News Feature: Biology research, no cells required

Background: Essential proteins play important roles in the survival or reproduction of an organism and support the stability of the system. Essential proteins are the minimum set of proteins absolutely required to maintain a living cell. The identification of essential proteins is a very important topic not only for a better comprehension of the minimal requirements for cellular life, but also for a more efficient discovery of the human disease genes and drug targets. Traditionally, as the experimental identification of essential proteins is complex, it usually requires great time and expense. With the cumulation of high-throughput experimental data, many computational methods that make useful complements to experimental methods have been proposed to identify essential proteins. In addition, the ability to rapidly and precisely identify essential proteins is of great significance for discovering disease genes and drug design, and has great potential for applications in basic and synthetic biology research. Objective: The aim of this paper is to provide a review on the identification of essential proteins and genes focusing on the current developments of different types of computational methods, point out some progress and limitations of existing methods, and the challenges and directions for further research are discussed.

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Application of Conservation Biology Research to Management

Conservation Biology ◽

10.1046/j.1523-1739.2000.99443.x ◽

2000 ◽

Vol 14 (6) ◽

pp. 1898-1902 ◽

Cited By ~ 14

Author(s):

David J. Flaspohler ◽

Brian R. Bub ◽

Beth A. Kaplin

Keyword(s):

Conservation Biology ◽

Biology Research

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Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data

Applied Sciences ◽

10.3390/app11135999 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5999

Author(s):

Diego A. Camacho-Hernández ◽

Victor E. Nieto-Caballero ◽

José E. León-Burguete ◽

Julio A. Freyre-González

Keyword(s):

Systems Biology ◽

Clustering Analysis ◽

Quantitative Assessment ◽

Clustering Algorithm ◽

Statistical Evaluation ◽

Quantitative Methodology ◽

Typical Problem ◽

Statistical Validation ◽

Common Features ◽

Biology Research

Identifying groups that share common features among datasets through clustering analysis is a typical problem in many fields of science, particularly in post-omics and systems biology research. In respect of this, quantifying how a measure can cluster or organize intrinsic groups is important since currently there is no statistical evaluation of how ordered is, or how much noise is embedded in the resulting clustered vector. Much of the literature focuses on how well the clustering algorithm orders the data, with several measures regarding external and internal statistical validation; but no score has been developed to quantify statistically the noise in an arranged vector posterior to a clustering algorithm, i.e., how much of the clustering is due to randomness. Here, we present a quantitative methodology, based on autocorrelation, in order to assess this problem.

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A Geroscience Perspective on COVID-19 – Joint Program from the American Aging Association (AGE) and The Gerontological Society of America

Innovation in Aging ◽

10.1093/geroni/igaa057.1716 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 532-532

Author(s):

Rozalyn Anderson

Keyword(s):

Real World ◽

Gerontological Society ◽

Joint Program ◽

The Face ◽

Biology Of Aging ◽

Biology Research ◽

American Aging ◽

Aging Association

Abstract Faculty will focus on the biology of aging as a contributor to the vulnerability in COVID-19. Faculty will present the latest concepts and insights that will advance our ability to confront this global outbreak. Our goal for this session is to connect with the concept of Geroscience and how ideas from aging biology research can be incorporated to improve outcomes and informed practice. Although the emphasis is on biology, the goal is to provide insight in a manner that is readily accessible to researchers across the aging spectrum that they might translate these ideas in the face of a very real-world challenge.

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How to Use Online Tools to Generate New Hypotheses for Mammary Gland Biology Research: A Case Study for Wnt7b

Journal of Mammary Gland Biology and Neoplasia ◽

10.1007/s10911-020-09474-z ◽

2021 ◽

Author(s):

Yorick Bernardus Cornelis van de Grift ◽

Nika Heijmans ◽

Renée van Amerongen

Keyword(s):

Mammary Gland ◽

In Silico ◽

Online Tools ◽

Mammary Gland Biology ◽

User Friendly ◽

In Silico Analyses ◽

Biology Research ◽

Selection Of

AbstractAn increasing number of ‘-omics’ datasets, generated by labs all across the world, are becoming available. They contain a wealth of data that are largely unexplored. Not every scientist, however, will have access to the required resources and expertise to analyze such data from scratch. Fortunately, a growing number of investigators is dedicating their time and effort to the development of user friendly, online applications that allow researchers to use and investigate these datasets. Here, we will illustrate the usefulness of such an approach. Using regulation of Wnt7b expression as an example, we will highlight a selection of accessible tools and resources that are available to researchers in the area of mammary gland biology. We show how they can be used for in silico analyses of gene regulatory mechanisms, resulting in new hypotheses and providing leads for experimental follow up. We also call out to the mammary gland community to join forces in a coordinated effort to generate and share additional tissue-specific ‘-omics’ datasets and thereby expand the in silico toolbox.

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Cardioinformatics: the nexus of bioinformatics and precision cardiology

Briefings in Bioinformatics ◽

10.1093/bib/bbz119 ◽

2019 ◽

Cited By ~ 2

Author(s):

Bohdan B Khomtchouk ◽

Diem-Trang Tran ◽

Kasra A Vand ◽

Matthew Might ◽

Or Gozani ◽

...

Keyword(s):

Cardiovascular Disease ◽

Cancer Research ◽

Computational Biology ◽

Cause Of Death ◽

Cancer Mortality ◽

Unmet Need ◽

Mortality Rates ◽

Cardiovascular Medicine ◽

Biology Research

Abstract Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17 million deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, most bioinformatics and computational biology research and funding to date has been concentrated predominantly on cancer research, with a relatively modest footprint in CVD. In this paper, we review the existing literary landscape and critically assess the unmet need to further develop an emerging field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, which we refer to as ‘cardioinformatics’.

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