You need to break down each sentence into a list of words through tokenization, while clearing up all the messy text in the process. Bigrams are two words frequently occurring together in the document. Trigrams are 3 words frequently occurring. The higher the values of these param, the harder it is for words to be combined to bigrams. The bigrams model is ready. The two main inputs to the LDA topic model are the dictionary id2word and the corpus. Gensim creates a unique id for each word in the document.
For example, 0, 1 above implies, word id 0 occurs once in the first document. Likewise, word id 1 occurs twice and so on. We have everything required to train the LDA model. In addition to the corpus and dictionary, you need to provide the number of topics as well. Apart from that, alpha and eta are hyperparameters that affect sparsity of the topics. According to the Gensim docs, both defaults to 1. The above LDA model is built with 20 different topics where each topic is a combination of keywords and each keyword contributes a certain weightage to the topic.
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Looking at these keywords, can you guess what this topic could be? Model perplexity and topic coherence provide a convenient measure to judge how good a given topic model is. In my experience, topic coherence score, in particular, has been more helpful. Now that the LDA model is built, the next step is to examine the produced topics and the associated keywords. Each bubble on the left-hand side plot represents a topic.
The larger the bubble, the more prevalent is that topic. A good topic model will have fairly big, non-overlapping bubbles scattered throughout the chart instead of being clustered in one quadrant. A model with too many topics, will typically have many overlaps, small sized bubbles clustered in one region of the chart. Alright, if you move the cursor over one of the bubbles, the words and bars on the right-hand side will update.
These words are the salient keywords that form the selected topic. Given our prior knowledge of the number of natural topics in the document, finding the best model was fairly straightforward. You only need to download the zipfile, unzip it and provide the path to mallet in the unzipped directory to gensim. See how I have done this below. My approach to finding the optimal number of topics is to build many LDA models with different values of number of topics k and pick the one that gives the highest coherence value.
Picking an even higher value can sometimes provide more granular sub-topics. If the coherence score seems to keep increasing, it may make better sense to pick the model that gave the highest CV before flattening out. This is exactly the case here. One of the practical application of topic modeling is to determine what topic a given document is about.
As such, automated function prediction AFP has become increasingly important in reducing the gap between the huge number of protein sequences and very limited experimental annotations 4 , 5. By using a time-delayed evaluation procedure, CAFA assesses the accuracy of protein function prediction submitted by participants. A few months later T1 , target proteins with experimental annotations were then used as a benchmark for performance evaluation.
The benchmark data in CAFA was grouped into two categories: no-knowledge and limited-knowledge. The no-knowledge benchmark proteins refer to those who do not have experimental annotations before T0, but instead have at least one experimental annotation before T1. The limited-knowledge benchmark proteins are those who have the first experimental annotations in the target domain between T0 and T1, as well as experimental annotations in at least one other domain before T0. This means that AFP for no-knowledge protein is valuable to biologists. From a machine learning viewpoint, AFP is a problem of a large-scale multilabel classification, where multiple GO terms labels can be assigned to a protein instance 6.
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AFP faces two main challenges from the sides of the GO label and protein instance. All of these GO terms are organized in a hierarchical structure under the three GO ontologies.
If a protein is assigned by a GO term, for example, all GO terms located at its ancestor nodes in GO of this term should be assigned to this particular protein as well. The experimental GO annotations of human proteins in Swissprot 7 December reveal that one human protein can be annotated by 74 GO terms on average. On the protein side, information about proteins is not limited to sequences.
Sequences are just part of all information about proteins. Sequences are static and genetic, while proteins are alive and dynamic. Thus, an imperative issue is how to effectively integrate multiple types of data other than protein sequences for AFP. GOLabeler deems the AFP as a ranking problem, and utilizes a learning to rank LTR 12 framework to seamlessly integrate multiple types of sequence-based evidence, such as homology, domain, family, motif, amino acid k-mer, and biophysical properties.
Despite this fact, many protein functions cannot be inferred from protein sequences only. A natural question then arises as to whether other types of protein information can further improve the performance of GOLabeler for AFP. The advantages of NetGO are as follows: NetGO addresses both sides of the challenges: i the label side by using LTR; and ii the instance protein side by incorporating network-based information;.
Each node corresponds to a protein in this organism, while an edge represents an interaction association between two proteins. Within the framework of learning to rank LTR 12 , NetGO integrates both protein sequence and network information effectively and efficiently to improve the performance of the large-scale AFP. As a powerful machine learning paradigm, LTR aims to rank instances in terms of their optimal ordering, rather than to produce a numerical score for each of the instances. As mentioned before, the essence of AFP lies in ranking GO terms labels in order of their relevance to a given query protein.
The detailed method used in our LTR is a pairwise approach, which can be cast as a problem of pairwise classification. In this kind of the approach, given pairs of GO terms with respect to a specific protein, the LTR model tries to tell which GO term is more relevant by ranking more relevant GO terms at top positions in the list. During the testing, the top rank GO terms are chosen as the true labels, after they are ordered by their prediction scores.
This is because it has demonstrated a good performance in several international machine learning competitions, such as BioASQ challenge 16 , 17 and Yahoo Learning to Rank competition Figure 1 illustrates the whole framework of NetGO. On the other hand, the newly developed component, called Net-KNN, makes use of network information. The framework of NetGO with seven steps. The top five component methods use sequence information, while Net-KNN relies on network information. NetGO has to be trained before accepting test queries proteins.
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Note that Step 6 relies on Step 5 of Ranking model that has been learned from the training data. The training data contains a number of instances that consist of protein sequences, their network information, and their associated ground-truth GO annotations. In other words, a protein is associated with a number of GO terms in the form of a pair of protein-a GO term and their score score 1 for relevant and score 0 for irrelevant.
During the training, given a training protein, NetGO first relies on each component method in Step 3 to predict the association score of each GO term to this protein.
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See the Result section. For each candidate GO term, we use their association scores to form a six-dimensional feature vector. Second, Step 4 of LTR tries to learn a ranking model to minimize the number of incorrectly ordered pairs in the training data. This minimization of the cost function is achieved by adjusting the parameters of Steps 3, 4 and 5.
In particular, LTR aims to produce an optimal ordering of GO annotations for all pairs of the proteins in the training data. As such, LTR does not care much about the exact score that each candidate obtains, but does care about the relative ordering among all pairs of the candidate in the output list.
During a test, NetGO accepts a protein query with its network information. Again, the six components in Step 3 use their already learned parameters to extract the features of this protein, producing a score feature vector of length six. Candidate GO terms, i. In the following, we briefly describe the six component methods of NetGO. Note that the details of the top five component methods can be found in 11 , and the formula for Net-KNN is given in the supplement.
Naive is an official baseline of CAFA. For a given P j , the score that P j is associated with G i is defined as the relative frequency of G i in D.
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The higher the normalized sum of bit-scores of homologous proteins of P j is associated with G i , the bigger S G i , P j will become. If found, this protein will be used as PV j , together with its neighboring nodes PV k ; and. The higher the weight of two proteins in all of the m individual networks is, the higher their aggregated weight is. Note that the different types of networks and the various ways of their combinations affect the weights and the final performance.
This database covers 9 proteins from organisms with interactions in total. The networks of organisms appearing in the training data were used in Net-KNN.
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NetGO made use of the six different types of networks in STRING: 0:neighbourhood, 1:fusion, 2:co-occurrence, 3:co-expression, 4:experiment and 5:database. Specifically, four datasets have been generated for NetGO training and testing, where the proteins are annotated at different time stamps. Training: the training data for the component methods. All data experimentally annotated after October by October and not before October Table 1 in the supplementary materials reports the number of proteins in the above datasets. As a standard evaluation metric in machine learning, AUPR punishes false positive prediction.
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