IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Computationalbiology

Elysium Technologies Private Limited
ISO 9001:2008 A leading Research and Development Division
Madurai | Chennai | Trichy | Coimbatore | Kollam| Singapore
Website: elysiumtechnologies.com, elysiumtechnologies.info
Email: info@elysiumtechnologies.com

IEEE Project List 2011 - 2012

Abstract COMPUTATIONAL BIOLOGY AND
BIO INFORMATICS 2011 - 2012

01 3D Shape Reconstruction of Loop Objects in X-Ray Protein Crystallography

Knowledge of the shape of crystals can benefit data collection in X-ray crystallography. A preliminary step is the
determination of the loop object, i.e., the shape of the loop holding the crystal. Based on the standard set-up of
experimental X-ray stations for protein crystallography, the paper reviews a reconstruction method merely requiring 2D
object contours and presents a dedicated novel algorithm. Properties of the object surface (e.g., texture) and depth
information do not have to be considered. The complexity of the reconstruction task is significantly reduced by slicing the
3D object into parallel 2D cross-sections. The shape of each cross-section is determined using support lines forming
polygons. The slicing technique allows the reconstruction of concave surfaces perpendicular to the direction of projection.
In spite of the low computational complexity, the reconstruction method is resilient to noisy object projections caused by
imperfections in the image-processing system extracting the contours. The algorithm developed here has been
successfully applied to the reconstruction of shapes of loop objects in X-ray crystallography.

02 A Biologically Inspired Measure for Co expression Analysis

Two genes are said to be coexpressed if their expression levels have a similar spatial or temporal pattern. Ever since the
profiling of gene microarrays has been in progress, computational modeling of coexpression has acquired a major focus.
As a result, several similarity/distance measures have evolved over time to quantify coexpression similarity/dissimilarity
between gene pairs. Of these, correlation coefficient has been established to be a suitable quantifier of pairwise
coexpression. In general, correlation coefficient is good for symbolizing linear dependence, but not for nonlinear
dependence. In spite of this drawback, it outperforms many other existing measures in modeling the dependency in
biological data. In this paper, for the first time, we point out a significant weakness of the existing similarity/distance
measures, including the standard correlation coefficient, in modeling pairwise coexpression of genes. A novel measure,
called BioSim, which assumes values between 1 and þ1 corresponding to negative and positive dependency and 0 for
independency, is introduced. The computation of BioSim is based on the aggregation of stepwise relative angular deviation
of the expression vectors considered. The proposed measure is analytically suitable for modeling coexpression as it
accounts for the features of expression similarity, expression deviation and also the relative dependence. It is
demonstrated how the proposed measure is better able to capture the degree of coexpression between a pair of genes as
compared to several other existing ones. The efficacy of the measure is statistically analyzed by integrating it with several
module-finding algorithms based on coexpression values and then applying it on synthetic and biological data. The
annotation results of the coexpressed genes as obtained from gene ontology establish the significance of the introduced
measure. By further extending the BioSim measure, it has been shown that one can effectively identify the variability in the
expression patterns over multiple phenotypes. We have also extended BioSim to figure out pairwise differential expression
pattern and coexpression dynamics. The significance of these studies is shown based on the analysis over several real-life
data sets. The computation of the measure by focusing on stepwise time points also makes it effective to identify partially

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Madurai Trichy Kollam
Elysium Technologies Private Limited Elysium Technologies Private Limited Elysium Technologies Private Limited
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eMail: info@elysiumtechnologies.com Contact : 91431 - 4002234. eMail: elysium.kollam@gmail.com
eMail: elysium.tiruchy@gmail.com

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coexpressed genes. On the whole, we put forward a complete framework for coexpression analysis based on the BioSim
measure.

03 A cDNA Microarray Gene Expression Data Classifier for Clinical Diagnostics Based on Graph Theory

Despite great advances in discovering cancer molecular profiles, the proper application of microarray technology to routine
clinical diagnostics is still a challenge. Current practices in the classification of microarrays’ data show two main
limitations: the reliability of the training data sets used to build the classifiers, and the classifiers’ performances, especially
when the sample to be classified does not belong to any of the available classes. In this case, state-of-the-art algorithms
usually produce a high rate of false positives that, in real diagnostic applications, are unacceptable. To address this
problem, this paper presents a new cDNA microarray data classification algorithm based on graph theory and is able to
overcome most of the limitations of known classification methodologies. The classifier works by analyzing gene expression
data organized in an innovative data structure based on graphs, where vertices correspond to genes and edges to gene
expression relationships. To demonstrate the novelty of the proposed approach, the authors present an experimental
performance comparison between the proposed classifier and several state-of-the-art classification algorithms.

04 A Comprehensive Statistical Model for Cell Signaling

Protein signaling networks play a central role in transcriptional regulation and the etiology of many diseases. Statistical
methods, particularly Bayesian networks, have been widely used to model cell signaling, mostly for model organisms and
with focus on uncovering connectivity rather than inferring aberrations. Extensions to mammalian systems have not
yielded compelling results, due likely to greatly increased complexity and limited proteomic measurements in vivo. In this
study, we propose a comprehensive statistical model that is anchored to a predefined core topology, has a limited
complexity due to parameter sharing and uses micorarray data of mRNA transcripts as the only observable components of
signaling. Specifically, we account for cell heterogeneity and a multilevel process, representing signaling as a Bayesian
network at the cell level, modeling measurements as ensemble averages at the tissue level, and incorporating patient-to-
patient differences at the population level. Motivated by the goal of identifying individual protein abnormalities as potential
therapeutical targets, we applied our method to the RAS-RAF network using a breast cancer study with 118 patients. We
demonstrated rigorous statistical inference, established reproducibility through simulations and the ability to recover
receptor status from available microarray data.

05 A Consensus Tree Approach for Reconstructing Human Evolutionary History and Detecting Population Substructure

The random accumulation of variations in the human genome over time implicitly encodes a history of how human
populations have arisen, dispersed, and intermixed since we emerged as a species. Reconstructing that history is a
challenging computational and statistical problem but has important applications both to basic research and to the
discovery of genotypephenotype correlations. We present a novel approach to inferring human evolutionary history from
genetic variation data. We use the idea of consensus trees, a technique generally used to reconcile species trees from
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divergent gene trees, adapting it to the problem of finding robust relationships within a set of intraspecies phylogenies
derived from local regions of the genome. Validation on both simulated and real data shows the method to be effective in
recapitulating known true structure of the data closely matching our best current understanding of human evolutionary
history. Additional comparison with results of leading methods for the problem of population substructure assignment
verifies that our method provides comparable accuracy in identifying meaningful population subgroups in addition to
inferring relationships among them. The consensus tree approach thus provides a promising new model for the robust
inference of substructure and ancestry from large-scale genetic variation data.

06 A Comprehensive Statistical Model for Cell Signaling

The random accumulation of variations in the human genome over time implicitly encodes a history of how human
populations have arisen, dispersed, and intermixed since we emerged as a species. Reconstructing that history is a
challenging computational and statistical problem but has important applications both to basic research and to the
discovery of genotypephenotype correlations. We present a novel approach to inferring human evolutionary history from
genetic variation data. We use the idea of consensus trees, a technique generally used to reconcile species trees from
divergent gene trees, adapting it to the problem of finding robust relationships within a set of intraspecies phylogenies
derived from local regions of the genome. Validation on both simulated and real data shows the method to be effective in
recapitulating known true structure of the data closely matching our best current understanding of human evolutionary
history. Additional comparison with results of leading methods for the problem of population substructure assignment
verifies that our method provides comparable accuracy in identifying meaningful population subgroups in addition to
inferring relationships among them. The consensus tree approach thus provides a promising new model for the robust
inference of substructure and ancestry from large-scale genetic variation data.

07 A Continuous-Time, Discrete-State Method for Simulating the Dynamics of Biochemical Systems

Computational systems biology is largely driven by mathematical modeling and simulation of biochemical networks, via
continuous deterministic methods or discrete event stochastic methods. Although the deterministic methods are efficient
in predicting the macroscopic behavior of a biochemical system, they are severely limited by their inability to represent the
stochastic effects of random molecular fluctuations at lower concentration. In this work, we have presented a novel method
for simulating biochemical networks based on a deterministic solution with a modification that permits the incorporation of
stochastic effects. To demonstrate the feasibility of our approach, we have tested our method on three previously reported
biochemical networks. The results, while staying true to their deterministic form, also reflect the stochastic effects of
random fluctuations that are dominant as the system transitions into a lower concentration. This ability to adapt to a
concentration gradient makes this method particularly attractive for systems biologybased applications.

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08 A Fast Algorithm for Computing Geodesic Distances in Tree Space

Comparing and computing distances between phylogenetic trees are important biological problems, especially for models
where edge lengths play an important role. The geodesic distance measure between two phylogenetic trees with edge
lengths is the length of the shortest path between them in the continuous tree space introduced by Billera, Holmes, and
Vogtmann. This tree space provides a powerful tool for studying and comparing phylogenetic trees, both in exhibiting a
natural distance measure and in providing a euclidean-like structure for solving optimization problems on trees. An
important open problem is to find a polynomial time algorithm for finding geodesics in tree space. This paper gives such an
algorithm, which starts with a simple initial path and moves through a series of successively shorter paths until the
geodesic is attained.

09 A Fast Hierarchical Clustering Algorithm for Functional Modules Discovery in Protein Interaction Networks

As advances in the technologies of predicting protein interactions, huge data sets portrayed as networks have been
available. Identification of functional modules from such networks is crucial for understanding principles of cellular
organization and functions. However, protein interaction data produced by high-throughput experiments are generally
associated with high false positives, which makes it difficult to identify functional modules accurately. In this paper, we
propose a fast hierarchical clustering algorithm HC-PIN based on the local metric of edge clustering value which can be
used both in the unweighted network and in the weighted network. The proposed algorithm HC-PIN is applied to the yeast
protein interaction network, and the identified modules are validated by all the three types of Gene Ontology (GO) Terms:
Biological Process, Molecular Function, and Cellular Component. The experimental results show that HC-PIN is not only
robust to false positives, but also can discover the functional modules with low density. The identified modules are
statistically significant in terms of three types of GO annotations. Moreover, HC-PIN can uncover the hierarchical
organization of functional modules with the variation of its parameter’s value, which is approximatively corresponding to
the hierarchical structure of GO annotations. Compared to other previous competing algorithms, our algorithm HC-PIN is
faster and more accurate.

10 A Framework for Semi supervised Feature Generation and Its Applications in Biomedical Literature Mining

Feature representation is essential to machine learning and text mining. In this paper, we present a feature coupling
generalization (FCG) framework for generating new features from unlabeled data. It selects two special types of features,
i.e., example-distinguishing features (EDFs) and class-distinguishing features (CDFs) from original feature set, and then

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generalizes EDFs into higher-level features based on their coupling degrees with CDFs in unlabeled data. The advantage is:
EDFs with extreme sparsity in labeled data can be enriched by their co-occurrences with CDFs in unlabeled data so that the
performance of these low-frequency features can be greatly boosted and new information from unlabeled can be
incorporated. We apply this approach to three tasks in biomedical literature mining: gene named entity recognition (NER),
protein-protein interaction extraction (PPIE), and text classification (TC) for gene ontology (GO) annotation. New features
are generated from over 20 GB unlabeled PubMed abstracts. The experimental results on BioCreative 2, AIMED corpus, and
TREC 2005 Genomics Track show that 1) FCG can utilize well the sparse features ignored by supervised learning. 2) It
improves the performance of supervised baselines by 7.8 percent, 5.0 percent, and 5.8 percent, respectively, in the tree
tasks. 3) Our methods achieve 89.1, 64.5 F-score, and 60.1 normalized utility on the three benchmark data sets

11 A General Framework for Analyzing Data from Two Short Time-Series Microarray Experiments

We propose a general theoretical framework for analyzing differentially expressed genes and behavior patterns from two
homogenous short time-course data. The framework generalizes the recently proposed Hilbert-Schmidt Independence
Criterion (HSIC)-based framework [34], [35] adapting it to the time-series scenario by utilizing tensor analysis for data
transformation. The proposed framework is effective in yielding criteria that can identify both the differentially expressed
genes and time-course patterns of interest between two time-series experiments without requiring to explicitly cluster the
data. The results, obtained by applying the proposed framework with a linear kernel formulation, on various data sets are
found to be both biologically meaningful and consistent with published studies.

12 A Genetic Optimization Approach for Isolating Translational Efficiency Bias

The study of codon usage bias is an important research area that contributes to our understanding of molecular evolution,
phylogenetic relationships, respiratory lifestyle, and other characteristics. Translational efficiency bias is perhaps the most
well-studied codon usage bias, as it is frequently utilized to predict relative protein expression levels. We present a novel
approach to isolating translational efficiency bias in microbial genomes. There are several existent methods for isolating
translational efficiency bias. Previous approaches are susceptible to the confounding influences of other potentially
dominant biases. Additionally, existing approaches to identifying translational efficiency bias generally require both
genomic sequence information and prior knowledge of a set of highly expressed genes. This novel approach provides more
accurate results from sequence information alone by resisting the confounding effects of other biases. We validate this
increase in accuracy in isolating translational efficiency bias on 10 microbial genomes, five of which have proven
particularly difficult for existing approaches due to the presence of strong confounding biases.

13 A Markov-Blanket-Based Model for Gene Regulatory Network Inference

An efficient two-step Markov blanket method for modeling and inferring complex regulatory networks from large-scale
microarray data sets is presented. The inferred gene regulatory network (GRN) is based on the time series gene expression
data capturing the underlying gene interactions. For constructing a highly accurate GRN, the proposed method performs: 1)
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discovery of a gene’s Markov Blanket (MB), 2) formulation of a flexible measure to determine the network’s quality, 3)
efficient searching with the aid of a guided genetic algorithm, and 4) pruning to obtain a minimal set of correct interactions.
Investigations are carried out using both synthetic as well as yeast cell cycle gene expression data sets. The realistic
synthetic data sets validate the robustness of the method by varying topology, sample size, time delay, noise, vertex in-
degree, and the presence of hidden nodes. It is shown that the proposed approach has excellent inferential capabilities and
high accuracy even in the presence of noise. The gene network inferred from yeast cell cycle data is investigated for its
biological relevance using well-known interactions, sequence analysis, motif patterns, and GO data. Further, novel
interactions are predicted for the unknown genes of the network and their influence on other genes is also discussed.

14 A Max-Flow-Based Approach to the Identification of Protein Complexes Using Protein Interaction and Microarray Data

The emergence of high-throughput technologies leads to abundant protein-protein interaction (PPI) data and microarray
gene expression profiles, and provides a great opportunity for the identification of novel protein complexes using
computational methods. By combining these two types of data, we propose a novel Graph Fragmentation Algorithm (GFA)
for protein complex identification. Adapted from a classical max-flow algorithm for finding the (weighted) densest
subgraphs, GFA first finds large (weighted) dense subgraphs in a protein-protein interaction network, and then, breaks
each such subgraph into fragments iteratively by weighting its nodes appropriately in terms of their corresponding log-fold
changes in the microarray data, until the fragment subgraphs are sufficiently small. Our tests on three widely used protein-
protein interaction data sets and comparisons with several latest methods for protein complex identification demonstrate
the strong performance of our method in predicting novel protein complexes in terms of its specificity and efficiency. Given
the high specificity (or precision) that our method has achieved, we conjecture that our prediction results imply more than
200 novel protein complexes.

15 A Note on the Fixed Parameter Tractability of the Gene-Duplication Problem

The NP-hard gene-duplication problem takes as input a collection of gene trees and seeks a species tree that requires the
fewest number of gene duplications to reconcile the input gene trees. An oft-cited, decade-old result by Stege states that
the gene-duplication problem is fixed parameter tractable when parameterized by the number of gene duplications
necessary for the reconciliation. Here, we uncover an error in this fixed parameter algorithm and show that this error cannot
be corrected without sacrificing the fixed parameter tractability of the algorithm. Furthermore, we show a link between the
geneduplication problem and the minimum rooted triplets inconsistency problem which implies that the gene-duplication
problem is 1) W[2]-hard when parameterized by the number of gene duplications necessary for the reconciliation and 2)
hard to approximate to better than a logarithmic factor.

16 A Partial Set Covering Model for Protein Mixture Identification Using Mass Spectrometry Data

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Protein identification is a key and essential step in mass spectrometry (MS) based proteome research. To date, there are
many protein identification strategies that employ either MS data or MS/MS data for database searching. While MS-based
methods provide wider coverage than MS/MS-based methods, their identification accuracy is lower since MS data have less
information than MS/MS data. Thus, it is desired to design more sophisticated algorithms that achieve higher identification
accuracy using MS data. Peptide Mass Fingerprinting (PMF) has been widely used to identify single purified proteins from
MS data for many years. In this paper, we extend this technology to protein mixture identification. First, we formulate the
problem of protein mixture identification as a Partial Set Covering (PSC) problem. Then, we present several algorithms that
can solve the PSC problem efficiently. Finally, we extend the partial set covering model to both MS/MS data and the
combination of MS data and MS/MS data. The experimental results on simulated data and real data demonstrate the
advantages of our method: 1) it outperforms previous MS-based approaches significantly; 2) it is useful in the MS/MS-based
protein inference; and 3) it combines MS data and MS/MS data in a unified model such that the identification performance is
further improved.

17 A Practical Algorithm for Reconstructing Level-1 Phylogenetic Networks

Recently, much attention has been devoted to the construction of phylogenetic networks which generalize phylogenetic
trees in order to accommodate complex evolutionary processes. Here, we present an efficient, practical algorithm for
reconstructing level-1 phylogenetic networks—a type of network slightly more general than a phylogenetic tree—from
triplets. Our algorithm has been made publicly available as the program LEV1ATHAN. It combines ideas from several known
theoretical algorithms for phylogenetic tree and network reconstruction with two novel subroutines. Namely, an
exponential-time exact and a greedy algorithm both of which are of independent theoretical interest. Most importantly,
LEV1ATHAN runs in polynomial time and always constructs a level-1 network. If the data are consistent with a phylogenetic
tree, then the algorithm constructs such a tree. Moreover, if the input triplet set is dense and, in addition, is fully consistent
with some level-1 network, it will find such a network. The potential of LEV1ATHAN is explored by means of an extensive
simulation study and a biological data set. One of our conclusions is that LEV1ATHAN is able to construct networks
consistent with a high percentage of input triplets, even when these input triplets are affected by a low to moderate level of
noise.

18 A Spectral Approach to Protein Structure Alignment

A new intrinsic geometry based on a spectral analysis is used to motivate methods for aligning protein folds. The geometry
is induced by the fact that a distance matrix can be scaled so that its eigenvalues are positive. We provide a mathematically
rigorous development of the intrinsic geometry underlying our spectral approach and use it to motivate two alignment
algorithms. The first uses eigenvalues alone and dynamic programming to quickly compute a fold alignment. Family
identification results are reported for the Skolnick40 and Proteus300 data sets. The second algorithm extends our spectral
method by iterating between our intrinsic geometry and the 3D geometry of a fold to make high-quality alignments. Results
and comparisons are reported for several difficult fold alignments. The second algorithm’s ability to correctly identify fold
families in the Skolnick40 and Proteus300 data sets is also established.

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19 A Survey on Methods for Modeling and Analyzing Integrated Biological Networks

Understanding how cellular systems build up integrated responses to their dynamically changing environment is one of the
open questions in Systems Biology. Despite their intertwinement, signaling networks, gene regulation and metabolism have
been frequently modeled independently in the context of well-defined subsystems. For this purpose, several mathematical
formalisms have been developed according to the features of each particular network under study. Nonetheless, a deeper
understanding of cellular behavior requires the integration of these various systems into a model capable of capturing how
they operate as an ensemble. With the recent advances in the “omics” technologies, more data is becoming available and,
thus, recent efforts have been driven toward this integrated modeling approach. We herein review and discuss
methodological frameworks currently available for modeling and analyzing integrated biological networks, in particular
metabolic, gene regulatory and signaling networks. These include network-based methods and Chemical Organization
Theory, Flux-Balance Analysis and its extensions, logical discrete modeling, Petri Nets, traditional kinetic modeling, Hybrid
Systems and stochastic models. Comparisons are also established regarding data requirements, scalability with network
size and computational burden. The methods are illustrated with successful case studies in large-scale genome models and
in particular subsystems of various organisms.

20 A Theoretical Analysis of the Prodrug Delivery System for Treating Antibiotic-Resistant Bacteria

Simulations were carried out to analyze a promising new antimicrobial treatment strategy for targeting antibiotic-resistant
bacteria called the -lactamase-dependent prodrug delivery system. In this system, the antibacterial drugs are delivered as
inactive precursors that only become activated after contact with an enzyme characteristic of many species of antibiotic-
resistant bacteria ( - lactamase enzyme). The addition of an activation step contributes an extra layer of complexity to the
system that can lead to unexpected emergent behavior. In order to optimize for treatment success and minimize the risk of
resistance development, there must be a clear understanding of the system dynamics taking place and how they impact on
the overall response. It makes sense to use a systems biology approach to analyze this method because it can facilitate a
better understanding of the complex emergent dynamics arising from diverse interactions in populations. This paper
contains an initial theoretical examination of the dynamics of this system of activation and an assessment of its therapeutic
potential from a theoretical standpoint using an agent-based modeling approach. It also contains a case study comparison
with real-world results from an experimental study carried out on two prodrug candidate compounds in the literature.

21 A Weighted Principal Component Analysis and Its Application to Gene Expression Data

In this work, we introduce in the first part new developments in Principal Component Analysis (PCA) and in the second part
a new method to select variables (genes in our application). Our focus is on problems where the values taken by each
variable do not all have the same importance and where the data may be contaminated with noise and contain outliers, as is
the case with microarray data. The usual PCA is not appropriate to deal with this kind of problems. In this context, we
propose the use of a new correlation coefficient as an alternative to Pearson’s. This leads to a so-called weighted PCA

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(WPCA). In order to illustrate the features of our WPCA and compare it with the usual PCA, we consider the problem of
analyzing gene expression data sets. In the second part of this work, we propose a new PCA-based algorithm to iteratively
select the most important genes in a microarray data set. We show that this algorithm produces better results when our
WPCA is used instead of the usual PCA. Furthermore, by using Support Vector Machines, we show that it can compete with
the Significance Analysis of Microarrays algorithm

22 Accurate Construction of Consensus Genetic Maps via Integer Linear Programming

We study the problem of merging genetic maps, when the individual genetic maps are given as directed acyclic graphs. The
computational problem is to build a consensus map, which is a directed graph that includes and is consistent with all (or,
the vast majority of) the markers in the input maps. However, when markers in the individual maps have ordering conflicts,
the resulting consensus map will contain cycles. Here, we formulate the problem of resolving cycles in the context of a
parsimonious paradigm that takes into account two types of errors that may be present in the input maps, namely, local
reshuffles and global displacements. The resulting combinatorial optimization problem is, in turn, expressed as an integer
linear program. A fast approximation algorithm is proposed, and an additional speedup heuristic is developed. Our
algorithms were implemented in a software tool named MERGEMAP which is freely available for academic use. An
extensive set of experiments shows that MERGEMAP consistently outperforms JOINMAP, which is the most popular tool
currently available for this task, both in terms of accuracy and running time. MERGEMAP is available for download at
http://www.cs.ucr.edu/~yonghui/mgmap.html.

23 Accurate Reconstruction for DNA Sequencing by Hybridization Based on a Constructive Heuristic

Sequencing by hybridization is a promising cost-effective technology for high-throughput DNA sequencing via microarray
chips. However, due to the effects of spectrum errors rooted in experimental conditions, an accurate and fast
reconstruction of original sequences has become a challenging problem. In the last decade, a variety of analyses and
designs have been tried to overcome this problem, where different strategies have different trade-offs in speed and
accuracy. Motivated by the idea that the errors could be identified by analyzing the interrelation of spectrum elements, this
paper presents a constructive heuristic algorithm, featuring an accurate reconstruction guided by a set of well-defined
criteria and rules. Instead of directly reconstructing the original sequence, the new algorithm first builds several accurate
short fragments, which are then carefully assembled into a whole sequence. The experiments on benchmark instance sets
demonstrate that the proposed method can reconstruct long DNA sequences with higher accuracy than current approaches
in the literature.

24 An Approximation Algorithm for the Noah’s Ark Problem with Random Feature Loss

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The phylogenetic diversity (PD) of a set of species is a measure of their evolutionary distinctness based on a phylogenetic
tree. PD is increasingly being adopted as an index of biodiversity in ecological conservation projects. The Noah’s Ark
Problem (NAP) is an NP-Hard optimization problem that abstracts a fundamental conservation challenge in asking to
maximize the expected PD of a set of taxa given a fixed budget, where each taxon is associated with a cost of conservation
and a probability of extinction. Only simplified instances of the problem, where one or more parameters are fixed as
constants, have as of yet been addressed in the literature. Furthermore, it has been argued that PD is not an appropriate
metric for models that allow information to be lost along paths in the tree. We therefore generalize the NAP to incorporate a
proposed model of feature loss according to an exponential distribution and term this problem NAP with Loss (NAPL). In
this paper, we present a pseudopolynomial time approximation scheme for NAPL.

25 An Improved Heuristic Algorithm for Finding Motif Signals in DNA Sequences

The planted ðl; dÞ-motif search problem is a mathematical abstraction of the DNA functional site discovery task. In this
paper, we propose a heuristic algorithm that can find planted ðl; dÞ-signals in a given set of DNA sequences. Evaluations
on simulated data sets demonstrate that the proposed algorithm outperforms current widely used motif finding algorithms.
We also report the results of experiments on real biological data sets..

26 Asymmetric Comparison and Querying of Biological Networks

Comparing and querying the protein-protein interaction (PPI) networks of different organisms is important to infer
knowledge about conservation across species. Known methods that perform these tasks operate symmetrically, i.e., they
do not assign a distinct role to the input PPI networks. However, in most cases, the input networks are indeed
distinguishable on the basis of how the corresponding organism is biologically well characterized. In this paper a new idea
is developed, that is, to exploit differences in the characterization of organisms at hand in order to devise methods for
comparing their PPI networks. We use the PPI network (called Master) of the best characterized organism as a fingerprint to
guide the alignment process to the second input network (called Slave), so that generated results preferably retain the
structural characteristics of the Master network. Technically, this is obtained by generating from the Master a finite
automaton, called alignment model, which is then fed with (a linearization of) the Slave for the purpose of extracting, via the
Viterbi algorithm, matching subgraphs. We propose an approach able to perform global alignment and network querying,
and we apply it on PPI networks. We tested our method showing that the results it returns are biologically relevant.

27 Bayesian Models and Algorithms for Protein Beta-Sheet Prediction

Prediction of the 3D structure greatly benefits from the information related to secondary structure, solvent accessibility,
and nonlocal contacts that stabilize a protein’s structure. We address the problem of Beta-sheet prediction defined as the

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prediction of Beta-strand pairings, interaction types (parallel or antiparallel), and Beta-residue interactions (or contact
maps). We introduce a Bayesian approach for proteins with six or less Beta-strands in which we model the conformational
features in a probabilistic framework by combining the amino acid pairing potentials with a priori knowledge of Beta-strand
organizations. To select the optimum Beta-sheet architecture, we significantly reduce the search space by heuristics that
enforce the amino acid pairs with strong interaction potentials. In addition, we find the optimum pairwise alignment
between Beta-strands using dynamic programming in which we allow any number of gaps in an alignment to model -
bulges more effectively. For proteins with more than six Beta-strands, we first compute Beta-strand pairings using the
BetaPro method. Then, we compute gapped alignments of the paired Beta-strands and choose the interaction types and -
residue pairings with maximum alignment scores. We performed a 10-fold cross-validation experiment on the BetaSheet916
set and obtained significant improvements in the prediction accuracy.

28 Cancer Classification from Gene Expression Data by NPPC Ensemble

The most important application of microarray in gene expression analysis is to classify the unknown tissue samples
according to their gene expression levels with the help of known sample expression levels. In this paper, we present a
nonparallel plane proximal classifier (NPPC) ensemble that ensures high classification accuracy of test samples in a
computer-aided diagnosis (CAD) framework than that of a single NPPC model. For each data set only, a few genes are
selected by using a mutual information criterion. Then a genetic algorithm-based simultaneous feature and model selection
scheme is used to train a number of NPPC expert models in multiple subspaces by maximizing cross-validation accuracy.
The members of the ensemble are selected by the performance of the trained models on a validation set. Besides the usual
majority voting method, we have introduced minimum average proximity-based decision combiner for NPPC ensemble. The
effectiveness of the NPPC ensemble and the proposed new approach of combining decisions for cancer diagnosis are
studied and compared with support vector machine (SVM) classifier in a similar framework. Experimental results on cancer
data sets show that the NPPC ensemble offers comparable testing accuracy to that of SVM ensemble with reduced training
time on average.

29 Comparison of Galled Trees Gabriel

Galled trees, directed acyclic graphs that model evolutionary histories with isolated hybridization events, have become very
popular due to both their biological significance and the existence of polynomial-time algorithms for their reconstruction. In
this paper, we establish to which extent several distance measures for the comparison of evolutionary networks are metrics
for galled trees, and hence, when they can be safely used to evaluate galled tree reconstruction methods.

30 Component-Based Modeling and Reachability Analysis of Genetic Networks

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Genetic regulatory networks usually encompass a multitude of complex, interacting feedback loops. Being able to model
and analyze their behavior is crucial for understanding their function. However, state space explosion is becoming a
limiting factor in the formal analysis of genetic networks. This paper explores a modular approach for verification of
reachability properties. A framework for component-based modeling of genetic regulatory networks, based on a modular
discrete abstraction, is introduced. Then a compositional algorithm to efficiently analyze reachability properties of the
model is proposed. A case study on embryonic cell differentiation involving several hundred cells shows the potential of
this approach.

31 Computing a Smallest Multilabeled Phylogenetic Tree from Rooted Triplets

We investigate the computational complexity of inferring a smallest possible multilabeled phylogenetic tree (MUL tree)
which is consistent with each of the rooted triplets in a given set. This problem has not been studied previously in the
literature. We prove that even the very restricted case of determining if there exists a MUL tree consistent with the input and
having just one leaf duplication is an NP-hard problem. Furthermore, we show that the general minimization problem is
difficult to approximate, although a simple polynomial-time approximation algorithm achieves an approximation ratio close
to our derived inapproximability bound. Finally, we provide an exact algorithm for the problem running in exponential time
and space. As a by-product, we also obtain new, strong inapproximability results for two partitioning problems on directed
graphs called ACYCLIC PARTITION and ACYCLIC TREE-PARTITION.

32 Data Mining on DNA Sequences of Hepatitis B Virus

Extraction of meaningful information from large experimental data sets is a key element in bioinformatics research. One of
the challenges is to identify genomic markers in Hepatitis B Virus (HBV) that are associated with HCC (liver cancer)
development by comparing the complete genomic sequences of HBV among patients with HCC and those without HCC. In
this study, a data mining framework, which includes molecular evolution analysis, clustering, feature selection, classifier
learning, and classification, is introduced. Our research group has collected HBV DNA sequences, either genotype B or C,
from over 200 patients specifically for this project. In the molecular evolution analysis and clustering, three subgroups have
been identified in genotype C and a clustering method has been developed to separate the subgroups. In the feature
selection process, potential markers are selected based on Information Gain for further classifier learning. Then,
meaningful rules are learned by our algorithm called the Rule Learning, which is based on Evolutionary Algorithm. Also, a
new classification method by Nonlinear Integral has been developed. Good performance of this method comes from the use
of the fuzzy measure and the relevant nonlinear integral. The nonadditivity of the fuzzy measure reflects the importance of
the feature attributes as well as their interactions. These two classifiers give explicit information on the importance of the
individual mutated sites and their interactions toward the classification (potential causes of liver cancer in our case). A
thorough comparison study of these two methods with existing methods is detailed. For genotype B, genotype C
subgroups C1, C2, and C3, important mutation markers (sites) have been found, respectively. These two classification
methods have been applied to classify never-seen-before examples for validation. The results show that the classification

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methods have more than 70 percent accuracy and 80 percent sensitivity for most data sets, which are considered high as
an initial scanning method for liver cancer diagnosis.

33 Determination of Glycan Structure from Tandem Mass Spectra

Glycans are molecules made from simple sugars that form complex tree structures. Glycans constitute one of the most
important protein modifications and identification of glycans remains a pressing problem in biology. Unfortunately, the
structure of glycans is hard to predict from the genome sequence of an organism. In this paper, we consider the problem of
deriving the topology of a glycan solely from tandem mass spectrometry (MS) data. We study, how to generate glycan tree
candidates that sufficiently match the sample mass spectrum, avoiding the combinatorial explosion of glycan structures.
Unfortunately, the resulting problem is known to be computationally hard. We present an efficient exact algorithm for this
problem based on fixed-parameter algorithmics that can process a spectrum in a matter of seconds. We also report some
preliminary results of our method on experimental data, combining it with a preliminary candidate evaluation scheme. We
show that our approach is fast in applications, and that we can reach very well de novo identification results. Finally, we
show how to count the number of glycan topologies for a fixed size or a fixed mass. We generalize this result to count the
number of (labeled) trees with bounded out degree, improving on results obtained using Po´ lya’s enumeration theorem.

34 Discriminative Motif Finding for Predicting Protein Subcellular Localization

Many methods have been described to predict the subcellular location of proteins from sequence information. However,
most of these methods either rely on global sequence properties or use a set of known protein targeting motifs to predict
protein localization. Here, we develop and test a novel method that identifies potential targeting motifs using a
discriminative approach based on hidden Markov models (discriminative HMMs). These models search for motifs that are
present in a compartment but absent in other, nearby, compartments by utilizing an hierarchical structure that mimics the
protein sorting mechanism. We show that both discriminative motif finding and the hierarchical structure improve
localization prediction on a benchmark data set of yeast proteins. The motifs identified can be mapped to known targeting
motifs and they are more conserved than the average protein sequence. Using our motif-based predictions, we can identify
potential annotation errors in public databases for the location of some of the proteins. A software implementation and the
data set described in this paper are available from http://murphylab.web.cmu.edu/software/ 2009_TCBB_motif/.

35 Disturbance Analysis of Nonlinear Differential Equation Models of Genetic SUM Regulatory Networks

Noise disturbances and time delays are frequently met in cellular genetic regulatory systems. This paper is concerned with
the disturbance analysis of a class of genetic regulatory networks described by nonlinear differential equation models. The
mechanisms of genetic regulatory networks to amplify (attenuate) external disturbance are explored, and a simple measure
of the amplification (attenuation) level is developed from a nonlinear robust control point of view. It should be noted that the
conditions used to measure the disturbance level are delay-independent or delay-dependent, and are expressed within the
framework of linear matrix inequalities, which can be characterized as convex optimization, and computed by the interior-
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point algorithm easily. Finally, by the proposed method, a numerical example is provided to illustrate how to measure the
attenuation of proteins in the presence of external disturbances.

36 Efficient Formulations for Exact Stochastic Simulation of Chemical Systems

One can generate trajectories to simulate a system of chemical reactions using either Gillespie’s direct method or Gibson
and Bruck’s next reaction method. Because one usually needs many trajectories to understand the dynamics of a system,
performance is important. In this paper, we present new formulations of these methods that improve the computational
complexity of the algorithms. We present optimized implementations, available from http://cain.sourceforge.net/, that offer
better performance than previous work. There is no single method that is best for all problems. Simple formulations often
work best for systems with a small number of reactions, while some sophisticated methods offer the best performance for
large problems and scale well asymptotically. We investigate the performance of each formulation on simple biological
systems using a wide range of problem sizes. We also consider the numerical accuracy of the direct and the next reaction
method. We have found that special precautions must be taken in order to ensure that randomness is not discarded during
the course of a simulation.

37 Encoding Molecular Motions in Voxel Maps

This paper builds on the combination of robotic path planning algorithms and molecular modeling methods for computing
large-amplitude molecular motions, and introduces voxel maps as a computational tool to encode and to represent such
motions. We investigate several applications and show results that illustrate the interest of such representation.

38 Ensemble Learning with Active Example Selection for Imbalanced Biomedical Data Classification

In biomedical data, the imbalanced data problem occurs frequently and causes poor prediction performance for minority
classes. It is because the trained classifiers are mostly derived from the majority class. In this paper, we describe an
ensemble learning method combined with active example selection to resolve the imbalanced data problem. Our method
consists of three key components: 1) an active example selection algorithm to choose informative examples for training the
classifier, 2) an ensemble learning method to combine variations of classifiers derived by active example selection, and 3)
an incremental learning scheme to speed up the iterative training procedure for active example selection. We evaluate the
method on six real-world imbalanced data sets in biomedical domains, showing that the proposed method outperforms
both the random under sampling and the ensemble with under sampling methods. Compared to other approaches to
solving the imbalanced data problem, our method excels by 0.03-0.15 points in AUC measure.

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39 Estimating Genome-Wide Gene Networks Using Nonparametric Bayesian Network Models on Massively Parallel Computers

We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene
expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures,
existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by
repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical
simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm
to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human
genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a
traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the
small network, as well as others that could not be captured during the small network estimation. The results also revealed
master-regulator genes that are not in the small network but that control many of the genes in the small network. These
analyses were impossible to realize without our proposed algorithm.

40 Estimating Haplotype Frequencies by Combining Data from Large DNA Pools with Database Information

We assume that allele frequency data have been extracted from several large DNA pools, each containing genetic material
of up to hundreds of sampled individuals. Our goal is to estimate the haplotype frequencies among the sampled individuals
by combining the pooled allele frequency data with prior knowledge about the set of possible haplotypes. Such prior
information can be obtained, for example, from a database such as HapMap. We present a Bayesian haplotyping method for
pooled DNA based on a continuous approximation of the multinomial distribution. The proposed method is applicable when
the sizes of the DNA pools and/or the number of considered loci exceed the limits of several earlier methods. In the
example analyses, the proposed model clearly outperforms a deterministic greedy algorithm on real data from the HapMap
database. With a small number of loci, the performance of the proposed method is similar to that of an EM-algorithm, which
uses a multinormal approximation for the pooled allele frequencies, but which does not utilize prior information about the
haplotypes. The method has been implemented using Matlab and the code is available upon request from the authors.

41 EvoMD: An Algorithm for Evolutionary Molecular Design

Traditionally, Computer-Aided Molecular Design (CAMD) uses heuristic search and mathematical programming to tackle the
molecular design problem. But these techniques do not handle large and nonlinear search space very well. To overcome
these drawbacks, graph-based evolutionary algorithms (EAs) have been proposed to evolve molecular design by mimicking
chemical reactions on the exchange of chemical bonds and components between molecules. For these EAs to perform their
tasks, known molecular components, which can serve as building blocks for the molecules to be designed, and known
chemical rules, which govern chemical combination between different components, have to be introduced before the
evolutionary process can take place. To automate molecular design without these constraints, this paper proposes an EA
called Evolutionary Algorithm for Molecular Design (EvoMD). EvoMD encodes molecular designs in graphs. It uses a novel

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crossover operator which does not require known chemistry rules known in advanced and it uses a set of novel mutation
operators. EvoMD uses atomics-based and fragment-based approaches to handle different size of molecule, and the value
of the fitness function it uses is made to depend on the property descriptors of the design encoded in a molecular graph. It
has been tested with different data sets and has been shown to be very promising.

42 Extensions and Improvements to the Chordal Graph Approach to the Multistate Perfect Phylogeny Problem

The multistate perfect phylogeny problem is a classic problem in computational biology. When no perfect phylogeny exists,
it is of interest to find a set of characters to remove in order to obtain a perfect phylogeny in the remaining data. This is
known as the character removal problem. We show how to use chordal graphs and triangulations to solve the character
removal problem for an arbitrary number of states, which was previously unsolved. We outline a preprocessing technique
that speeds up the computation of the minimal separators of a graph. Minimal separators are used in our solution to the
missing data character removal problem and to Gusfield’s solution of the perfect phylogeny problem with missing data.

43 F2Dock: Fast Fourier Protein-Protein Docking

The functions of proteins are often realized through their mutual interactions. Determining a relative transformation for a
pair of proteins and their conformations which form a stable complex, reproducible in nature, is known as docking. It is an
important step in drug design, structure determination, and understanding function and structure relationships. In this
paper, we extend our non uniform fast Fourier transform-based docking algorithm to include an adaptive search phase
(both translational and rotational) and thereby speed up its execution. We have also implemented a multithreaded version
of the adaptive docking algorithm for even faster execution on multi-core machines. We call this protein-protein docking
code F2Dock (F2 ¼ Fast Fourier). We have calibrated F2Dock based on an extensive experimental study on a list of
benchmark complexes and conclude that F2Dock works very well in practice. Though all docking results reported in this
paper use shape complementarity and Coulombic-potential-based scores only, F2Dock is structured to incorporate
Lennard-Jones potential and re ranking docking solutions based on desolvation energy.

44 Fast Surface-Based Travel Depth Estimation Algorithm for Macromolecule Surface Shape Description

Travel Depth, introduced by Coleman and Sharp in 2006, is a physical interpretation of molecular depth, a term frequently
used to describe the shape of a molecular active site or binding site. Travel Depth can be seen as the physical distance a
solvent molecule would have to travel from a point of the surface, i.e., the Solvent-Excluded Surface (SES), to its convex
hull. Existing algorithms providing an estimation of the Travel Depth are based on a regular sampling of the molecule
volume and the use of the Dijkstra’s shortest path algorithm. Since Travel Depth is only defined on the molecular surface,
this volume-based approach is characterized by a large computational complexity due to the processing of unnecessary

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samples lying inside or outside the molecule. In this paper, we propose a surface-based approach that restricts the
processing to data defined on the SES. This algorithm significantly reduces the complexity of Travel Depth estimation and
makes possible the analysis of large macromolecule surface shape description with high resolution. Experimental results
show that compared to existing methods, the proposed algorithm achieves accurate estimations with considerably reduced
processing times.

45 FEAST: Sensitive Local Alignment with Multiple Rates of Evolution

We present a pairwise local aligner, FEAST, which uses two new techniques: a sensitive extension algorithm for identifying
homologous subsequences, and a descriptive probabilistic alignment model. We also present a new procedure for training
alignment parameters and apply it to the human and mouse genomes, producing a better parameter set for these
sequences. Our extension algorithm identifies homologous subsequences by considering all evolutionary histories. It has
higher maximum sensitivity than Viterbi extensions, and better balances specificity. We model alignments with several
submodels, each with unique statistical properties, describing strongly similar and weakly similar regions of homologous
DNA. Training parameters using two submodels produces superior alignments, even when we align with only the
parameters from the weaker submodel. Our extension algorithm combined with our new parameter set achieves sensitivity
0.59 on synthetic tests. In contrast, LASTZ with default settings achieves sensitivity 0.35 with the same false positive rate.
Using the weak submodel as parameters for LASTZ increases its sensitivity to 0.59 with high error. FEAST is available at
http://monod.uwaterloo.ca/feast/.

46 Finding Significant Matches of Position Weight Matrices in Linear Time

Position weight matrices are an important method for modeling signals or motifs in biological sequences, both in DNA and
protein contexts. In this paper, we present fast algorithms for the problem of finding significant matches of such matrices.
Our algorithms are of the online type, and they generalize classical multipattern matching, filtering, and superalphabet
techniques of combinatorial string matching to the problem of weight matrix matching. Several variants of the algorithms
are developed, including multiple matrix extensions that perform the search for several matrices in one scan through the
sequence database. Experimental performance evaluation is provided to compare the new techniques against each other as
well as against some other online and indexbased algorithms proposed in the literature. Compared to the brute-force
OðmnÞ approach, our solutions can be faster by a factor that is proportional to the matrix length m. Our multiple-matrix
filtration algorithm had the best performance in the experiments. On a current PC, this algorithm finds significant matches
(p ¼ 0:0001) of the 123 JASPAR matrices in the human genome in about 18 minutes.

47 Fuzzy ARTMAP Prediction of Biological Activities for Potential HIV-1 Protease Inhibitors Using a Small Molecular Data Set

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Obtaining satisfactory results with neural networks depends on the availability of large data samples. The use of small
training sets generally reduces performance. Most classical Quantitative Structure-Activity Relationship (QSAR) studies for
a specific enzyme system have been performed on small data sets. We focus on the neuro-fuzzy prediction of biological
activities of HIV-1 protease inhibitory compounds when inferring from small training sets. We propose two computational
intelligence prediction techniques which are suitable for small training sets, at the expense of some computational
overhead. Both techniques are based on the FAMR model. The FAMR [1] is a Fuzzy ARTMAP (FAM) incremental learning
system used for classification and probability estimation. During the learning phase, each sample pair is assigned a
relevance factor proportional to the importance of that pair. The two proposed algorithms in this paper are: 1) The GA-
FAMR algorithm, which is new, consists of two stages: a) During the first stage, we use a genetic algorithm (GA) to optimize
the relevances assigned to the training data. This improves the generalization capability of the FAMR. b) In the second
stage, we use the optimized relevances to train the FAMR. 2) The Ordered FAMR is derived from a known algorithm. Instead
of optimizing relevances, it optimizes the order of data presentation using the algorithm of Dagher et al. [2], [3]. In our
experiments, we compare these two algorithms with an algorithm not based on the FAM, the FS-GA-FNN introduced in [4],
[5]. We conclude that when inferring from small training sets, both techniques are efficient, in terms of generalization
capability and execution time. The computational overhead introduced is compensated by better accuracy. Finally, the
proposed techniques are used to predict the biological activities of newly designed potential HIV-1 protease inhibitors.

48 Genetic Networks and Soft Computing

The analysis of gene regulatory networks provides enormous information on various fundamental cellular processes
involving growth, development, hormone secretion, and cellular communication. Their extraction from available gene
expression profiles is a challenging problem. Such reverse engineering of genetic networks offers insight into cellular
activity toward prediction of adverse effects of new drugs or possible identification of new drug targets. Tasks such as
classification, clustering, and feature selection enable efficient mining of knowledge about gene interactions in the form of
networks. It is known that biological data is prone to different kinds of noise and ambiguity. Soft computing tools, such as
fuzzy sets, evolutionary strategies, and neurocomputing, have been found to be helpful in providing low-cost, acceptable
solutions in the presence of various types of uncertainties. In this paper, we survey the role of these soft methodologies
and their hybridizations, for the purpose of generating genetic networks.

49 Graph Comparison by Log-Odds Score Matrices with Application to Protein Topology Analysis

A TOPS diagram is a simplified description of the topology of a protein using a graph where nodes are -helices and -
strands, and edges correspond to chirality relations and parallel or antiparallel bonds between strands. We present a
matching algorithm between two TOPS diagrams where the likelihood of a match is measured according to previously
known matches between complete 3D structures. This totally new 3D training is recorded on transition matrices that count
the likelihood that a given TOPS feature, or combination thereof, is replaced by another feature on homologs. The new

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IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Computationalbiology

IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Computationalbiology

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