The basic modeling problem begins with a set of observed data yn = {yt : t = 1, 2, …, n}, generated by some physical machinery, where the elements yt may be of any kind. Since no matter what they are they can be encoded as numbers we take them as such, i.e. natural numbers with or without the order if the data come from finite or countable sets, and real numbers otherwise. Often each number yt is observed together with others x1,t, x2,t, …, called explanatory data, written collectively as a K × n matrix X = {xi,j}, and the data then are written as yn ∣X. It is convenient to use the terminology “variables” for the source of these data. Hence, we say that the data {yt} come from the variable Y, and the explanatory data are generated by variables X1, X2, and so on.

In physics the explanatory data often determine the data yn of interest, called a “law,” but not so in statistical problems. Although by taking sufficiently many explanatory data we may also fit a function to the given set of observed data, but this is not a “law,” since if the same machinery were to generate additional data yn+1, x1,n+1, x2,n+1, … the function would not give yn+1. This is the reason the objective is to learn the statistical properties of the data yn, possibly in the context of the explanatory data.

The 1918 flu pandemic, also referred to as the Spanish flu, was a devastating infectious disease. It is estimated that 50 million people, about 3% of the world's population at the time, died of the disease. About 500 million people were infected. The causative agent was an influenza virus. In this chapter we will learn more about these viruses. We will make use of highly significant molecular biology databases and bioinformatics tools. These are useful not only for learning about influenza viruses, but are widely used to explore just about any topic in biology.

Short history of sequence databases

A vast amount of information is collected by projects around the world designed to characterize genomes, genes and proteins. The development with respect to DNA sequencing is particularly remarkable. One important task in bioinformatics is to store all of this information in databases and, importantly, to make it available to the scientific community for downloading and analysis. Numerous dedicated individuals working on database projects are the unsung heroes of bioinformatics and molecular biology (see also the quotation on stamp collecting above).

Many kinds of information technology can be used to make meetings more productive, some of which are related to what happens before and after meetings, while others are intended to be used during a meeting. Document repositories, presentation software, and even intelligent lighting can all play their part. However, the following discussion of user requirements will be restricted to systems that draw on the multimodal signal processing techniques described in the earlier chapters of this book to capture and analyze meetings. Such systems might help people understand something about a past meeting that has been stored in an archive, or they might aid meeting participants in some way during the meeting itself. For instance, they might help users understand what has been said at a meeting, or even convey an idea of who was present, who spoke, and what the interaction was like. We will refer to all such systems, regardless of their purpose or when they are used, as “meeting support technology.”

This chapter reviews the main methods and studies that elicited and analyzed user needs for meeting support technology in the past decade. The chapter starts by arguing that what is required is an iterative software process that through interaction between developers and potential users gradually narrows and refines sets of requirements for individual applications. Then, it both illustrates the approach and lays out specific user requirements by discussing the major user studies that have been conducted for meeting support technology.

General overview

Meetings are a rich resource of information that, in practice, is mostly untouched by any form of information processing. Even now it is rare that meetings are recorded, and fewer are then annotated for access purposes. Examples of the latter only include meetings held in parliaments, courts, hospitals, banks, etc., where a record is required for reasons of decision tracking or legal obligations. In these cases a labor-intensive manual transcription of the spoken words is produced. Giving much wider access to the rich content is the main aim of the AMI consortium projects, and there are now many examples of interest in that access – through the release of commercial hardware and software services. Especially with the advent of high-quality telephone and videoconferencing systems the opportunity to record, process, recognize, and categorize the interactions in meetings is recognized even by skeptics of speech and language processing technology.

Of course meetings are an audio-visual experience by nature and humans make extensive use of visual and other sensory information. To illustrate the rich landscape of information is the purpose of this book and many applications can be implemented even without looking at the spoken word. However, it is still verbal communication that forms the backbone of most meetings, and accounts for the bulk of the information transferred between participants. Hence automatic speech recognition (ASR) is key to access the information exchanged and is the most important part required for most higher level processing.

Methods of gene prediction

We saw in the previous chapter how prediction of CpG islands may be used to identify transcription start sites of protein-coding genes. However, there are many other elements and statistical properties of such genes that we may exploit for gene finding.

What are the methods available for computational gene finding? In general, one may distinguish between two major categories: de novo or ab initio methods and homology-based methods. The de novo methods make use of statistical signals in DNA sequences that are characteristic of protein-coding genes; the homology-based methods rely on the identification of exons by matching known mRNA or protein sequences or even profile HMMs to a genomic sequence. The homology-based methods are powerful but they require that mRNA or protein sequence information is available. Here we will focus on the de novo type of gene finding. What are the signals characteristic of proteincoding genes?

The RNA world

So far this book has focused on proteins and the genes that encode them. The human genome encodes some 21000 different proteins and the vast majority of them are important. On the other hand, there is a whole range of RNAs transcribed from the human genome that do not code for proteins, but have other functions. We refer to these RNAs as non-coding RNAs (ncRNAs). In fact, a major portion of the human genome is transcribed, although only about 1.5% of it corresponds to coding regions. We still do not know the function of many of these RNAs, but there are a large number of ncRNA families that have been characterized. Classic examples are tRNAs and ribosomal RNAs, which are part of the translation machinery. A set of U RNAs are involved in splicing (Chapter 16) and there are catalytically important RNA molecules of the RNA-processing enzymes RNases P and MRP. A vital and highly populated class of ncRNA is the RNAs involved in gene silencing as described in Chapter 3.

Meeting support technology evaluation can broadly be considered to be in three categories, which will be discussed in sequence in this chapter, in terms of goals, methods, and outcomes, following a brief introduction on methodology and undertakings prior to the AMI Consortium (Section 13.1). Evaluation efforts can be technology-centric, focused on determining how specific systems or interfaces performed in the tasks for which they were designed (Section 13.2). Evaluations can also adopt a task-centric view, defining common reference tasks such as fact finding or verification, which directly support cross-comparisons of different systems and interfaces (Section 13.3). Finally, the user-centric approach evaluates meeting support technology in its real context of use, measuring the increase in efficiency and user satisfaction that it brings (Section 13.4).

These aspects of evaluation differ from the component evaluation that accompanies each of the underlying technologies described in Chapters 3 to 10, which is often a black-box evaluation based on reference data and distance metrics (although task-centric approaches have been adopted for summarization evaluation, as shown in Chapter 10). Rather, the evaluation of meeting support technology is a stage in a complex software development process for which the helix model was proposed in Chapter 11. We think back on this process in the light of evaluation undertakings, especially for meeting browsers, at the end of this chapter (Section 13.5).

Approaches to evaluation: methods, experiments, campaigns

The evaluation of meeting browsers, as pieces of software, should be related (at least in theory) to a precise view of the specifications they answer.

This book has two parts: the first summarizes the facts of coding and information theory which are needed to understand the essence of estimation and statistics, and the second describes a new theory of estimation, which also covers a good part of statistics as well. After all, both estimation and statistics are about extracting information from the often chaotic looking data in order to learn what it is that makes the data behave the way they do. The first part together with an outline of the algorithmic information in Appendix A is meant for the statistician who wants to understand his or her discipline rather than just learn a bag of tricks with programs to apply them to various data, tricks that are not based on any theory and do not stand a critical examination although some of them can be quite useful, providing solutions for important statistical problems.

The word information has many meanings, two of which have been formalized by Shannon. The first is fundamental in communication as just the number of messages, strings of symbols, either to be stored or to be sent over some communication channel, the practical question being the size of the storage device needed or the time it takes to send them. The second meaning is the measure of the strength of the statistical property a string has, which is fundamental in statistics, and very different from that in communication.

For this chapter, as well as Chapters 12 and 13, we turn to the important genomics and bioinformatics problem of identifying biological function based on nucleotide and amino acid sequences.

Assigning function based on sequence similarity

A common problem in molecular biology is that you are faced with a gene or a gene product and you have no clue from experimental studies as to its function. In this context a critical contribution of bioinformatics is to attribute the sequence of a gene or a gene product a function. As one example, a genome sequencing project may give rise to tens of thousands of predicted protein sequences. In such a case we want to assign as many of these as possible a biological function using computational tools. In this manner we avoid many laborious wetlab experiments. In addition to genome sequencing projects, there are other more specialized situations where we want to find functions of genes. For instance, we could identify genes as being related to a specific genetic trait or disease, or a set of genes as being expressed under certain conditions.

A number of computational tools are available to predict a biological function associated with a protein sequence. In this chapter we will see an example in which we assign a function to a protein based on sequence similarity. Consider the human gene encoding the protein BRCA1, originally sequenced in 1994 (Miki et al., 1994). It was found to be related in sequence to a yeast protein RAD9. This yeast protein is involved in cell cycle control. This observation gave scientists a hint about possible roles of the BRCA1 gene. We see here an example of inferring a function based on a homology relationship to a protein that has already been functionally characterized. We will see yet another example of this situation in this chapter, where we will make use of BLAST to identify a homology relationship. We already encountered BLAST in the context of the BCR–ABL fusion protein in Chapter 7.

This chapter will deal further with phylogenetic analysis. We will introduce methods in addition to those of neighbour-joining and we will use a Perl script to examine taxonomy data. For these topics we will take a closer look at an extinct animal, the Tasmanian tiger.

Extinction

The Tasmanian tiger was not, in fact, a tiger; it was a dog-like marsupial animal. Thylacine is the more adequate scientific name. In the early twentieth century it existed only in Tasmania, and even there it was very scarce. A farmer named Wilf Batty lived in the Mawbanna district of northeastern Tasmania. On 13 May 1930 he spotted a thylacine attempting to break into his chicken coop. Batty had observed the thylacine around his house for weeks, and this day he took his rifle and shot the animal. As it happened, this was the last wild thylacine to be killed. Another specimen, most likely a female, was captured in 1933 and kept at the Hobart Zoo in Tasmania. She died on 7 September 1936, apparently as a result of neglect. The animal was kept outdoors and was not allowed access to her den, despite difficult temperatures. Ironically, the death took place only two months after the thylacine species was given full legal protection by the Tasmanian government. There are sightings of the thylacine reported after 1936, but none of these are well documented and we unfortunately need to regard the thylacine as being extinct.