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Association for Computing Machinery

Articles Tagged: Computational biology

Articles & Features

Cancer: from biology to computer science

DEPARTMENT: Blogs

Cancer: from biology to computer science

Cancer research has been at the heart of life sciences for the past few decades. Since genetics play an important role in most cancers, computational methods are crucial in understanding the development of the disease.

By Abdelrahman Hosny, June 2017

PDF | HTML | In the Digital Library

DEPARTMENT: Hello world

On constructing the tree of life

By Marinka Zitnik, December 2013

PDF | HTML | In the Digital Library

Automated DNA sequencers

DEPARTMENT: Back

Automated DNA sequencers

By Finn Kuusisto, September 2012

PDF | HTML | In the Digital Library

Storage capacity comparison of neural network models for memory recall

The physiology of how the human brain recalls memories is not well understood. Neural networks have been used in an attempt to model this process.

Two types of networks have been used in several models of temporal sequence memory for simple sequences of randomly generated and also of structured patterns: auto- and hetero-associative networks. Previous work has shown that a model with coupled auto- and hetero-associative continuous attractor networks can robustly recall learned simple sequences. In this paper, we compare Hebbian learning and pseudo-inverse learning in a model for recalling temporal sequences in terms of their storage capacities. The pseudo-inverse learning method is shown to have a much higher storage capacity, making the new network model 700% more efficient by reducing calculations.

By Kate Patterson, December 2007

PDF | HTML | In the Digital Library

Achieving I/O improvements in a mass spectral database

Research in proteomics has created two significant needs: the need for an accurate public database of empirically derived mass spectrum information and the need for managing the I/O and organization of mass spectrometry data in the form of files and structures. Lack of an empirically derived database limits the ability of proteomic researchers to identify and study proteins. Managing the I/O and organization of mass spectrometry data is often time-consuming due to the many fields that need to be set and retrieved. As a result, incompatibilities and inefficiencies are created by each programmer handling this in his or her own way. Until recently, storage space and computing power has been the limiting factor in developing tools to handle the vast amount of mass spectrometry information. Now the resources are available to store, organize, and analyze mass spectrometry information.The Illinois Bio-Grid Mass Spectrometry Database is a database of empirically derived tandem mass spectra of peptides created to provide researchers with an organized and searchable database of curated spectrum information to allow more accurate protein identification. The Mass Spectrometry I/O Project creates a framework that handles mass spectrometry data I/O and data organization, allowing researchers to concentrate on data analysis rather than I/O. In addition, the Mass Spectrometry I/O Project leverages several cross-platform and portability-enhancing technologies, allowing it to be utilized on a variety of hardware and operating systems.

By Eric Puryear, Jennifer Van Puymbrouck, David Sigfredo Angulo, Kevin Drew, Lee Ann Hollenbeck, Dominic Battre, Alex Schilling, David Jabon, Gregor von Laszewski, December 2006

PDF | HTML | In the Digital Library

Modeling protein dependency networks using CoCoA

In an interdisciplinary effort to model protein dependency networks, biologists measure signals from certain proteins within cells over a given interval of time. Using this time series data, the goal is to deduce protein dependency relationships. The mathematical challenges is to statistically measure correlations between given proteins over time in order to conjecture probable relationships. Biologists can then consider these relationships with more scrutiny, in order to confirm their conjectures. One algorithm for finding such relationships makes use of interpolation of the data to produce next-state functions for each protein and the Deegan-Packel Index of Power voting method to measure the strength of correlations between pairs of proteins. The algorithm was previously implemented, but limitations associated with the original language required the algorithm to be re-implemented in a more computationally efficient language. Because of the algebraic focus of the Computational Commutative Algebra language, or CoCoA, the algorithm was re-implemented in this language, and results have been produced much more efficiently. In this paper I discuss the algorithm, the CoCoA language, the implementation of the algorithm in CoCoA, and the quality of the results.

By Grey Ballard, September 2006

PDF | HTML | In the Digital Library

A parallel algorithm for DNA alignment

By Thomas Royce, Rance Necaise, March 2003

PDF | HTML | In the Digital Library