Predict New Therapeutic Drugs for Hepatocellular Carcinoma Based on Gene Mutation and Expression

Abstract

Hepatocellular carcinoma (HCC) is the fourth most common primary liver tumor and is an important medical problem worldwide. However, the use of current therapies for HCC is no possible to be cured, and despite numerous attempts and clinical trials, there are not so many approved targeted treatments for HCC. So, it is necessary to identify additional treatment strategies to prevent the growth of HCC tumors. We are looking for a systematic drug repositioning bioinformatics method to identify new drug candidates for the treatment of HCC, which considers not only aberrant genomic information, but also the changes of transcriptional landscapes. First, we screen the collection of HCC feature genes, i.e., kernel genes, which frequently mutated in most samples of HCC based on human mutation data. Then, the gene expression data of HCC in TCGA are combined to classify the kernel genes of HCC. Finally, the therapeutic score (TS) of each drug is calculated based on the kolmogorov-smirnov statistical method. Using this strategy, we identify five drugs that associated with HCC, including three drugs that could treat HCC and two drugs that might have side-effect on HCC. In addition, we also make Connectivity Map (CMap) profiles similarity analysis and KEGG enrichment analysis on drug targets. All these findings suggest that our approach is effective for accurate predicting novel therapeutic options for HCC and easily to be extended to other tumors.

Identifying Antioxidant Proteins by Using Amino Acid Composition and Protein-Protein Interactions

“…The CKSAAP describes the composition of amino acids. The other is a feature that describes protein-protein interaction (PPI) information (Yu et al, 2010(Yu et al, , 2020Liu et al, 2019b;Zhao et al, 2020). Three adjacent amino acids are regarded as a linker to judge the charge properties and hydrophobicity of the target protein.…”

Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features

“…The regularization parameter C and the kernel width parameter γ were tuned via the grid search method. So far, the risk minimization of the SVM algorithm has become the latest research hotspot and it has been successfully applied to various fields [32][33][34][35][36][37][38], especially in the field of biological computing, such as in the prediction of protein sequence structure and in the classification of protein structure [28,[39][40][41][42][43][44][45][46]. In this paper, the LIBSVM algorithm has been used to predict various feature information, which can be downloaded from http://www.csie.ntu.edu.tw/cjlin/ libsvm/.…”

Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model

“…On the contrary, pre-impact fall detection can detect a fall in advance before a body segment hits the ground. It not only has the advantage of post-fall detection, but also can interlock the protective system to directly prevent injuries due to falls [13]. Although research on protective systems such as wearable airbag devices is at its early stage without any commercialized systems, it is very clear that injury prevention is possible by integrating pre-sensing devices [14].…”

“…A multi-class fall classification model architecture that includes falls in various directions utilizing three kinds of deep learning algorithms has been developed, showing a pre-impact fall detection accuracy of 99% [33]. convolutional neural network (CNN) and long short-term memory (LSTM) deep learning techniques have also been utilized to develop a pre-impact fall estimation model with accuracy up to 98.7% [13].…”