- Deliver scientific outcomes faster by working with our experts in computational sciences.
- Reduce uncertainty in high-risk R&D initiatives by leveraging our industry experience.
- Keep up to date with the latest applications of machine learning.
- Up-level your team through collaboration with our experts.
Manifold’s contribution has been invaluable. Our integration of machine learning algorithms will enable the dynamic adjustment of treatment...
—Chief Medical Officer, Digital Therapeutic
Natural Language Processing
Fine-tune large language models to your enterprise use cases
Search and text retrieval over large document corpuses
Human in the loop UI/UX for query generation, plotting, cohort building, and more
Natural language interface to chat with a corpus of documents
Extract structured data from unstructured clinical text data
Process large-scale imaging data, including histology slides, CT, MRI, and video
Extract semantic features from images like nodule size or cell count
Use techniques from data centric AI to create more training data for machine learning
Segment images from your instruments, from fluorescence microscopes to CT scans
Forecast disease progression or response to treatment by combining genotype and imaging data.
Develop digital biomarkers from multivariate time series data
Predict disease risk or adherence from longitudinal patient data sources
Probabilistic time series modeling for forecasting and anomaly detection
Discrete-time signal processing for linear and non-linear systems
Dynamical system system identification using deep learning
Unsupervised techniques to find anomalous patterns in time series data
Analyze genotypes and downstream traits to find associations for disease, drug response, or clinical outcome
Find biomarkers from NGS data to classify patients and develop companion diagnostics
Next Gen Sequencing
Process fastq files from NGS to do alignment, count transcripts, call variants, and more
Predict which patients are more likely to reach primary or secondary clinical endpoints
Use state-of-the-art generative AI models to predict molecular structures or binding
Estimate causal effects from observational data using DAGs and modern inference techniques
Multiomics Companion Diagnostic
Discovered novel multiomic biomarkers using parallelized machine learning pipelines.
Digital Biomarkers for Diabetes
Developed explainable digital biomarkers to predict treatment response to a digital behavioral intervention.
Deep Learning for PK/PD Modeling
Developed a deep learning based inverse model to learn the PK/PD of therapies that target hematopoiesis.
We are a multi-disciplinary team focused on developing scientific insights using a mix of applied mathematics, machine learning, and computing.