JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method

<div>IFS curve that shows the values of <svg height="12.1502pt" id="M97" style="vertical-align:-3.3838pt" version="1.1" viewbox="-0.0498162 -8.7664 33.8437 12.1502" width="33.8437pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M673 0V28C608 34 594 43 563 129C499 303 432 494 370 665L339 656L132 132C98 43 84 37 20 28V0H238V28C162 35 154 45 175 108C188 150 203 192 219 237H432C454 176 474 122 487 83S491 36 428 28V0H673ZM418 280H234C265 362 296 450 328 535H330L418 280Z" id="g121-63"></path><glyph.data ascent="989" descent="-360" horiz-adv-x="691" vert-adv-y="691"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,8.122,0)"><path d="M478 437H300V411C357 404 363 393 347 345C325 280 293 190 258 107C229 178 188 284 163 354C147 397 149 405 203 411V437H-2V411C52 403 59 396 82 339C130 222 176 109 220 -11H248C304 138 356 261 390 336C417 395 425 404 478 411V437Z" id="g121-116"></path><glyph.data ascent="989" descent="-360" horiz-adv-x="463" vert-adv-y="463"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,14.336,0)"><path d="M463 437C426 431 375 425 327 422C297 440 264 449 231 449H230C153 449 51 396 51 283C51 215 94 168 139 149C123 129 91 103 51 88C50 78 53 60 62 46C75 25 100 2 136 -9C112 -28 73 -59 53 -79C38 -94 29 -113 29 -135C30 -192 91 -257 203 -257C336 -257 452 -160 452 -59C452 39 371 59 309 59C275 59 240 58 203 58C158 58 140 77 140 96C140 110 157 129 170 138C186 135 205 133 221 133C306 133 396 185 396 293C396 328 384 360 366 381L423 378C439 387 459 413 468 429L463 437ZM219 418C277 418 314 362 314 284C314 205 275 166 231 165C176 165 137 221 137 299C137 376 177 418 219 418ZM241 -11C285 -11 314 -14 339 -24C367 -36 384 -61 384 -95C384 -157 335 -206 240 -206C166 -206 108 -165 108 -110C108 -82 128 -54 154 -32C172 -17 195 -11 241 -11Z" id="g121-101"></path><glyph.data ascent="989" descent="-360" horiz-adv-x="468" vert-adv-y="468"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,20.42,0)"><path d="M409 504C401 567 396 607 392 642C354 654 312 665 266 665C137 665 60 583 60 487C60 374 161 325 225 290C300 250 355 215 355 141C355 68 311 21 235 21C131 21 86 122 71 183L41 176C48 128 61 42 68 21C78 16 93 8 118 0C142 -7 175 -15 216 -15C349 -15 438 69 438 174C438 287 344 333 265 374C186 414 138 449 138 522C138 576 172 631 249 631C336 631 363 562 380 499L409 504Z" id="g121-81"></path><glyph.data ascent="989" descent="-360" horiz-adv-x="474" vert-adv-y="474"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,26.504,0)"><path d="M524 0V26C466 32 460 36 460 104V297C460 393 411 449 331 449C302 449 276 437 248 419C223 402 201 387 181 372V451C137 432 90 420 42 411V388C96 378 102 374 102 310V104C102 38 97 33 29 26V0H246V26C187 32 181 36 181 104V339C211 365 250 390 290 390C357 390 381 345 381 276V109C381 40 374 32 315 26V0H524Z" id="g121-108"></path><glyph.data ascent="989" descent="-360" horiz-adv-x="547" vert-adv-y="547"></glyph.data></g></svg> against feature subsets generated by the SU-IFS method.</div>

BioMed Research International

fig7

Figure 7

Figure 7: JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method 