Tag
A predictive modeling researcher describes challenges in developing a machine learning paper for disease prediction, facing criticism from non-ML experts on methods like imputation and class imbalance handling.
Introduces CellAudit, a method to audit input-use claims in AI virtual cells by examining source code and predictive contributions, using falsification to bridge the prediction–claim gap in agentic model discovery.
This paper proposes the Knowledge-enhanced Agriculture-informed Neural Network (KAINN) framework, which integrates domain knowledge into deep learning models to improve the accuracy and interpretability of nitrous oxide emissions predictions in agricultural systems.
Researchers developed a cost-sensitive XGBoost model to predict ESBL-producing Enterobacteriaceae infections using electronic health record data, aiming to guide empiric antibiotic selection and reduce carbapenem overuse in a 12-hospital study.
Google DeepMind introduces AlphaGenome Atlas, a platform with AI predictions for all 9 billion single-letter DNA variants in the human genome, accelerating biological research and disease understanding.
This paper presents a framework for predicting quantifiability from primary screens to prioritize dose-response profiling in drug discovery, showing that usability of potency estimates can be distinguished from biological activity.
Research on AI agents shows their collective dynamics follow statistical physics laws, predicting emergent behavior.
This study uses connected vehicle telemetry data from Sydney to predict high-risk driving hotspots, benchmarking machine learning and time-series models for proactive road safety interventions.
Chinese researchers have developed an AI model that uses brain scan data to predict the risk of depression up to four years in advance, potentially enabling earlier interventions.
This article updates AI Futures' timelines forecasts for Automated Coder, introducing coding uplift and revenue as new methods to refine predictions and improve confidence in AI development timelines.
This arXiv paper presents a shape-constrained predictive modeling approach to estimate nanoparticle size and dispersity in continuous flow nanodrug production, reducing the need for extensive experimental screening.
This paper compares five feature selection methods for EHR diagnosis codes in opioid use disorder prediction, finding that NTK sensitivity offers the best accuracy-stability balance while LLM-guided selection adds complementary clinical signals.
Google DeepMind describes how they used their 'Predicting the Past' AI skill to analyze ancient texts and uncover historical mysteries, including tracking a Roman ring thief and mapping networks of visitors to a Greek oracle.
The MS AI Frontiers team introduces BenchPress, a method that uses matrix completion to predict LLM benchmark scores from just five probes, showing the score matrix is effectively rank-2.
This study presents a hybrid predictive framework using CatBoost and SHAP to identify risk factors in tree-involved traffic crashes, highlighting restraint non-use as the most critical predictor of severe injury.
TRIBE v2 is a new predictive foundation model designed to understand how the human brain processes complex stimuli.