Background: Much learning has emerged as a versatile approach for predicting complex biological phenomena. However, the utility of biological discoveries so far been limited, given that generic neural network in providing some insight into the biological mechanisms that underlie successful predictions.
Here we demonstrate in-depth study on the biological network, where each node has an equivalent molecules, such as proteins or genes, and each edge has a mechanistic interpretation, such as regulatory interactions along the signal path.
Results: With the knowledge-primed neural network (KPNNs), we exploit the ability of deep learning algorithm to assign significant weight in the multi-layered network, thus broadly applicable approach to learning in the interpretation. We present a method of learning that increases the interpretability of KPNNs trained by stabilizing the node weights in the presence of redundancy, improve the interpretability quantitative node weights, and control for connectivity are uneven in biological tissue. We validate KPNNs simulated data with known ground truth and show their practical use and utility in five applications with single-cell biology of RNA-seq data for cancer and immune cells.
Conclusions: We introduce KPNNs as a method that combines the predictive power of deep learning with biological tissue interpretability. While shown here at the single-cell sequencing data, this method is broadly relevant to other research fields in which domain knowledge before it can be represented as a network.
neuroendocrine neoplasms (NENs) is a clinical cancer incomplete diverse and characterized challenging to classify. MicroRNAs (miRNAs) are small regulatory RNAs that can be used to classify cancer. More recently, morphology-based classification framework to evaluate NENs from different anatomical sites proposed by the experts, the need for enhanced integration of molecular data.
Here, we collected 378 miRNA expression profiling to examine the classification of Nen through a comprehensive miRNA profiling and data mining. The following data preprocessing, our final study cohort included 221 and 114 Nen-Nen non samples, representing 15 Nen type of pathological and 5 non-site-matched control group Nen.
Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data
Use Whole Genome Sequencing data for First Silico Evaluation Test Specificity of RT-qPCR Used for SARS-CoV-2 Detection
Disease coronavirus Flow 2019 (COVID-19) pandemic started in December 2019. COVID 19 cases are confirmed by the detection of SARS-CoV-2 RNA in biological samples by RT-qPCR. However, a limited number of SARS-CoV-2 genome is available as RT-qPCR method was first developed in January 2020 to early in silico evaluation of specificity and to verify whether the target locus highly conserved.
Now that the genome data more completely available, we use the tools of bioinformatics filtered and total 4755 publicly available SARS-CoV-2 genome, download at two different time points to evaluate the specificity 12 tests RT-qPCR (comprised of a total of 30 primary and probe sets) used for SARS-CoV-2 detection and the impact of the genetic evolution of the virus’ on four of them.
Description: Description of target: The related sulfinamides (R(S=O)NHR) are amides of sulfinic acids (R(S=O)OH) (see sulfinyl). Chiral sulfinamides such as tert-butanesulfinamide, p-toluenesulfinamide and 2,4,6-trimethylbenzenesulfinamide are relevant to asymmetric synthesis.;Species reactivity: General;Application: ;Assay info: Assay Methodology: Competitive Inhibition ELISA;Sensitivity:
Description: 10-DEBC hydrochloride is a selective inhibitor of Akt (or termed PKB) [1], with an IC50 value of approximate 48 ?M [2].Akt is a type of serine/threonine kinase. It phosphorylates and inactivates components in the apoptotic machinery, including Caspase 9 and BAD.
Description: AI-10-49 is a selective inhibitor of CBF? -SMMHC and RUNX1 interaction with a FRET IC50 value of 260nM. AI-10-49 restores RUNX1 transcriptional activity, displays favorable pharmacokinetics, and delays leukemia progression in mice.
Description: Fas C- Terminal Tripeptide,(C16H29N3O6), a tri-peptide with the sequence AC-SER-LEU-VAL-OH, it?s the C-terminal tripeptide of Fas, MW= 359.4.
Description: HG-10-102-01 is a potent and selective inhibitor of leucine-rich repeat kinase 2 (LRRK2) with the IC50 values of 20.3nM and 3.2nM for wild type LRRK2 and LRRK2 [G1019S], respectively [1].
Description: Amyloid precursor c-terminal peptide (APP) (C86H118N20O27S) has the amino acid sequence Gly-Tyr-Glu-Asn-Pro-Thr-Tyr-Lys-Phe-Phe-Glu-Gln-Met-Gln-Asn. Although it has been implicated as a regulator of synapse formation, neural plasticity and iron export, the primary function of APP is not known.
Exclusivity This method was also assessed using the human reference genome and the genomes of closely related 2624 other respiratory viruses. Specificity of this test is generally good and stable over time. The exception is the method was first developed by the Chinese Center for Disease Control and Prevention (CDC), which showed three primary discrepancy is present in 358 SARS-CoV-2 genome sequencing, especially in Europe from February 2020 onwards.
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