Common Use Cases
There are a wide range of use cases of data science and big data in biotechnology and life sciences. Spanning from healthcare and drug discovery efforts to medical devices and diagnostics, the use of machine learning in biotech spans efforts in bioresources, agriculture, and environmental applications. We use deep learning to monitor behavior, signals, symptoms, and populations while creating image classification algorithms that can be used in precision medicine for segmenting cells and tracking diseases.
We help businesses and organizations collect, annotate, and reuse medical imaging data. We apply deep learning and Artificial Intelligence to problems in clinical and research imaging from X-rays to MRIs, and beyond.
We create custom solutions and algorithms, solving complex R&D type problems. Our team creates end-to-end pipelines, transforming industry data scientists into valuable clinical and commercial assets.
Mass Spectrometry: Identify mass spec peaks, verify and quantify target ions, and add biological meaning to your data using big data and machine learning techniques.
MRI: The automated classification of MRI images and segmentation of key structures and anomalies is a necessary task to solve when faced with the increasing quantity of medical images. Recent advances in deep learning and GPU architecture make it a suitable tool for such computer vision problems.
With the recent improvements in high-throughput whole-slide (WSI) scanning systems and GPU computing, deep learning and neural networks (DNNs) are poised to make H&E slide analysis and other routinely stained and fluorescent molecular slides an automated process. Using such systems we can classify existing samples and have a transformative effect, combining historical records of patient outcomes and large biological data sets to create advanced technologies.
Diagnosis: Current technology-free and unaided approaches to histopathology are not maximizing the complex morphological information present for optimal patient management. We create workflows and systems for specific tissues and diseases, as such as for precision oncology.
Automation: To limit the use of molecular diagnostics and even with more objective tools, the need for highly uniform and reproducible diagnostic reports and testing limits the total value of individual samples. Machine learning (and deep learning) approaches may allow for multi-parametric feature extraction and aggregation that permit even subtle, but reproducible, groups of features to be identified. We can integrate individual cases and samples at very high numbers (millions of slides, cases) of unappreciated features and patient histories that can be correlated with specific outcomes, lab results, other tests, and molecular changes.
We build algorithms, use advanced analytics, and validation methods to help build the next generation of smart devices and diagnostic tools.
Hand-held: From monitoring and IoT sensor applications deployed on phones to advanced diagnostic devices for patients and real-world conditions in real-time, we develop precision and robust software around your application.
Validation & Quality Control: We provide analytical assay and methodology validation, including data processing and noise reduction techniques focusing on specificity, accuracy, precision, limits of quantification, range, and robustness. We help businesses build and improve their software.
Quality Assurance & Field Testing: We design tools and algorithms to ingest and process field, sample, and test data to validate protocols, results, and create automated quality management systems around your application.
Market data, claims, patient records, and test results are examples of large data repositories that can contain patterns that can help researchers to draw conclusions and predict events related to their business outcomes.
Machine Learning: Predictive analytics and learning algorithms can help understand clinical variables, molecular properties, and system properties to predict many processes and systems in life sciences. SFL Scientific has worked on disease detection, including onset, and relapse indicators, drug response prediction using DNA or RNA data and to sub-identify patients and predict improved treatment effects, and the comparison and integration of real-world data and diagnostic tests into models.