Results 81 to 90 of about 9,911,867 (381)

Functional analysis and transcriptional output of the Göttingen minipig genome [PDF]

open access: yes, 2015
In the past decade the Göttingen minipig has gained increasing recognition as animal model in pharmaceutical and safety research because it recapitulates many aspects of human physiology and metabolism.
Badi, Laura   +21 more
core   +9 more sources

From omics to AI—mapping the pathogenic pathways in type 2 diabetes

open access: yesFEBS Letters, EarlyView.
Integrating multi‐omics data with AI‐based modelling (unsupervised and supervised machine learning) identify optimal patient clusters, informing AI‐driven accurate risk stratification. Digital twins simulate individual trajectories in real time, guiding precision medicine by matching patients to targeted therapies.
Siobhán O'Sullivan   +2 more
wiley   +1 more source

Controversies in modern evolutionary biology: the imperative for error detection and quality control

open access: yesBMC Genomics, 2012
Background The data from high throughput genomics technologies provide unique opportunities for studies of complex biological systems, but also pose many new challenges.
Prosdocimi Francisco   +4 more
doaj   +1 more source

Modeling leaderless transcription and atypical genes results in more accurate gene prediction in prokaryotes

open access: yesGenome Research, 2018
In a conventional view of the prokaryotic genome organization, promoters precede operons and ribosome binding sites (RBSs) with Shine-Dalgarno consensus precede genes.
A. Lomsadze   +3 more
semanticscholar   +1 more source

Prediction of gene–phenotype associations in humans, mice, and plants using phenologs [PDF]

open access: yes, 2013
All authors are with the Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX 78712, USA.
Laurent, Jon M.   +4 more
core   +2 more sources

Thermostable neutral metalloprotease from Geobacillus sp. EA1 does not share thermolysin's preference for substrates with leucine at the P1′ position

open access: yesFEBS Letters, EarlyView.
Knowing how proteases recognise preferred substrates facilitates matching proteases to applications. The S1′ pocket of protease EA1 directs cleavage to the N‐terminal side of hydrophobic residues, particularly leucine. The S1′ pocket of thermolysin differs from EA's at only one position (leucine in place of phenylalanine), which decreases cleavage ...
Grant R. Broomfield   +3 more
wiley   +1 more source

Genomic and stress resistance characterization of Lactiplantibacillus plantarum GX17, a potential probiotic for animal feed applications

open access: yesMicrobiology Spectrum
Lactobacilli, recognized as beneficial bacteria within the human body, are celebrated for their multifaceted probiotic functions, including the regulation of intestinal flora, enhancement of body immunity, and promotion of nutrient absorption. This study
Yangyan Yin   +15 more
doaj   +1 more source

CNN-MGP: Convolutional Neural Networks for Metagenomics Gene Prediction

open access: yesInterdisciplinary Sciences Computational Life Sciences, 2018
Accurate gene prediction in metagenomics fragments is a computationally challenging task due to the short-read length, incomplete, and fragmented nature of the data.
Amani A. Al-Ajlan, Achraf El Allali
semanticscholar   +1 more source

Will gene markers predict hypertension? [PDF]

open access: yesHypertension, 1989
It may be unrealistic to expect a pure monogenic (single gene) explanation for most patients with a disorder as common and heterogeneous as essential hypertension. However, gene marker technology can be combined with risk factor epidemiology to try to quantitate the risk of hypertension and sort out ...
openaire   +3 more sources

PREDICTING GENE FUNCTION FROM GENE EXPRESSIONS AND ONTOLOGIES [PDF]

open access: yesBiocomputing 2001, 2000
We introduce a methodology for inducing predictive rule models for functional classification of gene expressions from microarray hybridisation experiments. The basic learning method is the rough set framework for rule induction. The methodology is different from the commonly used unsupervised clustering approaches in that it exploits background ...
Astrid Lægreid   +3 more
openaire   +3 more sources

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