Within the last decade, advances in data science, particularly in deep learning have impacted a number of fields of research and thousands of corporations. Organizations who embrace data science stand to benefit tremendously as novel machine learning techniques are increasingly applied with tremendous success to solve problems in the life sciences industry. Traditional computational chemistry and machine learning approaches have already made significant impact in SAR, drug discovery and formulation design, however deep learning implementation has not been impactful in the life sciences industry.
Artificial intelligence is a model or system that provide scientific, operation or business insight by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data.
- Machine Learning enables computers to learn from data with minimal programming.
- Neural Network is a type of machine learning that is made up of interconnected units that processes information by responding to external inputs, relaying information between each unit.
- Deep Learning is a type of machine learning that uses neural networks with many layers of processing units.
The Data Science Process:
Machine Learning Timeline:
There are three general approaches to machine learning. For supervised learning, the data set contains a target. Unsupervised learning is typically based on clustering algorithms. Reinforcement learning is where results are evaluated thoroughly, however, best answers are often unknown.
The Three Types of Machine Learning:
- Known the right answer
- Model figures out the map form inputs to the right answer
- Don’t have the answer
- Model evaluation is tricky
- Lots of clustering
- Often used with supervised learning
- Can be used for mapping decisions in an environment
- Can also be used if we can use other models to assess answers and provide rewards
Deep learning architectures take simple neural networks to the next level. Using these layers, deep learning networks can be built which enable machine learning. Data goes into a neural network through the input layer, which in-turn communicates to hidden layers. Processing takes place in the hidden layers through a system of weighed connections. Nodes in the hidden layer then combine data from the input layer with a set of coefficients and assigns appropriate weights to inputs. These input-weight products are then summed up. The sum is then passed through a node’s activation function, which determines the extent that a signal must progress further through the network to affect the final output. Finally, the hidden layers link to the output layer – where the outputs are retrieved.
Products offered by jNext utilize application data to create algorithms that help business make impactful decisions, utilizing the above mentioned tools.