Skip to main content

Deep Biology Program



About Deep Biology Program

Cere Labs is happy to start the Deep Biology program under the umbrella of CoE with Patkar-Varde College, Goregaon. This unique program brings together multiple departments in Patkar-Varde College, Goregaon to collaborate with CereLabs. The objective is to use Deep Learning and Machine Learning for Drug Discovery and Personalised Oncology.

The Deep Biology program took place in four phases:

Phase I - April ‘17 to May ‘17 - Decide Areas  

In the first phase the following two areas were decided:
Drug Discovery and Personalised Oncology
Drug design is an expensive process. A new drug takes 10 to 15 years and costs more than $250 billion to introduce it to market. Applying Machine Learning to drug discovery will reduce both the time and cost of discovering a new drug.

Personalized oncology 
Personalized oncology is the method of offering customized medicine for a cancer patient based on the person’s genetic makeup. Machine Learning techniques accelerates the process of finding accurate treatment.

Phase II - May ‘17 to June ‘17 - Training & Assignments
Students from Bioinformatics and Computer Science went through a seven days workshop on Bioinformatics and Machine Learning. This workshop helped them to start their research in drug discovery and personalized oncology.

Phase III - June ‘17 to September ‘17 - Literature survey and decide project topic

Following two projects were finalized

Project 1:Design chemical entity suitable for inhibition for HIV-1 Protease by combination machine learning techniques & structure based drug designing.

Description: Understanding the pathway of HIV virus and identifying important drug target (i.e. HIV-1 Protease) & validating active site in protein. Approved drug parameters are retrieved from DrugBank or PubChem. Creating analogs or similar structure and checking its activity using insilico tools. Combining data of approved and similar structure suitable for applying supervised machine learning technique and generate model/equation. Retrieving the parent molecule from collected data and performing lead optimization derive a new molecule. New molecule can be tested through the equation generated by machine learning to check activity/inactivity of molecule on HIV-1 Protease.

Expected Outcome: Determine parameters for best suited for chemical entity on selected protein target & model the structure of chemical entity for further analysis.

Project 2: Identifying Drug Candidate for multidrug resistance tuberculosis using drug repositioning method & machine learning.

Description: Machine learning is used to find patterns from gene expressions retrieved from GEO database which helps in identifying differential gene expression in healthy and diseased sample. Drugs are linked with gene expression to find enrichment score for each drug. Score above 30% indicates optimal drug suitable for further optimization and testing.

Expected Outcome: Identifying drug candidate from previously drugs, optimize the drug to reduce timeline of treatment. 

Phase IV -  September ‘17 onwards - Actual Working on project
 
Students have started on the projects. The task is of collecting data and training it using Machine Learning algorithms.
 
Labels:

Comments

Post a Comment

Popular posts from this blog

How is AI Saving the Future

Meanwhile the talk of AI being the number one risk of human extinction is going on, there are lot many ways it is helping humanity. Recent developments in Machine Learning are helping scientists to solve difficult problems ranging from climate change to finding the cure for cancer. It will be a daunting task for humans to understand enormous amount of data that is generated all over the world. Machine Learning is helping scientists to use algorithms that learn from data and find patterns. Below is a list of few of the problems AI is working on to help find solutions which otherwise would not have been possible: Cancer Diagnostics : Recently, scientists at University of California (UCLA) applied Deep Learning to extract features for achieving high accuracy in label-free cell classification. This technique will help in faster cancer diagnostics, and thus will save a lot of lives. Low Cost Renewable Energy : Artificial-intelligence is helping wind power forecasts of u...
6 comments
Read more

GPU - The brain of Artificial Intelligence

Machine Learning algorithms require tens and thousands of CPU based servers to train a model, which turns out to be an expensive activity. Machine Learning researchers and engineers are often faced with the problem of running their algorithms fast. Although initially invented for processing graphics in computer games, GPUs today are used in machine learning to perform feature detection from vast amount of unlabeled data. Compared to CPUs, GPUs take far less time to train models that perform classification and prediction. Characteristics of GPUs that make them ideal for machine learning Handle large datasets Needs far less data centre infrastructure Can be specialized for specific machine learning needs Perform vector computations faster than any known processor Designed to perform data parallel computation NVIDIA CUDA GPUs today are used to build deep learning image processing tools for  Adobe Creative Cloud. According to NVIDIA blog future Adobe appli...
7 comments
Read more

In the World of Document Similarity

How does a human infer whether two documents are similar? This question has dazzled cognitive scientists, and is one area under which a lot of research is taking place. As of  now there is no product that is able to match or surpass human capability in finding the similarity in documents. But things are improving in this domain, and companies such as IBM and Microsoft are investing a lot in this area. We at Cere Labs, an Artificial Intelligence startup based in Mumbai, also are working in this area, and have applied LDA and Word2Vec techniques, both giving us promising results: Latent Dirichlet Allocation (LDA) : LDA is a technique used mainly for topic modeling. You c an leverage on this topic modeling to find the similarity between documents. It is assumed that more the topics two documents overlap, more are the chances that those documents carry semantic similarity. You can study LDA in the following paper: https://www.cs.princeton.edu/~blei/papers/BleiNgJordan20...
Post a Comment
Read more