Cancers are the group of diseases, which arise because of the uncontrolled behavior of some of the genes in our cells. There are possibilities of gene amplifications, overexpressions, deletions and other anomalies which might lead to the development and spread of cancer. One of the most dangerous ways to the cancers is the mutations of the genes. The mutated genes can start unstoppable proliferation of cells, their uncontrolled motility, protection from apoptosis, the DNA mutation enhancement as well as other anomalies, leading to the cancer. This review focuses on the genes, which are frequently mutated in various cancers and are known to be important in the advance and progression of colorectal cancer and melanoma, namely KRAS, NRAS and BRAF.

Uncontrolled proliferation is the hallmark of cancer and other proliferative disorders and abnormal cell cycle regulation is, therefore, common in these diseases. Cyclin-dependent kinases (CDKs) play a crucial role in the control of the cell cycle and proliferation. These kinases are frequently deregulated in various cancers, viral infections, neurodegenerative diseases, ischemia and some proliferative disorders. This led to a rigorous pursuit for small-molecule CDK inhibitors for therapeutic uses. Early efforts to block CDKs with nonselective CDK inhibitors led to little specificity and efficacy but apparent toxicity, but the recent advance of selective CDK inhibitors allowed the first successful efforts to target these kinases for the therapies of several diseases. Major ongoing efforts are to develop CDK inhibitors as monotherapies and rational combinations with chemotherapy and other targeted drugs.

The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data.