Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

https://cdr.lib.unc.edu/downloads/v692t8920

A global reference for human genetic variation.

Comprehensive Integration of Single-Cell Data.

Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

/pdf/finding-the-missing-heritability-of-complex-diseases-3b6mbqztnq.pdf

Finding the missing heritability of complex diseases

By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98% of accessible single nucleotide polymorphisms at a frequency of 1% in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations.

/pdf/an-integrated-map-of-genetic-variation-from-1-092-human-4vxat9g1wd.pdf

An integrated map of genetic variation from 1,092 human genomes

Publisher Summary Determining the DNA sequence of the human genome was the first step for molecular understanding of complex human diseases with a genetic component. Knowing a reference DNA sequence led to the formation of an international consortium dedicated to develop a catalog of common human genomic variation. The International Haplotype Mapping Consortium set its initial goal of identifying all common single nucleotide polymorphisms (SNPs), defined as SNPs with minor allele frequency> 5%, every 5 kilo bases (kb) across the genome. This highly successful project ( International HapMap Consortium, 2005 ) not only provided a resource for studying genomic variations that contribute susceptibility to common disease, but also stimulated the development of molecular tools for highly multiplexed examination of genetic abnormalities, in high resolution, across the entire genome

Genotype and Epigenotype by Single Nucleotide Polymorphism (SNP) Analysis

Poster presentado en la 58th Annual Meeting of the American Society of Human Genetics (ASHG 2008), Philadelphia, PA, Nov 11-15, 2008.

/pdf/duplications-in-the-17p13-3-miller-dieker-syndrome-region-26l4y4d1cj.pdf

Duplications in the 17p13.3 Miller-Dieker syndrome region: Increased expression of LIS1 affects human and mouse brain development

A method for amplifying nucleic acids, comprising (a) providing (i) an array of amplification sites having one or more capture agents and (ii) an amplifying agent comprising a solution comprising a plurality of nucleic acids different target, wherein the number of different nucleic acid target in the solution exceeds the number of amplification sites in the matrix, wherein the different nucleic acid target have a fluidic access to the plurality of amplification sites, and wherein each one of the amplification sites can be occupied by multiple target nucleic acids of the plurality of different target nucleic acids; and (b) reacting the amplification reagent with the matrix of amplification sites, such that a plurality of amplification sites comprising each amplicons clonal individual target solution nucleic acid, wherein the reaction comprises simultaneously (i ) conveying the different target to sites amplification nucleic acids to plant sites amplification, and (ii) amplifying the target nucleic acids that have been planted in the amplification sites where the amplification rate exceeds the rate of seed, such amplicons generated to prevent additional target nucleic acids that are planted in each of said plurality of amplification sites.

Amplification kinetic exclusion of nucleic acid libraries

The current document discusses a detection system comprising a mechanical-change sensor that exhibits one or more mechanical changes when specifically interacting with entities within a target, each entity having a type, a mechanical-change-to-signal transducer that transduces the one or more mechanical changes into a signal, and an analysis subsystem that determines the types of entities within the target using the signal.

Sequence-detection system

The current document discusses electromechanical sequence detectors that transduce changes in the shape of a shape-change sensor component into an electrical signal from which one or more derived values are generated. In a disclosed implementation, the sequence-detection system comprises a mechanical-change sensor that changes shape when specifically interacting with entities within a target, a shape-to-signal-transduction component that transduces changes in the shape of the mechanical-change sensor into an electrical signal, an analysis subsystem that determines the types of entities within the target using the electrical signal, and a control subsystem that continuously monitors operational characteristics of the sequence-detection system and adjusts sequence-detection system operational parameters.

Kevin L. Gunderson

Papers

Genotype and Epigenotype by Single Nucleotide Polymorphism (SNP) Analysis

Duplications in the 17p13.3 Miller-Dieker syndrome region: Increased expression of LIS1 affects human and mouse brain development

Amplification kinetic exclusion of nucleic acid libraries

Sequence-detection system

Tuning and calibration features of sequence-detection system