Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Objective:
This update reviews the epidemiology and surgical management, and the controversies of gastric adenocarcinoma. We provide the relevance of outcome data to surgical decision-making and discuss the application of gene-expression analysis to clinical practice.

Gastric adenocarcinoma: review and considerations for future directions.

The epidemiological link of Helicobacter pylori and gastric carcinoma was confirmed and the reported rate of gastric carcinoma development in the Japanese population with H. pylori gastritis was 2.9% during a 7.8-year period. Studies showed that gastric atrophy and intestinal metaplasia may partially regress after H. pylori eradication, but whether this will result in a decrease in the development of malignant lesions remains to be confirmed in large studies. Little progress was made towards the identification of criteria applicable to individualized populations that would benefit from H. pylori screening and surveillance of gastric malignancy. Studies of low-grade MALT lymphoma reported clinical and molecular features that may be useful to identify cases that may be responsive to H. pylori eradication therapy, namely evaluation of lymphoma stage by EUS and t(11;18). The role of H. pylori eradication therapy in high-grade MALT lymphoma needs to be evaluated in larger prospective studies. The application of methods of global analysis of gene expression (microarray studies) resulted in the explosion of information on the molecular pathways activated by H. pylori in gastric epithelial cells. New pathways that may play an important role in H. pylori carcinogenesis have been discovered in several studies.

Helicobacter pylori and gastric malignancies.

How to find early gastric cancer cells in vivo is a great challenge for the diagnosis and therapy of gastric cancer. This study is aimed at investigating the feasibility of using fluorescent magnetic nanoparticle (FMNP)-labeled mesenchymal stem cells (MSCs) to realize targeted imaging and hyperthermia therapy of in vivo gastric cancer. The primary cultured mouse marrow MSCs were labeled with amino-modified FMNPs then intravenously injected into mouse model with subcutaneous gastric tumor, and then, the in vivo distribution of FMNP-labeled MSCs was observed by using fluorescence imaging system and magnetic resonance imaging system. After FMNP-labeled MSCs arrived in local tumor tissues, subcutaneous tumor tissues in nude mice were treated under external alternating magnetic field. The possible mechanism of MSCs targeting gastric cancer was investigated by using a micro-multiwell chemotaxis chamber assay. Results show that MSCs were labeled with FMNPs efficiently and kept stable fluorescent signal and magnetic properties within 14 days, FMNP-labeled MSCs could target and image in vivo gastric cancer cells after being intravenously injected for 14 days, FMNP-labeled MSCs could significantly inhibit the growth of in vivo gastric cancer because of hyperthermia effects, and CCL19/CCR7 and CXCL12/CXCR4 axis loops may play key roles in the targeting of MSCs to in vivo gastric cancer. In conclusion, FMNP-labeled MSCs could target in vivo gastric cancer cells and have great potential in applications such as imaging, diagnosis, and hyperthermia therapy of early gastric cancer in the near future.

/pdf/fluorescent-magnetic-nanoparticle-labeled-mesenchymal-stem-33mn1seweo.pdf

Fluorescent magnetic nanoparticle-labeled mesenchymal stem cells for targeted imaging and hyperthermia therapy of in vivo gastric cancer

High incidence of gastric cancer-related death is mainly due to diagnosis at an advanced stage in addition to the lack of adequate neoadjuvant therapy. Hence, new tools aimed at early diagnosis would have a positive impact in the outcome of the disease. Using cDNA arrays having 376 genes either identified previously as altered in gastric tumors or known to be altered in human cancer, we determined expression signature of 99 tissue fragments representing normal gastric mucosa, gastritis, intestinal metaplasia, and adenocarcinomas. We first validated the array by identifying molecular markers that are associated with intestinal metaplasia, considered as a transition stage of gastric adenocarcinomas of the intestinal type as well as markers that are associated with diffuse type of gastric adenocarcinomas. Next, we applied Fisher's linear discriminant analysis in an exhaustive search of trios of genes that could be used to build classifiers for class distinction. Many classifiers could distinguish between normal and tumor samples, whereas, for the distinction of gastritis from tumor and for metaplasia from tumor, fewer classifiers were identified. Statistical validations showed that trios that discriminate between normal and tumor samples are powerful classifiers to distinguish between tumor and nontumor samples. More relevant, it was possible to identify samples of intestinal metaplasia that have expression signature resembling that of an adenocarcinoma and can now be used for follow-up of patients to determine their potential as a prognostic test for malignant transformation.

https://cancerres.aacrjournals.org/content/canres/64/4/1255.full.pdf

Molecular Classifiers for Gastric Cancer and Nonmalignant Diseases of the Gastric Mucosa

https://cbio.mines-paristech.fr/~jvert/svn/bibli/local/Meireles2003Differentially.pdf

Differentially expressed genes in gastric tumors identified by cDNA array

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Differentially expressed genes in gastric tumors identified by cDNA array