## Abstract

**Summary:** Gene prioritization refers to a syndicate of computational techniques for inferring disease genes through a determine of trail genes and cautiously chosen similarity criteria. Test genes are scored based on their average similarity to the train plant, and the rankings of genes under versatile similarity criteria are aggregated via statistical methods. The contributions of our work are threefold : ( one ) first, based on the realization that there is no unique way to define an optimum aggregate for rankings, we investigate the predictive choice of a number of newfangled collection methods and known fusion techniques from machine learn and social choice theory. Within this context, we quantify the charm of the number of training genes and similarity criteria on the diagnostic quality of the aggregate and perform in-depth cross-validation studies ; ( two ) second, we propose a newfangled approach to genomic data collection, termed HyDRA ( Hybrid Distance-score Rank Aggregation ), which combines the advantages of score-based and combinatorial collection techniques. We besides propose incorporating a new top-versus-bottom ( TvB ) weighting sport into the hybrid schemes. The TvB feature ensures that aggregates are more authentic at the top of the number, preferably than at the bottomland, since only top candidates are tested experimentally ; ( three ) third base, we propose an iterative procedure for gene discovery that operates via successful augmentation of the set of training genes by genes discovered in previous rounds, checked for consistency. **Motivation:** Fundamental results from social choice theory, political and calculator sciences, and statistics have shown that there exists no consistent, average and alone way to aggregate rankings. rather, one has to decide on an collection approach using predefined set of desirable properties for the aggregate. The collection methods fall into two categories, score- and distance-based approaches, each of which has its own drawbacks and advantages. This work is motivated by the observation that merging these two techniques in a computationally effective manner, and by incorporating extra constraints, one can ensure that the predictive quality of the resulting collection algorithm is very high. **Results:** We tested HyDRA on a count of gene sets, including autism, breast cancer, colorectal cancer, endometriosis, ischemic stroke, leukemia, lymphoma and osteoarthritis. furthermore, we performed iterative gene discovery for glioblastoma, meningioma and breast cancer, using a consecutive augment list of training genes related to the Turcot syndrome, Li-Fraumeni condition and other diseases. The methods outperform state-of-the-art software tools such as ToppGene and Endeavour. Despite this witness, we recommend as best practice to take the union of top-ranked items produced by different methods for the final aggregate tilt. **Availability and implementation:** The HyDRA software may be downloaded from : hypertext transfer protocol : //web.engr.illinois.edu/∼mkim158/HyDRA.zip

**Contact:** mkim158 @ illinois.edu **Supplementary information:** Supplementary data are available at Bioinformatics on-line .

## 1 Introduction

identification of genes that predispose an person to a disease is a problem of great interest in medical sciences and systems biota ( Adie et al., 2006 ). The most accurate and powerful methods used for identification are experimental in nature, involving convention and disease samples ( Cardon et al., 2001 ). Experiments are time consuming and dearly-won, complicated by the fact that typically, multiple genes have to be jointly mutated to trigger the attack of a disease. Given the big total of human genes ( ≥25 000 ), testing flush relatively minor subsets of pairs of campaigner genes is prohibitively expensive ( Risch and Merikangas, 1996 ). To mitigate this write out, a arrange of predictive analytic and computational methods have been proposed under the collective name gene prioritization techniques. Gene prioritization refers to the complex procedure of ranking genes according to their likelihoods of being linked to a certain disease. The likelihood function is computed based on multiple sources of evidence, such as sequence similarity, linkage analysis, gene note, functionality and expression activity, gene merchandise attributes—all determined with esteem to a laid of train genes. A wide range of tools has been developed for identifying genes involved in a disease ( Köhler et al., 2008 ; Kolde et al., 2012 ; Pihur et al., 2009 ), as surveyed ( Tiffin et al., 2006 ). Existing software includes techniques based on network information, such as GUILDify ( Guney et al., 2014 ) and GeneMANIA ( Warde-Farley et al., 2010 ), data mine and machine learning-based approaches as described in ( Perez-Iratxeta et al., 2002 ), POCUS ( Turner et al., 2003 ) SUSPECTS ( Adie et al., 2006 ) and ( Yu et al., 2008 ), and methods using statistical analysis, including Endeavour ( Aerts et al., 2006 ; De Bie et al., 2007 ), ToppGene ( Chen et al., 2009 ) and NetworkPrioritizer ( Kacprowski et al., 2013 ). here, we focus on statistical approaches coupled with raw combinatorial algorithm for gene prioritization, and emphasize one aspect of the prioritization operation : membership collection. The problem of aggregating rankings of clear-cut objects or entities provided by a number of experts, voters or search engines has a rich history ( Fishburn, 1970 ). One of the identify findings is that versatile voting paradoxes arise when more than three candidates are to be ranked : it is frequently potential not to have a candidate that wins all pairwise competitions ( the Condorcet paradox ) and it is theoretically impossible to guarantee the universe of an aggregate solution that meets certain predefined set of criteria [ such as those imposed by Arrow ’ s impossibility theorem ( Fishburn, 1970 ) ]. These issues carry over to aggregation methods used for gene discovery, and as a result, the rank-ordered lists of genes heavily depend on the particular collection method acting used. ) and statistical methods. In the bioinformatics literature, the aggregation methods of choice are statistical in nature, relying on pre-specified hypotheses to evaluate the distribution of the gene rankings. One of the earliest prioritization softwares, Endeavour, uses the Q-statistics for multiple significance testing, and measures the minimum false discovery rate at which a test may be called significant. In particular, rankings based on different similarity criteria are combined via order statistics approaches. For this purpose, one uses the rank ratio (normalized ranking) of a gene g for m different criteria,

r1 ( guanine ), …, rm ( deoxyguanosine monophosphate ) and recursively computes the Q-value, defined as Qi ( r1 ( gigabyte ), …, rm ( gravitational constant ) ) =m ! ∫0r1 ( deoxyguanosine monophosphate ) ∫s1r2 ( gigabyte ) …∫sm−1rm ( gram ) dsmdsm−1…ds1. Two families of methods have found wide applications in rank collection : combinative methods ( including score- and distance-based approaches ) ( Kemeny, 1959 ) and statistical methods. In the bioinformatics literature, the collection methods of choice are statistical in nature, relying on pre-specified hypotheses to evaluate the distribution of the gene rankings. One of the earliest prioritization softwares, Endeavour, uses the Q-statistics for multiple meaning test, and measures the minimum false discovery rate at which a test may be called significant. In particular, rankings based on different similarity criteria are combined via order statistics approaches. For this purpose, one uses the rank and file ratio ( anneal ranking ) of a gene g for meter different criteria, and recursively computes the Q-value, defined as Post-processed Q-values are used to create the resulting rank of genes. The drawbacks of the method are that it is based on a null hypothesis that is unmanageable to verify in practice, and that it is computationally expensive, as it involves evaluating an m-fold built-in. To enable efficient scale of the method, Endeavour resorts to approximating the Q-integral. The influence of the estimate errors on the final rate is hard to assess, as small changes in scores may result in significant changes of the aggregate orderings. similarly, ToppGene uses a well-known statistical approach, called the Fisher χ2 method acting. It first base determines the p-values of similarity score indexed by joule, denoted by phosphorus ( j ), for j=1, …, m. The p-values are computed through multiple preprocessing stages, involving estimate of the information contents ( i.e. weights ) of note terms, setting-up a similarity criteria based on Sugeno fuzzy measures ( i.e. non-additive measures ) ( Popescu et al., 2006 ), and performing meta-testing. The manipulation of fuzzed measures ensures that all similarities are non-negative. then, under the guess of independent tests, ToppGene uses Fisher ’ sulfur inverse χ2 solution, stating that −2∑j=1mlogp ( joule ) →χ2 ( 2m ) . here, χ2 ( 2m ) stands for the chi-square distribution with 2 m degrees of freedom. The result is asymptotic in nature, and based on possibly impractical independence assumptions. A number of methods, and additive score methods in detail, have the drawback that they tacitly or implicitly trust on the assumption that ( one ) only the sum score matters, and the symmetry between the number of criteria that highly ranked the gene and those that ranked it very moo is irrelevant. For example, outlier rankings may reduce the overall ranking of a gene to the period that it is not considered a disease gene candidate, while the outlier itself may be a baffling criterion. To illustrate this observation, consider a gene that was ranked 1st, 2nd, 1st, 20th by four criteria. At the lapp clock, consider another gene that was ranked 6th by all four criteria. It may be unclear which of these two genes is more probable to be involved in the disease, given that additive score methods would rank the two genes evenly ( as one has ( 1 + 2 + 1 + 20 ) /4 = 6 ). however, it appears reasonable to assume that the first candidate is a more dependable choice for a disease gene, as it had a very eminent rank for three out of four criteria ; and ( two ) no eminence is made about the accuracy of ranking genes in any separate of the list ; i.e. the aggregate rank has to be uniformly accurate at the top, middle and penetrate of the list. Clearly, neither of the two aforesaid assumptions is absolve in the gene prioritization summons : there are many instances where genes similar entirely under a few criteria ( such as sequence similarity or linkage distance ) are involved in the lapp disease pathway. furthermore, as the goal of prioritization is to produce a tilt of genes to be experimentally tested, lone the highest rate campaigner genes are important and should have higher accuracy than early genes in the tilt. In addition, most known collection methods are highly sensitive to outliers and ranking errors. We propose a fresh overture to gene prioritization by introducing a numeral of fresh collection paradigm, which we jointly refer to as HyDRA ( Hybrid Distance-score Rank Aggregation ). The kernel of HyDRA is to combine combinative approaches that have universal axiomatic underpinnings with statistical attest pertaining to the accuracy of individual rankings. Our prefer distance measure for combinatorial collection is the Kendall distance ( Kendall, 1938 ), which counts the issue of pairwise disagreement between two rankings, and was axiomatically postulated by Kemeny ( 1959 ). The Kendall distance is closely related to the Kendall absolute correlation coefficient ( Dwork et al., 2001 ; Kendall, 1948 ). As such, it has many properties useful for gene prioritization, such as monotonicity, support and Pareto efficiency ( Thanassoulis, 2001 ). The Kendall distance can be generalized to take into account positional relevance of items, as was done in our companion article ( Farnoud et al., 2012, 2014 ). There, it was shown that by assigning weights to pairs of positions in rankings, it is possible to ( i ) eliminate negative outliers from the collection process, ( two ) include quantitative data into the aggregate and ( three ) ensure higher accuracy at the top of the rank than at the bottom. The contributions of this workplace are threefold. First, we introduce new weighted distance measures, where we compute the weights based on statistical tell of a function of the difference between p-values of adjacently ranked items. Aggregation weights based on statistical evidence improve the accuracy of the combinatorial collection routine and make them more robust to estimate errors. second, we describe how to scale the weights obtained based on statistical evidence by a decreasing sequence of TvB ( Top versus Bottom ) multipliers that ensure even higher accuracy at the top of the aggregate tilt. As collection under the Kendall metric is NP-hard ( Non-deterministic Polynomial-time hard ) ( Bartholdi et al., 1989 ), and the same is true of the slant Kendall metric unit, we propose a 2-approximation method acting that is stable under small perturbations. Aggregation is accomplished via burden bipartite coordinated, such as the Hungarian algorithm and derivatives thereof ( Kuhn, 1955 ). third gear, we test HyDRA within two operational scenarios : cross-validation and disease gene discovery. In the early subject, we assess the performance of different hybrid methods with respect to the choice of the weight function and different act of test and training genes. In the latter case, we adapt collection methods to gene discovery via a new iterative re-ranking procedure.

## 2 Systems and methods

In our subsequent exposition, we use greek lower character letters to denote complete linear orders ( permutations ), and unless explicitly mentioned otherwise, our findings besides hold for partial ( incomplete ) permutations. Latin lower case letters are reserved for score vectors or scalar scores, and which of these entities we refer to will be clear from the context. The act of test genes equals n, while the number of similarity criteria equals m. Throughout the article, we besides use [ k ] to denote the set { 1, …, k } and Sn to denote the rig of all permutations on n elements—the symmetrical group of orderliness newton ! . For a permutation σ= ( σ ( 1 ), …, σ ( newton ) ) , the social station of chemical element one in σ, rankσ ( i ) , equals σ−1 ( i ) , where σ−1 denotes the inverse permutation of σ. For a vector of scores x= ( x ( one ) ) i=1n∈Rn, σx represents a permutation describing the scores in decreasing order, i.e. σx ( one ) = argmax k∈Tix ( kelvin ) , where Ti is defined recursively as Ti=Ti−1∖σx ( one ) , with T0= [ n ] . For example, if x = ( 2.5, 3.8, 1.1, 0.7 ), then σx= ( 2,1,3,4 ) . note that if phosphorus is a vector of p-values, higher scores are associated with smaller p-values, so that argmax should be replaced by argmin. The terms gene and chemical element are used interchangeably, and each permutation is tacitly assumed to be produced by one similarity criteria. For a set of permutations Σ= { σ1, …, σm }, σi= ( σi ( 1 ), …, σi ( n ) ) , an aggregate permutationσ* is a permutation that optimally represents the rankings in Σ. Combinatorial aggregates may be obtained using score- and distance-based methods. note that score and distance-based methods do not make consumption of quantitative data, such as, e.g., p-values ( for the shell of gene prioritization ) or ratings ( for the case of sociable option hypothesis and recommender systems ). In what follows, we briefly identify score and distance-based methods and introduce their hybrid counterparts, which allow to integrate p-values and relevance constraints into combinative collection approaches.

### 2.1 Score-based methods

Score-based methods are the simple and computationally least demanding techniques for rank collection. As inputs, they take a hardened of permutations or partial permutations, Σ= { σ1, …, σm }, σi= ( σi ( 1 ), …, σi ( newton ) ) . For each permutation σi∈Σ, the score rule awards mho ( σi ( 1 ), i ) points to element σi ( 1 ), s ( σi ( 2 ), i ) points to element σi ( 2 ), and sol on. For a fix one, the scores are non-increasing functions of their first index. Each element k∈ [ north ] is assigned a accumulative score equal to ∑j=1ms ( thousand, joule ) . The simplest score method acting is Borda ’ s reckon, for which sulfur ( k, joule ) =n−k+1 autonomous on joule. The Borda reckon and related score rules entirely use positional information in order to provide an aggregate rank. Ignoring actual p-values ( ratings ) may lead to aggregation problems, as illustrated by the adjacent example. **Example 1:** Assume that north = 5 elements were rated according to x= ( 7.0,7.01,0.2,0.45,7.001 ) . The rank induced by this evaluation equals σx= ( 2,5,1,4,3 ) , indicating that chemical element 2 received the highest evaluation, component 5 received the moment highest rate and sol on. According to the Borda dominion, element 2 receives 5 points, chemical element 5 receives 4 points, etc. Despite the fact that candidates 2 and 1 are about tied with scores of 7.01 and 7.0, and that the dispute in their scores may be attributed to computational impreciseness, element 2 receives 5 points while element 1 receives lone 3 points. As a solution, very small differences in ratings may result in large differences in Borda scores. One way to approach the trouble is to quantize the score and work with rankings with ties, rather of full linear orders ( i.e. permutations ). Elements tied in their rank receive the same number of points in the popularize Borda scheme. A prefer option, which we introduce in this workplace, is the Hybrid Borda method.

j, j=1, …, m. The cumulative score of element i in the hybrid Borda setting is computed as Si=∑j=1m ( ∑k≠imp ( potassium, joule ) 1 { p ( thousand, j ) ≥p ( one, j ) } phosphorus ( one, j ) ). Let p ( one, j ) denote the p-value of gene one computed under similarity criteria. The accumulative score of element iodine in the loanblend Borda set is computed as The overall aggregate is obtained by ordering S in a derive orderliness. It is aboveboard to see that the previous score function extends Borda method acting in sol far that it scores an component ( gene ) according to the sum sexual conquest of elements ranked lower than the element. Recall that in Borda ’ mho method, the chemical element ranked one is awarded n−i+1 points, as n−i+1 elements are ranked below it, each receiving the same score 1. In our Hybrid Borda method, each element is awarded a score in accord with the p-values of elements ranked below it. **Example 2:** Let n = 4 and m = 2, where the two ratings equal to p1= ( 0.2,0.3,0.01,0.12 ) and p2= ( 0.1,0.4,0.2,0.35 ) . The Hybrid Borda scores Si for genes i=1,2,3,4 are computed as S1=0.3/0.2+ ( 0.4+0.2+0.35 ) /0.1=11, S2 = 0, S3= ( 0.2+0.3+0.12 ) /0.01+ ( 0.4+0.35 ) /0.2=65.75 and S4= ( 0.2+0.3 ) /0.12+0.4/0.35=5.3. By ordering the values Si in a descending manner, we obtain the overall aggregate σHB= ( 3,1,4,2 ) . Si=∑j=1m ( ∑k≠iwm ( kelvin, joule ) p ( thousand, j ) \mathbbm { 1 } { phosphorus ( k, j ) ≥p ( iodine, j ) } wm ( iodine, j ) phosphorus ( one, j ) ). wm ( iodine, j ) =1n−rankσj ( i ) +1. The loanblend Borda method acting can be extended promote by adding a TvB sport, resulting in the Weighted loanblend Borda method acting. This is accomplished by including increasing ( multiplier ) weights into the seduce aggregates, therefore stressing the top of the list more than the bottom. More precisely, the seduce of gene i is computed as : where one simple choice for the weight unit multipliers that provides thoroughly empirical performance equals

### 2.2 Distance-based methods

Σ= { σ1, …, σm } . For a given distance function between two permutations σ and π,

five hundred ( σ, π ) , aggregation reduces to π=arg minσ∑i=1md ( σ, σi ) Another common approach to rank collection is distance-based crying collection. As ahead, assume that one is given a set of permutations. For a given distance serve between two permutations σ and π, , collection reduces to The aggregate π is frequently referred to as the median of the permutations, and is illustrated in Figure 1.

Fig. 1 .Open in new tabDownload slide Four rankings : σ1, σ2, σ3, σ4 and their sum ( median ) ranking π Fig. 1 .Open in new tabDownload slide Four rankings : σ1, σ2, σ3, σ4 and their sum ( median ) ranking π One of the most important features of distance-based approaches is the option of the distance officiate. table 1 lists two of the most frequently used distances, the Kendall tau distance and the Spearman footrule. As may be seen from the table, the distance measures are combinative in nature, and do not account for scores or p-values. furthermore, as already mentioned in the introduction, it is known that collection under the Kendall metric is computationally hard. however, there exists a number of techniques which provide demonstrable approximation guarantees for the aggregate, including the leaden Bipartite Graph Matching ( WBGM ) method acting ( using the fact that the Spearman distance aggregate is a 2-approximation for the Kendall aggregate ), analogue programming ( LP ) relaxation and Page Rank/Markov chain ( PR ) methods ( Dwork et al., 2001 ; Farnoud et al., 2012 ; Raisali et al., 2013 ).

Table 1.

Distance . | Measurement . | Example . |
---|---|---|

Spearman’s footrule | Sum of differences of ranks of elements. |
dF ( rudiment, cba ) =2+0+2=4 |

Kendall | Minimum number of adjacent swaps of entries for transforming one ranking into another. |
dK ( rudiment, cba ) =3 |

Distance . | Measurement . | Example . |
---|---|---|

Spearman’s footrule | Sum of differences of ranks of elements. |
dF ( rudiment, cba ) =2+0+2=4 |

Kendall | Minimum number of adjacent swaps of entries for transforming one ranking into another. |
dK ( rudiment, cba ) =3 |

Open in new tab

Table 1.

Distance . | Measurement . | Example . |
---|---|---|

Spearman’s footrule | Sum of differences of ranks of elements. |
dF ( rudiment, cba ) =2+0+2=4 |

Kendall | Minimum number of adjacent swaps of entries for transforming one ranking into another. |
dK ( rudiment, cba ) =3 |

Distance . | Measurement . | Example . |
---|---|---|

Spearman’s footrule | Sum of differences of ranks of elements. |
dF ( rudiment, cba ) =2+0+2=4 |

Kendall | Minimum number of adjacent swaps of entries for transforming one ranking into another. |
dK ( rudiment, cba ) =3 |

Open in new tab

The Kendall distance besides does not take into report the fact that the top of a list is more crucial than the remainder of the list. To overcome this problem, we introduced the notion of leaden Kendall distances, where each adjacent swap is assigned a cost, and where the cost is higher at the circus tent of a list. This ensures that in an sum, impregnable showings of candidates are emphasized compared with their watery showings, accounting for the fact that it is often sufficient to have strong similarity with deference to only a subset of criteria. furthermore, such weights ensure that higher importance is paid to the lead of the aggregate rank .w is to compute this distance as the shortest path in a graph describing swap relationships between permutations. The key concepts are illustrated in and , where each edge is assigned a length proportional to its weight W. This weight depends on the swap being made at the top or at some other position in the ranking. Given that it is computationally demanding to aggregate under the weighted Kendall distance, we use a specialized approximation function

Dw ( σ, θ ) for dw, of the form Dw ( σ, θ ) =∑i=1nw ( σ−1 ( one ) : θ−1 ( one ) ), ( 1 ) west ( kelvin : l ) = { ∑h=kl−1W ( h, h+1 ), if k

W ( · ) representing adjacent transpositions

( k k+1 ), ( k+1 k+2 ), …, ( l−1 l ), if k < l, the sum of the weights of edges

W ( · ) representing adjacent transpositions

( liter l+1 ), ( l+1 l+2 ), …, ( k−1 kilobyte ) , if l < k, and 0, if k = l. The theme behind the leaden Kendall outdistance dis to compute this distance as the shortest path in a graph describing barter relationships between permutations. The key concepts are illustrated in Figures 2 and 3, where each edge is assigned a length proportional to its weight W. This system of weights depends on the barter being made at the acme or at some other position in the rank. Given that it is computationally demanding to aggregate under the slant Kendall distance, we use a specify approximation functionfor five hundred, of the formwheredenotes the sum of the weights of edgesrepresenting adjacent transpositionsif kilobyte < lambert, the kernel of the weights of edgesrepresenting adjacent transpositions, if liter < potassium, and 0, if k = l .
Fig. 2 .Open in new tabDownload slide The weighted Kendall distance is the system of weights of the shortest path between two permutations, with edges having possibly different lengths ( weights ). Edges are labeled by the adjacent swaps used to move along the vertices Fig. 3 .Open in new tabDownload slide The burden Kendall distance is the weight of the shortest way between two permutations, with edges having possibly unlike lengths ( weights ). Edges are labeled by the adjacent swaps used to move along the vertices **Example 3:** Suppose that one is given four rankings, ( 1, 2, 3 ), ( 1, 2, 3 ), ( 3, 2, 1 ) and ( 2, 1, 3 ). There are two optimum aggregates according to the Kendall tau distance, namely ( 1, 2, 3 ) and ( 2, 1, 3 ). Both have accumulative distance four from the set of given permutations. If the transposition weights are non-uniform, say such that W ( 12 ) > W ( 23 ) , the solution becomes singular and peer to ( 1, 2, 3 ). If the last ranking is changed from ( 2, 1, 3 ) to ( 2, 3, 1 ), precisely three permutations are optimum from the perspective of Kendall tau collection : ( 1, 2, 3 ), ( 2, 1, 3 ) and ( 2, 3, 1 ). These three solutions give wide different predictions of what one should consider the top campaigner. Nevertheless, by choosing once more W ( 12 ) > W ( 23 ) the solution becomes singular and equal to ( 1, 2, 3 ). 1/2Dw ( σ, θ ) ≤dw ( π, σ ) ≤Dw ( σ, θ ) It can be shown that for any non-negative weight serve west, and for two permutations σ and θ, one has In a company article ( Farnoud et al., 2012 ), we presented extensions of the WBGM and PR collection methods for slant Kendall distances. here, we will pursue the WBGM model, and propose a fresh method acting to compute the weights W ( · ) of edges ( swaps ) based on the p-values of the genes within each similarity criteria ranking. We refer to the resulting leaden model as the Hybrid Kendall method.

P*= ( p* ( i, j ) ) . We use the following

( n−1 ) ×m swap weight matrix

W, with entries W ( one, j ) =c ( P* ( i+1, j ) −P* ( i, joule ) P* ( i+1, joule ) ) ×dn−i, To start, arrange the p-values of all genes based on all similaritycriteria into an nitrogen × thousand matrix P. Next, rearrange the p-values of genes for each criterion in an increasing club, and denote the resultingrearranged matrix by. We use the followingswap slant matrix, with entriesindicating how much it costs to swap positions i and i + 1 for criteria j. The parameters c, d are constants independent of north and molarity, used for standardization and for emphasizing the TvB constraint, respectively. For our simulations, we set speed of light = 10 and d = 1.05, as these choices provided thoroughly empirical performance on synthetic data. The barter matrix put high weight to the top of the list.

Dw ( θ, σ ) , we only need to accumulate each of the contributions from the training permutations in Σ. This may be achieved by using a n × n total cost matrix C, with entry C(i, j) indicating how much it would ‘cost’ for gene i to be ranked at position j: C ( one, j ) =1m∑k=1m∑l=min ( j, σpk ( one ) ) soap ( j, σpk ( i ) ) −1W ( fifty, potassium ) To compute the aggregate based on the approximate distance, we alone need to accumulate each of the contributions from the education permutations in Σ. This may be achieved by using a normality × north sum cost matrix C, with entry C ( iodine, j ) indicating how a lot it would ‘ cost ’ for gene one to be ranked at status joule : The total cost matrix C is the input signal to the WBGM algorithm, where C ( i, j ) denotes the weight of an boundary connecting gene iodine with position j ( see Fig. 4 for an exercise of the bipartite graph, with the left-hand side nodes denoting genes and the right side nodes denoting their possible positions ; the minimum weight coordinated is represented by boldface edges ). To find the minimum cost solution, or the maximum weight match, we used the classical Hungarian algorithm ( Kuhn, 1955 ) implemented in ( Melin, 2006 ).

Fig. 4 .Open in new tabDownload slide A meet in a leaden bipartite graph. Fig. 4 .Open in new tabDownload slide A duplicate in a slant bipartite graph .**Example 4:** Let n = 4 and m = 2, where the two ratings equal to

p1= ( 0.2,0.3,0.01,0.12 ) and

p2= ( 0.1,0.4,0.2,0.35 ) . Then P*= [ 0.010.10.120.20.20.350.30.4 ], W= [ 10.615.794.414.733.51.31 ], C= [ 7.515.15.237.6715.186.982.402.95.39.8812.2810.572.372.24.61 ]. Let newton = 4 and m = 2, where the two ratings equal toand. then For exercise, since gene 3 was ranked 1st and 2nd by the two criteria, C ( 3,3 ) =1/2 ( 10.61+4.41 ) +1/2 ( 4.73 ) =9.88. The minimum cost solution of the matching with cost matrix C, based on the hungarian algorithm yields the aggregate σHK= ( 3,1,4,2 ) .

### 2.3 The Lovász-Bregman divergence method

A previously reported distance measure represents another possible base for performing HyDRA. The so call Lovász-Bregman method acting ( Iyer and Bilmes, 2013 ) calls for a distance measurement between real-valued vectors x∈R≥0n and permutations.

f:2V→R, and for all

S, T⊂V, it holds

f ( S ) +f ( T ) ≥f ( S∪T ) +f ( S∩T ) . The Lovász extension of f, fL(x), equals florida ( x ) =∑i=1nx ( σx ( i ) ) [ f ( Sjσx ) −f ( Sj−1σx ) ],

Sjσx denotes the set

{ σx ( 1 ), …, σx ( j ) } . Note that under some mild conditions, the Lovász extension is convex. Let us next define the differential of f as hσxf ( σx ( joule ) ) =f ( Sjσx ) −f ( Sj−1σx ) dr ( x||σ ) =x· ( hσxf−hσf ) To define the Lovász-Bregman deviation that acts as a distance proxy between rankings and ratings, we start with a submodular set-function, i.e. a function fluorine such that for a finite ground set V, , and for all, it holds. The Lovász reference of degree fahrenheit, degree fahrenheit ( x ), equalswheredenotes the sic. note that under some mild conditions, the Lovász elongation is convex. Let us following define the differential of degree fahrenheit asThen the Lovász-Bregman deviation is defined via the point productDespite its apparently building complex construction, the Lovász-Bregman divergence allows for close form collection for a bombastic class of submodular functions f. The optimum aggregate reduces to the ranking induced by the total of real-valued fink vectors, ordered in a decreasing manner. L ( iodine ) =∑j=1mp ( one, j ) 1n∑i=1np ( i, j ),

σL, where

L= ( L ( one ) ) i=1n. If, as ahead, phosphorus ( one, j ) denotes the p-value of gene i under criteria j, we define the anneal Lovász-Bregman seduce for gene iodine aswhere the total of p-values over criteria is normalized by the average of the p-values for each criterion. The aggregate equals, where **Example 5:** Let n = 4 and m = 2, where the two ratings equal to p1= ( 0.2,0.3,0.01,0.12 ) and p2= ( 0.1,0.4,0.2,0.35 ) . note that 1/n∑i=1np ( i,1 ) =1/4 ( 0.2+0.3+0.01+0.12 ) =0.1575, and 1/n∑i=1np ( i,2 ) =1/4 ( 0.1+0.4+0.2+0.35 ) =0.2625. The Lovász-Bregman scores L ( iodine ), i=1,2,3,4, peer L ( 1 ) = 0.2/0.1575 + 0.1/0.2625 = 1.65, L ( 2 ) = 0.3/0.1575 + 0.4/0.2625 = 3.43, L ( 3 ) = 0.01/0.1575 + 0.2/0.2625 = 0.83, L ( 4 ) = 0.12/0.1575 + 0.35/0.2625 = 2.1. By ordering L ( iodine ) in an ascend manner, one arrives at σLB= ( 3,1,4,2 ) .

## 3 Algorithms and implementation

We now turn our attention to testing different collection methods on lists of p-values generated by Endeavour and ToppGene. The aforesaid methods trust on a dress of train genes known to be involved in a disease. The test genes are compared with all the training genes according to a set of similarity criteria, and the p-value of each comparison is computed in the summons. For example, if the standard is sequence similarity, the p-value reflects the z-value, describing the number of standard deviations above the think of for a given observation. Given the p-values, the question of interest becomes how to aggregate them into one rank. Computing the p-values is a routine procedure, and the challenge of the prioritization summons is to most meaningfully and efficiently perform the collection step. There are two settings in which one can use the collection algorithm. The first context is cross-validation, a verification step that compares the output of an collection algorithm with existing, validated cognition. This mode of operation is aimed at discovering shortcomings and advantages of different methods. In the second determine, termed gene discovery, the bearing is to identify sets of genes implicated in a disease which are not included in the database. clearly, cross-validation studies are necessary first steps in gene discovery procedures, as they explain best collection strategies for different datasets and different similarity and discipline conditions. For both methods, a list of genes involved in a certain disease ( referred to as onset genes ) was obtained from the publicly available databases Online mendelian inheritance in Man ( OMIM ) ( Hamosh et al., 2005 ) and/or the Genetic Association Database ( GAD ) ( Becker et al., 2004 ). Both of these sources trust on the literature for familial association for huge act of diseases, but OMIM typically provides a more conservative ( i.e. short ) number than the GAD. Onset genes were tested along with random genes, obtained by randomly permuting 19, 231 human genes in the GeneCards database ( Safran et al., 2002 ), and retaining the top helping of the list according to the choose number of screen genes.

### 3.1 Cross-validation

We performed a taxonomic, relative performance analysis of the ToppGene and Endeavour collection algorithm and the newly proposed hybrid methods. Given a tilt of roentgen onset genes, we beginning selected deoxythymidine monophosphate attack genes to serve as target genes ( henceforth referred to as target onset genes ) for establishment ; we used the remaining r−t onset genes as education genes. Of the north screen genes, n−t genes were selected randomly from GeneCards ( Safran et al., 2002 ). Our cross-validation procedure closely followed that of Endeavour and ToppGene : we fixed triiodothyronine = 1, and tested all r individual genes from the pool of onset genes, and then averaged the results. Averaging was performed as follows : we took target onset genes one-by-one and averaged their rankings over ( rt ) t=1=r experiments. note that in principle, one may besides choose metric ton ≥ 2 ; in this font, the lowest rank of the thymine genes ( i.e. the highest positional value that a target onset gene assumed ) should serve as a adept measure of performance. One would then proceed to average the result rankings over ( rt ) experiments, producing a ‘ worst case scenario ’ for rank of target onset genes. For fair comparison with Endeavour and ToppGene, we alone used the first described method with triiodothyronine = 1 and the same set of p-values as inputs. As will be described in subsequent sections, we used deoxythymidine monophosphate ≥ 2 for gene discovery procedures.

### 3.2 Gene discovery

The ultimate goal of gene prioritization is to discover genes that are probably to be involved in a disease without having any prior experimental cognition about their function. We describe adjacent a new, iterative gene discovery method. The method uses collection techniques or combinations of collection techniques deemed to be most effective in the cross-validation study. Given a certain disease with roentgen onset genes, we first identify mho suspect genes. Suspect genes are genes that are known to be involved in diseases related to that under study ( as an exercise, a fishy gene for glioblastoma may be a gene known to be implicated in another form of brain cancer, say meningioma ), but have not been tested in this potential function. Suspect genes are processed in an iterative manner, as illustrated in Algorithm 1. In the first iteration, roentgen onset genes are used for educate, and mho suspect genes, along with normality − south randomly selected genes, are used as test genes. From the aggregate results provided by different loanblend algorithm, we selected q top-ranked genes and moved them to the laid of educate genes and simultaneously declared them as potential disease genes. The option for the parameter q is governed by the number of education and test genes, ampere well as the empirical performance of the collection methods observed during multiple rounds of testing. The moment iteration starts with roentgen + q training genes, sulfur − q fishy genes, and normality − s + q randomly selected genes ; the routine is repeated until a preset stop criteria is met, such as the size of the set of potential disease genes exceeding a given threshold. **Input:** Set of attack genes, O= { o1, o2, …, or } , set of suspect genes, S= { s1, s2, …, sulfur } , number of test genes, n∈Z+, a cut-off doorway, τ∈Z+, and the number of let iterations, l∈Z **Output:** Set of electric potential disease genes, denoted by A **Initialization:**

- Set one = 1, A=∅, R= { r1, r2, …, rn−s } – a fructify of randomly chosen genes, training set TR = O, examination set TS=S∪R

**For** i≤l **do**

- Run a gene prioritization cortege using the training set TR, trial adjust TS, and m similarity criteria
- Run k collection methods on the p-values produced in Step 1, and denote the result rankings by σ1, …, σk
- Let B= { σ1 ( 1 ), …, σ1 ( τ ) } ∪···∪ { σk ( 1 ), …, σk ( τ ) }
- A←A∪B ; TR←TR∪B ; S←S∖B
- TS←S∪R′, R′= rig of n−|S| randomly chosen genes
- i←i+1

**End** **Return** A

## 4 Results

We performed across-the-board cross-validation studies for eight diseases using both Endeavour- and ToppGene-generated p-values. Our results indicate that the similarity criteria that exhibits the strongest influence on the performance of the ToppGene and the Endeavour method acting is the PubMed and literature criteria, which award genes according to their citations in the disease associate publications. In order to explore this issue far, we performed extra cross-validation studies for both ToppGene and Endeavour datasets to examine how ejection of the literature criteria changes the performance of the two methods american samoa well as our loanblend schemes. Our results reveal that HyDRA collection methods outperform Endeavour and ToppGene procedures for a majority of timbre criteria, but they besides highlight that each method acting offers unique advantages in prioritization for some specific diseases. For gene discovery, we again used Endeavour and ToppGene p-values, and investigated three diseases—glioblastoma, meningioma and summit cancer—including all criteria available. We recommend as best drill a nest collection method acting, i.e. aggregating the aggregates of Endeavour, HyDRA and ToppGene, coupled with iterative education set augmentation.

### 4.1 Cross-validation

Cross-validation for HyDRA methods was performed on autism, breast cancer, colorectal cancer, endometriosis, ischemic accident, leukemia, lymphoma and osteoarthritis. board 2 provides the drumhead of our results, pertaining to the modal rank of one selected aim gene. board 2 illustrates that HyDRA methods offer optimum operation in 11 out of 16 tests when compared with ToppGene aggregates, and in 12 out of 16 cases when compared with Endeavour aggregates. In the early subject, the Weighted Hybrid Kendall method outperformed all early techniques. A detailed review of our cross-validation results is given in the auxiliary data section S1. note that in for all eight diseases, we performed two tests, in one of which we excluded those similarity criteria that control solid prior information about disease genes, such as the ‘ Disease ’ and ‘ PubMed ’ class. mesa 2 demonstrates the significant differences in average ranks of the target genes when literature information is excluded, suggesting that ToppGene and Endeavour both significantly benefit from this prior onset gene data when ranking the target genes. The Supplementary data Section S2 contains a detailed description of our results.

Table 2.

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Table 2.

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Another means for evaluating the performance of HyDRA algorithms compared with that of ToppGene and Endeavour is to examine the liquidator operating feature ( ROC ) curves of the techniques. In this place setting, we follow the same approach as used by both ToppGene and Endeavour. Sensitivity is defined as the frequency of tests in which prospect genes were ranked above a particular threshold position, and specificity as the percentage of expectation genes ranked below this threshold. As an case, a sensitivity/specificity pair of values 90/77 indicates that the presumably discipline disease gene was ranked among the top-scoring 100−77=23 % of the genes in 90 % of the prioritization tests. The ROCs plot the dependence between sensitivity and the reflect specificity, and the sphere under the crook ( AUC ) represents another useful operation measure. The higher the AUC and specificity, the better the performance of the method acting. Endeavour reported 90/74 sensitivity/specificity values for their chosen determined of test and educate genes, vitamin a well as an AUC score of 0.866. similarly, ToppGene reported 90/77 sensitivity/specificity values and an AUC score of 0.916 for their tests of interest. Our specificity/sensitivity and AUC values are listed in table 3, with best AUC and Sensitivity/Specificity values shaded in grey. eminence that although the AUC values appear cheeseparing in all cases, the HyDRA methods have very abject overall computational complexity ( Figs. 5 and 6 ).

Fig. 5Open in new tabDownload slide Cross-validation results : ROC curves for disease listed in mesa 2 using all criteria and Endeavour data. Fig. 5Open in new tabDownload slide Cross-validation results : ROC curves for disease listed in mesa 2 using all criteria and Endeavour data .

Fig. 6 .Open in new tabDownload slide Cross-validation results : ROC curves for disease listed in table 2 using all criteria and ToppGene data. Fig. 6 .Open in new tabDownload slide Cross-validation results : ROC curves for disease listed in board 2 using all criteria and ToppGene data .

Table 3.

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Table 3.

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### 4.2 Gene discovery

The genetic factors behind glioblastoma, the most park and aggressive primary brain tumor, are still stranger. We study this disease, ampere well as meningioma and breast cancer, in the gene discovery phase. Our choice is governed by the fact that few publications are available pointing towards the causes of this form of genius cancer, and by the fact that it is wide believed that the familial base of this disease is related to the genetic base of the Von Hippel-Lindau ( VHL ), Li-Fraumeni ( LF ), and Turcot Syndromes ( TS ), Neurofibromatosis ( N ) and tuberous Sclerosis ( TS ) ( Kyritsis et al., 2009 ). furthermore, holocene findings ( Pandey, 2014 ) indicate that brain cancers and breast cancers share a common agate line of mutations in the family of Immunoglobulin GM genes, and that the Human cytomegalovirus puts patients at gamble of both brain and front cancer. consequently, we used genes documented to be involved in glioblastoma as coach genes for three discovery tests. In the first gear trial, for the distrust genes we selected a subset of 15 genes known to be implicated in the VHL, LF, TS, N and TS syndromes. We subsequently ran Algorithm1 with l = 3, s = 15, n = 100, τ = 3. In the second test, we selected 18 genes known to be involved in breast cancer as fishy genes for glioblastoma, and run Algorithm1 with fifty = 3, s = 18, n = 100, τ = 3. ultimately, we performed the lapp analysis on distrust genes known to be involved in meningioma, by setting the parameters of iterative HyDRA gene discovery to l = 3, s = 19, nitrogen = 100, τ = 3. The results are shown in table 4. note that in our algorithmic investigation, we used fifty = 3 ( i.e. top-three ) ranked genes, since this parameter choice offered a adept tradeoff between the size of the union of the top-ranked genes and the accuracy of the genes produced by the HyDRA discovery methods. The numeral of fishy genes was governed by the size of the available pool in OMIM/GAD and was targeted to be approximately 20 % of the size of the test set. Such a percentage is deemed to be sufficiently high to allow for meaningful discovery, so far sufficiently low to prevent routine gene identification.

Table 4.

Test disease . | Iteration 1 . | Iteration 2 . |
---|---|---|

Breast cancer | AKT1, ATM, BRIP1, CDH1, CHEK2, GSTM2, KAAG1, RAD51, TP73 |
BARD1, CASP7, ITGA4, KRAS, PALB2, PHB, SMAD7, UMOD |

VHL, LF, TS, N, TS | CCND1, CD28, CD74, CDK4, CHEK2, MLH1, MSH2, MSH6, NBPF4, PMS2, PRNT, TSC2 |
ALCAM, APC, MRC1, NCL, NF1, NF2, SNCA, TAF7, TOPBP1, TSC1, VHL |

Meningioma | CCND1, HLA-DQB1, KLF6, KRAS, TGFB1, TGFBR2, XRCC5 |
BAGE, BAP1, CAV1, CD4, CDH1, NF2, PDGFB, PSMC2, RFC1, SAMD9L, SERPING1, SMARCB1 |

Test disease . | Iteration 1 . | Iteration 2 . |
---|---|---|

Breast cancer | AKT1, ATM, BRIP1, CDH1, CHEK2, GSTM2, KAAG1, RAD51, TP73 |
BARD1, CASP7, ITGA4, KRAS, PALB2, PHB, SMAD7, UMOD |

VHL, LF, TS, N, TS | CCND1, CD28, CD74, CDK4, CHEK2, MLH1, MSH2, MSH6, NBPF4, PMS2, PRNT, TSC2 |
ALCAM, APC, MRC1, NCL, NF1, NF2, SNCA, TAF7, TOPBP1, TSC1, VHL |

Meningioma | CCND1, HLA-DQB1, KLF6, KRAS, TGFB1, TGFBR2, XRCC5 |
BAGE, BAP1, CAV1, CD4, CDH1, NF2, PDGFB, PSMC2, RFC1, SAMD9L, SERPING1, SMARCB1 |

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Table 4.

Test disease . | Iteration 1 . | Iteration 2 . |
---|---|---|

Breast cancer | AKT1, ATM, BRIP1, CDH1, CHEK2, GSTM2, KAAG1, RAD51, TP73 |
BARD1, CASP7, ITGA4, KRAS, PALB2, PHB, SMAD7, UMOD |

VHL, LF, TS, N, TS | CCND1, CD28, CD74, CDK4, CHEK2, MLH1, MSH2, MSH6, NBPF4, PMS2, PRNT, TSC2 |
ALCAM, APC, MRC1, NCL, NF1, NF2, SNCA, TAF7, TOPBP1, TSC1, VHL |

Meningioma | CCND1, HLA-DQB1, KLF6, KRAS, TGFB1, TGFBR2, XRCC5 |
BAGE, BAP1, CAV1, CD4, CDH1, NF2, PDGFB, PSMC2, RFC1, SAMD9L, SERPING1, SMARCB1 |

Test disease . | Iteration 1 . | Iteration 2 . |
---|---|---|

Breast cancer | AKT1, ATM, BRIP1, CDH1, CHEK2, GSTM2, KAAG1, RAD51, TP73 |
BARD1, CASP7, ITGA4, KRAS, PALB2, PHB, SMAD7, UMOD |

VHL, LF, TS, N, TS | CCND1, CD28, CD74, CDK4, CHEK2, MLH1, MSH2, MSH6, NBPF4, PMS2, PRNT, TSC2 |
ALCAM, APC, MRC1, NCL, NF1, NF2, SNCA, TAF7, TOPBP1, TSC1, VHL |

Meningioma | CCND1, HLA-DQB1, KLF6, KRAS, TGFB1, TGFBR2, XRCC5 |
BAGE, BAP1, CAV1, CD4, CDH1, NF2, PDGFB, PSMC2, RFC1, SAMD9L, SERPING1, SMARCB1 |

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table 4 reveals a number of results presently not known from the literature. The genes KRAS and CDH1, both implicated in summit cancer and meningioma, adenine well as CCND1 involved in meningioma ( deoxyadenosine monophosphate well as in colorectal cancer ) appear to be highly similar to genes implicated with glioblastoma. KRAS is a gene encoding for the K-Ras protein that is involved in regulating cell division, and therefore an obvious candidate for being implicated in cancer. On the early hand, CDH1 is creditworthy for the production of the E-cadherin protein, whose affair is to aid in cellular telephone attachment and to regulate transmittance of chemical signals within cells, and control cell festering. E-cadherin besides much acts as a tumor suppressor protein. GeneCards reveals that the CCND1 gene is implicated in altering cell cycle progress, and is mutated in a variety show of tumors. Its character in glioma tumorogenesis appears to be good documented ( Buschges et al., 1999 ), but amazingly, neither KRAS nor CDH1 nor CCND1 are listed in the OMIM/GAD database as likely glioblastoma genes. Another matter to receive involves genes ranked among the top three candidates, but not identified as ‘ suspect ’ genes. For case, according to GeneBank, GSTM2 regulates an individual ’ second susceptibility to carcinogens and toxins and may suggest glioblastoma being in function caused by toxic and other environmental conditions ; KAAG1 appears to be implicated with kidney tumors, while TP73 belongs to the p53 syndicate of arrangement factors and is known to be involved in neuroblastoma.

## 5 Discussion

We start by discussing the results in mesa 2. The first notice is that the Lovász-Bregman method acting performs worse than any other collection method acting. This rule may be attributed to the fact that the p-values have a boastfully bridge, and small values may be ‘ masked ’ by larger ones. Scaling all p-values may be a entail to improve the operation of this proficiency, but how precisely to accomplish this task remains a doubt. In about all cases, except for Leukemia and Lymphoma, the average rankings produced by ToppGene and the Weighted Kendall distance appear to be about identical. But modal values may be misleading, as individual rankings of genes may vary well between the methods, as can be seen from the auxiliary material. It is due to this reason that we recommend merging lists generated by unlike methods as best collection practice. Another significant observation is that HyDRA methods have significantly lower computational complexity than ToppGene and specially, Endeavour, and hence scale good for large datasets. Another find is the fact that the good performance of ToppGene and all other methods largely depends on including prior literature on the genes into the collection summons. We observed situations where the rank of an element dropped by approximately 90 positions when this prior was not available. This implies that for gene discovery, it is bad to rely on any unmarried method, and it is again good commit to merge top-ranked entries generated by different methods. last, it is not clear how to optimally choose the number of training genes for a given put of quiz genes, or frailty versa. Choosing more prepare genes may appear to be beneficial at first glance, but it creates a more diverse pool of candidates for which some similarity criteria will inevitably fail to identify the right genes. In this lawsuit, we recommend using the Weighted Kendall to eliminate outliers, and in accession, we recommend the use of a fairly large TvB scale argument.

## Acknowledgements

The work was supported in part by the National Science Foundation ( NSF ) under grants CCF 0809895, CCF 1218764, CSoI-CCF 0939370, and IOS 1339388. conflict of interest : none declared.

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## Author notes

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