12853199
Parallelization with Tree Skeletons
Trees are useful data structures, but to design efficient parallel programs over trees is known to be more difficult than to do over lists. Although several important tree skeletons have been proposed to simplify parallel programming on trees, few studies have been reported on how to systematically use them in solving practical problems; it is neither clear how to make a good combination of skeletons to solve a given problem, nor obvious even how to find suitable operators used in a single skeleton. In this paper, we report our first attempt to resolve these problems, proposing two important transformations, the tree diffusion transformation and the tree context preservation transformation. The tree diffusion transformation allows one to use familiar recursive definitions to develop his parallel programs, while the tree context preservation transformation shows how to derive associative operators that are required when using tree skeletons. We illustrate our approach by deriving an efficient parallel program for solving a nontrivial problem called the party planning problem, the tree version of the famous maximum-weight-sum problem.
9579545
An Injective Language for Reversible Computation
Erasure of information incurs an increase in entropy and dissipates heat. Therefore, information-preserving computation is essential for constructing computers that use energy more effectively. A more recent motivation to understand reversible transformations also comes from the design of editors where editing actions on a view need to be reflected back to the source data. In this paper we present a point-free functional language, with a relational semantics, in which the programmer is allowed to define injective functions only. Non-injective functions can be transformed into a program returning a history. The language is presented with many examples, and its relationship with Bennett’s reversible Turing machine is explained. The language serves as a good model for program construction and reasoning for reversible computers, and hopefully for modelling bi-directional updating in an editor.
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.
21046907
Construction of List Homomorphisms by Tupling and Fusion
List homomorphisms are functions which can be efficiently computed in parallel since they ideally suit the divide- sand-conquer paradigm. However, some interesting functions, e.g., the maximum segment sum problem, are not list homomorphisms. In this paper, we propose a systematic way of embedding them into list homomorphisms so that parallel programs are derived. We show, with an example, how a simple, and “obviously” correct, but possibly inefficient solution to the problem can be successfully turned into a semantically equivalent almost homomorphism by means of two transformations: tupling and fusion.
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).
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.
6684027
iProLINK: an integrated protein resource for literature mining
The exponential growth of large-scale molecular sequence data and of the PubMed scientific literature has prompted active research in biological literature mining and information extraction to facilitate genome/proteome annotation and improve the quality of biological databases. Motivated by the promise of text mining methodologies, but at the same time, the lack of adequate curated data for training and benchmarking, the Protein Information Resource (PIR) has developed a resource for protein literature mining--iProLINK (integrated Protein Literature INformation and Knowledge). As PIR focuses its effort on the curation of the UniProt protein sequence database, the goal of iProLINK is to provide curated data sources that can be utilized for text mining research in the areas of bibliography mapping, annotation extraction, protein named entity recognition, and protein ontology development. The data sources for bibliography mapping and annotation extraction include mapped citations (PubMed ID to protein entry and feature line mapping) and annotation-tagged literature corpora. The latter includes several hundred abstracts and full-text articles tagged with experimentally validated post-translational modifications (PTMs) annotated in the PIR protein sequence database. The data sources for entity recognition and ontology development include a protein name dictionary, word token dictionaries, protein name-tagged literature corpora along with tagging guidelines, as well as a protein ontology based on PIRSF protein family names. iProLINK is freely accessible at http://pir.georgetown.edu/iprolink, with hypertext links for all downloadable files.
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/
29782824
Hamilton connectivity of line graphs and claw-free graphs
38894685
A Strongly Polynomial Algorithm for the Inverse Shortest Arborescence Problem
Abstract In this paper an inverse problem of the weighted shortest arborescence problem is discussed. That is, given a directed graph G and a set of nonnegative costs on its arcs, we need to modify those costs as little as possible to ensure that T, a given v1 -arborescence of G, is the shortest one. It is found that only the cost of T needs modifying. An O(n3) combinatorial algorithm is then proposed. This algorithm also gives an optimal solution to the inverse weighted shortest path problem.
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