Using transfer learning and loss function adaptation for RNA secondary structure prediction

openThe problem of predicting RNA secondary structure is a challenging topic, which involves various fields of computer science. Accurate solutions to this problem are helpful in the disciplines of medicine for vaccine development, to design stable mRNA molecules, or biology for discerning between different functions of various RNA molecules according to their shape. The objective of this project is to study an emerging Machine Learning-based approach to the problem of RNA secondary structure prediction via integration of deep learning techniques like transfer learning and convolutional neural net- works, aided by adaptations made for the specific problem at hand, like data representation and loss function. The objective of this project is to provide a new robust Machine Learning-based approach to the problem of RNA secondary structure prediction via integration and improvement of emerging Deep Learning techniques.The problem of predicting RNA secondary structure is a challenging topic, which involves various fields of computer science. Accurate solutions to this problem are helpful in the disciplines of medicine for vaccine development, to design stable mRNA molecules, or biology for discerning between different functions of various RNA molecules according to their shape. The objective of this project is to study an emerging Machine Learning-based approach to the problem of RNA secondary structure prediction via integration of deep learning techniques like transfer learning and convolutional neural net- works, aided by adaptations made for the specific problem at hand, like data representation and loss function. The objective of this project is to provide a new robust Machine Learning-based approach to the problem of RNA secondary structure prediction via integration and improvement of emerging Deep Learning techniques

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include a myriad of properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field

PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.

Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions