Difference between revisions of "Template:Article of the week"

From LIMSWiki
Jump to navigationJump to search
(Updated article of the week text)
(Updated article of the week text)
 
(172 intermediate revisions by the same user not shown)
Line 1: Line 1:
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bhattacharya FrontInOnc2019 9.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''"[[Journal:AI meets exascale computing: Advancing cancer research with large-scale high-performance computing|AI meets exascale computing: Advancing cancer research with large-scale high-performance computing]]"'''
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''
 
[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information|Full article...]]''')<br />


The application of data science in [[cancer]] research has been boosted by major advances in three primary areas: (1) data: diversity, amount, and availability of biomedical data; (2) advances in [[artificial intelligence]] (AI) and machine learning (ML) algorithms that enable learning from complex, large-scale data; and (3) advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build ''in silico'' ML models that can provide transformative insights from data, including molecular dynamics simulations, [[Sequencing|next-generation sequencing]], omics, [[Molecular imaging|imaging]], and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next-generation high-performance computers; and (3) assessing robustness and reliability in the AI models. ('''[[Journal:AI meets exascale computing: Advancing cancer research with large-scale high-performance computing|Full article...]]''')<br />
<br />
''Recently featured'':
''Recently featured'':
: ▪ [[Journal:Building infrastructure for African human genomic data management|Building infrastructure for African human genomic data management]]
{{flowlist |
: ▪ [[Journal:Process variation detection using missing data in a multihospital community practice anatomic pathology laboratory|Process variation detection using missing data in a multihospital community practice anatomic pathology laboratory]]
* [[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]
: ▪ [[Journal:Development and validation of a fast gas chromatography–mass spectrometry method for the determination of cannabinoids in Cannabis sativa L|Development and validation of a fast gas chromatography–mass spectrometry method for the determination of cannabinoids in Cannabis sativa L]]
* [[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]
* [[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]
}}

Latest revision as of 18:03, 10 June 2024

Fig2 Berezin PLoSCompBio23 19-12.png

"Ten simple rules for managing laboratory information"

Information is the cornerstone of research, from experimental data/metadata and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems (LIMS) to transform this large information load into useful scientific findings. The development of mathematical models that can predict the properties of biological systems is the holy grail of computational biology. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... (Full article...)

Recently featured:

Retrieved from "https://www.limswiki.org/index.php?title=Template:Article_of_the_week&oldid=64398"