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1 n such populations across many environments (phenomics).
2 ertension Through Pulmonary Vascular Disease Phenomics).
3 ertension Through Pulmonary Vascular Disease Phenomics).
4 eptual and technical challenges facing plant phenomics.
5  To address these challenges, we present MDR-Phenomics, a novel approach based on the multifactor dim
6 tility of systematic human and cross-species phenomics analyses in highly heterogeneous genetic disor
7 ntelligence (AI), routine clinical genomics, phenomics and environment, and returning value across di
8 t integrate several processes for functional phenomics and genetic analysis, which will lead to a gre
9  potential of zebrafish models in behavioral phenomics and high-throughput genetic/small molecule scr
10                                   We applied phenomics and machine-learning approaches with Schizosac
11 gether, we demonstrate that a combination of phenomics and metabolomics is ideal to identify the opti
12                         Combining functional phenomics and root economics is a promising approach to
13  Next, we present strategies for multi-scale phenomics, and describe how major improvements in imagin
14                 Recent advances in genomics, phenomics, and transcriptomics allow in-depth analysis o
15 the ESCRT proteins and then used an unbiased phenomics approach to probe the cellular role of Nhx1.
16                               Therefore, the phenomics approach, which characterizes phenotypes as a
17 s programmes allied to high-throughput mouse phenomics are now addressing this challenge and systemat
18 es of applications of deep learning in plant phenomics, best practices, and open challenges.
19 c magnetic resonance-generated 4-dimensional phenomics cardiac magnetic resonance imaging (4DPcmr) le
20 riment 1), and another at the National Plant Phenomics Centre (NPPC), Aberystwyth, UK (Experiment 2),
21               Using large-scale longitudinal phenomics data as input, TEP-Finder first encodes the co
22 ar aging, showing that these predictions and phenomics data provide a rich resource to uncover new pr
23       We show how high-dimensional molecular phenomics data sets can be leveraged to accurately predi
24 tational tools are needed to analyze complex phenomics data, which consists of multiple traits/behavi
25 ata types, including genomic, epigenetic and phenomics data, will take advantage of big data approach
26 nts, thus studying both values and trends of phenomics data.
27 reatly increased the volume and diversity of phenomics data.
28 sion cluster memberships with an independent phenomics dataset and found that genes that consistently
29         We finally validate our methods on a phenomics dataset of growth conditions.
30                                   These rich phenomics datasets associate lincRNA mutants with hundre
31 ndered by a vast amount of disease genomics, phenomics, drug treatment, and genetic pathway and uniqu
32 ngoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide
33 such distinctions, we hypothesized that deep phenomics employing a clinical sample that has not been
34              Recent advances in genomics and phenomics for a range of plant species, particularly cro
35 as only slightly smaller power than does MDR-Phenomics for single-locus analysis but has considerably
36 n open-source, artificial intelligence-based phenomics framework, combining facial recognition techno
37 her, these constitute the Shape Analysis for Phenomics from Imaging Data (SAPID) method.
38                         Finally, advances in phenomics have unlocked rapid screening of populations f
39 roteomics, transcriptomics, epigenomics, and phenomics-have transformed our understanding of plant st
40 vision that grasviq will be adopted for vein phenomics in maize and other grass species.
41  the development and evaluation of Microbial Phenomics Information Extractor (MicroPIE, version 0.1.0
42  the development of a framework to integrate phenomics information with powerful ML for prediction en
43 We discuss clinical phenomenology, genomics, phenomics, intestinal microbiota, and functional genomic
44                                              Phenomics is essential for understanding the mechanisms
45                         We conclude that MDR-Phenomics is more powerful than MDR-PDT and SP-MDR when
46                                              Phenomics is the study of the properties and behaviors o
47                   As a result, the field of 'phenomics' is being born.
48         PVDOMICS (Pulmonary Vascular Disease Phenomics) is a precision medicine initiative to charact
49                   Here we survey the current phenomics landscape, including data resources and handli
50 ons are investigated through high-throughput phenomics, microscopy, RNA-sequencing, differential expr
51 endence between child outcomes, the Bayesian phenomics model showed that maternal prenatal asthma sym
52                               We used a deep phenomics modeling approach to elucidate the quantitativ
53              Generalized linear and Bayesian phenomics models were used to determine the relation bet
54                   Pulmonary Vascular Disease Phenomics (National Heart, Lung, and Blood Institute) co
55 tions to bridge the gap between genomics and phenomics of leafroll disease.
56              In addition, a new discipline, "phenomics" or "phenometrics," could be initiated that wo
57 a is a versatile, high-throughput cell-based phenomics platform and we showcase its utility by identi
58          To quantitate phenotypic changes, a phenomics platform was used to grow wild-type and mutant
59 igh levels of morphological plasticity, crop phenomics presents distinct challenges compared with stu
60 edefining Pulmonary Hypertension through PVD Phenomics program (PVDOMICS) to determine clinical and e
61 ertension through Pulmonary Vascular Disease Phenomics) program undergo an extensive invasive hemodyn
62 ertension through Pulmonary Vascular Disease Phenomics) project.
63                    Most of the current human phenomics research is based primarily on European popula
64 ents an important challenge and major gap in phenomics research.
65                                    Deep hair phenomics shows that the progressive hair loss is associ
66 oming important in insect systematics and in phenomics studies of insect behavior and physiology.
67 aches can inform and complement experimental phenomics studies.
68 ertension through Pulmonary Vascular Disease Phenomics study were analyzed.
69 ertension through Pulmonary Vascular Disease Phenomics) study.
70 a foundation of accurate and valid omics and phenomics that can be harnessed at scale from electronic
71  part of a new revolution in high-throughput phenomics that promises to help understand ecological an
72                                        Plant phenomics, the collection of large-scale plant phenotype
73                                              Phenomics, the comprehensive study of phenotypes, is the
74                     Finally, by applying MDR-Phenomics to autism, a complex disease in which multiple
75  inaccessible to nonmorphologists and causes phenomics to lag behind genomics in elucidating evolutio
76 ts demonstrate the efficacy of our deep hair phenomics tool for characterizing factors that modulate
77                                   The Cancer Phenomics Toolkit was used to calculate radiomic feature
78  segmentations and publicly available Cancer Phenomics Toolkit, or CaPTk, software was used to comput
79 from proteomics, metabolomics, metagenomics, phenomics, transcriptomics, and epigenomics have been la
80 haracterization at multiple levels-including phenomics, transcriptomics, proteomics, chromosome segre
81                     Software is available at phenomics.uky.edu/PhenoCurve.
82                              To validate MDR-Phenomics, we compared it with two MDR-based methods: (1
83 red from the Consortium for Neuropsychiatric Phenomics, we further demonstrated that the connectivity
84                                              Phenomics, which ideally involves in-depth phenotyping a
85                      We further compared MDR-Phenomics with conditional logistic regression (CLR) for
86 ks, proteomics, metabolomics, lipidomics and phenomics with informatics techniques to provide new ins
87 tion in winter wheat, we combined functional phenomics with trait economic theory, utilizing genetic