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

Data Mining - Concepts and Techniques.

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

* Starting from Need* Evolution of the Toyota Production System* Further Development* Genealogy of the Toyota Production System* The True Intention of the Ford System* Surviving the Low-Growth Period

Toyota production system : beyond large-scale production

Introduction: Heart Failure (HF) and stroke are complex diseases with multiple phenotypes. While many risk factors for these diseases are well known, it is likely that there are as-yet unidentified...

Abstract 21081: Coffee Intake Affects Heart Failure and Stroke Survival and is Significant in Predicting Heart Failure and Stroke Risk

Developers must actively maintain status information about the programming tasks they are engaged in. Unfortunately, much of this knowledge does not exist in tangible form. Commonly, developers are forced to recover the details of this knowledge after encountering an unanticipated interruption. In this paper, we present a technique enabling developers to quickly reengage in a task after such an interruption. A dashboard visualization, called TaskBoard, of intermediate task knowledge is constructed from recent activities and program executions and then displayed to assist reengagement. Developers can annotate its contents with task descriptions and prospective cues.

TaskBoard: Tracking pertinent task artifacts and plans

Background: The interpretation of the results from genome-scale experiments is a challenging and important problem in contemporary biomedical research. Biological networks that integrate experimental results with existing knowledge from biomedical databases and published literature can provide a rich resource and powerful basis for hypothesizing about mechanistic explanations for observed gene-phenotype relationships. However, the size and density of such networks often impede their efficient exploration and understanding. Results: We introduce a visual analytics approach that integrates interactive filtering of dense networks based on degree-of-interest functions with attribute-based layouts of the resulting subnetworks. The comparison of multiple subnetworks representing different analysis facets is facilitated through an interactive super-network that integrates brushing-and-linking techniques for highlighting components across networks. An implementation is freely available as a Cytoscape app. Conclusions: We demonstrate the utility of our approach through two case studies using a dataset that combines clinical data with high-throughput data for studying the effect of β-blocker treatment on heart failure patients. Furthermore, we discuss our team-based iterative design and development process as well as the limitations and generalizability of our approach.

Visual analysis of biological data-knowledge

In this paper we discuss how different kinds of models can be combined in an educational setting to enable students to experience natural phenomena, here earthquakes. Different tools enable students to manipulate virtual models, construct physical models and formulate mathematical models. Ideally, the modelling processes and the resulting models complement each other to some degree. Virtual and physical models can then be driven by real data as well as mathematical models of the phenomena.

Next generation modeling II - applications: experiencing natural phenomena with virtual, constructed and mathematical models

The goal of the 21th International Workshop on Data Mining in Bioinformatics (BIOKDD 2022) is to encourage KDD researchers to solve the numerous problems and challenges in Bioinformatics using Data Mining technologies. Based on the organizers' expertise and communities, BIOKDD 2022 features 2 closely related themes "Biomedical Ontologies" and "Biological Data Visualization". These themes encourage the knowledge graph community and the data visualization community to work together to solve the many challenging problems in Bioinformatics with new perspectives. The key goal is to accelerate the convergence between Data Mining and Bioinformatics communities to expedite discoveries in basic biology, medicine and healthcare.

Carsten Görg

Papers

Abstract 21081: Coffee Intake Affects Heart Failure and Stroke Survival and is Significant in Predicting Heart Failure and Stroke Risk

TaskBoard: Tracking pertinent task artifacts and plans

Visual analysis of biological data-knowledge

Next generation modeling II - applications: experiencing natural phenomena with virtual, constructed and mathematical models

21th International Workshop on Data Mining in Bioinformatics (BIOKDD 2022)