dataesr/scientific-paragraphs-categorization huggingface dataset:
In the African countries of the dataset that we used, the treatment is ZDV/ d4T (NRTI) + 3TC (NRTI) + NVP/EFV (NNRTI) in most cases  [10] , and this treatment is generalized to the affected population, with poorer follow-up and adherence than in the UK. This discrepancy could lead to different mutations arising in both datasets, however since the treatment strategy is a combination of both NRTI and NNRTI drug classes, as in many countries, similar RAMs arise  [10]. Furthermore, there is potentially more uncertainty in the African dataset than in the UK. For example some individuals may have unofficially taken antiretroviral drugs, but still identify themselves as RTI-naive, or report having some form of ART while not having been treated for HIV  [25]. All of this explains the high prevalence of multiple resistance in the African data set: the median number of RAMs in sequences containing at least one RAM is 3 in the African sequences, while it is 1 in UK sequences (Table  1 ).
Thus, we can say that African sequences are highly resistant, with possibly different mutations and epistatic effects, compared to their UK counterparts. All these differences between the two datasets helped us to assess the generalizability of our method and the robustness of the results. That is to say, if signal extracted from the UK dataset was still relevant on such a different dataset as the African one, we could be fairly reassured in regard to the biological and epidemiological relevance of the observed signal. Sequences in both African and UK datasets were already aligned. In order to avoid overly gappy regions of our alignment we selected only positions 41 to 235 of RT for our analysis. We used the Sierra web service (https://hivdb. stanford. edu/page/webservice/) to get amino acid positions relative to the reference HXB2 HIV genome.
This allowed us to determine all the amino acids present at each reference position in both datasets, among which we distinguished the "reference amino acids" for each position, corresponding to the B and C subtype reference sequences obtained from the Los Alamos sequence database (http://www. hiv. lanl. gov/). All the other, non-reference amino acids are named "mutations" in the following, and the set of mutations was explored to reveal new potential RAMs. To train our supervised classification methods  [26] [27] [28] , the sequence data needed to be encoded to numerical vectors. A common and intuitive way to do so is to create a single feature in the dataset for each position of the sequence to encode. Each amino acid is then assigned an integer value, and an amino acid sequence is represented by a succession of integers corresponding to each amino acid.
There is, however, one drawback with this method: by assigning an integer value to amino acids, we transform a categorical variable into an ordinal variable. Any ordering of amino acids is hard to justify and might introduce bias. To avoid this, we represented each sequence by a binary vector using one-hot encoding. For each position in the sequence to be encoded, amino acids corresponding to mutations are mapped to a binary vector denoting its presence or absence in the sequence. For example, at site 184, amino acids M, G, I, L, T and V are present in the UK dataset. After encoding we will have 5 binary features corresponding to the M184G, M184I, M184L, M184T and M184V mutations. We did not encode the reference amino acid M, but only the mutated amino acids. With this method each mutation in the dataset (n = 1, 318) corresponds to a single feature.
Classifier training In order to find new potential RAMs, we first followed the conventional multiple testing approach  [10]. We first used Fisher exact tests to identify which of these mutations were significantly associated with anti-retroviral treatment. All the resulting p-values were then corrected for multiple testing using the Bonferroni correction  [29]. Those for which the corrected pvalue was � 0. 05 were then considered as significantly associated with treatment and potentially implicated in resistance. This method was complemented by our parallel, machine learning based approach. In order to extract potential RAMs, we trained several classifiers to discriminate between RTIexperienced and RTI-naive sequences represented by the binary vectors described above. This classification task does not need any phenotypic resistance measure, allowing us to use much larger and more readily available datasets than other machine learning based approaches previously mentioned.
Naive Bayes is a very simple approach, generally fairly accurate, and in between the two others in terms of explanatory power  [28]. In order to be able to compare all these approaches in a common framework, we devised a very simple classifier out of the results of the Fisher exact tests. This "Fisher classifier" (FC) predicts a sequence as RTI-experienced if it has at least one of the mutations significantly associated to treatment. In this way, we were able to compute metrics for all classification methods and compare their performance. It is important to note that in all of these approaches we chose to discriminate RTI-naive from RTI-experienced sequences, regardless of the type of RTI received. One of the reasons is that we did not have detailed enough treatment history for sequences in the UK and African datasets.
This resulted in training a set of classifiers on all subtype B sequences of the UK dataset and testing them on subtype C sequences from the UK dataset, training another set of classifiers on the subtype C sequences of the UK dataset and testing on the subtype B sequences from the UK dataset, as well as training a final set of classifiers on the whole UK dataset, but testing it on the smaller African dataset with a completely different phylogenetic makeup and treatment context  [10]. Furthermore, in order to identify novel RAMs and study the behavior of the classifiers, we repeated this training scheme on both datasets, each time removing resistance-associated signal incrementally: first by removing all representation features corresponding to known RAMs from the dataset, and second by removing all sequences that had at least one known RAM.
Measuring classifier performance To compare the performance of our classifiers we used balanced accuracy  [35] , which is the average of accuracies (i. e. percentages of well-classified sequences) computed separately on each class of the test set. This score takes into account, and corrects for, the imbalance between RTI-naive and RTI-experienced samples, which would lead to a classifier always predicting a sequence as RTI-naive getting a classical accuracy score of up to 77% (i. e. the frequency of naive sequences in the UK dataset). We also computed the adjusted mutual information (AMI) between predicted and true sequence labels, which is a normalized version of MI allowing comparison of performance on differently sized test sets  [36]. Additionally, mutual information (MI) was used to compute p-values and assess the significance of the classifiers' predictive power.
Classifier performance & interpretation As can be seen in Fig 1A  and 1B , when all RAM features and sequences were kept in the training and testing sets, classifiers had good prediction accuracy, with the machine learning classifiers slightly outperforming the "Fisher" classifier. When removing RAM features from the training and testing sets, the classifiers retained a significant prediction accuracy, especially with the African data set and its multiple RAMs that are observed in a large number of sequences (but removed in this experiment). In this configuration the ML classifiers had a similar performance to the "Fisher" classifier, except for the random forest that is slightly less accurate, likely due to overfitting. Also, when removing sequences that had known RAMs, every classifier lost all prediction accuracy, and none could distinguish RTI-naive from RTIexperienced sequences.
Additional classification results The fact that, when looking at classifiers trained without known RAMs, "Fisher" classifiers perform as well as the machine learning ones, leads us to believe that there is little interaction between mutations that would explain resistance better than taking each mutation separately. It is therefore likely that the kind of epistatic phenomena we were looking for, combining several mutations that do not induce any resistance when taken separately, do not come into play Fisher Classifier, RD: Agnostic random probabilistic classifier (this classifier predicts, as the probability of a sample belonging to a class, the frequency of that class in the training data). A) Adjusted mutual information (higher is better) between ground truth and predictions by classifiers trained on dataset with all features (blue), without features corresponding to known RAMs (orange) and without RAM features and without sequences that have at least 1 known RAM (green).
Hatching indicates the training set on which a classifier was trained and the testing set on which the performance was measured. The expected value for a null classifier is 0, and 1 for a perfect classifier and a � denotes that the p-value derived from mutual information is � 0. 05. For example when trained with all features all the classifiers have a significative MI. Conversely when removing RAM features and RAM sequences none of the classifiers have a significative MI and only LR trained on the entirety of the UK dataset has an AMI >10 -3. B) Balanced Accuracy score, i. e. average of accuracies perclass (higher is better) for the same classifiers as in A). The red line at y = 0. 5 is the expected balanced accuracy for a null classifier that only predicts the majority class as well as a random uniform (i.
As can be seen in Fig 2A  and 2B , the classifiers trained to discriminate sequences that have at least one known RAM from those that have none, on datasets from which all features corresponding to known RAMs were removed, perform much better than classifiers trained to discriminate RTI-experienced from RTI-naive sequences. This increase in performance is especially visible for classifiers tested on UK sequences (more difficult to classify than the African ones, see above), with an AMI often almost one order of magnitude higher for the known-RAM presence/absence classification task. This further reinforces our belief that all there is a fairly strong residual resistance-signal in sequences that contain known RAMs, due to new accessory and compensatory mutations identified by our classifiers and Fisher tests.
Identifying new mutations from classifiers We assessed the importance of each mutation in the learned internal model of all the classifiers, in the setting where all known RAMs have been removed from the training dataset. For the Fisher classifier, we used one minus the p-value of the exact Fisher test as the importance value, therefore the more significantly associated mutations have the higher importance value and were ranked first. For a given classification task, we ranked each mutation according to the appropriate importance value for each classifier (see above), trained on the B or C subtypes, with the highest importance value having a rank of 0. We then computed the average rank for each mutation and each classification task (RTI-naive/RTI-experienced and RAM present/ RAM absent). This gave us, for each classification task, a ranking of mutations potentially associated with resistance that took into account the importance given to this new mutation by each classifier trained on this task.
To get a general idea of this over-representation, for each new mutation we computed RR(new, treatment) comparing the prevalence of the new mutation in RTI-experienced and RTI-naive sequences. We also computed RR(new, any RAM) comparing the prevalence the new mutation in sequences having at least one known RAM and sequences that have none. Both of these RRs are shown in Table  3  for each new mutation. We then computed RR(new, RAM) for each known RAM present in more than 0. 1% of UK sequences and the new mutations. In Fig  3  we see the RRs for which the lower bound of the 95% confidence interval, computed on 1000 bootstrap samples from the UK dataset, was greater than 4.
Detailed analysis of potentially resistance-associated mutations As can be seen in Table  3 , all of these new mutations except for I135L, are highly over-represented in RTI-experienced sequences and sequences that already have known RAMs, with lower bounds on the 95% RR CI always greater than 5, and often exceeding 10. When looking The genetic barrier to resistance for each of these new mutations is quite low, with a minimum of 1 base change for each of them (Table  3 ). We also computed the average codon distance (i. e. number of different bases), weighted by the prevalence of wild and mutated codons at the given positions in the UK (Table  3 ) and Africa (S1 Table) datasets, and in each case the average codon distance was always close to 1. In other words, at the amino acid level these mutations are expected to be relatively frequent.
However, their frequencies are much higher in treated/with-RAM sequences than in naive/without-RAM ones (Table  3 ). Moreover, if we look at the BLOSUM62 scores (Table  3 ), some of these mutations induce some substantial changes in physicochemical properties, most notably at site 228, which reinforces again the likelihood that these mutations are associated with resistance. These metrics were also computed for all known RAMs (Table  3 ). For all these metrics, and the 6 new potential Codon distance: For each new mutation we computed the minimum number of nucleotide mutations to go from the wild amino acid codons to those of the mutated amino acid, as well as the average codon distance between both amino acids, weighted by the prevalence of each wild and mutated codon at the given position in the UK dataset.
B62: BLOSUM62 similarity values (e. g. D218E = 2, reflecting that E and D are both negatively charged and highly similar). Count: We looked at the number of occurrences of each new potential RAM in the UK dataset and the corresponding prevalence in parentheses. Relative risks: We computed RR(new, treatment) (e. g. L228R is 18. 1 times more prevalent in RTI-experienced sequences compared to RTI-naive sequences in the UK dataset). We also computed RR(new, any RAM) (e. g. L228R is 115. 7 times more prevalent in sequences that have at least one known RAM than in sequences that have none in the UK dataset). The 95% confidence intervals shown under each RR were computed with 1000 bootstrap samples of size n = 55, 000 drawn with replacement from the whole UK dataset. p-values: Fisher exact tests were done on the African dataset (to avoid confounding effects due to phylogenetic correlation) to see if each of these new mutations were more prevalent in RTIexperienced sequences.
The p66 subunit also has a regulatory pocket behind L228R/L228H. L228R is the most important of these new mutations according to the feature importance ranking done above. This is reflected in the very high over-representation in RTI-experienced sequences and sequences with known RAMs shown in Table  3. When looking at the detailed RRs shown in Fig  3 , we observe that L228R presents high RR values with mainly NRTI RAMs, but also with NNRTI RAMs such as Y181C and L100I, and this is even more so for RRs computed on the African dataset (S1 Fig). L228H is very similar in all regards to L228R, however its highest RRs are exclusively with NRTI RAMs. 3 ). Arginine (R) and Histidine (H) are both less hydrophobic than Leucine (L), and have positively charged side-chains. This important change in physicochemical properties could explain the role they both seem to have in NRTI resistance.
D218E. D218E is also highly over-represented in both RTI-experienced sequences and sequences with known RAMs. It has infinite RR values in the African dataset (Table  3 ), because it is quite rare in this dataset, and all of its 25 occurrences are in sequences that have at least one known RAM and are RTI-experienced. In fact, from the UK dataset we can see that D218E has some of the highest RR values for individual RAMs (along with H208Y). The majority of these very high RR values occur for NRTI RAMs. Site 218 on the p66 subunit is quite close to the RT active site, which could explain the role D218E seems to have in NRTI resistance. Aspartic acid (D) and Glutamic acid (E) are very similar amino acids, both acidic with negatively charged side-chains, as reflected in their fairly high BLOSUM62 score, the main difference between both being molecular weight, with E being slightly larger than D.
Discussion and perspectives Our method has allowed us to identify six mutations that might play a role in drug resistance in HIV. These mutations are significantly over-represented in RTI-experienced sequences, as well as sequences exhibiting at least one other known RAM. The fact that models trained on the UK are still performant on such a different dataset as the African one strongly suggests that the learned classifier models have acquired generalized knowledge on resistance. For all of these new mutations their spatial positioning on HIV-1 RT is consistent with our conclusions, as all were either close to the active site or the regulatory binding pocket. Some of the mutations we have identified as potentially associated with resistance have been mentioned in previous studies. L228R/H have been observed before  [39]  and were suggested to be associated with reduced susceptibility to didanosine  [40, 41].
With our machine classifiers we seem to have found some RAMs of an accessory nature, over-represented in sequences already containing known RAMs. This is a form of epistasis, where the interaction between the main RAM and the accessory RAM is important. However, we did not manage to find subtler forms of epistasis, in our dataset, where two mutations separately have no effect on resistance but have an effect together. This is partly indicated by the fact that there is a limited performance gap between the Fisher exact tests and more sophisticated classifiers, that are able to reveal significant association of mutations, while each individual mutation has low prediction power. However, one advantage of machine learning classifiers, is that they are probabilistic, meaning that they can give more nuanced insights into the nature or resistance level of a given sequence than the classical binary presence/absence of RAMs approach.
In this regard logistic regression appears as a method of choice, showing similar or better performance than other classifiers, and an easy interpretation that is facilitated by the lasso regularization which performs a simple feature selection and retains the most important ones. Similar results were already observed on other sequence analysis tasks  [46]. In order to investigate the second form of epistasis further we tested each pair of mutations in the UK dataset (n = 867, 903) with Fisher exact tests to see if they were linked to treatment status. In order to mitigate the effects of phylogenetic correlation which are sure to have an effect in this type of setting, we tested the pairs that were significantly associated to treatment (n = 1, 309) again on the African dataset. We also compared these results to the Fisher exact tests executed for each single mutation.
05. For example when trained with all features all the classifiers have a significative MI. Conversely when removing RAM features and RAM sequences none of the classifiers have a significative MI and only LR trained on the entirety of the UK dataset has an AMI >10 -3. B) Balanced Accuracy score, i. e. average of accuracies perclass (higher is better) for the same classifiers as in A). The red line at y = 0. 5 is the expected balanced accuracy for a null classifier that only predicts the majority class as well as a random uniform (i. e. 50/50) classifier. C) Brier score, which is the mean squared difference between the sample's experience to RTI and the predicted probability of being RTI experienced (lower is better), for the same classifiers as in A) and B).
Data. Archive of figure generating data. A zip archive containing the processed data used to generate each panel of the main figures. (ZIP) S2 Data. List of known DRMs. A. csv file containing all the known RAMs used in this project as well as the corresponding feature name in the encoded datasets. Obtained from (hivdb. stanford. edu/dr-summary/comments/NRTI/) and (hivdb. stanford. edu/dr-summary/ comments/NNRTI/). (CSV).
Acknowledgments We thank  Anna Zhukova , Frederic Lemoine and  Marie Morel  for their help and suggestions. We also thank the  UK HIV Drug Resistance Database  and the UK Collaborative HIV Cohort: Barts Health NHS Trust, London (Mark Hopkins );  Molecular Diagnostic Unit, Imperial College, London  ( Simon Dustan );  University College London Hospitals  ( Stuart Kirk ); West of  Scotland Specialist Virology Laboratory, Gartnavel, Glasgow  ( Rory Gunson ,  Amanda Bradley-Stewart ).
This work was supported by:  Horizon 2020 framework programme  (Award number  634650 ,  VIROGENESIS  project, OG recipient, LB master salary);  Agence Nationale de la Recherche  (Award number  ANR-19-P3IA-0001 ,  PIA3  programme,  PRAIRIE  project, OG recipient, LB PhD salary);  UK Medical Research Council  (Award number  164587 , AT recipient);  European Research Council (ERC)   Starting Grant  (Award number  757688 , awarded to  Katherine E. Atkins , CJVA). These funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
S1 Data. Archive of figure generating data. A zip archive containing the processed data used to generate each panel of the main figures. (ZIP) S2 Data. List of known DRMs. A. csv file containing all the known RAMs used in this project as well as the corresponding feature name in the encoded datasets. Obtained from (hivdb. stanford. edu/dr-summary/comments/NRTI/) and (hivdb. stanford. edu/dr-summary/ comments/NNRTI/). (CSV).
PI3K and PI3Ka-specific transcriptomic signature predicts aggressive pancreatic cancer We sought to determine in an unbiased manner which signalling pathway are associated with aggressive features in PDAC. We analysed publicly available data set to distinguish a normal pancreas from chronic pancreatitis (CP) and primary tumours from localised PDAC (PDACloc) or metastatic PDAC (PDACmet)  Dataset EV1). PDACmet and CP patients share the same enrichment of mRNA expression-based hallmarks of biological pathways compared to normal, except for 3 hallmarks. The PI3K/Akt/mTOR pathway was the most differentially expressed with the lowest Pvalue  (Fig 1A). Differential enrichment analysis demonstrates that the PI3K/Akt/mTOR pathway is constantly (equally) enriched in localised and metastatic PDAC compared to normal parenchyma.
Those cells also presented increased levels of pS473Akt/Akt levels compared to nontumoural ductal cells  (Appendix Fig S1C)  analysed by Western blot. This gene subset of the PI3Ka activation signature was increased in all metastatic patients  (Fig 1C). We extended our findings to two larger independent cohorts of PDAC patients (Dataset EV2). High scoring of PI3Ka activation was significantly increased in patients with the poorest prognosis, regardless of their stage  (Fig 1D). In both cohorts, PI3Ka activation signature was mostly associated with pure basal-like RNA subtype as described in Appendix Fig S2B. The PI3Ka activation signature discriminates between localised patients and those with an early risk of relapse and death  (Fig 1E). Even though oncogenic mutations of PI3Ka are rare in PDAC  (Eser et al, 2013) , as confirmed in the PAAD database  (Dataset EV3, Appendix Fig S2) , high scoring of non-mutated PI3Ka activation was a worse prognostic factor, irrespective of the stage of the disease.