No free lunch in search and optimizationIn computational complexity and optimization the no free lunch theorem is a result that states that for certain types of mathematical problems, the computational cost of finding a solution, averaged over all problems in the class, is the same for any solution method. The name alludes to the saying "no such thing as a free lunch", that is, no method offers a "short cut". This is under the assumption that the search space is a probability density function. It does not apply to the case where the search space has underlying structure (e.
Cancer researchCancer research is research into cancer to identify causes and develop strategies for prevention, diagnosis, treatment, and cure. Cancer research ranges from epidemiology, molecular bioscience to the performance of clinical trials to evaluate and compare applications of the various cancer treatments. These applications include surgery, radiation therapy, chemotherapy, hormone therapy, immunotherapy and combined treatment modalities such as chemo-radiotherapy.
AlgorithmIn mathematics and computer science, an algorithm (ˈælɡərɪðəm) is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algorithms are used as specifications for performing calculations and data processing. More advanced algorithms can use conditionals to divert the code execution through various routes (referred to as automated decision-making) and deduce valid inferences (referred to as automated reasoning), achieving automation eventually.
Phenotypic screeningPhenotypic screening is a type of screening used in biological research and drug discovery to identify substances such as small molecules, peptides, or RNAi that alter the phenotype of a cell or an organism in a desired manner. Phenotypic screening must be followed up with identification (sometimes referred to as target deconvolution) and validation, often through the use of chemoproteomics, to identify the mechanisms through which a phenotypic hit works. Phenotypic screening historically has been the basis for the discovery of new drugs.
High-content screeningHigh-content screening (HCS), also known as high-content analysis (HCA) or cellomics, is a method that is used in biological research and drug discovery to identify substances such as small molecules, peptides, or RNAi that alter the phenotype of a cell in a desired manner. Hence high content screening is a type of phenotypic screen conducted in cells involving the analysis of whole cells or components of cells with simultaneous readout of several parameters.
Merge sortIn computer science, merge sort (also commonly spelled as mergesort) is an efficient, general-purpose, and comparison-based sorting algorithm. Most implementations produce a stable sort, which means that the relative order of equal elements is the same in the input and output. Merge sort is a divide-and-conquer algorithm that was invented by John von Neumann in 1945. A detailed description and analysis of bottom-up merge sort appeared in a report by Goldstine and von Neumann as early as 1948.
Cancer stem cellCancer stem cells (CSCs) are cancer cells (found within tumors or hematological cancers) that possess characteristics associated with normal stem cells, specifically the ability to give rise to all cell types found in a particular cancer sample. CSCs are therefore tumorigenic (tumor-forming), perhaps in contrast to other non-tumorigenic cancer cells. CSCs may generate tumors through the stem cell processes of self-renewal and differentiation into multiple cell types.
ConsiderationConsideration is a concept of English common law and is a necessity for simple contracts but not for special contracts (contracts by deed). The concept has been adopted by other common law jurisdictions. The court in Currie v Misa declared consideration to be a “Right, Interest, Profit, Benefit, or Forbearance, Detriment, Loss, Responsibility”. Thus, consideration is a promise of something of value given by a promissor in exchange for something of value given by a promisee; and typically the thing of value is goods, money, or an act.
Hill climbingIn numerical analysis, hill climbing is a mathematical optimization technique which belongs to the family of local search. It is an iterative algorithm that starts with an arbitrary solution to a problem, then attempts to find a better solution by making an incremental change to the solution. If the change produces a better solution, another incremental change is made to the new solution, and so on until no further improvements can be found. For example, hill climbing can be applied to the travelling salesman problem.
Travelling salesman problemThe travelling salesman problem (TSP) asks the following question: "Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city?" It is an NP-hard problem in combinatorial optimization, important in theoretical computer science and operations research. The travelling purchaser problem and the vehicle routing problem are both generalizations of TSP.
