A Computational Algorithm to Predict shRNA Potency

Applying Two Computational Classification Methods to Predict the Risk of Breast Cancer: A Comparative Study

Introduction: Lack of a proper method for early detection and diagnostic errors in medicine are some fundamental problems in treating cancer. Data analysis techniques may significantly help early diagnosis. The current study aimed at applying and evaluating neural networks and decision tree algorithm on breast cancer patients’ data for early cancer prediction. Methods: In the current stu...

Applying Two Computational Classification Methods to Predict the Risk of Breast Cancer: A Comparative Study

Introduction: Lack of a proper method for early detection and diagnostic errors in medicine are some fundamental problems in treating cancer. Data analysis techniques may significantly help early diagnosis. The current study aimed at applying and evaluating neural networks and decision tree algorithm on breast cancer patients’ data for early cancer prediction. Methods: In the current stu...

Introduction of Mismatches in a Random shRNA-Encoding Library Improves Potency for Phenotypic Selection

RNA interference (RNAi) is a mechanism for interfering with gene expression through the action of small, non-coding RNAs. We previously constructed a short-hairpin-loop RNA (shRNA) encoding library that is random at the nucleotide level [1]. In this library, the stems of the hairpin are completely complementary. To improve the potency of initial hits, and therefore signal-to-noise ratios in lib...

Computational approaches to predict bacteriophage–host relationships

Metagenomics has changed the face of virus discovery by enabling the accurate identification of viral genome sequences without requiring isolation of the viruses. As a result, metagenomic virus discovery leaves the first and most fundamental question about any novel virus unanswered: What host does the virus infect? The diversity of the global virosphere and the volumes of data obtained in meta...

Computational Approaches to Predict Protein Interaction