Independent hierarchy

Basic modules: different levels of division, followed by large-scale matrix operations

Expert systems, artificial intelligence, human-machine thinking

Databases: models, programming

Knowledge, as experience, is the iterative premise of Bayesian networks

The information query of the database can obtain the relationship of new variables through a certain logical combination, and can also be matched and counted to a certain extent

Data visualization is a hierarchical operation, in a way that people can understand, with a relatively large granularity, conducive to the transmission of information, with a relatively large efficiency. At the same time www.biquge.info this is a kind of high-dimensional data extraction, and there may be certain patterns emerging

Relative data relationships, the relative proportions of different levels are high-dimensional relationships

Breaking is a prerequisite for a larger level of coupling

The meaning of high and low is the overall change of the network, the results of different processing are different, we need to explore the structure of high dimensions, which can replace the change of a sufficient number of times per unit time from a sufficient number of different times. Such as the degree of correlation between specific changes (the relationship between a disease and a certain molecular concentration). Combined with the idea of a collection of images in photosynth software, our biomedical molecular interactions (like the basic units of images) can be reconstructed into a complete network. Every time a picture is taken an observation, and the set of multiple observations establishes a certain connection sequence according to a certain similarity, that is, a certain panorama, which can refer to genome sequencing

The projection of different levels of complex systems, i.e., simplification, is reductionism, and the next combination is holism, i.e., selective expression

Combine physiological knowledge to build a bio-specific expert system (domains, vocabulary, rules)

Knowledge is the experience of large-scale data formation, the description of knowledge is the matching of different functions, the semantic network is a holistic model, and the rules are certain simplified essences, just like logic

Metabolism (exchange), excitability (response to change) and reproduction (stages of growth and development) of vital activities. Thresholds are an intrinsic of the operation of matrix networks

Everything is the language of probability, and the reasoning at each step has a certain credibility

A graphical rules language that deals with facts and relationships: attribute predicates and values

The overall intrinsic of a non-10100101 network is information, and a certain range of discrete or continuous values can also be delineated

A collection of complex rules is like having the brain of an experienced expert in an inference machine

Language-function-function calls

The CF range determines the direction of progression, and the definition of the context can be similar to the metabolic network, linked by different probabilities

The rule base is modular, an atom whose logical judgments can form matrices and be coupled with other matrices

I'm biased towards relational networks built by database similarity matching

List processing is a smart move of the LISP language, which is the basis for large-scale data use, and the introduction of the atomic idea of physics is the basis of modularity, and it is also the beginning of the application of the statistical laws of physics

Data structures: stacks, queues, linked lists, trees, and lookups, heaps, and graphs

Algorithms: sorting, enumeration, depth and breadth-first search, traversal of graphs

The mathematical form of the network is the coexistence and regeneration of all levels, which is a self-organizing structure that simulates nature and society. The hierarchy can be processed into various groups, and whether the contact information of the group is a channel or an exchange, I think it is coexistence and competition, and it is a selective expression of the network. The various invariants of the network are intrinsic

The topological nature of a network is not only a variety of invariants, but also an overall relationship that maintains stable changes in the midst of change

Detection technology is the premise of the development of the network, and the deeper layer of error may be an intrinsic characteristic, that is, distribution

Similarity of networks and differential equations

The interconversion of concentration, current, voltage, temperature, etc., can make the measurement valid (ascending to the network and then improving the selective expression and reducing the dimensionality), and most importantly the frequency (the time domain of Fourier analysis--- the frequency domain is a high-dimensional structure), just like the observation of a microscope, to find the two points of the minimum recognition

Constructing a network of circuits may improve accuracy and take advantage of the similarity of the networks

Relativity is taken into account in the measurement of frequency

Linear range and multi-linear coupling, the coupling of different methods can improve the accuracy

For example, the detection of the kit is the intrinsic search for the network of specific physiological and pathological processes: there will always be individual expressions that are closely related to the overall expression of the network or have great correlation

When the research reaches the molecular level, the next step is to bottom out, that is, to use the network of systems to integrate information at the molecular level, so that its combination can be described by a certain pattern. Using big data and probability theory, the law of large numbers and the median value theorem, we can find the real essence, that is, the description of the wave function of the network, so that the different expressions of different environments can be integrated, which is the theorem of everything at the biological level. Bioinformatics has great potential in this regard: the relationship between sequence matching and hierarchical coupling, the similarity of alignment and matrix scoring and the nature of the network, various algorithms such as the description of probability networks by Hidden Markov, large-scale operations are games between levels, the exclusion and path selection of infinite permutations and combinations, and the convergence and relativity of each level

The knowledge extraction of expert systems is based on the emergence of big data, and the knowledge network is based on certain rules

LISP language

The extension of thinking, based on network-based experience, allows the selective expression of the environment to be close to the intrinsic, i.e. effective and rapid

Knowledge allows us to deal with probabilities, and the general explanation is a combination of probabilities that allows knowledge to be added. Build a basic network

Strategy and knowledge are coupled constructs

hierarchical structure

Data tables, arrays, strings, linear joins, functions, data and program equivalence

A multiplicity hit is the extraction of knowledge from experience, i.e., a coupling that increases the probability according to the Bayesian formula

Formal knowledge

Combining database, CAD, pattern recognition, data acquisition, interface technology

Network hierarchy of knowledge

The knowledge representation of natural language represents the intrinsic nature of the network through the frequency of keywords

The construction of a learning expert system is more suitable for me, and the most important thing is that I can grow and constantly add new knowledge

Development: organization and management, integration of programs, errors in knowledge, syntax, chain of reasoning, etc. (checking the degree of coupling of passing probabilities)

Rule-based: Fact list (data), knowledge base (rules), inference machine

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