The results suggest that combining drugs targeting AXL and ERBB receptors can be a better way to deal with tumors

quantitativeapproaches

Similarity matrix, the evolution of organic matter

Grading, molecular clocks, speculating germline occurrence with molecular sequences and structures, such as RNA, DNA, etc

First-order: sequence alignment, exploring homology, and comparing the phylogenetic relationships

Second order: structure, energy minimization, can be --- RNA, DNA--- protein, RNA, --- RNA from DNA. Pen | fun | pavilion www. ｂｉｑｕｇｅ。 info matrix

Calculate with statistics

The hidden Markov model predicts infinite possibilities and the convergence distribution of its quantum energy levels, and builds a framework based on probability

The function and its expression level are coupled and expressed in differential equations. Expressed in a bit matrix?

Transcription, splicing, small RNA molecules, translation, epigenetics, protein structure/function, development, evolution, disease, and other multi-level coupling

The common denominator of the structure may be the various probabilities and distributions, such as the proportion of primitives in the secondary structure, the proportion and distribution of the secondary in the tertiary structure, and the spatiotemporal expression of various genes, which are also proportions and probabilities and distributions. It is important to regulate the formation of loops

Migration of omics

Ecological niche, energy level distribution

There are also different levels of matching, similar nature, different lengths, and the idea of modularity is also important

SimilarityImpliesSimilarityInFunction(and/orstructure)) is used to guess function (sequencesimilarityimpliessimilarityinfunction(and/orstructure)) but there is spatial structural homology, and it seems that convolution-probability similarity can be used to predict structure and function (path selection)

Sequencing can take advantage of different biases to bind to viral sequencing

Use the scoring matrix to determine the shortest path and replace the matrix to correct it

Assuming that the velocity also obeys some kind of distribution, the variation can also be hierarchical, such as single, multi-base variation

Mutations are more common at the interface

Nonlinearity is an approximation of a sequence of linear combinations

Continuous space is accumulative

Grading is an important criterion: size, range of action, cell composition, relationships...... The above is also coupled to each other. Simulating with a wave function is a mathematical operation and a reasonable transformation

Protein structure is ultimately to take into account the existence of symmetry and is a deeper mathematical geometry

The reciprocating cycle of interaction is infinite, but mathematically convergent. At the same time, as with the self-circulation of the sequence of the chain of suspicion, the higher order is of little significance. Maxwell's equations reveal that we can understand it in terms of the high-dimensional geometry of the whole, so that we do not have an infinite dead loop at a particular stage. Then to determine the primitive: the vector, there is a relatively definite path of motion, and finally there can be a definite value of the flux in two dimensions (three-dimensional volume).

The degree of cohesion provided by proteins is the probability space, which is a large background that makes emergence-emergence

Homology prediction--- hierarchical of heterometric measures--- finding equivalent nodes using matrices--- docking using point mutations---, three-dimensional structural sequences-reaction possibilities, Bayesian networks

p(s，g，r，d)=p(s)p(g|s)p(r|g，s)p(a|s，g，r)

=p(s)p(g|s)p(r|s)p(a|g，r)

The form of the system is expressed in a network structure, the system is an abstraction of biology, and biology itself is a multi-level coupling: molecule-cell-tissue-organ-system-organism, each level is relatively independent, and its coupling is reflected in the similarity of sequences

Structure and function are fixed and variable

Each movement is a multi-level co-competition, selective expression, and thus at the overall level it is a hidden Markov pattern, with hierarchical decomposition and Taylor series unfolding

The multi-data of the gene chip is a specific multi-level one-dimensional embodiment

Emergence: is the inevitable expression of the probability of interaction on a large scale (property = small probability * number of interacting objects)

The multi-level nature of the network is reflected in different evaluation criteria, such as concentration, velocity, position, proportion, combination, modification, time, etc

The eigenlevel embodied in the fixed point principle and the median value theorem, which is the result of the transition from low dimension to high dimension, is also the result of statistics, and is also a sublayer structure, a unit with a certain convergence radius. Or the access of the network is an intrinsic characteristic

The top-to-bottom and bottom-to-top methods are like a layered coupling of yin and yang

Omics, a multidimensional expression of a unidirectional sequence, stratified by nature such as mass

Pattern of formation, which is the information enrichment of the first-order sequence (CPG islands and methylation regions), the distribution of energy, the pattern of node formation (new nodes tend to connect high-throughput regions)

Palindromic structure and crisper

The dynamic equilibrium of the various levels of the network structure is the relationship

Coupling is not necessarily an offset of operations, but rather a collection of information curled up, a closed body of the hidden Markov model, with a specific selective expression in a particular environment

First-order: variable and fixed regions of the receptor (recognition of ligands), second-order: sequences of receptor activation or inhibition form a certain structure, one-to-many or many-to-one, and third-order: coupling of pathways resulting from changes, which can be combined with hierarchical (transformable) probabilistic connections of neural networks

The solution of differential equations is also periodic

Network probability does not emphasize the exact matching of sequences, but the overall probability as a repository, some of which are selectively expressed as our signaling pathways, when the environment changes, the original non-pattern sequences can be reassembled, and finally there is a certain proportion of successful expressions

Topological coupling, such as the two-dimensional surface of the three-dimensional structure of the Mobius band, the network is such a structure, distribution, and coupling with modification heterogeneous effects (phosphorylation and kinase activation).

The robustness of the network, the possibility of refactoring

The coordination required for the behavior of the network is the result of coupling, such as reciprocal inhibition (collateral excitatory inhibitory nerves, central coordination) and regressive inhibition (self-feedback, keeping the whole consistent)

Sequence similarity leads to hierarchical coupling, connection of nodes. Similarity of complexity and entropy

The high dimensionality of probability

Different levels of matching, different lengths, scales

The ratio of the paired drug target to the disease gene is calculated according to the distance, which is the mechanism of action of the compound and determines the tightness in the network

Heredity and epigenetics are coupled to behavior and are secondary networks

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