AIMC Topic: HEK293 Cells

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CRISPRpred(SEQ): a sequence-based method for sgRNA on target activity prediction using traditional machine learning.

BMC bioinformatics
BACKGROUND: The latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accurate genome editing tool using ...

Revealing cytotoxic substructures in molecules using deep learning.

Journal of computer-aided molecular design
In drug development, late stage toxicity issues of a compound are the main cause of failure in clinical trials. In silico methods are therefore of high importance to guide the early design process to reduce time, costs and animal testing. Technical a...

Synthesis and biological characterization of MnZnEuDyFeO nanoparticles by sonochemical approach.

Materials science & engineering. C, Materials for biological applications
Metallic nanoparticles (NPs) possess unique properties which makes them attractive candidates for various applications especially in field of experimental medicine and drug delivery. Many approaches were developed to synthesize divers and customized ...

Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics.

Nature methods
We engineered light-gated channelrhodopsins (ChRs) whose current strength and light sensitivity enable minimally invasive neuronal circuit interrogation. Current ChR tools applied to the mammalian brain require intracranial surgery for transgene deli...

Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning.

Nature communications
Highly specific Cas9 nucleases derived from SpCas9 are valuable tools for genome editing, but their wide applications are hampered by a lack of knowledge governing guide RNA (gRNA) activity. Here, we perform a genome-scale screen to measure gRNA acti...

A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.

Cell
Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequence alone. We trained our model (APARENT, APA REgression NeT) on isoform expression data from over ...

Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning.

Nature methods
In mass-spectrometry-based proteomics, the identification and quantification of peptides and proteins heavily rely on sequence database searching or spectral library matching. The lack of accurate predictive models for fragment ion intensities impair...

PatcherBot: a single-cell electrophysiology robot for adherent cells and brain slices.

Journal of neural engineering
OBJECTIVE: Intracellular patch-clamp electrophysiology, one of the most ubiquitous, high-fidelity techniques in biophysics, remains laborious and low-throughput. While previous efforts have succeeded at automating some steps of the technique, here we...

Large-Scale Screening of Natural Products Transactivating Peroxisome Proliferator-Activated Receptor α Identifies 9S-Hydroxy-10E,12Z,15Z-Octadecatrienoic Acid and Cymarin as Potential Compounds Capable of Increasing Apolipoprotein A-I Transcription in Human Liver Cells.

Lipids
Increasing apolipoprotein A-I (apoA-I), the predominant protein of high-density lipoprotein (HDL) particles, has favorable effects on atherogenic risk factors. Here, we investigated the effects of peroxisome proliferator-activated receptor α (PPARα) ...

Hypoxia-responsive micelles self-assembled from amphiphilic block copolymers for the controlled release of anticancer drugs.

Journal of materials chemistry. B
Amphiphilic block copolymers poly(ethylene glycol)-block-poly(methacrylic acid-co-2-nitroimidazole methacrylate) (PEG-b-P(MAA-co-NIMA)) were synthesized by the combination of atom transfer radical polymerization (ATRP), hydrolysis and EDC reactions. ...