Machine learning
Natasa Miskov-Zivanov, an assistant professor of electrical and computer engineering at the University of Pittsburgh, has been awarded a $581,503 Faculty Early Career Development (CAREER) Award by the National Science Foundation (NSF) for her project on artificial intelligence-driven immunotherapy design. Miskov-Zivanov aims to develop an automated system that leverages
Machine learning
Caris Life Sciences, a commercial-stage company, has made a significant mark on the public markets with its upsized IPO debut, raising $494 million to support its precision medicine plans. The company's AI-driven offerings, which include molecular profiling services and companion diagnostics, are revolutionizing cancer treatment by identifying targets
Machine learning
As India's pharmaceutical sector prepares for a future of potentially $350 billion in exports by 2047, its emphasis on innovation has become crucial. The nation aims to transition from its role as the "Pharmacy of the World," focusing on manufacturing excellence to advancing into breakthrough innovation.
Machine learning
Researchers from Canakkale Onsekiz Mart University have conducted a study to compare the effectiveness of traditional Multiple Linear Regression (MLR) and deep learning Extreme Gradient Boosting (XGBoost) algorithms in predicting agricultural land prices. The study aimed to examine the relationship between agricultural land prices and economic indicators such as the
Machine learning
Researchers at the University of Freiburg Medical Center have made significant advancements in personalized medicine, particularly in the management of acute kidney injury (AKI) patients. The study, published in Heliyon, utilized machine learning algorithms to develop risk prediction models for severe AKI patients and investigated the impact of sodium bicarbonate
Machine learning
Research conducted by Merck & Company has led to a significant advancement in the field of medicinal chemistry, with the development of predictive models that enable faster discovery of safer and more efficacious therapeutics. The study highlights the potential of deep learning models, particularly graph neural networks, in improving model
Machine learning
The global drug discovery platforms market is undergoing significant expansion, driven by technological innovation and increasing research and development investments. Experts are leveraging high-throughput screening, AI-driven modeling, and advanced microfluidics to accelerate candidate identification. By 2025, the market size is estimated to be valued at USD 211.3 Mn, with
Machine learning
Researchers at Stanford University are leveraging machine learning to improve the efficacy and safety of targeted cell and gene therapies. By using machine learning algorithms to predict which proteins are more likely to trigger an immune response, the team has made significant progress in designing proteins that avoid such issues
Machine learning
A recent study published in Scientific Reports has made a significant contribution to the field of personalized medicine by developing a machine learning-derived angiogenesis signature for clinical prognosis and immunotherapy guidance in colon adenocarcinoma. The study, conducted by researchers at Tengzhou Central People's Hospital, found that an integrative
Machine learning
In a groundbreaking development in the field of computer vision, a patent application has been filed for a method and system for small object detection in images using machine learning and signal processing techniques. The application, developed by inventors Sanjay Chawla and Mohammad Amin Sadeghi, aims to address the challenge
Machine learning
Researchers at the University of Baghdad have developed an effective two-phase phishing detection system, employing unsupervised feature selection and supervised classification. The system utilizes machine learning to precisely identify malicious websites, addressing a critical problem in cybersecurity. The development aims to enhance the accuracy and efficiency of phishing detection, with
Machine learning
Research has highlighted the need for adaptable diagnostic tools that can detect COVID-19 from diverse and evolving data sources. A study from the National Technical University of Athens aimed to develop a comprehensive framework that monitors model drift and uses adaptation mechanisms to mitigate performance fluctuations in COVID-19 detection models
