PepMLM generates binders to challenging therapeutic targets across cancer and neurological disease using protein sequence and no structure.

To investigate the processing and representation of different Argument Structure Constructions (ASCs) in a recurrent neural language model, we created a custom dataset using GPT-4. This dataset was ...

A research team led by a Duke professor recently developed a novel computational model to predict antibody structures, a significant breakthrough for disease prevention efforts. A study published ...

TensorFlow 2.18 introduces new features that promise to optimize machine learning workflows, focusing on enhanced compatibility and improved computational efficiency.

Large Language Models (LLMs) have shown great potential in intelligent visualization systems, especially for domain-specific applications. Integrating LLMs into visualization systems presents ...

R, a popular programming language among statisticians and data scientists, offers a rich ecosystem for creating compelling data visualizations. This article will guide you on how to use R for data ...

For example, Data2Vis conceptualizes visualization generation as a sequence translation task, employing an encoder-decoder neural architecture. Similarly, RGVisNet initiates the text-to-vis process by ...

TensorFlow Large Model Support TensorFlow Large Model Support (TFLMS) is a feature in the TensorFlow provided by IBM Watson Machine Learning Community Edition (WML CE) that allows the successful ...

This study highlights the potential of integrating various protein language models and fine-tuning that may yield better performance than MSA-based methods and other single-sequence methods on ...