Computational Biology CB_Q0004

Title : Regularization and noise injection for improving genetic network models
Author : Eugene van Someren, Lodewyk Wessels, Marcel Reinders and Eric Backer
Year Publish : 2006
Place of Publish : : Springer Berlin / Heidelberg
Abstract :

Computational Biology CB_Q0003

Title : Discovering relationships among dispersed repeats using spatial association rule mining
Author : Surya Saha, Susan Bridges, Zenaida Magbanua and Daniel G Peterson
Year Publish : 2008
Place of Publish : BioMed Central Ltd
Abstract :

Background
DNA in eukaryotic genomes is characterized, and often dominated, by repetitive, non-genic DNA sequences. Initially thought to be non-functional, repeats have been found to influence gene expression [1] and provide diversity to the genome via mutation. Mobile repeat sequences [2](transposons) have played a prominent role in the evolutionary histories of eukaryotic genomes [3,4], and their persistence in eukaryotic DNA indicates that they have, on the whole, been evolutionarily advantageous. While there are an increasing number of algorithms that have been developed for discovering novel dispersed repeats [5-7], significant analysis of the repeats and their relationships to other genome features will be required before we can truly understand the complex ways in which dispersed repeat sequences contribute to evolutionary fitness. We propose a spatial proximity rule based data mining technique to discover highly fragmented repeat regions for which only the conserved parts are reported by a computational repeat finder.

Computational Biology CB_Q0002

Title : The protein sequence design problem in canonical model on 2D and 3D lattices
Author : Piotr Berman, Bhaskar DasGupta, Dhruv Mubayi, Robert Sloan, Gy?rgy Tur?n and Yi Zhang
Year Publish : 2004
Place of Publish : Springer Berlin / Heidelberg
Abstract :

In this paper we investigate the protein sequence design (PSD) problem (also known as the inverse protein folding problem) under the Canonical modelon 2D and 3D lattices [12,25]. The Canonical model is specified by (i) a geometric representation of a target protein structure with amino acid residues via itscontact graph, (ii) a binary folding code in which the amino acids are classified as hydrophobic (H) or polar(P), (iii) an energy function? defined in terms of the target structure that should favor sequences with adense hydrophobic core and penalize those with many solvent-exposed hydrophobic residues (in the Canonical model, the energy function ? gives an H-H residue contact in the contact graph a value of –1 and all other contacts a value of 0), and (iv) to prevent the solution from being a biologically meaningless all H sequence, the number of H residues in the sequence S is limited by fixing an upper bound ? on the ratio between H and P amino acids. The sequence S is designed by specifying which residues are H and which ones are P in a way that realizes the global minima of the energy function ?. In this paper, we prove the following results:
(1) An earlier proof of NP-completeness of finding the global energy minima for the PSD problem on 3D lattices in [12] was based on the NP-completeness of the same problem on 2D lattices. However, the reduction was not correct and we show that the problem of finding the global energy minima for the PSD problem for 2D lattices can be solved efficiently in polynomial time. But, we show that the problem of finding the global energy minima for the PSD problem on 3D lattices is indeed NP-complete by a providing a different reduction from the problem of finding the largest clique on graphs.
(2) Even though the problem of finding the global energy minima on 3D lattices is NP-complete, we show that an arbitrarily close approximation to the global energy minima can indeed be found efficiently by taking appropriate combinations of optimal global energy minima of substrings of the sequence S by providing a polynomial-time approximation scheme (PTAS). Our algorithmic technique to design such a PTAS for finding the global energy minima involves using the shifted slice-and-dice approach in [6,17,18]. This result improves the previous best polynomial-time approximation algorithm for finding the global energy minima in [12] with a performance ratio of 1\over 2.

Computational Biology CB_Q0001

Title : Overview of the entity relations (REL) supporting task of BioNLP Shared Task 2011
Author : Sampo Pyysalo_ Tomoko Ohta_ Jun’ichi Tsujii
Year Publish : 2011
Place of Publish : Omnipress, Inc
Abstract :

This paper presents the Entity Relations (REL) task, a supporting task of the BioNLP Shared Task 2011. The task concerns the extraction of two types of part-of relations between a gene/protein and an associated entity. Four teams submitted final results for the REL task, with the highest-performing system achieving 57.7% F-score. While experiments suggest use of the data can help improve event extraction performance, the task data has so far received only limited use in support of event extraction. The REL task continues as an open challenge, with all resources available from the shared task website.

Computational Biology CB_J0010

Title : Data mining the yeast genome in a lazy functional languag
Author : Amanda Clare and Ross D. King
Year Publish : 2003
Place of Publish : Springer Berlin / Heidelberg
Abstract :

Critics of lazy functional languages contend that the languages are only suitable for toy problems and are not used for real systems. We present an application (PolyFARM) for distributed data mining in relational bioinformatics data, written in the lazy functional language Haskell. We describe the problem we wished to solve, the reasons we chose Haskell and relate our experiences. Laziness did cause many problems in controlling heap space usage, but these were solved by a variety of methods. The many advantages of writing software in Haskell outweighed these problems. These included clear expression of algorithms, good support for data structures, abstraction, modularity and generalisation leading to fast prototyping and code reuse, parsing tools, profiling tools, language features such as strong typing and referential transparency, and the support of an enthusiastic Haskell community. PolyFARM is currently in use mining data from the Saccharomyces cerevisiae genome and is freely available for non-commercial use at http://www.aber.ac.uk/compsci/Research/bio/dss/polyfarm/.

Computational Biology CB_J0009

Title : Agent-based modeling of ductal carcinoma in situ: application to patient-specific breast cancer modeling
Author : Paul Macklin, Jahun Kim, Giovanna Tomaiuolo, Mary E. Edgerton and Vittorio Cristini
Year Publish : 2010
Place of Publish : Springer Berlin / Heidelberg
Abstract :

Ductal carcinoma in situ (DCIS) of the breast is the most common precursor to invasive carcinoma (IC), the second-leading cause of death in women in USA. There has been great progress in modeling DCIS at both the cellular scale (e.g., using cellular automata and agent-based models) and the population scale (e.g., using partial differential equations or systems of ordinary differential equations), but these past efforts have been difficult to calibrate with patient-specific molecular and cellular measurements. We develop a biophysically justified, agent-based cellular model of DCIS that is well-suited to patient-specific calibration. The model is modular in nature and can thus be readily extended to incorporate more advanced biology. We give an example of recently developed, patient-specific calibration of the model and conduct parameter studies that generate testable biological hypotheses.

Computational Biology CB_J0008

Title : Power boosts for cluster tests
Author : David Sankoff and Lani Haque
Year Publish : 2005
Place of Publish : Springer Berlin / Heidelberg
Abstract :

Gene cluster significance tests that are based on the number of genes in a cluster in two genomes, and how compactly they are distributed, but not their order, may be made more powerful by the addition of a test component that focuses solely on the similarity of the ordering of the common genes in the clusters in the two genomes. Here we suggest four such tests, compare them, and investigate one of them, the maximum adjacency disruption criterion, in some detail, analytically and through simulation.