"DNA元基催化与肽计算——德塔自然语言图灵系统修订版V00056051"

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本文介绍了DNA元基催化与肽计算的相关内容，其中包括德塔自然语言图灵系统、分词优化方式、神经网络索引、动态POS等知识来源和应用。此外，本文还简要介绍了DNA元基催化与肽计算的PPT无源码第五修订版的目录及作者信息。 DNA元基催化与肽计算是一项重要的研究领域，通过利用DNA元基的催化作用和肽计算的方法，可以实现对生物信息的处理和分析。德塔自然语言图灵系统是一种自然语言处理系统，它基于德塔分词的催化切词优化方式，通过对文本进行分词、排序和索引等操作，提高了系统的处理效率和准确性。分词是自然语言处理中的一项重要任务，它将连续的文本切分成独立的词语，为后续的处理提供基础。德塔分词使用催化切词优化方式，通过对分词过程进行优化和改进，使得切词的结果更加准确和稳定。排序是对文本进行有序排列的过程，在德塔自然语言图灵系统中，排序操作用于对分词结果进行排序，以便更好地进行后续的索引和搜索过程。通过排序操作，可以将相关的信息集中在一起，提高搜索的效率和精度。神经网络索引是一种基于神经网络的索引方法，它能够对大规模文本进行高效的索引和搜索。在德塔自然语言图灵系统中，神经网络索引的应用可以进一步提高系统的处理速度和搜索准确度。动态POS是指在文本处理过程中，根据上下文和语义的变化，对词语的词性进行动态调整。在德塔自然语言图灵系统中，动态POS的应用可以使得系统更加准确地理解文本，并达到更高的处理效果。此外，本文还提到了DNA元基催化与肽计算的PPT无源码第五修订版的目录和作者信息。该PPT以中英双语的形式展示了DNA元基催化与肽计算的相关内容，为读者提供了一种全面了解和学习该领域的途径。总之，DNA元基催化与肽计算是一项重要的研究领域，通过德塔自然语言图灵系统、分词优化方式、神经网络索引和动态POS等方法的应用，可以实现对生物信息的处理和分析。希望本文能够为读者对该领域的了解和学习提供一定的帮助。

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DNA 元基催化与肽计算_第 5 修订版本 V00056 16

Nero Network Index Forest

1 Deta Parser did a word segment indexed map by using humanoid speech verbal dictionary, for the reason why using JDK8+ tool

to do the map search logic, is that it had already integrated the binary search tree, balanced map tree arrangement and other

technologies.

2 Deta Parser’s balanced binary search tree method makes an observer mode of averaged classification with all types of the

reflection java concurrent maps, those maps include the char word length, verbal types and Part of speech corpus, etc. The author

did it to accelerate the NERO marching speed for searching the words.

3 Deta Parser supports the secondary indexing computing combinations, this way could be suitable for the distributed cache

searching systems. The author does not suggest this technology be used on a single desktop.

4 For the computing logic, Finally Deta Parser functions use string builder to accelerate the searching engine.

神经网络索引的价值主要体现在 2 个地方, 切词的关联索引上和词汇 map 索引上. 切词的关联索引价值, 主要体现在将

词汇的文字进行链化提取, 这种链化计算方式将词库中本相对独立的海量词汇进行了按人类语言文学中的顶针方法进行

了有效的前后长度关联（NERO）, 其价值有利于大文本的文字进行有必要关联链的小段小段的提取（NLP）, 类似挤牙

膏一样, 挤出来就刷牙用掉（POS）.

词汇 map 索引价值, 主要体现在词汇的文字进行链化合理切分, 这种链化切分方式将词库中根据不同属性的分类 map

来组合匹配按人类语言文学中的词汇词性和主谓宾搭配严谨定义来切分. 其价值在这些分类 map 可以自适应设计和多

样化扩展. 增加切词准确度和灵活度, 适应各种不同的场景, 类似牙刷机制, 挤出牙膏根据匹配不同的牙刷和刷牙方法

（NERO + POS）, 匹配适应不同的口腔环境. 描述人罗瑶光 , 稍后优化下

The accomplishment of the neural network index is mainly reflected in two sections, 1, the relevance index of word segmentation

and 2, the lexical index map. The associated relevance index value of word segmentation is mainly reflected in the chained

extraction of words. This chained calculation method effectively correlates the relatively independent of a large number of words

in the thesaurus, according to the Thimble Theory in human language and Literature (Nero). The value of the big data document

process splits the word chain links list into a small chars token(max 4) sections, and It is similar to squeezing toothpaste, and

brushing the teeth (POS) when squeezed out by the DetaParser marching engine.

The index value of the lexical map is mainly reflected in the reasonable chain segmentation of lexical characters. This chain of

word segmentation method combines and matches the classified maps in the thesaurus according to different attributes. And then

separates them according to the rigorous definition of lexical POS and SVO collocation in human literature languages. The

adaptive industrial system design and diversified expansion of this classification, would increase the accuracy and flexibility of

word segmentation and adapt to different segment scenes. Similar to the way of toothbrushes, the extruded toothpaste is matched

to adapt to different oral cavity environments according to different toothbrushes and brushing methods (Nero + POS).

Author: Luo Yaoguang

DNA 元基催化与肽计算_第 5 修订版本 V00056 17

分词在线性文本搜索中应用,

1 德塔分词的搜索建立在 map 类的权重计算方法上, 不同的权重叠加产生的打分进行排序输出. refer page 下册 64

2 权重的计算方法按词性的主谓宾如代名动形 , 和 POS 如动名形谓介分类. refer page 下册 66

3 权重与词长, 词频进行耦合 bit 叠加计算(bit 位计算比乘法要快一个数量级), 生成最终输出结果. refer page 下册 68

4 权重与词长的比值可以精度调节, 确定搜索的精确性和记录个人搜索偏好. refer page 下册 68

The Deta Parser word segmentation and its applications in the linear text document environments.

1 There has a lot of rights weight by each indexed map, based on those right weights, Deta Parser did a marching score system to

do the computation and calculation for the Chinese word segmentation logic.

2 the search weight of the computing logic, such as Subject Predicate Object(SVO), and part of speech(POS), for instance, noun,

verb, adjective etc.

3 to make a computing acceleration, the author injected a combination factor in the marching logics, such as bit calculation,

frequency statics and word length observations. similars with the theory of Count Down Latch and Cyclic Barrier logic (makes

definitions first then proves, or proves first then did a conclusion) ways etc

4 Above all things and logic once became JAVA transportations, the author set all global and local valuable scales to build the

Foolishman- Self-Controller components to make the algorithms easy and simple.

动态 POS 函数流水阀门细化遍历内核匹配

1 动态的核分为前序核和后序核两种. 根据词汇分析的位置进行实时变动更新. refer page 97

2 前序核主要缓存存储词汇的位置和词性, 用于 POS 词性搭配的 POS 函数流水阀门细化遍历计算. refer page 97

3 后序核主要缓存词汇的切词链后面准备跟进的词语. 用于 POS 语法的修正计算, 如连词匹配. refer page 97

4 内核采用 StringBuilder 做核载体进行计算加速. refer page 97

Dynamic River Flows Gate Function Marching and Circustantly Loop the POS Kernel Computing.

1 Dynamic kernel contains prefix and postfix two types can read the word token one by one. It does dynamic computing also at

the same time.

2 Prefix kernel stores a POS cache buffer by each current word piece of information such as positions, frequency etc, to accelerate

the word marching.

3 Postfix relevant to the optimization of word marching and segmentation. For example, checking the conjunction relationship

and continuing the word token link list.

4 The algorithms kernel uses StringBuilder to do higher computing affections according to computer language grammar.

DNA 元基催化与肽计算_第 5 修订版本 V00056 19

POS function gate river flows and their relationships. For example, the author did the word segmentation by using '如果是

非常理想' in this sentence. At the first through the indexed forest map dictionary, Deta Parser could cut '如果是非常理想'

into ‘如果’, ‘是非常’, ‘理想’those three associated chars word sets token list. And in this result list, ‘如果’and‘理想’ these

two lexical words seems to be immutably boned. ‘是非常’was a three chars word token then did an inner marching

computing by using POS function gate river flows theory. and at this time, the orthos corpus map base of the author's Deta

Parser system which could not find any verbals such as‘是非常’, then continued do the two chars marched for the next

step. About more powerful of these algorithms, was the Chinese chars literacy grammar marching system, for the chars

segment section, ‘是非常’did a separation into two types such as ‘是非-常’ and ‘是-非常’, then analyzed contrast and

distinguishment by these two segments. after analysis of each word and its prefix and postfix, POS combined relationships,

（the prefix token of‘是非’was‘如果’, the prefix token of‘非常’was‘是’, the prefix tokens of‘常’were‘是非' and‘非’, and the

prefix tokens of '理想’were‘常’and‘非常’）. This POS word segmentations theory was fixedly and immutably, which means

it should not contain any probability events here. if at this time the DetaPaser does not find any associated chars

relationships, then promoted to the next steps as reading cutting sequence list chars single one by one. Above all, the

result of the sample graph did a good show that DetaParser did a ‘如果-是-非常’response because the priority of

Conjunction- Adj,v- Adj,v is higher than conjunction- noun- adj,v.

Author: Yaoguang Luo

2019 年 3 月 18 日之前作者 Github 的该算法函数编码框架已经出现

https://github. com/yaoguangluo/Deta_Parser/commit/25b90c9847d15df85c5c991448f2c271e0ad8106

注意：链接的 CNN 关键词的历史记录属于作者用词错误, 作者当年基础学术累积不够, 关于卷积的知识仅仅学了计算

机视觉的理论课, 以为带内核计算的都叫 CNN 卷积

剩余304页未读，继续阅读

江水流春去

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"DNA元基催化与肽计算——德塔自然语言图灵系统修订版V00056051"

DNA元基催化与肽计算_第5修订版本V00059091

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未定义函数或变量 'readcell'。 出错 Untitled (line 3) dna_seqs = readcell(dna_file);

影响DNA甲基化的因素

DNA甲基化程度可能有所不同为什么是第一个位置

DNA甲基化去卷积分析的意义

解释def copy(): dna=Dna.get() complementary_bases = {"A": "T", "T": "A", "C": "G", "G": "C"} new_dna_seq = "" for base in dna: new_dna_seq += complementary_bases[base] result="DNA replication:"+new_dna_seq label2.config(text=result)

python编写DNA转录为RNA

DNA甲基化异质性是什么意思

已知六段dna序列，由FASTA格式存储在文件1.txt中。读取文件中的DNA序列，并将各DNA序列的互补链输出到1.txt相应的位置，并求解原DNA序列的G, C数量。

python DNA翻译

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未定义函数或变量 'readcell'。出错 Untitled (line 3) dna_seqs = readcell(dna_file);