Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 38% of its field
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literature
Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
8 authors at 1 institution in 1 country.
Yichuan LiuCenter for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.ORCID 0000-0003-2023-072X
Hui-Qi QuCenter for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.ORCID 0000-0001-9317-4488
Xiao ChangCenter for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
Lifeng TianCenter for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.ORCID 0000-0003-1880-5811
Jingchun QuCenter for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.ORCID 0000-0003-1974-2496
Joseph GlessnerCenter for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
Patrick M A SleimanCenter for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
Hakon HakonarsonCenter for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.ORCID 0000-0003-2814-7461
Children's Hospital of Philadelphia · US
Funding
VESICULAR LOCALIZATION AND FUNCTION OF PRESENILIN 1 FRAGMENTP50AG005138 · NIA · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI GROSSMAN, HILLEL · 1985 to 2019
$37.9M
QUANTITATIVE ANALYSIS OF MICROVASCULAR CHANGES IN THE AGING BRAINP01AG002219 · NIA · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI HAROUTUNIAN, VAHRAM · 1985 to 2009
$19.2M
White Matter Abnormalities in SchizophreniaP50MH066392 · NIMH · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI O'DONOVAN, MICHAEL J · 2002 to 2012
$18.6M
Project 5: In vivo measurement of GABA transmission in healthy controls & subjectP50MH084053 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LEWIS, DAVID A · 2008 to 2012
$11.5M
International Cohort Collection for Bipolar DisorderR01MH085542 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI SMOLLER, JORDAN W · 2008 to 2012
$10.7M
Genetics Association in Schizophrenia and Other DisordersR37MH057881 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI DEVLIN, BERNIE · 2013 to 2022
$5.4M
Src mediates molecular alterations leading to NMDAR hypofunction in schizophreniaR01MH075916 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI BORGMANN-WINTER, KARIN, HAHN, CHANG-GYU · 2006 to 2019
$3.3M
Identifying therapeutic targets for autism using Shank3-deficient miceR01MH093725 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI BUXBAUM, JOSEPH D. · 2011 to 2015
$2.5M
1/3-Networks from Multidimensional Data for Schizophrenia and Related DisordersR01MH097276 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI SCHADT, ERIC E, SKLAR, PAMELA · 2012 to 2014
$2.5M
Plasma and CSF Abeta peptides in late-onset major depressionR01MH080405 · NIMH · NATHAN S. KLINE INSTITUTE FOR PSYCH RES · PI POMARA, NUNZIO · 2007 to 2011
$1.6M
NIA NIH HHS P01 AG002219NIA NIH HHS P50 AG005138NIDA NIH HHS HHSN271201300031CNIMH NIH HHS P50 MH066392NIMH NIH HHS P50 MH084053NIMH NIH HHS R01 MH075916NIMH NIH HHS R01 MH080405NIMH NIH HHS R01 MH085542NIMH NIH HHS R01 MH093725NIMH NIH HHS R01 MH097276NIMH NIH HHS R37 MH057881The study and the APC were supported by Institutional Development Funds from the Children's Hospital of Philadel-phia to the Center for Applied Genomics, The Children's Hospital of Philadelphia Endowed Chair in Genomic Research to HH. na
6 · The paper itself
Abstract
RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as biomarkers. This study used machine learning to reduce gene/non-coding RNA features. Dorsolateral prefrontal cortex (dlpfc) RNA-seq data from 254 individuals was obtained from the CommonMind consortium. The average predictive accuracy for SCZ patients was 67% based on coding genes, and 96% based on long non-coding RNAs (lncRNAs). Machine learning is a powerful algorithm to reduce functional biomarkers in SCZ patients. The lncRNAs capture the characteristics of SCZ tissue more accurately than mRNA as the former regulate every level of gene expression, not limited to mRNA levels.
Indexed as
Machine LearningMultigene FamilyAlgorithmsBiomarkersComputational BiologyDiagnosis, Computer-AssistedFactor Analysis, StatisticalHumansPrefrontal CortexRNA, Long NoncodingRNA, MessengerRNA-SeqRNA, UntranslatedSchizophreniaTranscriptomeBiomarkersRNA, Long NoncodingRNA, MessengerRNA, Untranslatedlong non-coding RNAsmachine learningschizophreniatranscriptome
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters. · full record | OpenQuestion