Tempus AI, Inc.

Computational methods and systems for analyzing biological data (e.g., genomic, proteomic, clinical) to diagnose diseases, predict patient prognosis, assess treatment response, or stratify patients for therapy.

Identification and measurement of specific nucleic acid sequences (DNA, RNA), their expression levels, or epigenetic modifications (e.g., methylation) as indicators for disease presence, progression, risk, or treatment response.

Utilizing machine learning, particularly deep learning, to analyze medical data such as images, sensor readings, or physiological signals for disease prediction, diagnosis, or treatment assessment.

Digital platforms and systems that deliver tailored therapeutic interventions, guidance, or recommendations to patients based on their individual health data, biometric feedback, and computational models (e.g., AI/ML, physiological simulations).

Applying computational methods, often involving machine learning and multiomics data, to design, analyze, and understand biomolecules, genetic sequences, or complex biological systems.

Systems and methods for non-invasive or minimally invasive collection and analysis of physiological data (e.g., blood pressure, electrolytes, genetic markers, B cell repertoire) to assess patient health status, screen for conditions, or aid in diagnosis.

Methods and systems for combining and analyzing diverse biological datasets (e.g., genomics, transcriptomics, proteomics, metabolomics) to uncover complex biological relationships, disease mechanisms, or temporal trajectories.

Methods and reagents designed to improve the specificity, efficiency, or yield of nucleic acid capture, ligation, amplification, or library preparation steps, particularly for sequencing applications or quantitative analysis.

Systems that integrate digital technology, sensors, or connectivity to monitor, track, or automate aspects of medication administration, often providing data feedback, personalized recommendations, or secure logging.

Self-contained or modular devices designed to automate and integrate multiple steps of molecular diagnostic assays, from sample preparation to result interpretation, often for point-of-care or high-throughput applications.

Techniques for generating human-like text or other content using large pre-trained models, often involving prompt engineering, speculative decoding, or multi-modal inputs for content creation.

Techniques for combining and analyzing information from multiple distinct data modalities (e.g., text, image, video, audio, sensor data) to derive richer insights or improve system performance and decision-making.

Systems and methods for automating the lifecycle of machine learning models, including pipeline deployment, model management, versioning, and configuring for different inference environments.

Systems and methods utilizing artificial intelligence, particularly large language models and neural networks, to extract, summarize, generate, or categorize information from unstructured or semi-structured data sources.

Systems and methods that use imaging technologies, computer vision, and augmented reality to provide real-time guidance, localization, and visualization during surgical procedures or for detailed anatomical assessment.

Methods and systems for identifying, extracting, and structuring specific entities, relationships, or insights from text-based documents, often involving techniques like named entity recognition, relation extraction, or summarization.

Computational techniques and algorithms for processing, aligning, and interpreting raw biological sequence data (DNA, RNA, protein), including identifying genetic variations, classifying organisms, or predicting sequence attributes.

Processes for creating or manipulating three-dimensional digital representations of objects or environments, including mesh generation, surface fitting, and depth estimation from multiple views.