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Advancements in technology and medical research have paved the way for a new era of cancer prognosis and detection. A recent study comparing classification- and regression-based approaches for predicting continuous biomarkers in cancer has shown promising results. The studys regression model, CAMIL, outperformed other methods in predicting HRD status and other biomarkers in pathology images across multiple cancer types. With higher AUROCs, improved class separability, and stronger correlation coefficients, CAMILs robust and generalizable prognostic features make it a promising approach for clinical applications of Deep Learning (DL) systems.

Various machine learning techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs), and Decision Trees (DTs), have shown promise in categorizing cancer patients into high or low-risk groups. Specific algorithms like SVM, Random Forest (RF), K-Nearest Neighbors (KNN), and Logistic Regression (LR) have been used to diagnose breast cancer with high accuracy. Convolutional Neural Networks (CNNs) have also been investigated for breast cancer prediction using medical scans, demonstrating the potential of these techniques in improving early detection and ultimately, saving lives.

Studies have developed risk signatures based on mitochondrial related genes to improve prognosis prediction and risk stratification in breast cancer patients. One such study used transcriptome data and clinical features of breast cancer samples from the TCGA as the training set and the METABRIC as the independent validation set. The risk signature, comprising 8 mitochondrial related genes, was identified as an independent risk predictor for breast cancer patient survival. Patients in the low-risk group showed a more favorable prognosis, distinct mutation landscapes, and greater sensitivity to anti-tumor drugs.

A comprehensive study on the prognostic and immunogenic characteristics of DNA methylation regulators in Lung Adenocarcinoma (LUAD) utilized eight LUAD cohorts and one immunotherapeutic cohort of lung cancer. They constructed a DNA methylation regulators related signature (DMRRS) using univariate and multivariate COX regression analysis. The DMRRS defined low-risk group was associated with a favorable prognosis, tumor inhibiting microenvironment, more sensitivity to targeted therapy drugs, and better immune response.

Artificial intelligence (AI) is having a transformative impact on Positron Emission Tomography (PET) imaging, particularly through deep learning implementations in cancer diagnosis and therapy. AI is being used for image generation, integration into clinical practice, and multimodal data mining. However, the application of AI to PET imaging also brings with it certain limitations and ethical considerations.

Deep learning models are increasingly being used for predicting continuous biomarkers in cancer patients. The regression-based DL approach, such as the one used by the CAMIL model, shows promising results. This approach provides a robust and generalizable method for predicting cancer prognosis, offering hope for more precise and accurate cancer treatment in the future.

In conclusion, the integration of machine learning and deep learning techniques into cancer prognosis and detection is opening up new possibilities in medical research and patient care. The CAMIL regression model, with its robust and generalizable prognostic features, is leading the way in the clinical application of DL systems, demonstrating the immense potential of these technologies in the fight against cancer.

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Advancements in Cancer Prognosis and Detection using Machine Learning and Deep Learning Techniques - Medriva

Aimed at regulating the rapidly evolving field of AI, the EU AI Act is a landmark piece of legislation designed to ensure ethical and safe development of AI technologies. At its core, the EUs AI Act is a comprehensive legal framework intended to manage the risks associated with AI technologies. The parliaments priority is to make sure that AI systems used in the EU are safe, transparent, and traceable. In many cases this requires AI systems to be overseen by people, rather than by automation, to prevent harmful outcomes.

Non-compliance with the AI Act can result in significant fines, ranging from 7.5 million euros or 1.5% of global turnover for minor violations to 35 million euros or 7% for severe offences.

These key requirements of the EUs AI Act present challenges to AI companies like OpenAI with its ChatGPT, Googles DeepMind and their recently revealed Gemini, as well as Meta, Anthropic, Glean and other AI pioneers. The challenge lies in the machine learning systems they use in building their products and the opaque nature of their training datasets. These systems, developed through deep learning, process vast amounts of data in ways that even their creators might not fully comprehend, complicating the ability to provide comprehensive documentation or clear explanations of their decision-making processes.

In contrast to the challenges faced by traditional machine learning (ML) systems, Tau, a powerful software development tool based on logical AI, is paving a more reliable and safe way in AI application and software development. Tau revolutionizes industrial software development by allowing users to describe their desired software in logical sentences, which are directly executable as correct-by-construction software. As the description of the software is the software itself, this eliminates traditional testing cycles. This proprietary methodology created by Tau is called Software-as-Sentences.

Taus approach showcases several key strengths that inherently align with the AI Acts requirements:

Transparency Through Logical Structure and Clarity: Taus logical and structured format enhances the clarity of operations and decision-making processes, aligning seamlessly with user requirements and in turn, with the transparency requirements outlined in the AI Act. Unlike complex lines of code used in ML models that can be challenging to decipher, Tau utilizes formal specifications that look like sentences that are directly executable as working software. Tau outperforms conventional formal methods with its inherent expressiveness, infinite data values and its extensibility to add more languages. It allows complete system specification of a wider and more complex range of software, while ensuring correct-by-construction output. The use of Software as Sentences, removes code and verification steps, offering an intuitive and straightforward approach to developing software.

Human Knowledge Instead of Opaque Data: Unlike probabilistic ML-based AI models, Taus AI is not trained on large datasets, but rather, it derives its intelligence from the collective knowledge of its user base. Tau uses a novel logical engine to enable simultaneous development by multiple people on the same part of the software, including non-technical and end-users. Tau facilitates large-scale discussion with agreement detection, using ontologies and logical reasoning to clarify decisions and agreements. The agreed-upon software descriptions then can be run as correct-by-construction software. Furthermore, Tau supports Knowledge Representation languages, allowing users to capture real-world concepts and knowledge in software descriptions, improving the quality and readability of the software.

Safety Through Post-Release Control: Taus extensive behavior control helps ensure that software adheres to implemented security and safety conditions post-release. Tau enables specifying exactly what updates or changes will be accepted post-release and any update or tampering that doesnt comply with expressed rules is automatically rejected by the software, providing an added layer of security. This feature also helps ensure that software developed using Tau remains compliant with the regulations expressible in Tau Language, as they evolve over time.

The EUs AI Act marks a turning point in AI regulation, emphasizing safe, transparent, and responsible AI. Although this presents challenges for machine learning-based projects, it also provides them with an opportunity to leverage Taus logic-based solutions, simplifying compliance with the EUs AI Act. As the AI landscape evolves to meet these new regulatory standards, Tau and its products stand out as pioneers of a compliant, ethical, and transparent approach to AI development.

More:
Tau Excels Where Traditional Machine Learning Projects Struggle Under EU's AI Act - Medium

Groundbreaking Insights into Language Acquisition

In a pioneering research study, scientists have made significant strides in understanding early language acquisition in children. This has been achieved through a machine learning model that mimics how children learn languages, providing fresh insights into the process. The model was trained using first-person perspective data from video and audio recordings of a young child over a year. The results from this research contribute to our comprehension of language development in children and have significant implications for early education and language therapy.

The study of language development in children is fraught with challenges due to the complexity and variability of language data. However, the advent of machine learning models has opened up new possibilities in this field. These models can analyze large datasets of language samples, identifying patterns and predicting language development milestones in young children. Machine learning thus holds immense potential for the study of early language acquisition.

Another noteworthy study utilized dynamic topic modeling to analyze a corpus of over 1,600 articles on early reading. The research identified 11 cardinal topics, including the impact of interventions on early reading competencies, foundational elements of early reading, phonological awareness, letters and spelling, and early literacy proficiencies in children with autism spectrum disorder. The study underscored the importance of early reading in language acquisition and child development. It also highlighted how cognitive abilities, language skills, reading motivation, home and school environments, and gene-environment interaction can influence early reading abilities.

Artificial Intelligence (AI) has also found applications in monitoring child development. AI can aid in the early identification of developmental issues, enhancing the potential for effective clinical outcomes. A review of over 2,800 articles revealed that while AI applications are being used in developmental monitoring, many have not been evaluated in clinical practice. The main research areas involve cognitive, social, and language development, as well as the early detection of autism. However, clinical outcomes and stakeholder acceptance of AI remain underexplored areas.

Researchers have developed a machine learning model that mirrors the way children acquire language. By using video and audio recordings from a young childs perspective, the researchers discovered synchronized changes in gut bacteria and brain regions related to appetite and addiction. This suggests a complex, bidirectional brain-gut-microbiome axis. The study also revealed that the superior colliculus, a part of the brain, plays a crucial role in distinguishing objects from backgrounds, providing new insights into our understanding of vision.

The research findings carry significant implications for early education and language therapy. Understanding the patterns and milestones in early language acquisition can help educators and therapists formulate more effective teaching strategies. Additionally, the role of AI and machine learning in identifying developmental issues can lead to early interventions and improved outcomes in child development.

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Insights into Language Acquisition and Child Development | Health News - Medriva

Tracking progress towards the Sustainable Development Goals (SDGs) is a priority for the development community, and the subject of the recent 2023 SDG Atlasa groundbreaking data visualization and interactive storytelling platform, powered by the World Bank, which keeps track of the progress made towards achieving the 17 Global Goals.

One of these goals, SDG 2, pertains to achieving zero hunger by 2030. Recent official data on the indicators used to track progress in this specific goal reveal a concerning trend: SDG 2 data is off track. Particularly data on SDG 2.1.2, which measures the Prevalence of Moderate or Severe Food Insecurity, has been on the rise since inception of the data series in 2014.

This claim is not new. A recent blog "Are SDGs 1 and 2 Diverging?" highlights the need for better data to monitor progress towards SDGs related to poverty and food security. It points out that the divergence between SDG 1 (No Poverty) and SDG 2 (Zero Hunger) is amplified in data-poor regions, emphasizing the importance of improving data coverage, standards, and transparency, particularly in low and middle-income countries. The blog suggested leveraging new tools like machine learning, satellite imagery, and remote sensing data to enhance data accuracy and fill gaps in surveys.

To aid in tracking progress towards SDG 2, the World Bank has introduced the World Food Security Outlook (WFSO) database, an innovative amachine learning model-based data series. This database is focused on the monitoring and analysis of global severe food security issues.

The database encompasses historical, preliminary, and forecast data concerning severe food insecurity on a global scale. The primary goal of this dataset is to offer more timely and comprehensive statistics for all countries, thereby contributing to the development of more effective strategies and improved outcomes, particularly in regions with limited data availability. Additionally, the WFSO data enhances transparency in global food security numbers by tracking regional and global figures and breaking them down by individual countries.

The WFSO is published three times a year, aligned with the releases of the International Monetary Fund's World Economic Outlook (WEO) and the Food and Agricultural Organizations The State of Food Security and Nutrition in the World (SOFI) report. Its key components encompass various aspects such as the prevalence of severe food insecurity, estimates for countries without official data, the population sizes of severely food-insecure individuals, and the necessary financial support for safety nets.

One of the primary purposes of the WFSO is to complement the official data from the Food and Agriculture Organization (FAO) published in the State of Food Security and Nutrition in the World (SOFI) report. It plays a crucial role in filling data gaps for countries that are not covered by SOFI. Additionally, it offers a forward-looking perspective based on a machine learning model that utilizes data from the World Bank's World Development Indicators (WDI) database for model training and the International Monetary Fund's (IMF) World Economic Outlook (WEO) to generate food security projections that align with economic forecasts. This can help policymakers integrate food security considerations into their economic planning. The WFSO also includes estimates for safety net financing needs, following past International Development Association (IDA) approaches originally used in IDA (2020).

These innovative methods for addressing data gaps and generating more timely estimates are part of a larger initiative to not only enhance data quality but also facilitate prompt decision-making. Previous versions of the outlookhave been used to estimate demand for Early Response Financing under the IDA Crisis Response Window, in Bankregional economic updates and to update G24 on food security financing perspectives. The preliminary estimates also informed the World Banks comprehensive global response to the ongoing food security crisis, with up to $30 billion in existing and new projects in areas such as agriculture, nutrition, social protection, water and irrigation. More recently, motivated by the long-term outlook, The World Bank has included food and nutrition security among the eight global challenges to address at scale. In the 15 months leading to June 2023, the Bank has mobilized $45 billion surpassing its initial projected commitment of $30 billion.

The WFSO is supported by Food Systems 2030, a multi-donor Trust Fund, initiated by the World Bank, aimed to establish a sustainable food system that enhances livelihoods and ensures safe, affordable, and nutritious diets for all. It focuses on innovative business practices and reshaping market and institutional incentives for better food systems. The fund also provides policy advice, analytical products, and engages with the private sector to achieve a food system beneficial for health, the planet, and the economy.

As indicated by the map widget, historical estimates offer crucial insights into country-level contributions to global figures and uncover interesting trends. It's important to note that historical estimates from earlier periods are subject to higher levels of uncertainty. Additionally, the Prevalence of Severe Food Insecurity indicator is calculated based on the Food Insecurity Experience Scale (FIES), which may encompass psychological aspects of food insecurity not necessarily reflected in historical malnutrition data.

But, when playing over the time-lapse, one can see that the global improvements in food security conditions observed in the early 2000s were primarily driven by advancements in larger countries in Latin America and Asia. Meanwhile, many smaller-population African countries have been experiencing a long-term deterioration in food security, which accelerated after the great financial crisis of 2008 and the coinciding global food price crisis that resurged once more in 2011. Following the Arab Spring starting around 2010, some Middle Eastern and North African countries worsened their food insecurity indicators.

Globally, the scenario is not much different. Official data starting from 2014 shows a continued rise in food insecurity, with an acceleration during the pandemic and the Russian invasion of Ukraine.

Nonetheless, looking ahead, projections indicate that global food insecurity conditions are expected to stabilize. The World Bank Food Security Update for December 2023 further analyzes the trends projected in the October 2023 WFSO. In summary, the latest projections suggest that global food security conditions are gradually stabilizing, but disparities between income groups are increasing.

For further readings on food security trends, please visit our Agriculture & Food blog.

Continued here:
Navigating the future of food security with machine learning - World Bank