Biocomputing

در هزاره سوم بكارگيري تكنولوژي براي توسعه اقتصاد، دستيابي به علوم و فنون جديد، آموزش، توسعه اجتماعي، امري اجتناب ناپذير است. به واسطه
دستيابي به تكنولوژي نوين داشتن اطلاعات و برقراري ارتباطات نيز ضروري و نفي در آن انكار ناپذير است.
وري اطلاعات در توسعه اقتصادي و اشتغال كه خود بحث مفصل و خارج از موضوع تعيين Ĥ در اين مقاله نگارنده سعي بر آن دارد صرفنظر از كاربرد فن
شده است در نگاهي تخصصي به چگونگي بكارگيري از اين علم در ارائه خدمات مشاورهاي، تسهيل در راهنماي انتخاب شغل متناسب با علايق افراد
بپردازد.
وري اطلاعات Ĥ تاسيس مراكز مشاوره شغلي و ارائه خدمات مشاورهاي از جمله موضوعات روز در كشورهاي توسعه يافته است. با روند رو به رشد فن
ميدان فعاليت متخصصان و صاحبان كسب و كار محدود به حوزه و حرفه خاصي نبوده و بر خلاف نظر برخي از نظريه پردازان مبني بر جايگزيني تكنولوژي
به جاي نيروي انسان، جذب نيروهاي كارآمد ومتخصص، مطابق با نيازهاي بازار كار از مهمترين اهداف صاحبان صنعت و سرمايهگذاران صنعت ميباشد.
وري اطلاعات و ارتباطات، سعي در بكارگيري اين علم از دو منظر Ĥ از اينرو در كشورهاي توسعه يافته ضمن جامعيت و عموميت بخشيدن به تكنولوژي فن
نمودهاند.

Today, biomimetic approaches to unconventional computing have been exploring neurotransmitters' roles. But hormonal aspects of artificial cognition are still unexplored. And from a cognitive perspective, such a complex biochemical combination allows fundamental mechanisms of information processing, from abductive tasks to attention or intentionality. This paper will explore the theoretical ways by which hormonal computers could open a different way to create not only innovative machines but also programming ways of dealing with such bioinspiration.

Integral to the reproductive processes of the biota of several forest, shrub, and grassland biome-types, wildfire ignites some 3,400,000km2 of Earth’s vegetated surface annually. Though a highly complex phenomena coupled with not one, but several Earth systems, human actions are both directly and indirectly changing wildfire frequencies, intensities, severities, and behaviours, and to the detriment of both environment and society. Nowhere is this felt more so than the wildland urban interface, which home to roughly 1/3 of the nation’s populous and 40% of its housing stock, is the fastest-growing land-use type in the conterminous United States. A place where fire-averse architectures meet increasingly fire-prone lands, loss of lives, properties, and livelihoods to a series of wildfire complexes of proportions unprecedented in living memory have rendered there an urgent need to reconsider the challenge of living with wildfire as a vital landscape process. The product of a transdisciplinary study which converged state-of-the-knowledge from fields as diverse as the fire, ecological, and wider Earth sciences; information, communication, computing, and related technologies, both digital and biological; evolutionary, smart and living materials, architectures, and urban systems; philosophy, anthropology, psychology, and policymaking, this thesis presents a new wildland urban interface paradigm modelled on the biochemistries, behaviours, and systems of fire-adapted flora and the fire regimes they form. Migrating biomimetics from the level of species to systems, relying not on generic notions of nature and its workings, nor assumptions more generally, but on rigorous interrogation of the interplay between biotic and abiotic processes from the molecular to landscape to planetary scale, and across both human and geological timescales, several original theoretical and technical architectural and urban concepts are discussed, together with their possible applications and implications both within and beyond the wildland urban interface. Integrating insights from local and global indigenous and ancient fire cultures, the findings conclude that not merely is a reconciliation of human and non-human systems at the interface of fire-prone wild and urban lands possible, but therein resides potent ecological, social, and technical potentialities that merit further research in the years ahead.

Chapter Overview: Having discussed the underlying paradigmatic principles of a tri-part pangenical approach to architecture and urban design in the wildland urban interface of the western U.S., this chapter explores the codification potentialities, both present and possible future, through discussion, scenarios, and speculations.

To date, a number of researchers are seeking for and/or designing novel molecules which function as arithmetic molecular engines. Biomolecules such as deoxyribonucleic acid (DNA) and proteins are examples of promising candidate molecules. In the present article, we showed our view that DNA-based molecules could be used as a novel class of platforms for discrete mathematical operations or tools for natural computation. Here, we report on a novel molecular logic circuit combining exclusive disjunction (XOR) gate and conjunction (AND) gate implemented on a single DNA molecule performing arithmetic operations with simple binary numbers through polymerase chain reactions (PCR); which was inspired by previously developed protein-based computing model allowing simple polynomial algebra over fields through algebraic representation of cyclic inter-conversions in the catalytic modes of a plant enzyme as a cyclic additive group. In addition, we showed that DNA can be used as the platform for image coding and processing leading to DNA-coded animation by using novel PCR-based protocols. Lastly, we discussed the significance of recent attempts in the stream of natural computing and synthetic biological research, by handling DNA and related biomolecules as the media for discrete mathematical operations.

This Workshop will give an overview of key topics in the young field of Translational Bioinformatics (TBI). TBI has been defined as the "development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data, and genomic data, into proactive, predictive, preventive, and participatory health."[1] With PSB's stated focus on research in databases, algorithms, interfaces, natural language processing, and modeling, the bioinformatics aspect of TBI is a natural fit for this audience. Further, the US government's recently announced Precision Medicine Initiative makes it particularly timely for researchers to learn about and explore the translational side of our field. Specifically, this workshop provides context for how the various bioinformatics methods may be applied toward the enhancement of human health, enabling healthcare providers to deliver the right intervention for the right person at the right time. This workshop covers major themes within the field of TBI, as put forth by a recent review in IMIA's (International Medical Informatics Association) Yearbook of Medical Informatics[2], and supplements Pacific Symposium on Biocomputing 2016 552 those topics with a section on relevant data standards from the clinical and translational domain. Each presenter is a nationally or internationally recognized expert in their respective areas.

Brains are characterized by every property that engineers and computer scientists detest and avoid. They are chaotic, unstable, nonlinear, nonstationary, non-Gaussian, asynchronous, noisy, and unpredictable in fine grain, yet undeniably they are among the most successful devices that a billion years of evolution has produced. No one can justifiably claim that he or she has modeled brains, but they are a flowing spring of new concepts, and they provide a gold standard of what we can aspire to accomplish in developing more intelligent machines. The most fertile source of ideas with which to challenge and break the restrictions that characterize modern engineering practice i s the electroencephalogram (EEG). It was the action potential of single neurons that provided the foundation of neurobiology for the 20th century, and in its time it supported the development of digital computers, neural networks, and computational neuroscience. Now in the 21st century, the EEG will lead us in a remarkably different direction of growth for the computing industry, which will be dominated by highly parallel, hierarchically organized, distributed analog machines. These devices now exist in prototype form. They feed on noise in support of chaotic attractor landscapes, which are shaped by reinforcement learning through self-governed experience, not training by 'teachers', and they may solve many of the problems of interfacing between finite state automata and the infinite complexity of the real world.

Single Nucleotide Polymorphisms (SNPs) are the most common source of genetic variations. There has been enormous research in the area of Biocomputing and Bioinformatics on identification and analysis of SNPs. A large number of methods have been developed for their identification ever since the importance of SNPs in understanding of diseases emerged with the completion of Human Genome Project. This paper reviews Single Nucleotide Polymorphisms, their importance, their association to diseases, Biocomputing approaches and tools available for their identification up to 2017.

Modern genomic research has access to a plethora of knowledge sources. Often, it is imperative that researchers combine and integrate knowledge from multiple perspectives. Although some technology exists for connecting data and knowledge bases, these methods are only just begin- ning to be successfully applied to research in modern cell biology. In this paper, we argue that one way to integrate multiple knowledge sources is through anatomy—both generic cellular anatomy, as well as anatomic knowledge about the tissues and organs that may be studied via microarray gene expression experiments. We present two examples where we have combined a large ontology of human anatomy (the FMA) with other genomic knowledge sources: the gene ontology (GO) and the mouse genomic databases (MGD) of the Jackson Labs. These two initial examples of knowledge integration provide a proof of concept that anatomy can act as a hub through which we can usefully combine a variety of genomic knowledge and data. ...

Background. Drug-drug interaction (DDI) is a major cause of morbidity and mortality. DDI research includes the study of different aspects of drug interactions, from in vitro pharmacology, which deals with drug interaction mechanisms, to pharmaco-epidemiology, which investigates the effects of DDI on drug efficacy and adverse drug reactions. Biomedical literature mining can aid both kinds of approaches by extracting relevant DDI signals from either the published literature or large clinical databases. However, though drug interaction is an ideal area for translational research, the inclusion of literature mining methodologies in DDI workflows is still very preliminary. One area that can benefit from literature mining is the automatic identification of a large number of potential DDIs, whose pharmacological mechanisms and clinical significance can then be studied via in vitro pharmacology and in populo pharmaco-epidemiology.

Biophotonic(Bio-optical) quantum information based DNA molecular computing By Joel Taylor(S.T.A.R. LABS) Biophotonic(Bio-optical) quantum information based DNA molecular computing is a convergence of biological and photonic computing. It is a topological nonlinear optical quantum computing architecture that uses bioluminescence produced by a biological system for information processing. Bioluminescence is the production and emission of light by a living organism. The bioluminescence is a result of the interaction between DNA and photons.The biological systems are organic nonlinear optical materials. These organic nonlinear optical materials are mechanically interlocked molecular architectures. Mechanically interlocked molecular architectures such as organic nonlinear optical materials(MIMAs) are molecules or ions that are connected as a consequence of their topology. Organic materials are expected to have relatively strong nonlinear optical properties due to delocalized electrons at pi-pi * orbitals. The mechanically interlocked molecular architectures are known as DNA algorithmic self-assemblies. DNA algorithmic self-assemblies are arrangements of DNA with the guidance of algorithms. DNA algorithmic self-assemblies are called tiling arrays. The tiling arrays act as topological transistors and logic gates.Topology provides a convenient way of describing and predicting the molecular structure within the constraints of three-dimensional (3-D) space. The transistor is known as an optical transcriptor. An optical transcriptor is a device made of DNA that amplifies or switches optical signals. The logic gate is known as a DNA-photon translocation logic gate, A DNA-photon translocation logic gate uses photons to shift DNA to perform operations and produce output. Biophotonic(Bio-optical) quantum information based DNA molecular computing is useful in the development of artificial consciousness, exosuits, networks, power sources, and transmutation.

Standard analysis methods for genome wide association studies (GWAS) are not robust to complex disease models, such as interactions between variables with small main effects. These types of effects likely contribute to the heritability of complex human traits. Machine learning methods that are capable of identifying interactions, such as Random Forests (RF), are an alternative analysis approach. One caveat to RF is that there is no standardized method of selecting variables so that false positives are reduced while retaining adequate power. To this end, we have developed a novel variable selection method called relative recurrency variable importance metric (r2VIM). This method incorporates recurrency and variance estimation to assist in optimal threshold selection. For this study, we specifically address how this method performs in data with almost completely epistatic effects (i.e. no marginal effects). Our results show that with appropriate parameter settings, r2VIM can identify ...

Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcomes and decreasing the cost of healthcare delivery in the United States. Patient readmission rates are relatively high for conditions like heart failure (HF) despite the implementation of high-quality healthcare delivery operation guidelines created by regulatory authorities. Multiple predictive models are currently available to evaluate potential 30-day readmission rates of patients. Most of these models are hypothesis driven and repetitively assess the predictive abilities of the same set of biomarkers as predictive features. In this manuscript, we discuss our attempt to develop a data-driven, electronic-medical record-wide (EMR-wide) feature selection approach and subsequent machine learning to predict readmission probabilities. We have assessed a large repertoire of varia...

Circadian rhythm mechanisms involve multi-scale interactions between endogenous DNA-transcription oscillators. We present the application of efficient, numerically wellconditioned algorithms for abstracting (potentially large) systems of differential equation models of circadian oscillators into compact, accurate phase-only macromodels. We apply and validate our auto-extracted phase macromodelling technique on mammalian and Drosophila circadian systems, obtaining speedups of 9 − 13× over conventional timecourse simulation, with insignificant loss of accuracy, for single oscillators being synchronized by day/night light variations. Further, we apply the macromodels to simulate a system of 400 coupled circadian oscillators, achieving speedups of 240× and accurately reproducing synchronization and locking phenomena amongst the oscillators. We also present the use of parameterized phase macromodels for these circadian systems, and elucidate insights into circadian timing effects directly provided by our auto-extracted macromodels.

We present a feasibility study using text classification to classify tweets about alcohol use. Alcohol use is the most widely used substance in the US and is the leading risk factor for premature morbidity and mortality globally. Understanding use patterns and locations is an important step toward prevention, moderation, and control of alcohol outlets. Social media may provide an alternate way to measure alcohol use in real time. This feasibility study explores text classification methodologies for identifying alcohol use tweets. We labeled 34,563 geo-located New York City tweets collected in a 24 hour period over New Year's Day 2012. We preprocessed the tweets into stem/ not stemmed and unigram/ bigram representations. We then applied multinomial naïve Bayes, a linear SVM, Bayesian logistic regression, and random forests to the classification task. Using 10 fold cross-validation, the algorithms performed with area under the receiver operating curve of 0.66, 0.91, 0.93, and 0.94 respectively. We also compare to a human constructed Boolean search for the same tweets and the text classification method is competitive with this hand crafted search. In conclusion, we show that the task of automatically identifying alcohol related tweets is highly feasible and paves the way for future research to improve these classifiers.

A minimum description length MDL and stochastic complexity approach for model selection in robust linear regression is studied in this paper. Computational aspects and implementation of this approach to practical problems are the focuses of the study. P articularly, w e provide both algorithms and a package of S language programs for computing the stochastic complexity and proceeding with the associated model selection. A simulation study is then presented for illustration and comparing the MDL approach with the commonly used AIC and BIC methods. Finally, an application is given to a physiological study of triathlon athletes.

Pacific Symposium on Biocomputing 2015 443 Biocomputing 2015 Downloaded from www.worldscientific.com by 205.142.197.33 on 02/05/15. For personal use only. Proper control of gene expression is vital to cellular function, and improper control is thought to play a role in many diseases. There are >50,000 datasets currently available in the Gene Expression Omnibus database [1], covering a wide variety of disease states, tissue types, and conditions. Development of methods for extracting relevant information from these rich data remains a limiting step in the longstanding goal to translate them into useful clinical information. Differential expression analysis is by far the most popular method for identifying genes that are mis-expressed in a disease state, having been utilized in hundreds of studies to date (reviewed in ). Such analyses apply statistical methods such as t-tests to cohorts of individuals in order to identify genes with high expression levels in the disease state and low expression levels in normal conditions (or vice versa) (reviewed in [10]). However, these analyses often result in large lists of genes that must subsequently be further decomposed into specific dysregulated pathways and expression programs. Furthermore, differences in gene expression levels between individuals due to factors such as genotype, disease severity or stage, and age/gender/race may confound results from standard differential expression analysis methods.

The quality of seedlings colonized by Tuber melanosporum is one of the main factors that contributes to the success or failure of a truffle crop. Truffle cultivation has quickly grown in European countries and elsewhere, so a commonly shared seedling evaluation method is needed. Five evaluation methods are currently published in the literature: three are used in Spain and two in France and Italy. Although all estimate the percentage colonization by T. melanosporum mycorrhizae, they do it in different ways. Two methods also estimate total number of mycorrhizae per seedling. Most are destructive. In this work, ten batches of holm oak seedlings inoculated with T. melanosporum from two different nurseries were evaluated by means of the five methods noted above. Some similarity was detected between the percentages of T. melanosporum mycorrhizae estimated by each method but not in their ability to assess the suitability of each batch. We discuss the advantages and disadvantages for each method and suggest approaches to reach consensus within the truffle culture industry for certifying mycorrhizal colonization by T. melanosporum and seedling quality.

The measurement of performance of Internet Protocol IP network can be done by Transmission Control Protocol TCP because it guarantees send data from one end of the connection actually gets to the other end and in the same order it was send, otherwise an error is reported. There are several methods to measure the performance of TCP among these methods genetic algorithms, neural network, data mining
etc, all these methods have weakness and can’t reach to correct measure of TCP performance. This paper proposed a new method of measuring TCP performance for real time IP network using Biocomputing, especially molecular calculation because it provides wisdom results and it can exploit all
facilities of phylogentic analysis. Applying the new method at real time on Biological Kurdish Messenger BIOKM model designed to measure the TCP performance in two types of protocols File Transfer Protocol FTP and Internet Relay Chat Daemon IRCD. This application gives very close result of TCP
performance comparing with TCP performance which obtains from Little’s law using same model (BIOKM), i.e. the different percentage of utilization (Busy or traffic industry) and the idle time which are obtained from a new method base on Bio-computing comparing with Little’s law was @ 0.13%.

Synthetic biology aims to the rational design of gene circuits with predictable behaviours. Great efforts have been done so far to introduce in the field mathematical models that could facilitate the design of synthetic networks. Here we present a mathematical model of a synthetic gene-circuit with a negative feedback. The closed loop configuration allows the control of transcription by an inducer molecule (IPTG). Escherichia coli bacterial cells were transformed and expression of a fluorescent reporter (GFP) was measured for different inducer levels. Computer model simulations well reproduced the experimental induction data, using a single fitting parameter. Independent genetic components were used to assemble the synthetic circuit. The mathematical model here presented could be useful to predict how changes in these genetic components affect the behaviour of the synthetic circuit.

We present a new algorithm (SATCHMO) that simultaneously estimates a tree and generates a set of multiple sequence alignments given a set of protein sequences. Alignments are constructed for each node in the tree. These alignments predict the structurally conserved elements of the sequences in a subtree and are therefore of different lengths, and represent different amino acid preferences, at different nodes. Hidden Markov Models (HMMs) are also generated for each node and are used to determine branching order, to align sequences and to predict structurally alignable regions. In experiments on the BAliBASE benchmark alignment database, SATCHMO is shown to perform comparably to ClustalW and the UCSC SAM HMM software. Results using SATCHMO to identify protein domains are demonstrated on potassium channels, with implications for the mechanism by which tumor necrosis factor alpha affects potassium current.

DNA methylation has emerged as promising epigenetic markers for disease diagnosis. Both the differential mean (DM) and differential variability (DV) in methylation have been shown to contribute to transcriptional aberration and disease pathogenesis. The presence of confounding factors in large scale EWAS may affect the methylation values and hamper accurate marker discovery. In this paper, we propose a exible framework called methylDMV which allows for confounding factors adjustment and enables simultaneous characterization and identification of CpGs exhibiting DM only, DV only and both DM and DV. The proposed framework also allows for prioritization and selection of candidate features to be included in the prediction algorithm. We illustrate the utility of methylDMV in several TCGA datasets. An R package methylDMV implementing our proposed method is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#methylDMV.

We describe a system that extracts disease-gene relations from M edLine. We constructed a dictionary for disease and gene names from six public databases and extracted relation candidates by dictionary matching. Since dictionary matching produces a large number of false positives, we developed a method of machine learning-based named entity recognition (NER) to filter out false recognitions of disease/gene names. We found that the performance of relation extraction is heavily dependent upon the performance of NER filtering and that the filtering improves the precision of relation extraction by 26.7% at the cost of a small reduction in recall.

Biomedical text mining and other automated techniques are beginning to achieve performance which suggests that they could be applied to aid database curators. However, few studies have evaluated how these systems might work in practice. In this article we focus on the problem of annotating mutations in Protein Data Bank (PDB) entries, and evaluate the relationship between performance of two automated techniques, a text-mining-based approach (MutationFinder) and an alignment-based approach, in intrinsic versus extrinsic evaluations. We find that high performance on gold standard data (an intrinsic evaluation) does not necessarily translate to high performance for database annotation (an extrinsic evaluation). We show that this is in part a result of lack of access to the full text of journal articles, which appears to be critical for comprehensive database annotation by text mining. Additionally, we evaluate the accuracy and completeness of manually annotated mutation data in the PDB...

Even though the number a nd the size of sequence databases are growing rapidly, most new information relevant to biology research is still recorded as free text in journal articles and in comment elds of databases like the GenBank feature table annotations. As biomedical research e n ters the postgenome era, new kinds of databases that contain information beyond simple sequences are needed, for example, information on cellular localization, proteinprotein interactions, gene regulation and the context of these interactions. The forerunners of such databases include KEGG 1 , D I P 2 , B IND 3 , a mong others. Such databases are still small in size and are largely hand curated. A f a c t o r that can accelerate their growth is the development o f r eliable literature data mining technologies. This year is the third time the Paci c Symposium on Biocomputing has devoted an entire session to natural language processing and information extraction for biology. Compared to the last two y ears, the eld has made tremendous strides. Most of the early work on automated understanding of biomedical papers concentrated on analytical tasks such as identifying protein names 4 or relied on simple techniques such a s w ord co-occurrence 5 and pattern matching 6. Last year, we b e g an to see work based on more general natural language parsers that could handle considerably more complex sentences 7 8. This year, we s e e the emergence of more sophisticated natural language technologies that can handle anaphora, as well as extracting a broader range of information. Six papers were accepted under peer-review out of a total of seventeen submissions reviewed for this session. We b rie y introduce them here: The paper by D i n g et al. examines an issue that is fundamental to literature

There has been increased work in developing automated systems that involve natural language processing (NLP) to recognize and extract genomic information from the literature. Recognition and identification of biological entities is a critical step in this process. NLP systems generally rely on nomenclatures and ontological specifications as resources for determining the names of the entities, assigning semantic categories that are consistent with the corresponding ontology, and assignment of identifiers that map to well-defined entities within a particular nomenclature. Although nomenclatures and ontologies are valuable for text processing systems, they were developed to aid researchers and are heterogeneous in structure and semantics. A uniform resource that is automatically generated from diverse resources, and that is designed for NLP purposes would be a useful tool for the field, and would further database interoperability. This paper presents work towards this goal. We have automatically created lexical resources from four model organism nomenclature systems (mouse, fly, worm, and yeast), and have studied performance of the resources within an existing NLP system, GENIES 1. Using nomenclatures is not straightforward because issues concerning ambiguity, synonymy, and name variations are quite challenging. In this paper we focus mainly on ambiguity. We determined that the number of ambiguous gene names within the individual nomenclatures, across the four nomenclatures, and with general English ranged from 0%-10.18%, 1.187%-20.30%, and 0%-2.49% respectively. When actually processing text, we found the rate of ambiguous occurrences (not counting ambiguities stemming from English words) to range from 2.4%-32.9% depending on the organisms considered.

We propose a di erential equation model for gene expression and provide two methods to construct the model from a set of temporal data. We model both transcription and translation by kinetic equations with feedback loops from translation products to transcription. Degradation of proteins and mRNAs is also incorporated. We study two methods to construct the model from experimental data: Minimum Weight Solutions to Linear Equations (MWSLE), which determines the regulation by solving under-determined linear equations, and Fourier Transform for Stable Systems (FTSS), which re nes the model with cell cycle constraints. The results suggest that a minor set of temporal data may be su cient t o c o nstruct the model at the genome level. We also give a comprehensive discussion of other extended models: the RNA Model, the Protein Model, and the Time Delay Model.

This paper discusses the issues involved in implementing a dynamic programming algorithm for biological sequence comparison on a generalpurpose parallel computing platform based on a ne-grain event-driven multithreaded program execution model. Fine-grain multithreading permits e cient parallelism exploitation in this application both by taking advantage of asynchronous point-to-point synchronizations and communication with low o v erheads and by e ectively tolerating latency through the overlapping of computation and communication. We have implemented our scheme on EARTH, a ne-grain event-driven multithreaded execution and architecture model which has been ported to a numberof parallel machines with o -the-shelf processors. Our experimental results show that the dynamic programming algorithm can be e ciently implemented on EARTH systems with high performance e.g., speedup of 90 on 120 nodes, good programmability and reasonable cost.

The accuracy of phylogenetic inference was examined in simulated data sets up to nearly 10,000 taxa, the size of the largest set of homologous genes in existing molecular sequence databases. Even with a simple search algorithm (maximum parsimony without branch swapping), the number of characters needed to estimate 80% of a tree correctly can scale remarkably well at optimal substitution rates (on the order of log N, where N is the number of taxa). In other words, the number of taxa in an analysis can be doubled and only an arithmetic increase in the number of characters is required to maintain the same level of accuracy. Even substitution rates that are much higher than normally used in phylogenetic studies did not affect the scaling too adversely. However, scaling is usually worse than log N for more stringent levels of accuracy. Moreover, errors are not distributed randomly throughout the tree. Shallow nodes are remarkably easy to reconstruct and display favourable log-linear scal...

General pedigrees can be encoded as Bayesian networks, where the common MPE query corresponds to finding the most likely haplotype configuration. Based on this, a strategy for grid parallelization of a state-of-the-art Branch and Bound algorithm for MPE is introduced: independent worker nodes concurrently solve subproblems, managed by a Branch and Bound master node. The likelihood functions are used to predict subproblem complexity, enabling efficient automation of the parallelization process. Experimental evaluation on up to 20 parallel nodes yields very promising results and suggest the effectiveness of the scheme, solving several very hard problem instances. The system runs on loosely coupled commodity hardware, simplifying deployment on a larger scale in the future.

The measurement of performance of Internet Protocol IP network can be done by Transmission Control Protocol TCP because it guarantees send data from one end of the connection actually gets to the other end and in the same order it was send, otherwise an error is reported. There are several methods to measure the performance of TCP among these methods genetic algorithms, neural network, data mining etc, all these methods have weakness and can’t reach to correct measure of TCP performance.
This paper proposed a new method of measuring TCP Performance for real time IP network using Biocomputing, especially molecular calculation because it provides wisdom results and it can exploit all facilities of phylogentic analysis. Applying the new method at real time on Biological Kurdish Messenger BIOKM model designed to measure the TCP performance in two types of protocols File Transfer Protocol FTP and Internet Relay Chat Daemon IRCD. This application gives very close result of TCP performance comparing with TCP performance which obtains from Little’s law using same model (BIOKM), i.e. the different percentage of utilization (Busy or traffic industry) and the idle time which are obtained from a new method base on Bio-computing comparing with Little’s law was  ~ 0.13%.

KEYWORDS
Bio-computing, TCP performance, Phylogenetic tree, Hybridized Model (Normalized), FTP, IRCD

Although text mining shows considerable promise as a tool for supporting the curation of biomedical text, there is little concrete evidence as to its effectiveness. We report on three experiments measuring the extent to which curation can be speeded up with assistance from Natural Language Processing (NLP), together with subjective feedback from curators on the usability of a curation tool that integrates NLP hypotheses for protein-protein interactions (PPIs). In our curation scenario, we found that a maximum speed-up of 1/3 in curation time can be expected if NLP output is perfectly accurate. The preference of one curator for consistent NLP output and output with high recall needs to be confirmed in a larger study with several curators.

The regulation of human haematopoiesis is a complex biological system with numerous interdependent processes. In vivo Haematopoietic Stem Cells (HSCs) selfrenew so as to maintain a constant pool of these cells. It would be very interesting to maintain these cells in vitro, in view of their therapeutical importance. Unfortunately, there is currently no known process to activate HSCs self-renewal in vitro. Since the difficulties related to in vitro experiments, modeling and simulating this process is indispensable. Moreover, the complexity of haematopoiesis makes it necessary to integrate various functionalities: both discrete and continuous models as well as consumption and production of resources. We thus focus on the use of Hybrid Functional Petri Nets, which offer a number of features and flexibility. We begin by modeling and simulating the role of a specific cytokine, interleukin-6, in the regulation of early haematopoiesis. Results obtained in silico lead to the disappearence of HSCs, which is in agreement with in vitro results.

Rapid increases in e-cigarette use and potential exposure to harmful byproducts have shifted public health focus to e-cigarettes as a possible drug of abuse. Effective surveillance of use and prevalence would allow appropriate regulatory responses. An ideal surveillance system would collect usage data in real time, focus on populations of interest, include populations unable to take the survey, allow a breadth of questions to answer, and enable geo-location analysis. Social media streams may provide this ideal system. To realize this use case, a foundational question is whether we can detect ecigarette use at all. This work reports two pilot tasks using text classification to identify automatically Tweets that indicate e-cigarette use and/or e-cigarette use for smoking cessation. We build and define both datasets and compare performance of 4 state of the art classifiers and a keyword search for each task. Our results demonstrate excellent classifier performance of up to 0.90 and 0.9...

Bayesian network structure learning is a useful tool for elucidation of regulatory structures of biomolecular pathways. The approach however is limited by its acyclicity constraint, a problematic one in the cycle-containing biological domain. Here, we introduce a novel method for modeling cyclic pathways in biology, by employing our newly introduced Generalized Bayesian Networks (GBNs). Our novel algorithm enables cyclic structure learning while employing biologically relevant data, as it extends our cycle-learning algorithm to permit learning with singly perturbed samples. We present theoretical arguments as well as structure learning results from realistic, simulated data of a biological system. We also present results from a real world dataset, involving signaling pathways in T-cells.

Identifying positively selected amino acid sites is an important approach for making inference about the function of proteins; an amino acid site that is undergoing positive selection is likely to play a key role in the function of the protein. We present a new Bayesian method for identifying positively selected amino acid sites and apply the method to a data set of hemagglutinin sequences from the Influenza virus. We show that the results of the new methods are in accordance with results obtained using previous methods. More importantly, we also demonstrate how the method can be used for making further inferences about the evolutionary history of the sequences. For example, we demonstrate that sites that are positively selected tend to have a preponderance of conservative amino acid substitutions.

With the booming of new technologies, biomedical science has transformed into digitalized, data intensive science. Massive amount of data need to be analyzed and interpreted, demand a complete pipeline to train next generation data scientists. To meet this need, the transinstitutional Big Data to Knowledge (BD2K) Initiative has been implemented since 2014, complementing other NIH institutional efforts. In this report, we give an overview the BD2K K01 mentored scientist career awards, which have demonstrated early success. We address the specific trainings needed in representative data science areas, in order to make the next generation of data scientists in biomedicine.

The DNA-binding domain (DBD) structure of a regulatory transcription factor (TF) is important in determining its DNA sequence specificity, but it is unclear whether a relationship exists between DBD structure and general TF biological function or regulatory mechanism. We observed ...

The growing availability of inexpensive high-throughput sequence data is enabling researchers to sequence tumor populations within a single individual at high coverage. But, cancer genome sequence evolution and mutational phenomena like driver mutations and gene fusions are difficult to investigate without first reconstructing tumor haplotype sequences. Haplotype assembly of single individual tumor populations is an exceedingly difficult task complicated by tumor haplotype heterogeneity, tumor or normal cell sequence contamination, polyploidy, and complex patterns of variation. While computational and experimental haplotype phasing of diploid genomes has seen much progress in recent years, haplotype assembly in cancer genomes remains uncharted territory. In this work, we describe HapCompass-Tumor a computational modeling and algorithmic framework for haplotype assembly of copy number variable cancer genomes containing haplotypes at different frequencies and complex variation. We extend our polyploid haplotype assembly model and present novel algorithms for (1) complex variations, including copy number changes, as varying numbers of disjoint paths in an associated graph, (2) variable haplotype frequencies and contamination, and (3) computation of tumor haplotypes using simple cycles of the compass graph which constrain the space of haplotype assembly solutions. The model and algorithm are implemented in the software package HapCompass-Tumor which is available for download from

Rapid increases in e-cigarette use and potential exposure to harmful byproducts have shifted public health focus to e-cigarettes as a possible drug of abuse. Effective surveillance of use and prevalence would allow appropriate regulatory responses. An ideal surveillance system would collect usage data in real time, focus on populations of interest, include populations unable to take the survey, allow a breadth of questions to answer, and enable geo-location analysis. Social media streams may provide this ideal system. To realize this use case, a foundational question is whether we can detect e-cigarette use at all. This work reports two pilot tasks using text classification to identify automatically Tweets that indicate e-cigarette use and/or e-cigarette use for smoking cessation. We build and define both datasets and compare performance of 4 state of the art classifiers and a keyword search for each task. Our results demonstrate excellent classifier performance of up to 0.90 and 0....

The Cell Index Database, (CELLX) (http://cellx.sourceforge.net) provides a computational framework for integrating expression, copy number variation, mutation, compound activity, and meta data from cancer cells. CELLX provides the computational biologist a quick way to perform routine analyses as well as the means to rapidly integrate data for offline analysis. Data is accessible through a web interface which utilizes R to generate plots and perform clustering, correlations, and statistical tests for associations within and between data types for ~20,000 samples from TCGA, CCLE, Sanger, GSK, GEO, GTEx, and other public sources. We show how CELLX supports precision oncology through indications discovery, biomarker evaluation, and cell line screening analysis.

We propose a novel strategy for discovering motifs from gene expression data. The gene expression data in our experiments comes from DNA Microarray analysis of the bacterium E. coli in response to recovery from nutrient starvation. We have annotated the data and identified the upregulated genes. Our interest is to find common regulatory motifs that are responsible for the upregulation of these specific genes. We assume that a common motif that a regulatory protein can bind to will be present in the upstream region of the upregulated genes and will not be present in the upstream regions of genes that showed a constant level of expression over time. Our objective is to find the common motifs that are present in at least some of the upstream sequences of upregulated genes and not present in the control set, which is the set of genes whose expression remained the same. Because it is possible that there could be several subsets of co-regulated genes under different control mechanisms amo...

There has been much work devoted to the mapping, alignment, and linking of ontologies (MALO), but little has been published about how to evaluate systems that do this. A fault model for conducting fine-grained evaluations of MALO systems is proposed, and its application to the system described in Johnson et al. [15] is illustrated. Two judges categorized errors according to the model, and inter-judge agreement was calculated by error category. Overall inter-judge agreement was 98% after dispute resolution, suggesting that the model is consistently applicable. The results of applying the model to the system described in [15] reveal the reason for a puzzling set of results in that paper, and also suggest a number of avenues and techniques for improving the state of the art in MALO, including the development of biomedical domain specific language processing tools, filtering of high frequency matching results, and word sense disambiguation.

Protein fold recognition sometimes called threading is the prediction of a protein's 3-dimensional shape based on its similarity to a protein of known structure. Fold predictions are low resolution; that is, no e ort is made to rotate the protein's component amino acid side chains into their correct spatial orientations. The goal is simply to recognize the protein family member that most closely resembles the target sequence of unknown structure and to create a sensible alignment of the target to the known structure i.e., a structure-sequence alignment. To facilitate this type of structure prediction, we h a v e designed a low resolution molecular graphics tool. ProtAlign introduces the ability t o i n teract with and edit alignments directly in the 3-dimensional structure as well as in the usual 2-dimensional layout. It also contains several functions and features to

Suppose that a biologist wishes to study some local property P of genetic sequences. If he can design (with a computer scientist) an algorithm C which efficiently compresses parts of the sequence which satisfy P, then our algorithm TurboOptLift locates very quickly where property P occurs by chance on a sequence, and where it occurs as a result of a significant process. Under some conditions, the time complexity of TurboOptLift is O(n log n). We illustrate its use on the practical problem of locating approximate tandem repeats in DNA sequences.