Prediction based on biological sequences (where Machine Learning meets Life Sciences)

Speaker:  Mohammad Sohel Rahman – Dhaka, Bangladesh
Topic(s):  Applied Computing

Abstract

Due to the rapid development of fast sequencing technologies, we now have tremendous amount data on different biological sequences. For example, the number of sequence-known proteins has grown exponentially in recent years. On the contrary, the biochemical experiments to learn the attributes of proteins are expensive and time consuming. A large gap thus exists between the number of sequence-known proteins and that of attribute-known proteins. To catch up, researchers have started to rely on state of the art computational intelligence based methods (e.g., Machine Learning) to predict different attributes of proteins and other biological sequences. 
 
In this lecture, we will discuss Machine Learning based methods for a number of prediction tasks in the domain of life sciences. We will discuss predictors that have been developed based on a machine learning based framework where the features are extracted from the primary sequence only. Overall, our research empirically asserts the natural belief that the functional and structural information of a biological sequence are intrinsically encoded within its primary sequence. This assertion culminates in generalizing a framework for sequence based feature extraction and selection that can be applied to any prediction problem in life sciences.

About this Lecture

Number of Slides:  91
Duration:  45 minutes
Languages Available:  English
Last Updated: 

Request this Lecture

To request this particular lecture, please complete this online form.

Request a Tour

To request a tour with this speaker, please complete this online form.

All requests will be sent to ACM headquarters for review.