Diego

Undergraduate student of genomics at LIIGH

[LCG UNAM Juriquilla (LCGEJ) @lcgejunam]

Felicitamos al alumno Carlos Javier Rojas por su graduación de la Licenciatura en Ciencias Genómicas, así como por su actuación y contribución a la clasificación 🥇 del equipo mexicano 🇲🇽 de tiro con arco a los #JuegosOlímpicos2024 @Paris2024
https://t.co/d9LTCjIxaW

[LIIGH @LIIGH_UNAM]

Felicitamos a los siete estudiantes de la Licenciatura en Ciencias Genómicas @lcgejunam que se graduaron el día de hoy. ¡La comunidad del LIIGH les desea muchos éxitos en sus planes futuros!
¡Goya, Goya, Universidad! 🎓

[Nature Methods @naturemethods]

Characterizing multiple cell types within a tumor requires accurate and deep analysis of thousands of heterogeneous cells. @LabGeiger discusses how high-throughput single-cell proteomics analysis will help understand the spatial context within tumors. https://t.co/QZGZN4QhZr

[BioDecoded @BioDecoded]

dnadna: a deep learning framework for population genetics inference | Bioinformatics
https://t.co/iOXWtmDWlA

#Bioinformatics

[BioDecoded @BioDecoded]

Variant calling enhances the identification of cancer cells in single-cell RNA sequencing data | PLOS Computational Biology
https://t.co/8rug1uOcSt

#Bioinformatics

[BioDecoded @BioDecoded]

spliceJAC: transition genes and state-specific gene regulation from single-cell transcriptome data | Molecular Systems Biology
https://t.co/BAVnvtubpo

#Bioinformatics

[Journal of Experimental Medicine @JExpMed]

.@DKerdidani, @tsoumakidou et al. show that lung tumor MHCII immunity depends on in situ antigen presentation by fibroblasts. https://t.co/o3suHQS1Zr

From our Mechanisms & Models of #Cancer https://t.co/Qax0iqEzH9
#CSHLCancer22

[Nature Methods @naturemethods]

Using a sequence-based deep neural network, scBasset facilitates various tasks of single-cell ATAC-seq analysis in a unified framework. @HY3952 @drklly @calico https://t.co/BtzFBNb1to

[Luke Mones @LukeMones]

you ever have a friend become a doctor and are like "oh no...are doctors dumb"

[Nature News & Views @NatureNV]

Genetic mutations that don’t alter a protein’s amino-acid sequence are just as likely to be harmful as those that do, a systematic analysis of yeast finds https://t.co/bEgrgEPWox

[BioDecoded @BioDecoded]

scAmpi—A versatile pipeline for single-cell RNA-seq analysis from basics to clinics | PLOS Computational Biology
https://t.co/N6Noa5Nq5u

#Bioinformatics

[BioDecoded @BioDecoded]

Identifying tumor cells at the single-cell level using machine learning | Genome Biology
https://t.co/m7kt1ah2Sr

#MachineLearning

[Nature Biotechnology @NatureBiotech]

Expanding RNAi therapeutics to extrahepatic tissues with lipophilic conjugates https://t.co/MijNd0tgCm

[BioDecoded @BioDecoded]

An interpretable deep learning workflow for discovering subvisual abnormalities in CT scans of COVID-19 inpatients and survivors | Nature Machine Intelligence
https://t.co/AoEixohndD

#DeepLearning

[Science Magazine @ScienceMagazine]

Gene therapy restored muscle function and greatly improved survival in older mice with a condition that closely resembles severe muscular dystrophy. https://t.co/v1CHlmdAqT @ScienceAdvances

[BioDecoded @BioDecoded]

One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data | Genome Biology
https://t.co/Qbuj3Oj7J0

#Bioinformatics

[BioDecoded @BioDecoded]

Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks | Bioinformatics
https://t.co/PxIkEdpLIf

#Bioinformatics

[Journal of Experimental Medicine @JExpMed]

Schad, Wolchok, Merghoub @MSKCancerCenter and colleagues show that antigen stimulation induces a subset of CD4+ and CD8+ T cells to differentiate into CD4+CD8+ T cell subsets gaining in polyfunctional characteristics within the tumor. https://t.co/Mcqo8y4cDW
#TumorImmunology

[BioDecoded @BioDecoded]

Bi-order multimodal integration of single-cell data | Genome Biology
https://t.co/zR4TMSL33l

#Bioinformatics

[Stephen Turner 🦋 @stephenturner.us @strnr]

findPC: An R package to automatically select the number of principal components in single-cell analysis https://t.co/GeeLiVskZ9 https://t.co/VPFnV2Mw6J