Project description:This model picks up an already published mechanistic model by Chassagnole et al. (2001) for the threonine synthesis pathway model of E. coli and turns it into a hybrid model with a deep neural network, fully compatible with the SBML format
Project description:Interventions: Deep GI detection and characterization (Deep GI) is computer-aided diagnosis colonoscopy systems,CAD EYE detection and characterization (CAD EYE) is computer-aided diagnosis colonoscopy systems,Standard colonoscopy;Active Comparator Device,Active Comparator Device,Active Comparator Device;Deep GI detection and characterization (Deep GI) ,CAD EYE detection and characterization (CAD EYE),High-definition white light colonoscopy (WLI)
Primary outcome(s): Adenoma detection rate at 12 months after end of the study Adenoma detection rate
Study Design: Randomized
Project description:Deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, “constrained hallucination,” optimizes sequences such that their predicted structures contain the desired functional site. The second approach, “inpainting,” starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network.
Project description:Identification of active molecules against Mycobacterium tuberculosis using an ensemble of data from ChEMBL25 (Target IDs 360, 2111188 and 2366634). The final model is a stacking model integrating four algorithms, including support vector machine, random forest, extreme gradient boosting and deep neural networks.
Model encoded by Amna Ali, and metadata submitted in BioModels by Zainab Ashimiyu-Abdusalam.
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos46ev
Project description:Chromatin immunoprecipitation followed by deep sequencing (ChIP seq), using L4-staged animals that express an integrated construct of lir-3 fused to a GFP tag
Project description:Primary outcome(s): The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of each modality in diagnosing SM-deep cancer in adenoma and early colorectal caner with non-extension sign as an index for the diagnosis of SM-deep cancer.
Project description:DamID with sPom121, Nup98 and control proteins (GFP and Cbx1) N-term tag sPom121, Nup98 or GFP and express in HeLa-C cells - conduct deep sequencing
Project description:We used ultra deep sequencing of MnaseI trated cells to faithfully position nucleosomes before and after treatment with TGFB growth factor for 1h in HepG2-cells.
Project description:Recently, we demonstrated that RDRs had a general function to synthesize antisense RNAs from sense transcripts of protein-coding genes. In this study, we analyzed whether RDR-mediated antisense RNAs were processed into small RNAs by deep sequencing using SOLiD. Deep sequencing identified 1,645 RDR1/2/6-mediated smRNA loci in drought stress and control conditions.
