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

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

▪ Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed

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An integrative theory of prefrontal cortex function

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

genome are dramatically reshaping the research and development pathways for drugs, vaccines, and diagnostics. The growth in the number of molecular entities entering the drug development pipeline has accelerated as a consequence of powerful discovery and screening technologies such as combinatorial chemistry, mass spectrometry, high throughput screening, celland tissue-based DNA microarrays, and proteomic approaches.1 As a consequence, there is an escalating number of therapeutic candidates, which has caused the need for new technologies and strategies to streamline the process to make safe and effective therapies available to patients. One approach to the achievement of more expeditious and informative therapeutic research is the use of precise clinical measurement tools to determine disease progression and the effects of interventions (drugs, surgery, and vaccines). For example, gene-based approaches such as single nucleotide polymorphism maps are now being developed to distinguish the molecular and cellular basis for variations in clinical response to therapy.2 Another approach is the use of a wide array of analytical tools to assess biological parameters, which are referred to as biomarkers. Biomarker measurements can help explain empirical results of clinical trials by relating the effects of interventions on molecular and cellular pathways to clinical responses. In doing so, biomarkers provide an avenue for researchers to gain a mechanistic understanding of the differences in clinical response that may be influenced by uncontrolled variables (for example, drug metabolism). There are a variety of ways that biomarker measurements can aid in the development and evaluation of COMMENTARY

Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework*

The value of "the putrescine experience".

The dopamine hypothesis of ADHD and brain response to stimulant medication.

Testing for Fentanyl - Urgent Need for Practice-Relevant and Public Health Research.

Body mass index (BMI) and obesity prevalence among substance abuse participants from NIDA's clinical trials network

Iron loading has been consistently reported in those with alcohol use disorder (AUD), but its effect on the clinical course of the disease is not yet fully understood. Here, we conducted a cohort study to examine whether peripheral iron measures, genetic variation in HFE rs1799945 and their interaction differed between 594 inpatient participants with alcohol use disorder (AUD) undergoing detoxification and 472 healthy controls (HC). We also assessed whether HFE rs1799945 was associated with elevated peripheral iron and can serve as a predictor of withdrawal severity. AUD patients showed significantly higher serum transferrin saturation than HC. Within the AUD group, transferrin saturation significantly predicted withdrawal symptoms (CIWA‐Ar) and cumulative dose of benzodiazepine treatment during the first week of detoxification, which is an indicator of withdrawal severity. HFE rs1799945 minor allele carriers showed elevated transferrin saturation compared to non‐carriers, both in AUD and healthy controls. Exploratory analyses indicated that, within the AUD cohort, HFE rs1799945 predicted CIWA withdrawal scores, and this relationship was significantly mediated by transferrin saturation. We provide evidence that serum transferrin saturation predicts alcohol withdrawal severity in AUD. Moreover, our findings replicated previous studies on elevated serum transferrin saturation in AUD and an involvement of HFE rs1799945 in serum transferrin saturation levels in both AUD and healthy controls. Future studies may use transferrin saturation measures as predictors for treatment or potentially treat iron overload to ameliorate withdrawal symptoms.

Nora D. Volkow

Papers

The value of "the putrescine experience".

The dopamine hypothesis of ADHD and brain response to stimulant medication.

Testing for Fentanyl - Urgent Need for Practice-Relevant and Public Health Research.

Body mass index (BMI) and obesity prevalence among substance abuse participants from NIDA's clinical trials network

Elevated transferrin saturation in individuals with alcohol use disorder: Association with HFE polymorphism and alcohol withdrawal severity