Machine Learning in Life Sciences
Examples of our work:
What is Machine Learning in Life Sciences?
Machine Learning is a field of methods and algorithms that seeks to explain different types of data and/or biophysical phenomena and emulate intelligent behavior in terms of computational processes and analysis. As a result, the system can identify hidden patterns in data and make predictions of the biophysical phenomena of interest. The application of Machine Learning to Life Sciences, Biotech and Pharma is gaining popularity because with the wealth of biological and clinical data it has tremendous potential for discoveries of new therapeutics. Contact us if you want to learn more.
Examples of application of Machine Learning in our projects
- medical image analysis
- medical diagnostics
- target discovery
- AI/ML augmented PK/PD prediction
- drug applicability prediction
- protein engineering and structural biology
- and more!
Curious about how Machine Learning & AI are applied in Protein Engineering?
With this video, you will learn about:
- capabilities in building and deploying AI/ML models to address challenging Life Science questions
- key challenges & solutions in Protein Engineering Applications
- applicability of Machine Learning & AI technologies
Frequently asked questions about Machine Learning in Life Sciences
The AI/ML projects are challenging because they require a combination of diverse domain knowledge and a technical understanding of the methods and algorithms used in the AI/ML field. With this in mind, we built at BISC Global an AI/ML Team that consists of experts in various biological domains who have moreover strong AI/ML backgrounds. In this way, we offer our clients a unique opportunity to use the dedicated project team with the optimal skill set. By working with our consultants (full-time employees) you get a guarantee of a state-of-the-art solution that will deliver Machine Learning discoveries in a reasonable time frame.
The ability of Deep Learning (DL) to learn from real-world data during the drug discovery process makes the DL methods uniquely situated for drug screening. At each stage of the development of our Deep Learning method, we establish Uncertainty Analysis and Quantifications (UQ). In this approach, we converge ML-based learned models with model-based predictions so that all numerical and experimental data can help self-inform predictions. This enables an effective building of UQ into cognitive computing.
- Shorter analysis time - AI/ML models are computed with high speed
- Innovative and impactful findings – AI/ML allows us to push further the boundaries of what’s possible and find novel solutions to healthcare problems
- Harnessing the full potential of the Big Data – in contrast to traditional data analysis methods, AI/ML enables efficient exploration of an enormous amount of data
- Compatible with Life Sciences questions - the great advantage of AI/ML models over classical statistical modeling is that the former can solve highly non-linear problems often encountered in Life Sciences
BISC Global has a large portfolio of successful projects covering different aspects of Life Sciences and Biotech topics. Please check our Case Studies page to see selected examples of our work.
Using the synergy between statistics and AI/ML, we offer our clients an opportunity to use AI/ML that is enables estimates of a minimum size of the data set and control over the quality of the AI/ML model as the data volume increases.
The Uncertainty Analysis and Quantifications (UQ) approach coupled with AI/ML, allows us to quantify and control the impact of low-quality data. Hence, UQ needs to be developed for ML to inform uncertainties in learned models from overall data. This enables the optimization and control of the data assimilation process.
AI/ML depends on many parameters and the size of a training set. Therefore, our models make certain predictions after which to better capture the full cloud of predictions. We develop an approach that converges AI/ML-based learned models with model-based predictions so that all numerical and experimental data can help self-inform predictions.
There are common metrics used to evaluate models and we typically provide a comprehensive report and analysis of these metrics. We also use new data when evaluating our model to prevent the likelihood of overfitting the training set. Additionally, while building a model, we use special strategies to create a validation set that allows us to evaluate and tune the model and assess which parameters are optimal for it.
AI/ML is one of the fastest-growing technology sectors today and there are dozens of different libraries that can cover all your AI/ML needs. We typically offer our clients a summarized overview of the data characteristics and the latest methods within the field applicable to the problem of interest.