2144083
Program transformation in calculational form
Correctness-preserving program transformation has recently received a particular attention for compiler optimization in functional programming [Kelsey and Hudak 1989; Appel 1992; Peyton Jones 1996]. By implementing a compiler using many passes, each of which is a transformation for a particular optimization, one can attain a modular compiler. It is no surprise that the modularity would increase if transformations are structured, i.e. constructed in a modular way. Indeed, the program transformation in calculational form (or program calculation) can help us to attain this goal.
38460463
Formal derivation of efficient parallel programs by construction of list homomorphisms
It has been attracting much attention to make use of list homomorphisms in parallel programming because they ideally suit the divide-and-conquer parallel paradigm. However, they have been usually treated rather informally and ad hoc in the development of efficient parallel programs. What is worse is that some interesting functions, e.g., the maximum segment sum problem, are basically not list homomorphisms. In this article, we propose a systematic and formal way for the construction of a list homomorphism for a given problem so that an efficient parallel program is derived. We show, with several well-known but nontrivial problems, how a straightforward, and “obviously” correct, but quite inefficient solution to the problem can be successfully turned into a semantically equivalent “almost list homomorphism.” The derivation is based on two transformations, namely tupling and fusion, which are defined according to the specific recursive structures of list homomorphisms.
15997088
A New Parallel Skeleton for General Accumulative Computations
Skeletal parallel programming enables programmers to build a parallel program from ready-made components (parallel primitives) for which efficient implementations are known to exist, making both the parallel program development and the parallelization process easier. Constructing efficient parallel programs is often difficult, however, due to difficulties in selecting a proper combination of parallel primitives and in implementing this combination without having unnecessary creations and exchanges of data among parallel primitives and processors. To overcome these difficulties, we propose a powerful and general parallel skeleton, accumulate, which can be used to naturally code efficient solutions to problems as well as be efficiently implemented in parallel using Message Passing Interface (MPI).
5453797
List Homomorphism with Accumulation
This paper introduces accumulation into list homomorphisms for systematic development of both efficient and correct parallel programs. New parallelizable recursive pattern called H-homomorphism is given, and transformations from sequential patterns in the H-form and Hform into (H-)homomorphism are shown. We illustrate the power of our formalization by developing a novel and general parallel program for a class of interesting and challenging problems, known as maximum marking problems.
13526098
Systematic Derivation of Tree Contraction Algorithms
While tree contraction algorithms play an important role in efficient tree computation in parallel, it is difficult to develop such algorithms due to the strict conditions imposed on contracting op...
17460651
A calculational fusion system HYLO
Fusion, one of the most useful transformation tactics for deriving efficient programs, is the process whereby separate pieces of programs are fused into a single one, leading to an efficient program without intermediate data structures produced. In this paper, we report our on-going investigation on the design and implementation of an automatic transformation system HYLO which performs fusion transformation in a more systematic and more general way than any other systems. The distinguished point of our system is its calculational feature based on simple application of transformation laws rather than traditional search-based transformations.
471313
Dependence network modeling for biomarker identification
MOTIVATION
Our purpose is to develop a statistical modeling approach for cancer biomarker discovery and provide new insights into early cancer detection. We propose the concept of dependence network, apply it for identifying cancer biomarkers, and study the difference between the protein or gene samples from cancer and non-cancer subjects based on mass-spectrometry (MS) and microarray data.


RESULTS
Three MS and two gene microarray datasets are studied. Clear differences are observed in the dependence networks for cancer and non-cancer samples. Protein/gene features are examined three at one time through an exhaustive search. Dependence networks are constructed by binding triples identified by the eigenvalue pattern of the dependence model, and are further compared to identify cancer biomarkers. Such dependence-network-based biomarkers show much greater consistency under 10-fold cross-validation than the classification-performance-based biomarkers. Furthermore, the biological relevance of the dependence-network-based biomarkers using microarray data is discussed. The proposed scheme is shown promising for cancer diagnosis and prediction.


AVAILABILITY
See supplements: http://dsplab.eng.umd.edu/~genomics/dependencenetwork/
53305921
Substring selection for biomedical document classification
Motivation: Attribute selection is a critical step in development of document classification systems. As a standard practice, words are stemmed and the most informative ones are used as attributes in classification. Owing to high complexity of biomedical terminology, general-purpose stemming algorithms are often conservative and could also remove informative stems. This can lead to accuracy reduction, especially when the number of labeled documents is small. To address this issue, we propose an algorithm that omits stemming and, instead, uses the most discriminative substrings as attributes. 
 
Results: The approach was tested on five annotated sets of abstracts from iProLINK that report on the experimental evidence about five types of protein post-translational modifications. The experiments showed that Naive Bayes and support vector machine classifiers perform consistently better [with area under the ROC curve (AUC) accuracy in range 0.92--0.97] when using the proposed attribute selection than when using attributes obtained by the Porter stemmer algorithm (AUC in 0.86--0.93 range). The proposed approach is particularly useful when labeled datasets are small. 
 
Contact: vucetic@ist.temple.edu 
 
Supplementary Information: The supplementary data are available from www.ist.temple.edu/PIRsupplement
197229
The PIR integrated protein databases and data retrieval system
The Protein Information Resource (PIR) provides many databases and tools to support genomic and proteomic research. PIR is a member of UniProt—Universal Protein Resource—the central repository of protein sequence and function, which maintains UniProt Knowledgebase with extensively curated annotation, UniProt Reference databases to speed sequence searches, and UniProt Archive to reflect sequence history. PIR also provides PIRSF family classification system based on evolutionary relationships of full-length proteins, and iProClass integrated database of protein family, function, and structure. These databases are easily accessible from PIR web site using a centralized data retrieval system for information retrieval and knowledge discovery.
12615323
PIRSF: family classification system at the Protein Information Resource
The Protein Information Resource (PIR) is an integrated public resource of protein informatics. To facilitate the sensible propagation and standardization of protein annotation and the systematic detection of annotation errors, PIR has extended its superfamily concept and developed the SuperFamily (PIRSF) classification system. Based on the evolutionary relationships of whole proteins, this classification system allows annotation of both specific biological and generic biochemical functions. The system adopts a network structure for protein classification from superfamily to subfamily levels. Protein family members are homologous (sharing common ancestry) and homeomorphic (sharing full-length sequence similarity with common domain architecture). The PIRSF database consists of two data sets, preliminary clusters and curated families. The curated families include family name, protein membership, parent-child relationship, domain architecture, and optional description and bibliography. PIRSF is accessible from the website at http://pir.georgetown.edu/pirsf/ for report retrieval and sequence classification. The report presents family annotation, membership statistics, cross-references to other databases, graphical display of domain architecture, and links to multiple sequence alignments and phylogenetic trees for curated families. PIRSF can be utilized to analyze phylogenetic profiles, to reveal functional convergence and divergence, and to identify interesting relationships between homeomorphic families, domains and structural classes.
29782824
Hamilton connectivity of line graphs and claw-free graphs