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

Methods and Kits for analyzing macromolecules, including peptides, polypeptides, and proteins, employing nucleic acid encoding are disclosed. The sample analysis kits employ nucleic acid encoding and/or nucleic acid recording of a molecular interaction and/or reaction, such as recognition events (e.g., between an antigen and an antibody, between a modified terminal amino acid residue, or between a small molecule or peptide therapeutic and a target, etc.). Assays that do not require the cyclic transfer of information between a coding tag and a recording tag are also disclosed, including single cycle assays.

Methods and kits using nucleic acid encoding and/or label

Détection de réactions d'acide nucléique sur les réseaux à perle

본 개시내용은 서열결정 핵산 방법을 제공한다. 방법은 핵산 주형에 대항하는 초기 핵산 가닥으로의 뉴클레오타이드의 중합효소 촉매된 혼입을 포함할 수 있고, 여기서 중합효소는 뉴클레오타이드 혼입 사건을 검출하는 전하 센서에 부착된다. 각각이 전하 센서에서 고유한 특징을 생성하는 하나 이상의 비-천연 뉴클레오타이드 유형은 주형 핵산의 상이한 뉴클레오타이드를 고유하게 확인하는데 사용될 수 있다.

전계-효과 장치 및 핵산 서열결정 방법

La presente invention concerne des procedes et des compositions pour l'analyse de sequences d'acide nucleique. Dans certains aspects, les procedes utilisent des objets clonaux (tels que des boules d'ADN) qui ont ete captures sur des billes. A l'aide des procedes decrits aux presentes, les compositions sont fabriquees. Une bille et un objet clonal sont lies par affinite ou hybrides l'un a l'autre au moyen d'une piece de liaison par affinite ou d'une piece d'hybridation sur la surface de la bille. L'invention concerne egalement une population de billes possedant des objets clonaux lies par affinite ou hybrides a un rapport de 1/1. L'invention concerne en outre des procedes d'amplification d'une molecule d'acide nucleique cible a l'aide des compositions decrites aux presentes.

Amplification clonale en phase solide et procédés associés

FIELD: biotechnology.SUBSTANCE: group of inventions relates to genetics and can be used for polynucleotide sequencing. That is ensured by using a polynucleotide sequencing composition comprising: a nanopore; plurality of nucleotides; first and second polynucleotides; polymerase; long-term connecting structure; a first fragment located on the elongated body; reporter region. Polynucleotide sequencing system comprising the composition and the polynucleotide sequencing method itself are also used.EFFECT: this group of inventions is more reliable, reproducible and sensitive.43 cl, 1 tbl, 12 dwg

Kevin L. Gunderson

Papers

Methods and kits using nucleic acid encoding and/or label

Détection de réactions d'acide nucléique sur les réseaux à perle

전계-효과 장치 및 핵산 서열결정 방법

Amplification clonale en phase solide et procédés associés

Compositions, systems and methods for sequencing polynucleotides using connective structures attached to polymerases located near nanopores