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Predict phenotypic differences
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Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging.
19p
vicwell
29-02-2024
4
2
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Genetic variance that is not captured by single nucleotide polymorphisms (SNPs) is due to imperfect linkage disequilibrium (LD) between SNPs and quantitative trait loci (QTLs), and the extent of LD between SNPs and QTLs depends on different minor allele frequencies (MAF) between them.
14p
vinarcissa
21-03-2023
4
1
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Recent single-cell transcriptomic studies report that IDH-mutant gliomas share a common hierarchy of cellular phenotypes, independent of genetic subtype. However, the genetic differences between IDH-mutant glioma subtypes are prognostic, predictive of response to chemotherapy, and correlate with distinct tumor microenvironments.
22p
viarchimedes
26-01-2022
7
0
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The inflammatory phenotypes of asthma predict the treatment response and prognosis. The phenotype distributions differ depending on the geographical region. This study aimed to assess the distribution of diferent infammatory phenotypes among asthma patients in Jilin Province, China.
11p
vimackenziebezos
29-11-2021
8
1
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Gene expression profiling in blood is a potential source of biomarkers to evaluate or predict phenotypic differences between pigs but is expensive and inefficient because of the high abundance of globin mRNA in porcine blood.
10p
visilicon2711
20-08-2021
11
1
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Efforts to elucidate the function of enhancers in vivo are underway but their vast numbers alongside differing enhancer architectures make it difficult to determine their impact on gene activity. By systematically annotating multiple mouse tissues with super- and typical-enhancers, we have explored their relationship with gene function and phenotype.
22p
vijeeni2711
30-06-2021
14
1
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Glioblastoma is the most aggressive primary central nervous tumor and carries a very poor prognosis. Invasion precludes effective treatment and virtually assures tumor recurrence. In the current study, we applied analytical and bioinformatics approaches to identify a set of microRNAs (miRs) from several different human glioblastoma cell lines that exhibit significant differential expression between migratory (edge) and migrationrestricted (core) cell populations.
10p
vikentucky2711
26-11-2020
13
1
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Missense mutations in the first five exons of F9, which encodes factor FIX, represent 40% of all mutations that cause hemophilia B. A simple function that integrates information from different in silico programs yields the best prediction of mutated phenotypes.
13p
vijisoo2711
27-10-2020
12
1
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Cardiotoxicity, characterized by severe cardiac dysfunction, is a major problem in patients treated with different classes of anticancer drugs. Development of predictable human-based models and assays for drug screening are crucial for preventing potential drug-induced adverse effects. Current animal in vivo models and cell lines are not always adequate to represent human biology.
12p
vicolorado2711
22-10-2020
21
0
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In metastatic breast cancer (MBC), antigen profiles of metastatic tissue and primary tumor differ in up to 20 % of patients. Reassessment of predictive markers, including human epidermal growth factor receptor 2 (HER2) expression, might help to optimize MBC treatment. While tissue sampling is invasive and often difficult to repeat, circulating tumor cell (CTC) analysis requires only a blood sample and might provide an easy-to-repeat, real-time “liquid biopsy” approach.
7p
viamsterdam
18-09-2020
18
1
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A strong correlation between breast cancer (BC) molecular subtypes and axillary status has been shown. It would be useful to predict the probability of lymph node (LN) positivity.
10p
viputrajaya2711
22-06-2020
7
1
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The material for the study consisted of twenty genotypes of Bhindi, which were grown in a Randomized Block Design (RBD) with three replication under six environments (three different dates of sowing in Spring-summer and Kharif). The observations were recorded on five randomly selected plants for characters viz., plant height (cm). no. of branches per plant, days to first flowering, days to first harvest, number of fruits per plant, fruit length (cm), fruit diameter (cm), fruit weight (g), fruit yield per plant(kg) and fruit yield per plot(kg).
11p
nguaconbaynhay3
07-02-2020
16
0
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