We specialize in developing Deep Learning Models for Prognostic Application
OME-DL is a Artificial Intelligence powered Deep Learning Framework for Developing Predictive models. The platform has been successfully applied to Genomics, Healthcare, Drug Discovery and has potential to be used for a wide range of applications. It uses state of art technologies for Feature identification, Optimization of learning parameters, Network pruning and Cross validation. The framework is equipped with APIs to seamlessly integrate with existing IT infrastructure, Health Informatics systems, Diagnostics Lab and a wide range of existing systems.
Leveraging the Power of Neuron Learning
Our Brain has immense learning power. What if we focus all this in a specialized domain free from all distractions? Mimicking the brain learning process, scientist developed Deep Learning algorithm enabling the machines to make smart decisions. The ever-increasing computational power along with the advancement in algorithm is helping us develop Predictive models for wide range of applications.
SEQOME-DL is designed to support wide range of data. Some of the well tested data-include
- Clinical Data: Patient specific data, Lab Results
- Gene Expression Data: RNA Sequencing, Microarrays
- Small RNA Data: miRNA, siRNA, piRNA, rasiRNA
- Next Generation Sequencing: Variant, Expression, Fusion
- Quantitative Proteomics: SILAC, ICAT, LC-MS
- Sequence Data: Splice, TSS, Pattern Prediction etc
- Biochemical Data: Compatible with all formats of data
- Any other Numeric or Image data
Key Features of OME-DL
- Auto-optimizer: The Platform automatically optimizes for best results
- Feature Selection algorithm: Feature Selection algorithm selects the features which are significant for the classifier and removes the irrelevant features from further training.
- Momentum and Decay functions: State of art features such as Momentum and Decay integrated to the learning process, so as to obtain the best classifier and avoid the model falling in a local minima
- Cross Validation: Multiple fold cross validation to check for the prediction accuracy.
- Cost Function: Variable cost function to achieve the most precise result
- Ease of Use: Easy to use as unified web application
- Integration: Seamless integration with existing IT system, LIMS, HMS etc.