Personal computing tools

Over the years, there has been a lot of progress and development when it comes to artificial intelligence.

Most of this progress has been geared towards the growth of businesses and industries. However, several companies are working on artificial intelligence tools fit for personal use. Artificial intelligence is said to be the next big thing, especially with all the evolution that is taking place in technology and information technology. Artificial intelligence is the core of the current technological advancements that are taking place. AI makes machines to learn from human experience, perform tasks, and adjust to the new inputs.

This article aims to give a list of artificial intelligence tools that you appropriate for personal use. Siri is only available to Apple users. This interaction is done in a seamless manner that allows you to converse with her.

You can also ask her any questions you may have and also ask her to execute various commands. Siri accesses all the applications on your phone like messages, contacts, safari, mail, maps, etc. Siri is also useful when you want to find a particular place like sports information, entertainment joints, making phone calls, and sending messages. Designed for Microsoft Windows phones, this voice-controlled assistant is highly useful for users. The AI makes various personalised recommendations based on the data stored in the Smartphone of a user.

This tool makes use of the Bing search. This tool can come in handy for you because it helps in planning meetings. Wondering how Amy works? Well, as soon as you get meeting requests or an email, Amy handles it. It pins down the location and time. The CCDB contains structural and protein distribution information derived from confocal, multiphoton, and electron microscopy, including correlated microscopy. Its main mission is to provide a means to make high-resolution data derived from electron tomography and high-resolution light microscopy available to the scientific community, situating itself between whole brain imaging databases such as the MAP project 47 and protein structures determined from electron microscopy, nuclear magnetic resonance NMR spectroscopy, and X-ray crystallography e.

The CCDB serves as a research prototype for investigating new methods of representing imaging data in a relational database system so that powerful data-mining approaches can be employed for the content of imaging data. The CCDB data model addresses the practical problem of image management for the large amounts of imaging data and associated metadata generated in a modern microscopy laboratory.

In addition, the data model has to ensure that data within the CCDB can be related to data taken at different scales and modalities. The data model of the CCDB was designed around the process of three-dimensional reconstruction from two-dimensional micrographs, capturing key steps in the process from experiment to analysis.

Figure 4. The types of imaging data stored in the CCDB are quite heterogeneous, ranging from large-scale maps of protein distributions taken by confocal microscopy to three-dimensional reconstruction of individual cells, subcellular structures, and organelles. The CCDB can accommodate data from tissues and cultured cells regardless of tissue of origin, but because of the emphasis on the nervous system, the data model contains several features specialized for neural data.

For each dataset, the CCDB stores not only the original images and three-dimensional reconstruction, but also any analysis products derived from these data, including segmented objects and measurements of quantities such as surface area, volume, length, and diameter. Users have access to the full resolution imaging data for any type of data, e.

For example, a three-dimensional reconstruction is viewed as one interpretation of a set of raw data that is highly dependent on the specimen preparation and imaging methods used to acquire it. Thus, a single record in the CCDB consists of a set of raw microscope images and any volumes, images, or data derived from it, along with a rich set of methodological details. These derived products include reconstructions, animations, correlated volumes, and the results of any segmentation or analysis performed on the data.

By presenting all of the raw data, as well as reconstructed and processed data with a thorough description of how the specimen was prepared and imaged, researchers are free to extract additional content from micrographs that may not have been analyzed by the original author or employ additional alignment, reconstruction, or segmentation algorithms to the data. The utility of image databases depends on the ability to query them on the basis of descriptive attributes and on their contents.

Of these two types of query, querying images on the basis of their contents is by far the most challenging. Although the development of computer algorithms to identify and extract image features in image data is advancing, 48 it is unlikely that any algorithm will be able to match the skill of an experienced microscopist for many years.

The CCDB project addresses this problem in two ways. One currently supported way is to store the results of segmentations and analyses performed by individual researchers on the data sets stored in the CCDB. The CCDB allows each object segmented from a reconstruction to be stored as a separate object in the database along with any quantitative information derived from it.

The list of segmented objects and their morphometric quantities provides a means to query a dataset based on features contained in the data such as object name e. MacKenzie-Graham, E. Jones, D. Shattuck, I. Dinov, M. Bota, and A. Sinha, A. Bui, R. Taira, J. Dionisio, C. Morioka, D. Johnson, and H. It is also desirable to exploit information in the database that is not explicitly represented in the schema.

The properties of the surfaces can be determined through very general operations at query time that allow the user to query on characteristics not explicitly modeled in the schema e. In this example, the schema does not contain explicit indication of the shape of the dendritic shaft, but these characteristics can be computed as part of the query processing. Additional data types are being developed for volume data and protein distribution data.

A data type for tree structures generated by Neurolucida has recently been implemented. The CCDB is being designed to participate in a larger, collaborative virtual data federation. Thus, an approach to reconciling semantic differences between various databases must be found. Lacroix, ed. Anatomical entities may have multiple names e.

To minimize semantic confusion and to situate cellular and subcellular data from the CCDB in a larger context, the CCDB is mapped to several shared knowledge sources in the form of ontologies.

Thus, regardless of which term is preferred by a given individual, if they share the same ID, they are asserted to be the same. Conversely, even if two terms share the same name, they are distinguishable by their unique IDs. In addition, an ontology can support the linkage of concepts by a set of relationships. Because the knowledge required to link concepts is contained outside of the source database, the CCDB is relieved of the burden of storing exhaustive taxonomies for individual datasets, which may become obsolete as new knowledge is discovered.

NeuroNames is a comprehensive resource for gross brain anatomy in the primate. Ontologies for areas such as neurocytology and neurological disease are being built on top of the UMLS, utilizing existing concepts wherever possible and constructing new semantic networks and concepts as needed. In addition, imaging data in the CCDB is mapped to a higher level of brain organization by registering their location in the coordinate system of a standard brain atlas. Placing data into an atlas-based coordinate systems provides one method by which data taken across scales and distributed across multiple resources can reliably be compared.

Through the use of computer-based atlases and associated tools for warping and registration, it is possible to express the location of anatomical features or signals in terms of a standardized coordinate system. While there may be disagreement among neuroscientists about the identity of a brain area giving rise to a signal, its location in terms of spatial coordinates is at least quantifiable.

The expression of brain data in terms of atlas coordinates also allows them to be transformed spatially to offer alternative views that may provide additional information such as flat maps or additional parcellation.

Humphreys, D. Lindberg, H. Schoolman, and G. Ludascher, J. Grethe, and M. Bowden and M. Brevik, T. Leergaard M. Svanevik, J. Van Essen, H. Drury, J. Dickson, J. Harwell, D. Hanlon, and C. Although genomic databases such as GenBank receive the majority of attention, databases and algorithms that operate on databases are key tools in research into ecology and biodiversity as well.

These tools can provide researchers with access to information regarding all identified species of a given type, such as AlgaeBase 57 or FishBase; 58 they also serve as a repository for submission of new information and research. Other databases go beyond species listings to record individuals: for example, the ORNIS database of birds seeks to provide access to nearly 5 million individual specimens held in natural history collections, which includes data such as recordings of vocalizations and egg and nest holdings.

The data associated with ecological research are gathered from a wide variety of sources: physical observations in the wild by both amateurs and professionals; fossils; natural history collections; zoos, botanical gardens, and other living collections; laboratories; and so forth.

In addition, these data must placed into contexts of time, geographic location, environment, current and historical weather and climate, and local, regional, and global human activity. Needless to say, these data sources are scattered throughout many hundreds or thousands of different locations and formats, even when they are in digitally accessible format.

However, the need for integrated ecological databases is great: only by being able to integrate the totality of observations of population and environment can certain key questions be answered. Such a facility is central to endangered species preservation, invasive species monitoring, wildlife disease monitoring and intervention, agricultural planning, and fisheries management, in addition to fundamental questions of ecological science.

The first challenge in building such a facility is to make the individual datasets accessible by networked query. Over the years, hundreds of millions of specimens have been recorded in museum records. In many cases, however, the data are not even entered into a computer; they may be stored as a set of index cards dating from the s. Very few specimens have been geocoded. Museum records carry a wealth of image and text data, and digitizing these records in a meaningful and useful way remains a serious challenge.

Similarly, the NSF Partnerships for Enhancing Expertise in Taxonomy PEET program, 61 which emphasizes training in taxonomy, requires that recipients of funding incorporate collected data into databases or other shared electronic formats.

Ecological databases also rely on metadata to improve interoperability and compatibility among disparate data collections. These datasets are collected over long periods of time, possibly decades or even centuries, by a diverse set of actors for different purposes. A commonly agreed-upon format and vocabulary for metadata is essential for efficient cooperative access.

Furthermore, as data increasingly are collected by automated systems such as embedded systems and distributed sensor networks, the applications that attempt to fuse the results into formats amenable to algorithmic or human analysis must deal with high and always on data rates, likely contained in shifting standards for representation.

Again, early agreement on a basic system for sharing metadata will be necessary for the feasibility of such applications. In attempting to integrate or cross-query these data collections, a central issue is the naming of species or higher-level taxa. The Linnean taxonomy is the oldest such effort in biology, of course, yet because there is not yet nor likely can ever be complete agreement on taxa identification, entries in different databases may contain different tags for members of the same species, or the same tag for members that were later determined to be of different species.

Taxa are often moved into different groups, split, or merged with others; names are sometimes changed. ITIS data are of varying quality, and entries are tagged with three different quality indicators: credibility, which indicates whether or not data have been reviewed; latest review, giving the year of the last review; and global completeness, which records whether all species belonging to a taxon were included at the last review.

These measurements allow researchers to evaluate whether the data are appropriate for their use. In constructing such a database, many data standards questions arise. However, for the kingdom Protista, which at various times in biological science has been considered more like an animal and more like a plant, both standards might apply. Dates and date ranges provide another challenge: while there are many international standards for representing a calendar date, in general these did not foresee the need to represent dates occurring millions or billions of years ago.

ITIS employs a representation for geologic ages, and this illustrates the type of challenge encountered when stretching a set of data standards to encompass many data types and different methods of collection. For issues of representing observations or collections, an important element is the Darwin Core, a set of XML metadata standards for describing a biological specimen, including observations in the wild and preserved items in natural history collections.

Where ITIS attempts to improve communicability by achieving agreement on precise name usage, Darwin Core 64 and similar metadata efforts concentrates the effort on labeling and markup of data. This allows individual databases to use their own data structures, formats, and representations, as long as the data elements are labeled by Darwin Core keywords. Since the design demands on such databases will be substantially different, this is a useful approach.

These two approaches indicate a common strategic choice: simpler standards are easier to adopt, and thus will likely be more widespread, but are limited in their expressiveness; more complex standards can successfully. For a more extended discussion of the issues involved in maintaining ecological data, see W.

Michener and J. Brunt, eds. Agreement on standard terminology and data labeling would accomplish little if the data sources were unknown. The most significant challenge in creating large-scale ecological information is the integration and federation of the potentially vast number of relevant databases. The Global Biodiversity Information Facility GBIF 66 is an attempt to offer a single-query interface to cooperating data providers; in December of , it consisted of 95 providers totaling many tens of millions of individual records.

GBIF accomplishes this query access through the use of data standards such as the Darwin Core and Web services, an information technology IT industry standard way of requesting information from servers in a platform-independent fashion.

The CHM is intended as a way for information on biodiversity to be shared among signatory states and made available as a way to monitor compliance and as a tool for policy. Globally integrated ecological databases are still in embryonic form, but as more data become digitized and made available by the Internet in standard fashions, their value will increase. Integration with phylogenetic and molecular databases will add to their value as research tools, in both the ecological and the evolutionary fields.

Biological processes can take place over a vast array of spatial scales, from the nanoscale inhabited by individual molecules, to the everyday, meter-sized human world. They can take place over an even vaster range of time scales, from the nanosecond gyrations of a folding protein molecule to the seven decade or so span of a human life—and far beyond, if evolutionary time is included.

They also can be considered at many levels of organization, from the straightforward realm of chemical interaction to the abstract realm of, say, signal transduction and information processing.

Much of 21st century biology must deal with these processes at every level and at every scale, resulting in data of high dimensionality. Thus, the need arises for systems that can offer vivid and easily understood visual metaphors to display the information at each level, showing the appropriate amount of detail.

Such a display would be analogous to, say, a circuit diagram, with its widely recognized icons for diodes, transistors, and other such components. A key element of such systems is easily understood metaphors that present signals containing multiple colors over time on more than one axis. As an empirical matter, these metaphors are hard to find. Indeed, the problem of finding a visually or intellectually! The system would likewise offer easy and intuitive ways to navigate between levels, so that the user could drill down to get more detail or pop up to higher abstractions as needed.

Also, it would offer good ways to visualize the dynamical behavior of the system over time—whatever the appropriate time scale might be. Current-generation visualization systems such as those associated with BioSPICE 68 and Cytoscape 69 are a good beginning—but, as their developers themselves are the first to admit, only a beginning. Biologists use a variety of different data representations to help describe, examine, and understand data.

Biologists often use cartoons as conceptual, descriptive models of biological events or processes. A cartoon might show a time line of events: for example, the time line of the phosphorylation of a receptor that allows a protein to bind to it. As biologists take into account the simultaneous interactions of larger numbers of molecules, events over time become more difficult to represent in cartoons.

The most complex data visualizations are likely to be representations of networks. The complete graph in Figure 4. The graph was developed using the Osprey network visualization system.

Visualization of combined, large-scale interaction data sets in yeast. Each edge in the graph represents an interaction between nodes, which are colored according to Gene Ontology GO functional annotation.

Highly connected complexes within the dataset, shown at the perimeter of the central mass, are built from nodes that share at least three interactions within other complex members. The 20 highly connected complexes contain genes, 1, connections, and an average connectivity of 5.

Tyers and M. These software tools are designed to read, query, and edit cell pathways, and to visualize data in a pathway context.

Visual Cell creates detailed drawings by compactly formatting thousands of molecular interactions. The software uses DCL, which can visualize and simulate large-scale networks such as interconnected signal transduction pathways and the gene expression networks that control cell proliferation and apoptosis. DCL can visualize millions of chemical states and chemical reactions. A second approach to diagrammatic simulation has been developed by Efroni et al.

Behavior in Statecharts is described by using states and events that cause transitions between states. States may contain substates, thus enabling description at multiple levels and zooming in and zooming out between levels.

States may also be divided into orthogonal states, thus modeling concurrency, allowing the system to reside simultaneously in several different states. A cell, for example, may be described orthogonally as expressing several receptors, no receptors, or any combination of receptors at different stages of the cell cycle and in different anatomical compartments. Furthermore, transitions take the system from one state to another. In cell modeling, transitions are the result of biological processes or the result of user intervention.

A biological process may be the result of an interaction between two cells or between a cell and various molecules. Statecharts provide a controllable way to handle the enormous dataset of cell behavior by enabling the separation of that dataset into orthogonal states and allowing transitions.

Still another kind of graphical interface is used for molecular visualization. Interesting biomolecules usually consist of thousands of atoms. A list of atomic coordinates is useful for some purposes, but an actual image of the molecule can often provide much more insight into its properties—and an image that can be manipulated e. Virtual reality techniques can be used to provide the viewer with a large field of view, and to enable the viewer to interact with the virtual molecule and compare it to other molecules.

However, many problems in biomolecular visualization tax the capability of current systems because of the diversity of operations required and because many operations do not fit neatly into the current architectural paradigm.

As useful as graphical visualizations are, even in simulated three-dimensional virtual reality they are still two-dimensional. Tangible, physical models that a human being can manipulate directly with his or her hands are an extension of the two-dimensional graphical environment.

A project at the Molecular Graphics Laboratory at the Scripps Research Institute is developing tangible interfaces for molecular biology.

These efforts have required the development and testing of software for the representation of physical molecular models to be built by autofabrication technologies, linkages between molecular descriptions and computer-aided design and manufacture approaches for enhancing the models with additional physical characteristics, and integration of the physical molecular models into augmented-reality interfaces as inputs to control computer display and interaction.

Efroni, D. Harel, and I. Gillet, M. Sanner, D. Stoffler, D. Goodsell, and A. This approach assumes that co-occurrences are indicative of functional links, although an obvious limitation is that negative relations e. To overcome this problem, other natural language processing methods involve syntactic parsing of the language in the abstracts to determine the nature of the interactions.

There are obvious computation costs in these approaches, and the considerable complexity in human language will probably render any machine-based method imperfect. Even with limitations, such methods will probably be required to make knowledge in the extant literature accessible to machine-based analyses.

Rice and G. Copyright Elsevier. References omitted. Still another form of data presentation is journal publication. It has not been lost on the scientific bioinformatics community that vast amounts of functional information that could be used to annotate gene and protein sequences are embedded in the written literature. Rice and Stolovitzky go so far as to say that mining the literature on biomolecular interactions can assist in populating a network model of intracellular interaction Box 4.

So far, however, the availability of full-text articles in digital formats such as PDF, HTML, or TIF files has limited the possibilities for computer searching and retrieval of full text in databases.

In the future, wider use of structured documents tagged with XML will make intelligent searching of full text feasible, fast, and informative and will allow readers to locate, retrieve, and manipulate specific parts of a publication.

In the meantime, however, natural language provides a considerable, though not insurmountable, challenge for algorithms to extract meaningful information from natural text.

One common application of natural language processing involves the extraction from the published literature of information about proteins, drugs, and other molecules.

For example, Fukuda et al. Other work has investigated the feasibility of recognizing interactions between proteins and other molecules.

One approach is based on simultaneous occurrences of gene names and their use to predict their connections based on their occurrence statistics. Hirschman, J. Park, J. Tsujii, L. Wong, and C. Fukuda, et al. Cited in Hirschman et al. Stapley and G. Ding et al. For example, Putejovsky and Castano focused on relations of the word inhibit and showed that it was possible to extract biologically important information from free text reliably, using a corpus-based approach to develop rules specific to a class of predicates.

The detection of gene symbols and names, for instance, remains difficult, as researchers have seldom followed logical rules. In some organisms—the fruit fly Drosophila is an example—scientists have enjoyed applying gene names with primary meaning outside the biological domain.

Names such as vamp , eve , disco , boss , gypsy , zip or ogre are therefore not easily recognized as referring to genes. Also, both synonymy many different ways to refer to the same object and polysemy multiple meanings for a given word cause problems for search algorithms. Synonymy reduces the number of recalls of a given object, whereas polysemy causes reduced precision. The word insulin, for instance, can refer to a gene, a protein, a hormone or a therapeutic agent, depending on the context.

In addition, pronouns and definite articles and the use of long, complex or negative sentences or those in which information is implicit or omitted pose considerable hurdles for full-text processing algorithms. Grivell points out that algorithms exist e.

Ng and M. Putejovsky and J. Hahn, et al. Proux, F. Rechenmann, L. Julliard, V. Cited in Grivell, Note also that while gene names are often italicized in print so that they are more readily recognized as genes , neither verbal discourse nor text search recognizes italicization. In addition, because some changes of name are made for political rather than scientific reasons, and because these political revisions are done quietly, even identifying the need for synonym tracking can be problematic.

An example is a gene mutation, discovered in , that caused male fruit flies to court other males. See, for example, M. Besides the recognition of protein interactions from scientific text, natural language processing has been applied to a broad range of information extraction problems in biology. Hahn et al. Baclawski et al. The UMLS ontology was used to build keynets. Using both domain-independent and domain-specific knowledge, keynets parsed texts and resolved references to build relationships between entities.

Humphreys et al. This work illustrated the importance of template matching, and applied the technique to terminology recognition.

Rindflesch et al. EDGAR drew on a stochastic part-of-speech tagger, a syntactic parser able to produce partial parses, a rule-based system, and semantic information from the UMLS. Thomas et al. They developed and applied templates to every part of the texts and calculated the confidence for each match. The resulting system could provide a cost-effective means for populating a database of protein interactions.

The next papers [in this volume] focus on improving retrieval and clustering in searching large collections. Chang et al. They showed that supplementing sequence similarity with information from biomedical literature search could increase the accuracy of homology search result. Illiopoulos et al. Despite the minimal semantic analysis, clusters built here gave a shallow description of the documents and supported concept discovery.

An algorithm was given to produce themes and to cluster documents according to these themes. Stapley et al. The accuracy of the classifier on a benchmark of proteins with known cellular locations was better than that of a support vector machine trained on amino acid composition and was comparable to a handcrafted rule-based classifier Eisenhaber and Bork, Copyright Oxford University Press. Just as an electronic digital computer abstracts various continuous voltage levels as 0 and 1, DNA abstracts a three-dimensional organization of atoms as A, T, G, and C.

This has important biological benefits, including very high-accuracy replication, common and simplified ways for associated molecules to bind to sites, and low ambiguity in coding for proteins. For human purposes in bioinformatics, however, the use of the abstraction of DNA as a digital string has had other equally significant and related benefits. It is easy to imagine the opposite case, in which DNA is represented as the three-dimensional locations of each atom in the macromolecule, and comparison of DNA sequences is a painstaking process of comparing the full structures.

Indeed, this is very much the state of the art in representing proteins which, although they can be represented as a digital string of peptides, are more flexible than DNA, so the digital abstraction leaves out the critically important features of folding.

The digital abstraction includes much of the essential information of the system, without including complicating higher- and lower-order biochemical properties. The most basic feature of the abstraction is that it treats the arrangement of physical matter as information. An important advantage of this is that information-theoretic techniques can be applied to specific DNA strings or to the overall alphabet of codon-peptide associations.

For example, computer science-developed concepts such as Hamming distance, parity, and error-correcting codes can be used to evaluate the resilience of information in the presence of noise and close alternatives. A second and very practical advantage is that as strings of letters, DNA sequences can be stored efficiently and recognizably in the same format as normal text.

More broadly, this means that a vast array of tools, software, algorithms, and software packages that were designed to operate on text could be adapted with little or no effort to operate on DNA strings as well. More abstract examples include the long history of research into algorithms to efficiently search, compare, and transform strings.

Although this algorithm was developed long before the genome era, it is useful to DNA analysis nonetheless. Finally, the very foundation of computational theory is the Turing machine, an abstract model of symbolic manipulation. Some very innovative research has shown that the DNA manipulations of some single-celled organisms are Turing-complete, 86 allowing the application of a large tradition of formal language analysis to problems of cellular machinery.

Regev and E. Ideally, of course, a nucleotide could be stored using only two bits or three to include RNA nucleotides as well. ASCII typically uses eight bits to represent characters.

Wagner and M. Landweber and L. These comments should not be taken to mean that the abstraction of DNA into a digital string is cost-free. Although digital coding of DNA is central to the mechanisms of heredity, the nucleotide sequence cannot deal with nondigital effects that also play important roles in protein synthesis and function. Proteins do not necessarily bind only to one specific sequence; the overall proportions of AT versus CG in a region affect its rate of transcription; and the state of methylation of a region of DNA is an important mechanism for the epigenetic control of gene expression and can indeed be inherited just as the digital code can be inherited.

Because these nondigital properties can have important effects, ignoring them puts a limit on how far the digital abstraction can support research related to gene finding and transcription regulation. Last, DNA is often compared to a computer program that drives the functional behavior of a cell. Although this analogy has some merit, it is not altogether accurate. Because DNA specifies which proteins the cell must assemble, it is at least one step removed from the actual behavior of a cell, since the proteins—not the DNA—that determine or at least have a great influence on cell behavior.

A significant problem in molecular biology is the challenge of identifying meaningful substructural similarities among proteins. Although proteins, like DNA, are composed of strings made from a sequence of a comparatively small selection of types of component molecules, unlike DNA, proteins can exist in a huge variety of three-dimensional shapes. Such shapes can include helixes, sheets, and other forms generally referred to as secondary or tertiary structure. Similar structure may imply similar functionality or receptivity to certain enzymes or other molecules that operate on specific molecular geometry.

However, even for proteins whose three-dimensional shape has been experimentally determined through X-ray crystallography or nuclear magnetic resonance, finding similarities can be difficult due to the extremely complex geometries and large amount of data.

A rich and mature area of algorithm research involves the study of graphs, abstract representations of networks of relationships. For example, a graph might represent cities as nodes and the highways that connect them as edges weighted by the distance between the pair of cities. Graph theory has been applied profitably to the problem of identifying structural similarities among proteins. Recent work in this area has combined graph theory, data mining, and information theoretic techniques to efficiently identify such similarities.

For more on the influence of DNA methylation on genetic regulation, see R. Jaenisch and A. Indeed, some work even suggests that DNA methylation and histone acetylation may be connected.

See J. Dobosy and E. Mitchell, P. Artymiuk, D. Rice, and P. Huan, W. Wang, A. Washington, J. Prins, R. Shah, and A. A significant computational aspect of this example is that since the general problem of identifying subgraphs is NP-complete, 91 the mere inspiration of using graph theory to represent proteins is insufficient; sophisticated algorithmic research is necessary to develop appropriate techniques, data representations, and heuristics that can sift through the enormous datasets in practical times.

Similarly, the problem involves subtle biological detail e. Algorithms play an increasingly important role in the process of extracting information from large biological datasets produced by high-throughput studies. Algorithms are needed to search, sort, align, compare, contrast, and manipulate data related to a wide variety of biological problems and in support of models of biological processes on a variety of spatial and temporal scales.

For example, in the language of automated learning and discovery, research is needed to develop algorithms for active and cumulative learning; multitask learning; learning from labeled and unlabeled data; relational learning; learning from large datasets; learning from small datasets; learning with prior knowledge; learning from mixed-media data; and learning causal relationships.

The computational algorithms used for biological applications are likely to be rooted in mathematical and statistical techniques used widely for other purposes e. Because critical features of many biological systems are not known, algorithms must operate on the basis of working models and must frequently contend with a lack of data and incomplete information about the system under study though sometimes simulated data suffices to test an algorithm.

Thus, the results they provide must be regarded as approximate and provisional, and the performance of algorithms must be tested and validated by empirical laboratory studies. Algorithm development, therefore, requires the joint efforts of biologists and computer scientists.

Far from giving a comprehensive description, these sections are intended to illustrate the complex substrate on which algorithms must operate and, further, to describe areas of successful and prolific collaboration between computer scientists and biologists. Some of the applications described below are focused on identifying or measuring specific attributes, such as the identity of a gene, the three-dimensional structure of a protein, or the degree of genetic variability in a population.

At the heart of these lines of investigation is the quest to understand biological function, e. Further opportunities to address biological questions are likely to be as diverse as biology itself, although work on some of those questions is only nascent at this time.

Although the complete genomic sequences of many organisms have been determined, not all of the genes within those genomes have been identified. Difficulties in identifying genes from sequences of uncharacterized DNA stem mostly from the complexity of gene organization and architecture. Just a small fraction of the genome of a typical eukaryote consists of exons, that is, blocks of DNA that, when arranged according to their sequence in the genome, constitute a gene; in the human genome, the fraction is estimated at less than 3 percent.

The notion of an NP-complete problem is rooted in the theory of computational complexity and has a precise technical definition. For purposes of this report, it suffices to understand an NP-complete problem as one that is very difficult and would take a long time to solve. Thurn, C. Faloutsos, T. Mitchell, and L. Other untranscribed regions of unknown purpose are found between genes or interspersed within coding sequences.

Genes themselves can occasionally be found nested within one another, and overlapping genes have been shown to exist on the same or opposite DNA strands. In the process of transcription, the exons of a particular gene are assembled into a single mature mRNA. It is estimated that at least a third of human genes are alternatively spliced, 95 with certain splicing arrangements occurring more frequently than others.

Most consumers, however, waited until PCs could play games, do word processing, and manage spreadsheets. This drawing imagined a future of computing where information was stored, not on punch cards or tape, but on microchips. Generally considered the first usable microprocessor CPU design, this chip became the central processing unit in traffic lights and the Altair computer.

Produced and sold by Radio Shack, the TRS offered consumers a complete and easy-to-use microcomputer. In this cross-promotional advertainment comic, Superman taught kids to use the TRS and helped Radio Shack reach a younger market.