Many tasks involve predicting a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classification and graphical modeling. They combine the ability of graphical models to compactly model multivariate data with the ability of classification methods to perform prediction using large sets of inpu...

Prediction of protein secondary structures is one of the oldest problems in Bioinformatics. Although several different methods have been proposed to tackle this problem, none of these methods are perfect. Recently, it is proposed that addition of other structural information like accessible surface area of residues or prior information about protein structural class can significantly improve th...

MOTIVATION Manual inspection has been applied to and is well accepted for assessing critical assessment of protein structure prediction (CASP) free modeling (FM) category predictions over the years. Such manual assessment requires expertise and significant time investment, yet has the problems of being subjective and unable to differentiate models of similar quality. It is beneficial to incorpo...

It is not always clear how best to represent integrated data sets, and which application and database features allow a scientist to take best advantage of data coming from various information sources. To improve the use of integrated data visualisation in candidate gene finding, we carried out a user study comparing an existing general-purpose genetics visualisation and query system, Ensembl, t...

Protein fold recognition (PFR) is considered as an important step towards the protein structure prediction problem. It also provides crucial information about the functionality of the proteins. Despite all the efforts that have been made during the past two decades, finding an accurate and fast computational approach to solve PFR still remains a challenging problem for bioinformatics and comput...

Machine learning means different things to different people, and there is no general agreed upon core set of algorithms that must be learned. In this class we will therefore not focus so much on specific algorithms or machine learning models, but rather give an introduction to the overall approach to using machine learning in bioinformatics, as we see it. To us, the core of machine learning boi...

One of the major unsolved problems in modern day molecular biology is the protein folding problem: given an amino acid sequence, predict the overall three-dimensional structure of the corresponding protein. It has been known since the seminal work of Anfinsen (1973) in the early seventies that the sequence of a protein encodes its structure, but the exact details of the encoding still remain el...

Protein function prediction, i.e. classification of proteins according to their biological function, is an important task in bioinformatics. In this chapter, we illustrate that the presence of sequence motifs – elements that are conserved across different proteins – are highly discriminative features for predicting the function of a protein. This is in agreement with the biological thinking tha...

Certificate This is to certify that, Varun Aggarwal, (104/ECE/2000) a student of NSIT, Delhi, India did his summer training under me at Stockholm Bioinformatics Center for the months of June-July 2003. He worked on two projects documented in this report. Acknowledgement I will like to thanks Dr. Bob MacCallum for giving me this opportunity to work with his group. I hugely benefited and wish to ...

SUMMARY Chemical mapping experiments allow for nucleotide resolution assessment of RNA structure. We demonstrate that different strategies of integrating probing data with thermodynamics-based RNA secondary structure prediction algorithms can be implemented by means of soft constraints. This amounts to incorporating suitable pseudo-energies into the standard energy model for RNA secondary struc...