generate production-like data for testing purposes. This enables developers to validate applications under conditions that closely mimic real-world operations.

Furthermore, synthetic data can be used to create testing datasets for machine learning models, accelerating the quality assurance process by providing diverse and scalable data without any privacy concerns.

The Health sector also reaps benefits from synthetic data. For instance, synthetic medical records or claims can be generated for research purposes, boosting AI capabilities without violating patient confidentiality.

Similarly, synthetic CT/MRI scans can be created to train and refine machine learning models, ultimately improving diagnostic accuracy.

Financial Services can utilize synthetic data to anonymize sensitive client data, allowing for secure development and testing.

Moreover, synthetic data can be used to enhance scarce fraud detection datasets, improving the performance of detection algorithms.

In Insurance, synthetic data can be used to generate artificial claims data. This can help in modeling various risk scenarios and aid in creating more accurate and fair policies, while keeping the actual claimants data private.

These use cases are just the tip of the iceberg, demonstrating the transformative potential of synthetic data across industries.

In conclusion, the dynamic duo of Generative AI and Synthetic Data is set to transform the data landscape as we know it.

As weve seen, these technologies address critical issues, ranging from data scarcity and privacy concerns to regulatory compliance, thereby unlocking new potentials for AI development.

The future of Synthetic Data is promising, with an ever-expanding range of applications across industries. Its ability to provide an abundant, diverse, and privacy-compliant data source could be the key to unlocking revolutionary AI solutions and propelling us towards a more data-driven future.

As we continue to explore the depths of these transformative technologies, we encourage you to delve deeper and stay informed about the latest advancements.

Remember, understanding and embracing these changes today will equip us for the data-driven challenges and opportunities of tomorrow.

Follow this link:
Generative AI Applications: Episode #12: Synthetic Data Changing the Data Landscape - Medium

11 min read

One problem with many powerful machine learning algorithms is their uninterpretable nature. Algorithms such as neural networks and their many varieties take numbers in and spit numbers out while their inner workings, especially for sufficiently large networks, are impossible to understand. Because of this, its difficult to determine exactly what the algorithms have learned. This non-interpretability loses key information about the structure of the data such as variable importance and variable interactions.

However, other machine learning (ML) algorithms dont suffer these drawbacks. For example, decision trees, linear regression, and general linear regression provide interpretable models with still-powerful predictive capabilities (albeit typically less powerful than more complex models). This post will use a handful of technical indicators as input vectors for this type of ML algorithm to predict buy and sell signals determined by asset returns. The trained models will then be analyzed to determine the importance of the input variables, leading to an understanding of the trading decisions.

For simplicity, indicators readily available from FMPs data API will be used. If replicating, other indicators can easily be added to the dataset and integrated into the model to allow more complex trading decisions.

For demonstration, the indicators used as input to the ML models will be those readily available from FMPs API. A list of these indicators is below.

An n-period simple moving average (SMA) is an arithmetic moving average calculated using the n most recent data points.

FMP Endpoint:

https://financialmodelingprep.com/api/v3/technical_indicator/5min/AAPL?type=sma&period=10

The exponential moving average (EMA), is similar to the SMA but smooths the raw data by applying higher weights to more recent data points.

where S is a smoothing factor, typically 2, and V_t is the value of the dataset at the current time.

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Using Interpretable Machine Learning to Develop Trading Algorithms - DataDrivenInvestor

A Deep Dive into the Top 10 AI Tools Transforming Industries.

Artificial Intelligence (AI) has evolved rapidly over the past few years, transforming the way businesses operate and revolutionizing various industries. From machine learning algorithms to natural language processing, AI tools have become essential for automating tasks, gaining insights from data, and enhancing decision-making processes. In this article, we will explore the top 10 most powerful AI tools that are making a significant impact in the field.

In conclusion, the field of AI is advancing at an unprecedented pace, and these powerful tools are at the forefront of this technological revolution. From machine learning frameworks to cognitive computing platforms, these tools empower developers, data scientists, and businesses to harness the potential of AI and drive innovation across various domains. As AI continues to evolve, staying informed about the latest tools and technologies is crucial for those looking to leverage the full potential of artificial intelligence.

Read more here:
Top 10 Most Powerful AI Tools. A Deep Dive into the Top 10 AI Tools | by Token Tales | Jan, 2024 - Medium