Charlemagne N'
email: charlemagne.kopoin@inphb.ci

Diffon Kopoin

Ntakpe Tchimou
email: tchimou.ntakpe@gmail.com

Bernard Kouassi Saha

Michel Babri
email: michel.babri@inphb.ci

A Feature Extraction Method in Large Scale Prediction of Human Protein-Protein Interactions using Physicochemical Properties into Bi-gram

Keywords: protein-protein interaction, feature extraction technique, bigram features, Artificial neural network

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I. INTRODUCTION

The study of protein-protein interactions (PPIs) is an important area for the discovery of cell regulatory processes and therapeutic targeting. Some high throughput proteomic techniques have been invented to detect PPIs, including the proteomic chips [START_REF] Zhu | Global Analysis of Protein Activities Using Proteome Chips[END_REF], immunoprecipitation [START_REF] Liu | An algorithm for finding protein-DNA binding sites with applications to chromatinimmunoprecipitation microarray experiments[END_REF], two-yeast hybrid technique [START_REF] Ito | A comprehensive two-hybrid analysis to explore the yeast protein interactome[END_REF], etc. Many unknown interactions were revealed by these experiments, but they are time consuming and costly. These limitations are the basis for the motivation for the development of computer models to predict large-scale PPIs. To date, a large number of computer approaches have been suggested for predicting PPIs from different types of data, including gene fusion [START_REF] Chou | Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms[END_REF], gene ontology and annotation [START_REF] Chou | Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization[END_REF], 3D structural information [START_REF] Chou | ProtIdent: A web server for identifying proteases and their types by fusing functional domain and sequential evolution information[END_REF], etc. However, these approaches are not universal, their accuracy and reliability depend heavily on prior information on the proteins collected. In practice, the 3D structure of many proteins is unknown, gene ontology and annotation are incomplete [START_REF] Chou | A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins[END_REF] and PPIs for many species are rarely available. On the other hand, amino acid sequence datas are widely available and most approaches use datasets constructed solely from sequence information to predict PPIs. Indeed, many studies have shown that sequence-based prediction as the identification of proteases and their types [START_REF] Grantham | Amino Acid Difference Formula to Help Explain Protein Evolution[END_REF], the prediction of the subcellular localization of proteins [START_REF] Chou | Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization[END_REF], can provide very useful information for fundamental research, drug design and is therefore widely welcomed by scientists.

Among the many methods used to extract features, we can mention the Position Specific Scoring Matrix method (PSSM) [START_REF] Gao | Prediction of protein modification sites of gamma-carboxylation using position specific scoring matrices based evolutionary information[END_REF]. The PSSM was first introduced by Gribskov et al. in 1987 [START_REF] Gribskov | Profile analysis: detection of distantly related proteins[END_REF]. It provides information on the probability of substitution of a given amino acid according to its specific position along the sequence with the 20 amino acids of the genetic code. The advantage of using the PSSM is to match a sequence to the alignment table [START_REF] Mcginnis | BLAST: at the core of a powerful and diverse set of sequence analysis tools[END_REF] by giving higher scores to the retained positions. However, this method requires certain parameters such as the electronic value (e-value), the alignment database, the substitution matrix [START_REF] Jones | The rapid generation of mutation data matrices from protein sequences[END_REF]. Many authors use the PSI-BLAST tool [START_REF] Altschul | Gapped BLAST and PSI-BLAST: a new generation of protein database search programs[END_REF], to obtain PSSM values. Among the discrete models [START_REF] Chou | Pseudo Amino Acid Composition and its Applications in Bioinformatics[END_REF], we have the amino acid composition method (AAC) [START_REF] Shen | Predicting protein-protein interactions based only on sequences information[END_REF]. The AAC method simply counts the frequencies of appearance of each amino acid along the protein sequence. However, the AAC method does not consider sequence-order information. To solve this problem, Chou proposed the pseudo amino acid composition method, PseAAC [START_REF] Chou | Pseudo Amino Acid Composition and its Applications in Bioinformatics[END_REF]. The PseAAC method has been used to improve the quality of prediction of subcellular localization of proteins [START_REF] Chou | Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization[END_REF] and membrane protein types [START_REF] Chou | ProtIdent: A web server for identifying proteases and their types by fusing functional domain and sequential evolution information[END_REF]. Its goal is to continue to use a discrete model to represent a protein without completely losing its sequence-order information. The PseAAC method generates 20+m components, the first 20 of which constitute the 20 components of the AAC method and the other m components are the sequence-order components.

We also have the n-gram text methods [START_REF] Islam | Protein classification using modified n-gram and skip-gram models[END_REF] and the most commonly used n-gram method is the bi-gram method (n=2) [START_REF] Dehzangi | PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction[END_REF]- [START_REF] Martin | Algorithms for bigram and trigram word clustering1This paper is based on a communication presented at the ESCA Conference EUROSPEECH'95 and has been recommended by the EUROSPEECH'95 Scientific Committee.1[END_REF]. It is quite simple to use and can extract very relevant information from amino acid sequences. However, when used directly with amino acid residues, it is no longer as effective as it should be. For example, an amino acid sequence that does not contain all amino acid residues produces enough null values, which could affect the predictive performance of the machine learning methods adopted. As an alternative, some authors propose to use the linear probabilities obtained from the PSSM of the profiles of the different amino acids instead of the primary amino acid sequences [START_REF] Dehzangi | PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction[END_REF], [START_REF] Göktepe | Prediction of Protein-Protein Interactions Using An Effective Sequence Based Combined Method[END_REF], [START_REF] Sharma | A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition[END_REF]. However, as Chou says in [START_REF] Chou | Pseudo Amino Acid Composition and its Applications in Bioinformatics[END_REF], this kind of model failed to work when the query protein did not have significant homology to proteins with known characteristics.

In our study we used the bi-gram characteristics directly obtained from two physicochemical properties of the amino acids hydrophobicity [START_REF] Charles | Contribution of Hydrophobic Interactions to the Stability of the Globular Conformation of Proteins[END_REF] and hydrophilicity [START_REF] Hopp | Prediction of protein antigenic determinants from amino acid sequences[END_REF]. Then, we use an artificial neural networks (ANN) [START_REF] Marugán | A survey of artificial neural network in wind energy systems[END_REF] to effectively learn the representations of protein pairs. To demonstrate the effectiveness of our model, the proposed method is applied to a large set of human PPI datas from the work of Pan et al. [START_REF] Pan | Large-Scale Prediction of Human Protein-Protein Interactions from Amino Acid Sequence Based on Latent Topic Features[END_REF].

II. MATERIALS AND METHODS

A. Overview

We obtained human PPIs dataset from the HPRD database from the work of Pan et al [START_REF] Pan | Large-Scale Prediction of Human Protein-Protein Interactions from Amino Acid Sequence Based on Latent Topic Features[END_REF]. These datasets are reference data used by several authors in the prediction of PPIs and are freely accessible. For the rest, we used the developed technique to extract the sequence features, which allowed us to extract bigram physicochemical properties features. Finally, we applied artficial neural network with an architecture consisting of an input layer, two hidden layers and an output layer. Artiificial neural networks exceed in classification and recognition of form [START_REF] Du | DeepPPI: Boosting Prediction of Protein-Protein Interactions with Deep Neural Networks[END_REF]- [START_REF] Sun | A multimodal deep neural network for human breast cancer prognosis prediction by integrating multidimensional data[END_REF], which motivated the choice of this classifier. The results obtained are rather encouraging in the prediction of protein-protein interactions with greater predictive accuracy. Fig. 1 shows us the flow chart of our study.

B. Dataset

Our study focuses on the implementation of a sequencebased model for predicting human PPIs. We acquired a set of human PPI datas from the work of pan et al. [START_REF] Pan | Large-Scale Prediction of Human Protein-Protein Interactions from Amino Acid Sequence Based on Latent Topic Features[END_REF]. The positive data of this data set were acquired from the HPRD database. HPRD is one of the most comprehensive databases in terms of human PPI datas. Duplicate pairs of PPIs were eliminated to ensure accurate representativeness of the data. To form negative pairs (representing pairs without interaction), proteins located in distinct subcellular locations were matched. This locational information was obtained by selecting only human proteins from version 57.3 of the Swiss-prot database (uniprot.org). In addition, in order to maintain a high representation rate, terms marked with an ambiguity such as "likely" or "probable" and sequences composed of less than 50 amino acids or multiple locations have been excluded. Finally, we have a total of 36630 positive interactions from 9630 different human proteins. Concerning the pairs of negative PPIs, 36480 negative interactions were obtained from 1773 proteins from 6 subcellular locations. To avoid unbalanced data [START_REF] Martin | Predicting protein-protein interactions using signature products[END_REF], we took the same number of pairs of positive and negative interactions that is 36000 pairs of PPIs.

We also acquired PPIs data from Helicobacter Pylori (H. pylori) [START_REF] Martin | Predicting protein-protein interactions using signature products[END_REF]. The H. pylori dataset includes 2916 pairs of proteins, 1458 of which are interacting pairs and 1458 non interacting pairs as described by Martin et al [START_REF] Martin | Predicting protein-protein interactions using signature products[END_REF]. This data set will be used to compare our method with other methods.

C. Features extraction method

In this section, we present the proposed bi-gram feature extraction method using hydrophobicity and hydrophilicity distance values. The bi-gram method is one of the n-gram methods used to count all subsets consisting of n amino acids in the sequence. Depending on whether n =1, 2, 3, we will talk about monogram, bi-gram and trigram respectively. This extraction technique is simple and interesting. However, as mentioned in the introduction section, if used directly on primary amino acid sequences, there is a risk of having many zero values in the extracted vector, which may mean a lack of information. Therefore, this can affect the performance of the machine learning algorithm used and make the prediction method uninteresting. To correct it, some authors use the PSSM values obtained from the PSI-BLAST tool. But the approach used is not interesting when the query protein did not have significant homology to proteins with known characteristics. In this work, we do not use the primary protein to represent its sequence and we do not use the PSSM values obtained from the PSI-BLAST tool. We represent the sequence by the physicochemical distance values between the different amino acid residues that will be obtained from the values of the physico-chemical properties. Hydrophobicity and hydrophilicity are the two physicochemical properties used. Indeed, the distribution of hydrophobicity and hydrophilicity of the amino acid residues along a protein chain plays a very important role to its structure and function.

A protein P can be defined as a long chain L of amino acid (also known as amino acid residue). An amino acid residue is represented by a letter of the alphabet. There are 20 native amino acid residues represented by the letters in alphabetic order A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y.

We define a matrix C representing the physicochemical distance values of a given protein. The matrix C will have L rows and 20 columns where L is the length of the primary sequence. The element at ith-row and jth-column is denoted by Ci,j and is computed as follows: , 1 ( , ) , 1 ; 1 20

i j i j C D R R i L j i = ≤≤ ≤ ≤ (1)
Where 1 i is a rank weighting function and

( , )

i j D R R is the physicochemical distance from amino acid Ri to amino acid Rj that was derived based on the residue properties of hydrophobicity and hydrophilicity. It is computed based on the squared eucludian distance [START_REF] Perlibakas | Distance measures for PCA-based face recognition[END_REF] as folloows:

{ } 2 2 1 1 2 2 1 ( , ) ( ) ( ) ( ) ( ) 2 i j j i j i D R R H R H R H R H R     = - + -     (2)
where H1(Ri) and H1(Rj) are the hydrophobicity value for the amino acid Ri and the amino acid Rj resspectively ; while H2(Ri) and H2(Rj) are the hydrophilicity value for the amino acid Ri and the amino acid Rj resspectively. Note that before substituting the values of hydrophobicity and hydrophilicity into Equation (2), they were all subjected to a standard conversion as described by the following equation: A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W andY.

20 0 0 1 1 1 1 2 20 20 0 0 1 1 1 1 20 0 0 2 2 1 2 2 20 20 0 0 2 2 1 1 ( ) ( )/20 ( ) ( ) ( )/20 /20 ( ) ( )/20 ( ) ( ) ( )/20 /20 i k k i i k t k i k k i i k t k H R H H R H R H H R H H R H R H = = = = = = - =   -       - =   -                          (3)
Finally, the frequency of occurrence of transition from i-th amino acid to j-th amino acid is computed as follows:

1 , , 1 , 1 1,
..., [START_REF] Martin | Algorithms for bigram and trigram word clustering1This paper is based on a communication presented at the ESCA Conference EUROSPEECH'95 and has been recommended by the EUROSPEECH'95 Scientific Committee.1[END_REF] 1,.., 20

; ,

L i j k i k j k BP C C i j - + = = × = =  (4)
where L represents the length of the sequence, Ck,,i The physicochemical distance value at k-th row and i-th column and Ck+1,j is the physicochemical distance value at (k+1)-th row and j-th column. The (4) gives 400 values of frequencies which constitute the bi-gram occurrence matrix. We call its 400 elements the bi-gram physicochemical properties features and can be written as a vector V as:

  =   ( 5
)
where T is the transposing operator.

III. PREDICTION WITH ARTIFICIAL NEURAL NETWORK

A. Artificial Neural Network Artificial neural networks (ANN) are a group of models inspired by biological central nervous systems to achieve estimation functions that depend on a large number of inputs. ANNs have been well acknowledged as useful and costeffective tools to solve complex tasks [START_REF] Shi | Artificial neural network based mechanical and electrical property prediction of engineered cementitious composites[END_REF]. In computing, it is represented by a set of layers. These layers are categorized into three classes that are input, hidden and output. Indeed, each network has a single input and output layer. The number of neurons in the input layer is equal to the number of input variables in the processed data. The number of neurons in the output layer is equal to the number of outputs associated with each input.. The ANN model is then validated using an appropriate validation method such as k-fold validation or cross-validation. The real challenge here is to know the number of hidden layers and their neurons. Several architectures are practiced with artificial neural networks [START_REF] Hopp | Prediction of protein antigenic determinants from amino acid sequences[END_REF][START_REF] Zhang | A k-nearest neighbor based algorithm for multi-label classification[END_REF]. One of the basic concepts is the activation function [START_REF] Tong | Prediction of protein allergenicity using local description of amino acid sequence[END_REF].

B. Architecture of artificial neural network

An artificial neural network architecture is a multi-layer stack of single modules, the input layer receives the datas, then the datas information is transformed in a non-linear way through several hidden layers [START_REF] Marugán | A survey of artificial neural network in wind energy systems[END_REF], [START_REF] Bashir | HMV: A medical decision support framework using multi-layer classifiers for disease prediction[END_REF]. Calculate the average gradient and adjust the weights accordingly, before the final outputs are calculated in the output layer. For example, learning an artificial neural network with λ-hidden layer, where each . This output is determined using the following equation:

1 1 1 1 , 1 ( [ . ]
) , {1,..., }, {1,..., }

N i i jj i h f h b i N j N φ φ λ φ φ φ φ φ ω - - - - = = + ∀∈ ∈  ( 6
)
where ωi,j ∈ R are the weights, b φ-1 ∈ R the bias (one by layer), and f φ a non-linear function on the sum of weights.

C. Artificial neural network model

Our model consists of an input layer, two hidden layers and an output layer (Fig. 2.). The input layer has for number of neurons the number of inputs or dimensions of the training data (800 in our case). As for the hidden layers, we have adopted half of the inputs, 400 neurons exactly. we used the sigmoid function as a network activation function. In general, a sigmoid function shows mathematical behaviors, such as being differentiable, being in real value, having a first non-negative or positive derivative, having a local minimum and a local maximum. It is defined according to the following equation:

g(x)=sigmoid(x)= 1/ (1 + e -x ) (7)
where x represents the input from front layer. Such a function ranges the values of the valuations from 0 to 1 and is generally used to produce a Bernoulli distribution. For example:

{ 0 (x) 0.5 1 (x) 0.5 if g if g g ≤ > =  ( 8
)
We used only sigmoid function as activation function.

IV. RESULTS

In this section, we have provided information on the metrics used to evaluate our method. We then gave the results obtained and compared them with the results obtained by other methods and by other authors. We have implemented a sequence-based method for predicting human protein-protein interactions. This was proposed with Python version 3.6.5 and the Jupyter notebook application.

A. Evaluation

In this study, we use the following metrics to measure the performance of our method: overall prediction accuracy (ACC), precision (PRE), sensitivity (SEN) and Matthews correlation coefficient (MCC) which are commonly used to evaluate the performance of a machine learning method. These metrics are expressed by the following equations:

TP TN ACC TP TN FN FP

+ = + + + ( 9
)
TP PRE TP FP = + ( 10
)
TP SEN TP FN = + (11) ( ) ( ) ( ) ( ) ( ) ( )

TP TN FP FN MCC TP FP TP FN TN FP TN FN

× - × = + × + × + × + (12)
where TP and TN respectively represent the correctly predicted interaction and non-interaction pairs, FP and FN respectively represent the incorrectly predicted interaction and noninteraction pairs. The area under the receiver operator characteristics curve (AUC) is also used to evaluate our method. ROC curves are often used to evaluate and visualize the performance of classifiers. These are quite efficient and useful curves. It shows the performance of a classification model at all classification thresholds. The ROC curve traces two parameters including the True Positive Rate (TPR) which is a synonym for recall and is therefore defined as follows:

TP TPR TP FN = + (13)
and the False Positive Rate (FPR) defined as follows:

FP FPR FP FN = + (14)

B. Prediction performance of the proposed method

Through a 5-fold cross-validation on the benchmark training set, 5 models were trained. The results are shown in TABLE I. The best performances of the model are obtained in model 4 with ACC=96.94%, PRE=95.01%, SEN= 98.20% and MCC= 91.80%. The average performance is 96.62% for ACC, 95.38% for PRE, 97.81% for SEN and 91.77% for MCC. The values of the different metrics used are greater than 90%, which reflects a good performance of prediction.

TABLE I. THE RESULTS OF THE PREDICION OF HUMAN PPI WITH FIVE-FOLD CROSS VALIDATION

We compared our method with others feature extraction methods that are widely used in the literature on the same human dataset. These are the Moran autocorrelation method (Moran AC) [START_REF] Xia | Sequence-Based Prediction of Protein-Protein Interactions by Means of Rotation Forest and Autocorrelation Descriptor[END_REF], Pseudo-Amino Acid Composition method (PseAAC) [START_REF] Chou | Pseudo Amino Acid Composition and its Applications in Bioinformatics[END_REF], Local descriptor method (LD) [START_REF] Tong | Prediction of protein allergenicity using local description of amino acid sequence[END_REF]. For the Moran AC method, we took seven descriptors, which are hydrophobicity, hydrophilicity, net charge index of side chains, volume of side chains, polarity, polarizability, solvent accessible surface area. For PseAAC we took 10 tiers. The results in Many models for predicting protein-protein interaction have been proposed, and the most commonly used classifiers are super vector machine (SVM) [START_REF] Wei | Proteinprotein interaction sites prediction by ensembling SVM and sampleweighted random forests[END_REF], Random forest (RF) [START_REF] Genuer | Random Forests for Big Data[END_REF], Knearest neighbors (KNN) [START_REF] Zhang | A k-nearest neighbor based algorithm for multi-label classification[END_REF]. In this section, using the same feature extraction method, we will compare the prediction performance of our method with the method based on these different classifiers on HPRD dataset. To obtain optimal parameters for the SVM classifier, c and g, a grid search method was used. Here, we set c = 0.8 and g = 0.5 using the grid search method. We compared the performance of our model with those of other authors on all human PPI datas. TABLE IV gives us the results obtained. We can see that the performance of the different metrics obtained by these methods (except that of [START_REF] Huang | Using weighted sparse representation model combined with discrete cosine transformation to predict protein-protein interactions from protein sequence[END_REF] work) are on average between 73% and 90%, which is lower than that of our method which is between 90% and 98% on average. As for the [START_REF] Huang | Using weighted sparse representation model combined with discrete cosine transformation to predict protein-protein interactions from protein sequence[END_REF] work, our method has a better performance in accuracy and sensitivity which is 96.62% against 96.30% and 97.81% respectively. On the other hand, the [START_REF] Huang | Using weighted sparse representation model combined with discrete cosine transformation to predict protein-protein interactions from protein sequence[END_REF] work has a better performance in precision which is 99.59% compared to 95.38% with our method and also in the Mathews correlation coefficient which is 92.82% compared to 91.77% with our method. We also compared the performance of our method on H. Pylori dataset. TABLE V gives us the results. We can see that the performances obtained by our method is between 73% and 88% which is not satisfactory. We have 87.71% in accuracy, 86.35% in precision, 87.10% in sensitivity and 73.97% in MCC. This low rate overall can be explained by the fact that H. pylori dataset is not large. We also evaluated the results of our method using the AUC analysis on the HPRD and H. pylori datasets and compared them with the results of the authors who used the AUC analysis. the following figure shows the ROC curve in our case. Co-evolutionary divergence [START_REF] Liu | Human protein-protein interaction prediction by a novel sequence-based co-evolution method: coevolutionary divergence[END_REF] 70 LocFuse [START_REF] Zahiri | Proteinprotein interaction prediction from PSSM based evolutionary information[END_REF] 85 Weighted skip-sequential [START_REF] Göktepe | Prediction of Protein-Protein Interactions Using An Effective Sequence Based Combined Method[END_REF] 93 Our method

91.60

Hps [START_REF] Nanni | High performance set of PseAAC and sequence-based descriptors for protein classification[END_REF] 92.50

Weighted sparse [START_REF] Huang | Using weighted sparse representation model combined with discrete cosine transformation to predict protein-protein interactions from protein sequence[END_REF]

89,85

Weighted skip-sequential [START_REF] Göktepe | Prediction of Protein-Protein Interactions Using An Effective Sequence Based Combined Method[END_REF] 93,71 Our method 92.60

V. DISCUSSION

The bi-gram method allows to obtain the correlation values of two amino acids. It is a method widely used in the prediction of protein-protein interactions. However, when used directly with the primary amino acid sequence, it does not produce a vector of interest for a machine learning algorithm. To overcome this, the authors use PSSM values that can be obtained from the NCBI website with the PSI-BLAST tool. But, again, we may not have the expected results since the PSI-BLAST tool relies on the similarity of the protein for which we want the PSSM value and a protein from the database you have chosen.

The method presented in this paper uses two physicochemical distance values, hydrophobicity and hydrophilicity, to calculate the bi-gram characteristic vectors for each protein sequence. It must be noted that there are many physicochemical properties of amino acids, however in [START_REF] Charles | Contribution of Hydrophobic Interactions to the Stability of the Globular Conformation of Proteins[END_REF] and [START_REF] Hopp | Prediction of protein antigenic determinants from amino acid sequences[END_REF], we can see that hydrophobicity and hydrophilicity represent well the amino acids.

VI. CONCLUSION

The study of protein-protein interactions remains an important field for drug discovery. As biological and medical datas are constantly growing, algorithms must be adopted to support the large amount of datas. Among the large extraction technique adopted, we have the bi-gram method. Indeed, to correct the problem related to the application of the bi-gram method directly to the primary sequences, the authors use the PSSM values obtained from the PSI-BLAST tool. However, this approach failed to work when the query protein did not have significant homology to proteins with known characteristics. In this study, we used a feature extraction method based on the physicochemical properties into bi-gram and we adopted artificial neural network as machine learning algorithm. The architecture consisting of an input layer, two hidden layers and an output layer.

Our feature extraction method combined with ANN outperforms some predicting model in the literature and can be considered as an interesting method for predicting proteinprotein interactions. In future work, we will add other characteristic vectors to the vectors of our method in order to increase the number of key elements that best represent proteinprotein interaction.

  the original hydrophobicity and hydrophilicity values of the amino acid Ri respectively. Their values are taken from[START_REF] Charles | Contribution of Hydrophobic Interactions to the Stability of the Globular Conformation of Proteins[END_REF] and[START_REF] Hopp | Prediction of protein antigenic determinants from amino acid sequences[END_REF] respectively. Thek  (k = 1, …,20) is used to represent the 20 native amino acids according to the alphabetical order of their single-letter codes:

Fig. 1 .

 1 Fig. 1. Flow chart of our study. First, for each amino acid sequence in the dataset, we calculate the physicochemical distance values and then form a Lx20 matrix represented by the matrix C, where L is the length of the amino acid

. 1 {

 1 The first layer takes into consideration the global inputs of the network while the last layer returns the outputs as a posteriori probability H λ Now consider the output of the i-th neuron from the layer H φ

Fig. 2 .

 2 Fig. 2. Model of ANN

Fig. 3 .

 3 Fig. 3. ROC curve for ANN trained on the HPRD dataset We have obtained good results compared to some of the results of other authors. TABLES VI and VII give us the results on the HPRD and H. Pylori datasets respectively.

  80

  TABLE II clearly show that our feature extraction method exceeds in the different metrics with a minimum of 20% more than others in accuracy, 1.14% more than others in precision, 0.39% more than others in sensitivity and 1.62% more than others in Mathews correlation coefficient. We can say that our bi-gram physicochemical properties feature extraction method presents good interaction information.

	1.
	Test set	ACC (%)	PRE (%)	SEN (%)	MCC (%)
	1	95.74	94.90	97.89	90.92
	2	96.57	93.74	96.50	90.82
	3	96.74	95.67	97.29	92.68
	4	96.94	95.01	98.20	91.80
	5	96.89	95.24	98.44	92.74
	Average	96.62	95.38	97.81	91.77

TABLE II .

 II

		PERFORMANCE COMPARISON WITH DIFFERENT FEATURE
		EXTRACTION TECHNIQUE
	Methods	ACC (%)	PRE (%)	SEN (%)	MCC (%)
	AC-ANN	95.03	93.20	97.04	90.18
	PseAAC-ANN	95.24	94.12	98.01	91.05
	LD-ANN	93.91	92.57	95.43	87.94
	Our method	96.62	95.38	97.81	91.77

TABLE III .

 III

		PERFORMANCE COMPARISON WITH DIFFERENT CLASSIFIERS
	Methods	ACC (%)	PRE (%)	SEN (%)	MCC (%)
	SVM	96.75	93.69	97.25	90.21
	KNN	95.63	94.02	96.52	91.39
	RF	96.40	94.90	97.98	92.85
	Our method	96.62	95.38	97.81	91.77

TABLE IV .

 IV

	PERFORMANCE COMPARISON OF DIFFERENT METHODS ON THE
		HPRD DATASET
	Method	ACC (%)	PRE (%)	SEN (%)	MCC (%)
	ELM [39]	88.87	N/A	88.31	50.22
	DCT+SMR [40]	96.30	99.59	N/A	92.82
	Domain [41]	82.11	84.73	80.40	80.07
	Skip-sequential	73.81	74.11	73.24	72.79
	[18]
	Our method	96.62	95.38	97.81	91.77

TABLE V .

 V

	PERFORMANCE COMPARISON OF DIFFERENT METHODS ON THE
		H. PYLORI DATASET
	Method	ACC (%)	PRE (%)	SEN (%)	MCC (%)
	HKNN [42]	84	84	86	N/A
	Ens. ELM [39]	87.5	86.15	88.95	N/A
	Ens. KNN [43,	86.60	85	86.70	N/A
	p.]
	Multiscale [44]	86.74	86.43	87.01	76.99
	Weighted	73.81	74.11	73.24	72.79
	sparse [40]
	Our method	87.71	86.35	87.10	73.97

TABLE VI

 VI

	.	PERFORMANCE COMPARISON OF DIFFERENT METHODS ON THE HPRD DATASET USING AUC
		Prediction model	AUC (%)
	Mixed Bayesian model [45]

TABLE VII .

 VII

	PERFORMANCE COMPARISON OF DIFFERENT METHODS ON THE H. PYLORI DATASET USING AUC
	Prediction model	AUC (%)
	Wavelet [48]

ACKNOWLEDGMENT

Many thanks to all the professors, doctors and colleagues who helped me in carrying out the research work.


Olivier Boffoué

Bertrand Lenoir

Alexandre Jacquot

Valerie Brien

Christine Bellouard

Anne Dauscher
email: dauscher@mines.inpl-nancy.fr

Valérie Brien

Transport properties of Ti-Ni-Zr films grown by pulsed laser deposition

Keywords: numbers: 81.15.Fg, 61.44.Br, 73.50.-h A. Thin Films; D. Transport Properties, Quasicrystals

teaching and research institutions in France or abroad, or from public or private research centers.

Introduction

Quasicrystalline alloys have been discovered in 1984 by Shechtman et al. [1]. This class of materials, which exists in a variety of chemical systems, exhibits the particularity to crystallize in forbidden rotational symmetries structures (icosahedral, decagonal…) and to display a long-range aperiodic order not compatible with three-dimensional translation.

A constant effort has been devoted to the attainment of thermodynamically stable quasicrystals of high structural quality and to the understanding of their unusual and interesting chemical and physical properties, in relation with potential applications [2,3]. Deviations from standard temperature and composition dependences of the transport coefficients make that quasicrystalline and approximant alloys are positioned as intermediate materials between metals and semiconductors. [4, and refs therein]. Long-range quasiperiodicity, local atomic environment, or combined effects have been given as the possible origin of these particular features. Most of the experimental and theoretical works concerning transport properties has been conducted with Al-based materials, while Ti-based materials have not received much attention yet [5,6].

The aim of our work was to contribute to the study of the physical properties of the Ti-Ni-Zr icosahedral quasicrystalline phase that has attracted much attention until now for its ability to absorb reversibly large amounts of hydrogen making it an excellent candidate for constitutive materials in battery [7][8][9]. For these particular applications, the materials should be used in the thin film form. We previously showed that pulsed laser deposition (PLD) is an appropriate method to synthesize thermodynamically stable quasicrystalline Ti-Ni-Zr layers [10,11].

In this letter, we report on the electrical properties and thermopower measurements of Ti41.5Ni17Zr41.5 quasicrystalline films prepared by PLD at different substrate temperature.

Experimental

The deposition of the thin films was carried out in a high vacuum stainless steel chamber already described in [10], under a base pressure of 10 -7 -10 -8 mbar. The films were grown at different deposition temperatures Ts (298, 373 and 533 K) onto glass substrates fixed parallel to the target at a distance of 45 mm. The Ti-Ni-Zr targets of 18 mm in diameter were cut from an ingot prepared by radio-frequency melting. They were freshly polished before each new ablation. A Quantel Nd:YAG laser (= 1064 nm,  = 10 ns, 10 Hz repetition rate) was used as the ablation source. The laser fluence was fixed at 72 J/cm 2 .

The electrical resistivity and the thermoelectric power of both the layers and the target were measured simultaneously in the temperature range 77-310 K on a home-built sample holder similar to that developed by Stölzer et al. [START_REF] Stölzer | Proceedings of the 4 th European Workshop on Thermoelectrics[END_REF]. For the films, the geometry of the samples was defined by the shape of the mask through which the film was deposited. It consisted of a 20 x 3 mm 2 rectangular strip with tabs for potential leads. Film thickness was determined by mechanical profilometer measurements performed in the neighbourhood of the voltage leads. The electrical contacts to the sample were made with silver paste. The resistance was measured by a dc standard four-probe resistance technique using current reversals to subtract the thermal voltages. To measure the thermopower of the samples, a differential method has been used. Two small copper-constantan thermocouples (70 m in diameter) were pressed against the sample by springs. A temperature difference  (< 2 K), either positive or negative, was created on the sample by the alternating use of two small heaters in order to produce the thermoelectric voltage V. A least mean square linear regression was applied on the V - data to determine the slope and to extract the thermopower. All data were taken using high-speed computer data acquisition software and instrumentation.

Electrical resistivity, transverse magnetoresistance and Hall measurements of the layers were also conducted from 5 -300 K in a Quantum Design cryostat using an ac current at a frequency of 30 Hz. For these experiments, we also used rectangular shaped sample strips with tabs defined by a mask, but having a different size than previously (8 x 2 mm 2 ). In the case of the electrical resistivity, a good agreement of the two measurements performed on the two different experimental set-ups was observed in the temperature range 80 -300 K.

Results and discussion

All the films investigated show a similar thickness of about 700 nm. Electron probe microanalysis measurements show that the Ti41.5Ni17Zr41.5 composition is reached within a 1 at. % accuracy, independently of the substrate temperature Ts during growth. This composition was identified by Kelton et al. [9] as being the nominal composition of the icosahedral phase. Non congruent transfer of stoichiometry from the target to the substrate was shown to occur, contrarily to what is generally expected from the PLD method [10]. The observed loss of Ti explains why a target of nominal Ti45Ni17Zr38 composition has been used to ensure the attainment of the seeked composition. This target was made of several phases: mainly the icosahedral phase, the approximant phase of the icosahedral phase (phase W) and Laves phases.

Combined TEM and X-ray diffraction investigations revealed that the films are quasicrystalline, but that their morphology and microstructure are quite different, depending on the substrate temperature. X-ray patterns are displayed in figure 1. The sample deposited at the highest temperature (Ts = 533 K) is made of the icosahedral phase under the shape of around 50 nm wide textured columns constituting around 80% in volume of the 700 nm thick film. The columns lay on an underlying nano-quasicrystalline domain. The 5-fold axis of the icosahedral structure statistically matches with the growth direction. The film prepared at 373 K is essentially made of nanocrystallites whose statistical size is around 3 nm. The film prepared at 298 K is also made of nanocrystallites. The difference between this structure (prepared at 298 K) and the one deposited at 373 K is that the nanoparticles are statistically smaller (2 nm) and that they might be embedded in an amorphous matrix. More detailed information about the structure investigations of the films is reported elsewhere [11].

The room-temperature value of the electrical conductivity of the Ti-Ni-Zr layers is about 4850 Ω -1 .cm -1 whatever Ts is, while for the target  ~ 5300 Ω -1 .cm -1 . The deviation to the value of the films is attributed to the difference in both the chemical composition and structural disorder. Compared to the other most investigated quasicrystalline systems containing transition metals like Al-Cu-(Fe, Re, Ru) or Al-Pd-(Mn, Re),  is one or two orders of magnitude higher in the i-Ti41.5Ni17Zr41.5 alloy at room temperature. Figure 2 shows the temperature dependence of the normalized conductivities / (300 K) for the films and the target. The temperature coefficient  of the conductivity is positive for the films whatever Ts is. This result is consistent with the  - Mooij correlation [START_REF] Mooij | [END_REF] observed in lowconductivity metallic alloys and with previous results obtained by Zavaliche et al. [5] on rapidly quenched Ti-Ni-Zr ribbons, but is in disagreement with the recent study of Azhazha et al. [6] where  was found negative. We also observed a negative temperature coefficient for the target but we believe that it is correlated to the low target quality containing crystalline phases.

Little differences are observed in the behaviour of / (300 K) for the films prepared at 298 and 373 K, likely due to their very similar morphology and microstructure. For the film deposited at 533 K, that shows larger icosahedral domains, it is seen that the normalized conductivity becomes slowly temperature dependent. One general trend observed in quasicrystals is that the conductivity decreases when the perfection of the sample increases [14]. Figure 2 seems to suggest the contrary. If we exclude a compositional effect and the presence of minor impurity phases in the films, the deviation to the usual behaviour should find its origin in the different microstructure. Effects linked to the anisotropy could play a role since the film becomes more textured with an orientation of the 5-fold axis perpendicular to the substrate as Ts increases [11].

The temperature dependence of the normalized conductivity of the films was approximated by a power law following the procedure proposed in [15]. For Ts = 298 and 373 K, the exponent n is to 1.5 for T < 60 K suggesting a semi-metallic-like behaviour, and to 0.6 for T > 60 K suggesting a weak localization behaviour. For the film prepared at Ts = 533 K, two zones can also be observed: for T < 100 K, n ~ 0.8, and at higher temperatures n ~ 0.5. These crossovers of the changes in behaviour of the conductivity versus temperature from one conduction mechanism to another have also been observed by Zavaliche et al. [5] and have been attributed to a "depletion" of the electronic states near the Fermi level with increasing temperature.

The Hall coefficient of the films was found to be field independent in the range of fields, B, investigated (-4 ≤ B ≤ +4 T) over the entire temperature range studied. The Hall coefficient is low and positive whatever the substrate temperature Ts is. The Hall carrier concentration at room temperature is ~ 8 -9 x 10 22 cm -3 for films grown at Ts = 298 and 373 K and is lower for Ts = 533 K (~ 5.7 x 10 22 cm -3 ). These values are typical of those observed in disordered metallic glasses containing transition metals [16]. The Hall carrier concentration is slowly temperature dependent for Ts = 298 and 373 K (variations are less than 10%) over the temperature range investigated. The deviation from the metallic-glass behaviour is more pronounced in the case of Ts = 533 K with variations of the order of 30 % as illustrated in figure 3.

The transverse magnetoresistance, , of the films was found to be positive and remained very small in the range of fields studied. For example, at 5 K,  ~ 0.016 % and 0.031% for Ts = 298 and 373 K respectively. Higher values have been observed for the films prepared at Ts = 533 K (figure 4).

Figure 5 shows the thermopower S as a function of temperature for the Ti-Ni-Zr layers and the target. The films have small thermopower values, typical of a metal; S displays essentially linear temperature dependence, as also reported before [5,17], with positive S values consistent with the positive Hall coefficient measured. The value reached at room temperature strongly depends on the microstructure of the layers: 2.7 V/K for the films prepared at Ts = 298 and 373 K and 8.3 V/K for Ts = 533 K. The thermopower of the target varies also linearly but the value starts out negative at low temperatures, crosses zero, and has a room temperature value of 0.7 V/K.

The linear temperature dependence of the thermopower S is indicative of a diffusion thermopower. Sensitivity of S(T) to both composition and microstructure changes can be related to the special sensitivity of the thermopower to the details in the density of states around the Fermi Level.

Figure 1 :

 1 Figure 1 : X-ray diffraction patterns of the films prepared at a/ 298 K, b/ 373 K, and c/ 533 K. a.u. stands for arbitrary units.

Figure 2 :

 2 Figure 2: Temperature dependence of the normalized conductivity /(300 K) for the i-Ti41.5Ni17Zr41.5 films and the Ti45Zr38Ni17 target.

Figure 3 :

 3 Figure 3: Temperature dependence of the Hall carrier concentration for the film prepared at Ts = 533 K.

Figure 4 :

 4 Figure 4: Magnetoresistance / versus magnetic field for different temperatures and for the film prepared at Ts = 533 K.

Figure 5 :

 5 Figure 5: Temperature dependence of the thermopower S from 77 to 310 K for the i-Ti41.5Ni17Zr41.5 films and the Ti45Ni17Zr38 target.