CN106951735B - A kind of prediction technique of signal peptide and its cleavage site based on layering mixed model - Google Patents
A kind of prediction technique of signal peptide and its cleavage site based on layering mixed model Download PDFInfo
- Publication number
- CN106951735B CN106951735B CN201710140273.XA CN201710140273A CN106951735B CN 106951735 B CN106951735 B CN 106951735B CN 201710140273 A CN201710140273 A CN 201710140273A CN 106951735 B CN106951735 B CN 106951735B
- Authority
- CN
- China
- Prior art keywords
- signal peptide
- sequence
- amino acid
- cleavage site
- candidate
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
Landscapes
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Biophysics (AREA)
- Theoretical Computer Science (AREA)
- Spectroscopy & Molecular Physics (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Biology (AREA)
- Biotechnology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Data Mining & Analysis (AREA)
- Public Health (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Epidemiology (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Analytical Chemistry (AREA)
- Chemical & Material Sciences (AREA)
- Bioethics (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Peptides Or Proteins (AREA)
- Medicines That Contain Protein Lipid Enzymes And Other Medicines (AREA)
Abstract
The invention discloses the prediction techniques of a kind of signal peptide based on layering mixed model and its cleavage site, whether contain N-terminal hydrophobic patches using the SVM classifier identification of protein sequence based on amino acid residue feature in first layer first；Then applying in the second layer based on the corresponding naive Bayesian of amino acid residue feature and function domains characteristic and SVM classifier identification hydrophobic patches is signal peptide or N-terminal transbilayer helix；Finally in third layer, score is first trusted according to statistical learning Rules Filtering candidate cleavage site and counting statistics, then the similarity scores for calculating candidate signal peptide sequence by Needleman-Wunsch sequence alignment algorithms again determine the signal peptide cutting site of prediction to statistical trust score and sequence similarity score integral.
Description
Technical field
The present invention relates to the prediction techniques of a kind of signal peptide based on layering mixed model and its cleavage site, are using
The protein sequence known predicts whether the protein includes N-terminal signal peptide, and predicts its cleavage site, especially a kind of fusion
Amino acid residue and functional domain, merge it is statistical trust score and sequence similarity score, and from top and lower leaf is predicted
The algorithm of signal peptide and its cleavage site.
Background technique
1979, G.Blobel and D.Sabatini were based on Germicidal efficacy and have been put forward for the first time signal hypothesis (Signal
hypothesis).G.Blobel and D.Sabatini thinks have one section to play signal guiding function in the N-terminal of secreted protein sequence
Amino acid fragment, which can be shifted between each film with pilot protein matter, and by protein import to destination locations.
The amino acid fragment that this section plays signal guiding function is called signal peptide by they.In 1999, G.Blobel was by this achievement
Obtain Nobel prize's soul.
Research by years of researches to signal peptide, people further deepen the understanding of signal peptide.Signal peptide is one
Section appears in the short sequence of amino acid of protein sequence N-terminal, it in eucaryote and prokaryotes pilot protein matter in cell
In transfer and secretion.The N-terminal of almost all of secretory protein and many transmembrane protein amino acid sequences all includes signal
Peptide.As one " postcode ", signal peptide guidance nascent protein transports the correct position of intraor extracellular.If changing new life
The signal peptide of albumen n end, then protein may be transported to the cell position of mistake, this will lead to various strange diseases
Disease.Therefore, the knowledge of signal peptide is highly useful to the mechanism for disclosing some complex inheritance diseases.Furthermore according to signal
The knowledge of peptide, scientist can reprogram amino acid sequence in such a way that oneself is desired, this can be used for the thin of future
In born of the same parents and gene therapy, and thinking is provided for developing new drug object simultaneously.In fact, pharmaceutical science man passes through gene modification albumen
Confrontation induction signal peptide, for example, scientist can be marked simultaneously by adding special signature (signal peptide) to required protein
Their excretion is guided, so that they are easier to be acquired.Now, signal peptide has become searching novel drugs, carries out weight to cell
New program and the key tool for carrying out gene therapy.In addition, signal peptide prediction can also provide for the prediction of other related fieldss
Important reference information, such as in subcellular localization research, the information of signal peptide can directly provide the subcellular position of protein
Confidence breath.
Over the last couple of decades, science is always in the tireless effort of signal peptide prediction and its Locus Analysis in Shoots field,
And achieve outstanding research achievement.Up to the present, the method that multi-signal peptide analysis has not only been issued in field, but also
It issues and is widely used there are many signal peptide analysis software.Signal peptide analysis method in field has stepped up signal peptide point
The precision of analysis, and attempt analysis understanding signal peptide from different angles.Common signal peptide analysis software is counted, according to its progress
The prediction model applied when signal peptide analysis, they can be roughly divided into three classes by us: pre- based on the signal peptide for generating model
Survey method, the signal peptide prediction method based on discrimination model and the signal peptide prediction method based on sequence alignment.Based on differentiation mould
The signal peptide analysis method of type is usually that applied statistics analysis classifier (as refreshing by network, support vector machines etc.) carries out signal
Peptide analysis.The advantages of this method is that comparison is flexible, is suitble to the protein sequence of processing random length, the disadvantage is that due to each ammonia
Base acid residue is coded separately expression, and interrelated between each amino acid residue is just ignored.In addition, being based on discrimination model
Signal peptide analysis method carry out cleavage site judgement when, be easy true cleavage site the right and left fall into local optimum fall into
Trap.The letter of three functional areas (area N, the area H, the area C) in signal peptide structure is considered based on the signal peptide analysis method for generating model
Breath, such method pass through hidden Markov model (Hidden Markov by each interregional correlation of statistical signal peptide
Model, HMM) scheduling algorithm modeled.It is in the layers such as biology and chemistry based on the signal peptide analysis method advantage for generating model
There is more intuitive interpretation on face, and explore correlation between signal peptide functional domain, this will be helpful to understanding more profound
The function and structure of these three functional areas；The disadvantage is that being bad at processing signal peptide has the case where longer amino acid sequence.Base
It is constructed in thoughts such as evolutionary conservatism and the knowledge migrations that the signal peptide analysis method of sequence alignment is based on signal peptide, at this
In class signal peptide analysis method, it is necessary first to extract the data that signal peptide sequence composition includes annotation information from public database
Then signal peptide sequence in search sequence and data set is carried out sequence alignment by collection.The advantages of this kind of signal peptide analysis method
It is in biology and physically to have stronger interpretation, the class model is more flexible compared to other two class models, Ke Yitong
The data set that dynamic more new model uses is crossed to update the signal peptide analysis model of publication, does not need the weight on new data set
New training pattern.
For signal peptide prediction, signal peptide and non-secreting egg can be distinguished well based on amino acid residue feature classifiers
White matter.But it is in hydrophobic amino acid fragment that signal peptide and N-terminal transbilayer helix segment have one section in structure, so they
Amino acid residue feature has higher similitude.Therefore only with amino acid residue feature, classifier is difficult to correct identification signal peptide
And transbilayer helix, so that the false positive of signal peptide prediction is excessively high.Signal peptide cutting site prediction is all a difficulty all the time
Point, reason may be that signal peptide cutting site has complicated mode, and signal peptide distribution of lengths is more dispersed.
Summary of the invention
It is an object of the invention to aiming at the shortcomings in the prior art, provide the present invention to propose one kind based on layering hybrid guided mode
The signal peptide and its cleavage site prediction algorithm of type, the algorithm introduce Functional domains letter in signal peptide prediction field for the first time
It ceases to reduce the false positive rate of N-terminal transbilayer helix, merges signal peptide prediction after amino acid residue and functional structure domain information
Performance significantly improves.In terms of signal peptide cutting site prediction, which screens high quality cleavage site by statistical rules and waits
Then selected works calculate the sequence similarity score of candidate cleavage site by sequence alignment, trust score by the way that fusion is statistical
The final signal peptide cutting site with sequence similarity Score on Prediction.The present invention has lower false sun in terms of signal peptide prediction
Property, there is higher sensitivity in terms of signal peptide cutting site prediction.
Technical problem solved by the invention can be realized using following technical scheme:
Whether contain N using the SVM classifier identification of protein sequence based on amino acid residue feature in first layer first
Hold hydrophobic patches；Then it applies in the second layer based on the corresponding simple shellfish of amino acid residue feature and function domains characteristic
Ye Si and SVM classifier identification hydrophobic patches are signal peptide or N-terminal transbilayer helix；Finally in third layer, first according to statistics
Learning rules screen candidate cleavage site and counting statistics trusts score, then pass through Needleman-Wunsch sequence ratio again
The similarity scores for calculating algorithm candidate signal peptide sequence trust score to statistics and sequence similarity score integral determine
The signal peptide cutting site of prediction.
It is comprised the concrete steps that:
Step 1: using protein amino acid sequence extract PSSM matrix information, secondary structure information, soluble information and
Amino acid physical chemistry information carries out feature extraction using mRMR, generates amino acid residue feature.
Step 2: extracting the functionality of protein sequence from CDD (Conserved Domain Database) database
Structure domain information, and functional-structural information is mapped to by superfamily according to the clustering information that CDD is provided, and occur according to superfamily
Frequency generating functionality domains characteristic.
Step 3: SVM classifier and Naive Bayes Classifier use is respectively trained according to two category features of protein sequence
In signal peptide prediction.
Step 4: for the protein sequence for being predicted as signal peptide, using asymmetric sliding window [- 13 ,+2] in albumen
Amino acid sequence segments are generated in matter sequence.
Step 5: screening candidate's in the amino acid sequence segments of generation according to { -3, -1 ,+1 } of signal peptide rule
Signal peptide cutting site, and calculate the corresponding statistical trust score of each candidate cleavage site.
Step 6: generating corresponding signal peptide candidate sequence by candidate signal peptide cutting site, and signal peptide is candidate
Sequence and signal peptide sequence application Needleman-Wunsch algorithm containing annotation information do sequence alignment, and calculate every letter
The signal peptide cutting site number peptide candidate sequence corresponding sequence similarity score and obtained by sequence alignment.
Step 7: statistical trust score corresponding for each candidate's cleavage site and sequence similarity score accumulate
Point, calculate the signal peptide cutting site finally predicted.
Compared with prior art, beneficial effects of the present invention are as follows:
(1) Model Fusion amino acid residue and functional nucleotide sequence structure domain information, significantly reduce the false positive of signal peptide prediction；
(2) Model Fusion statistics score and sequence similarity score significantly improve signal peptide cutting site prediction
Sensitivity；
(3) from top and the model of lower leaf predicted signal peptide and its cleavage site, significantly improve signal peptide and its cutting
The performance of site estimation.
Detailed description of the invention
Fig. 1 is the signal of the signal peptide of the present invention based on layering mixed model and its cleavage site prediction technique
Figure.
Fig. 2 is the output knot of the signal peptide of the present invention based on layering mixed model and its cleavage site prediction technique
Fruit schematic diagram.
Specific embodiment
To be easy to understand the technical means, the creative features, the aims and the efficiencies achieved by the present invention, below with reference to
Specific embodiment, the present invention is further explained.
Referring to Fig. 1, the signal peptide and its cleavage site prediction technique of the present invention based on layering mixed model, first
Whether contain N-terminal hydrophobic patches using the SVM classifier identification of protein sequence based on amino acid residue feature in first layer；
Then it is applied in the second layer and is based on the corresponding naive Bayesian of amino acid residue feature and function domains characteristic and svm classifier
Device identifies that hydrophobic patches are signal peptide or N-terminal transbilayer helix；Finally in third layer, first waited according to statistical learning Rules Filtering
It selects cleavage site and counting statistics trusts score, then calculated again by Needleman-Wunsch sequence alignment algorithms candidate
The similarity scores of signal peptide sequence cut the statistical signal peptide for trusting score and the determining prediction of sequence similarity score integral
Cut site.
Lower mask body is illustrated:
Step 1: extracting the corresponding evolution information (PSSM) of amino acid residue, structural information according to protein sequence information
(SS) and physical chemistry information (PI), each residue in such protein sequence can be indicated with corresponding 30 dimensional vectors:
RF=[PSSM (20), SS (5), PI (5)] (1)
Step 2: taking the corresponding feature of L amino acid of protein sequence N-terminal, feature choosing then is carried out using mRMR algorithm
It selects the feature after generating optimization and is denoted as sRF；
Step 3: judging whether protein sequence includes hydrophobic patches (signal peptide using the SVM classifier based on sRF
With N-terminal transbilayer helix), if terminating without containing analysis；
Step 4: being believed using the Functional domains that RPS-BLAST software retrieval CDD database obtains protein sequence
Breath, and being generated according to the mapping relations of structural domain ID and structural domain superfamily can property domains characteristic (FDF)；
FDFCDD=[δ1 δ 2… δi … δM] (2)
Step 5: judging that hydrophobic amino acid fragment is signal peptide or N using the Naive Bayes Classifier based on FDF
Transbilayer helix is held, the protein of not FDF is predicted using the SVM classifier based on sRF feature；
Step 6: carrying out cleavage site prediction to the protein sequence comprising signal peptide is predicted as, use [- 13 ,+2] sliding
Dynamic window generates amino acid fragment, and the cleavage point set that root is candidate in { -3, -1 ,+1 } rule and statistical learning algorithm screening；
Γ={ rk},ifθk> φT (5)
Step 7: generating the sequence fragment assumed according to the candidate cleavage site filtered out, and by segment and contain annotation
The protein sequence of information does sequence alignment, calculates similarity scores；
Step 8: being accumulated to the corresponding statistical trust score of each candidate's cleavage site and sequence similarity score
Point, choosing the corresponding candidate cleavage site of maximum value is the signal peptide cutting site finally predicted.
Embodiment
An existing list entries, data are as follows:
>QuerySequence|SIGNAL 1 25
MIKSNRITACALAALFAGASFSASAWWGGPGYGNGLWDNMGDMFGDGYGDFNMSM
GGGGRGYGRGYGRGNGYGYGAPYGYGAPYGYGAPYGYGAPYGYGAPYGAMPYGA
MPPQMPAAPAQPQAAPSR
This is a sequence to be measured, as shown in Figure 2 using the software output result of the method for the present invention:
According to Signal-3L 2.0engine,the predicted signal peptide is:1-25
MIKSNRITACALAALFAGASFSASAWWGGPGYGNGLWDNMGDMFGDGYGDFNMSM
GGGGR GYGRGYGRGN
The protential cleavage sites and the credit scores
From the results, it was seen that this method is accurate and intuitively predicts signal peptide and its cleavage site.
The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (4)
1. a kind of prediction technique of signal peptide and its cleavage site based on layering mixed model, which is characterized in that including as follows
Step:
1) whether contain N-terminal using the SVM classifier identification of protein sequence based on amino acid residue feature in first layer first
Hydrophobic patches；
If 2) contain N-terminal hydrophobic patches, is applied in the second layer and be based on amino acid residue feature and function domains characteristic
Corresponding naive Bayesian and SVM classifier identification N-terminal hydrophobic patches are signal peptide or N-terminal transbilayer helix；
If 3) recognition result is signal peptide, first trusted according to statistical learning Rules Filtering candidate cleavage site and counting statistics
Then score calculates the similarity scores of candidate signal peptide sequence by Needleman-Wunsch sequence alignment algorithms again, right
Statistics trusts score and sequence similarity score integral determines the signal peptide cutting site of prediction.
2. the prediction technique of the signal peptide and its cleavage site according to claim 1 based on layering mixed model, special
Sign is, the step 1) method particularly includes:
A. PSSM matrix information, secondary structure information, soluble information and amino acid object are extracted using protein amino acid sequence
Physicochemical information carries out feature extraction using mRMR, generates amino acid residue feature；
B. the Functional domains letter of protein sequence is extracted from CDD (Conserved Domain Database) database
Breath, and functional-structural information is mapped to superfamily by the clustering information provided according to CDD, and raw according to the frequency that superfamily occurs
Functional domains characteristic.
3. the prediction technique of the signal peptide and its cleavage site according to claim 2 based on layering mixed model, special
Sign is, the step 2) method particularly includes:
C. it is pre- for signal peptide SVM classifier and Naive Bayes Classifier to be respectively trained according to two category features of protein sequence
It surveys.
4. the prediction technique of the signal peptide and its cleavage site according to claim 3 based on layering mixed model, special
Sign is, the step 3) method particularly includes:
D. raw on protein sequence using asymmetric sliding window [- 13 ,+2] for being predicted as the protein sequence of signal peptide
At amino acid sequence segments；
E. according to { -3, -1 ,+1 } of signal peptide rule, candidate signal peptide cleavage is screened in the amino acid sequence segments of generation
Site, and calculate the corresponding statistical trust score of each candidate cleavage site；
F. corresponding signal peptide candidate sequence is generated by candidate signal peptide cutting site, and by the signal peptide candidate sequence and contained
There is the signal peptide sequence application Needleman-Wunsch algorithm of annotation information to do sequence alignment, and it is candidate to calculate every bars peptide
Signal peptide cutting site sequence corresponding sequence similarity score and obtained by sequence alignment；
G. statistical trust score corresponding for each candidate's cleavage site and sequence similarity score integrate, and calculate most
The signal peptide cutting site predicted eventually.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710140273.XA CN106951735B (en) | 2017-03-10 | 2017-03-10 | A kind of prediction technique of signal peptide and its cleavage site based on layering mixed model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710140273.XA CN106951735B (en) | 2017-03-10 | 2017-03-10 | A kind of prediction technique of signal peptide and its cleavage site based on layering mixed model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106951735A CN106951735A (en) | 2017-07-14 |
| CN106951735B true CN106951735B (en) | 2019-06-04 |
Family
ID=59467230
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710140273.XA Active CN106951735B (en) | 2017-03-10 | 2017-03-10 | A kind of prediction technique of signal peptide and its cleavage site based on layering mixed model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106951735B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI709188B (en) | 2018-09-27 | 2020-11-01 | 財團法人工業技術研究院 | Fusion-based classifier, classification method, and classification system |
| CN110033822B (en) * | 2019-03-29 | 2020-12-08 | 华中科技大学 | Protein coding method and protein posttranslational modification site prediction method and system |
| CN110970090B (en) * | 2019-11-18 | 2021-06-29 | 华中科技大学 | Method for judging similarity between polypeptide to be processed and positive data set peptide fragment |
| CN111091871B (en) * | 2019-12-19 | 2022-02-18 | 上海交通大学 | Protein signal peptide and method for realizing prediction of cleavage site thereof |
| CN115035947B (en) * | 2022-06-10 | 2023-03-10 | 水木未来(北京)科技有限公司 | Protein structure modeling method and device, electronic device and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003014734A (en) * | 2001-06-15 | 2003-01-15 | Japan Science & Technology Corp | Method and computer program for discriminating signal peptide |
| CN1818916A (en) * | 2006-03-02 | 2006-08-16 | 上海交通大学 | Realization of kernel function predicted signal peptide and broken point based on sequence ratio |
| CN101175860A (en) * | 2005-03-15 | 2008-05-07 | 维莱尼姆公司 | Cellulases, nucleic acids encoding them and methods for making and using them |
| CN102012977A (en) * | 2010-12-21 | 2011-04-13 | 福建师范大学 | Signal peptide prediction method based on probabilistic neural network ensemble |
| CN102034029A (en) * | 2010-12-21 | 2011-04-27 | 福建师范大学 | Bayesian network-based signal peptide shearing site prediction method |
| CN106126972A (en) * | 2016-06-21 | 2016-11-16 | 哈尔滨工业大学 | A kind of level multi-tag sorting technique for protein function prediction |
-
2017
- 2017-03-10 CN CN201710140273.XA patent/CN106951735B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003014734A (en) * | 2001-06-15 | 2003-01-15 | Japan Science & Technology Corp | Method and computer program for discriminating signal peptide |
| CN101175860A (en) * | 2005-03-15 | 2008-05-07 | 维莱尼姆公司 | Cellulases, nucleic acids encoding them and methods for making and using them |
| CN1818916A (en) * | 2006-03-02 | 2006-08-16 | 上海交通大学 | Realization of kernel function predicted signal peptide and broken point based on sequence ratio |
| CN102012977A (en) * | 2010-12-21 | 2011-04-13 | 福建师范大学 | Signal peptide prediction method based on probabilistic neural network ensemble |
| CN102034029A (en) * | 2010-12-21 | 2011-04-27 | 福建师范大学 | Bayesian network-based signal peptide shearing site prediction method |
| CN106126972A (en) * | 2016-06-21 | 2016-11-16 | 哈尔滨工业大学 | A kind of level multi-tag sorting technique for protein function prediction |
Non-Patent Citations (1)
| Title |
|---|
| Signal-3L 2.0: A Hierarchical Mixture Model for Enhancing Protein Signal Peptide Prediction by Incorporating Residue-Domain Cross-Level Features;Yi-Ze Zhang .et al;《Journal of Chemical Information and Modeling》;20170315;第57卷(第4期);第988-999页 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106951735A (en) | 2017-07-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106951735B (en) | A kind of prediction technique of signal peptide and its cleavage site based on layering mixed model | |
| CN108734290B (en) | Convolutional neural network construction method based on attention mechanism and application | |
| CN103473786B (en) | Gray level image segmentation method based on multi-objective fuzzy clustering | |
| CN108389614A (en) | The method for building medical image collection of illustrative plates based on image segmentation and convolutional neural networks | |
| CN109558902A (en) | A kind of fast target detection method | |
| JP2003529131A (en) | Methods and devices for identifying patterns in biological systems and methods of using the same | |
| Li et al. | Protein contact map prediction based on ResNet and DenseNet | |
| CN109994151A (en) | Predictive genes system is driven based on the tumour of complex network and machine learning method | |
| CN112215847B (en) | Method for automatically segmenting overlapped chromosomes based on counterstudy multi-scale features | |
| CN110111895A (en) | A kind of method for building up of nasopharyngeal carcinoma far-end transfer prediction model | |
| US20070133857A1 (en) | Joint classification and subtype discovery in tumor diagnosis by gene expression profiling | |
| CN108764282A (en) | A kind of Class increment Activity recognition method and system | |
| CN110459264A (en) | Based on grad enhancement decision tree prediction circular rna and disease associated method | |
| Meng et al. | Enhancing differential evolution with novel parameter control | |
| CN117422704A (en) | Cancer prediction method, system and equipment based on multi-mode data | |
| Dewi et al. | Combination of resnet and spatial pyramid pooling for musical instrument identification | |
| Cruciani et al. | Personalizing activity recognition with a clustering based semi-population approach | |
| CN115690549A (en) | Target detection method for realizing multi-dimensional feature fusion based on parallel interaction architecture model | |
| Li et al. | High-order correlation-guided slide-level histology retrieval with self-supervised hashing | |
| CN106777350A (en) | It is a kind of based on bayonet socket data scheming to search drawing method and device | |
| Van Buren et al. | Artificial intelligence and deep learning to map immune cell types in inflamed human tissue | |
| CN110163130A (en) | A kind of random forest grader and classification method of the feature pre-align for gesture identification | |
| CN117333908A (en) | Cross-modal pedestrian re-recognition method based on attitude feature alignment | |
| CN116956138A (en) | Image gene fusion classification method based on multi-mode learning | |
| Zengyou | Data mining for bioinformatics applications |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |